PRECIO_BOSTON
ANY LOPEZ ROJAS
2026-10-11
1 Predicción de valores de Viviendas en la Ciudad de Boston
1.0.1 Descripción
Valores de las viviendas en los suburbios de Boston
El conjunto de datos Boston es un marco de datos (data frame) que contiene 506 filas y 14 columnas. Incluye variables relacionadas con las características de las viviendas, las condiciones socioeconómicas, la contaminación y la accesibilidad a los servicios en los suburbios de Boston.
Variables:
- crim: Tasa de criminalidad per cápita por localidad.
- zn: Proporción de terrenos residenciales destinados a lotes de más de 25 000 pies cuadrados.
- indus: Proporción de acres destinados a negocios no minoristas por localidad.
- chas: Variable indicadora del río Charles: toma el valor 1 si el terreno limita con el río y 0 en caso contrario.
- nox: Concentración de óxidos de nitrógeno, expresada en partes por cada 10 millones.
- rm: Número promedio de habitaciones por vivienda.
- age: Proporción de unidades ocupadas por sus propietarios construidas antes de 1940.
- dis: Media ponderada de las distancias a cinco centros de empleo de Boston.
- rad: Índice de accesibilidad a las autopistas radiales.
- tax: Tasa del impuesto predial calculada sobre un valor de 10 000 dólares.
- ptratio: Proporción de estudiantes por docente en cada localidad.
- black: Variable calculada mediante la expresión \(1000(B_k-0.63)^2\), donde \(B_k\) representa la proporción de población negra por localidad.
- lstat: Porcentaje de población de nivel socioeconómico bajo.
- medv: Valor mediano de las viviendas ocupadas por sus propietarios, expresado en miles de dólares.
2 1. Cargar librerías
library(MASS)
library(neuralnet)
library(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.2 ✔ tidyr 1.3.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::compute() masks neuralnet::compute()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks MASS::select()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(visdat)
library(tidyverse)
library(dplyr)
library(skimr)
library(readxl)
library(gmodels)
library(Hmisc)##
## Adjuntando el paquete: 'Hmisc'
##
## The following objects are masked from 'package:dplyr':
##
## src, summarize
##
## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(ggthemes)
library(GGally)
library(corrplot)## corrplot 0.95 loadedlibrary(PerformanceAnalytics)## Cargando paquete requerido: xts
## Cargando paquete requerido: zoo
##
## Adjuntando el paquete: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
##
## ######################### Warning from 'xts' package ##########################
## # #
## # The dplyr lag() function breaks how base R's lag() function is supposed to #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or #
## # source() into this session won't work correctly. #
## # #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop #
## # dplyr from breaking base R's lag() function. #
## # #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning. #
## # #
## ###############################################################################
##
## Adjuntando el paquete: 'xts'
##
## The following objects are masked from 'package:dplyr':
##
## first, last
##
##
## Adjuntando el paquete: 'PerformanceAnalytics'
##
## The following object is masked from 'package:graphics':
##
## legendlibrary(patchwork)##
## Adjuntando el paquete: 'patchwork'
##
## The following object is masked from 'package:MASS':
##
## area3 2. Cargar los datos
set.seed(65)
datos <- Boston
head(datos)## crim zn indus chas nox rm age dis rad tax ptratio black lstat
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## 5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
## 6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
## medv
## 1 24.0
## 2 21.6
## 3 34.7
## 4 33.4
## 5 36.2
## 6 28.7Interpretación: Boston contiene 506 observaciones y 14 variables: - 506 observaciones: áreas censales de los suburbios de Boston. - 13 variables predictoras: características de las áreas y sus viviendas. - 1 variable respuesta: medv, valor mediano de las viviendas ocupadas por sus propietarios, en miles de dólares.
4 3.Partición de la data
datos_completos <- datos
ids_entrenamiento <- sort(
sample(
seq_len(nrow(datos_completos)),
size = floor(0.80 * nrow(datos_completos)),
replace = FALSE
)
)
ids_prueba <- setdiff(
seq_len(nrow(datos_completos)),
ids_entrenamiento
)
#data de entrenamiento
data.trn <- datos_completos[
ids_entrenamiento, , drop = FALSE
]
#data de prueba
data.tst <- datos_completos[
ids_prueba, , drop = FALSE
]
cat("\n================ PARTICIÓN ================\n")##
## ================ PARTICIÓN ================cat("Entrenamiento:", nrow(data.trn), "observaciones\n")## Entrenamiento: 404 observacionescat("Prueba:", nrow(data.tst), "observaciones\n")## Prueba: 102 observacionesstopifnot(
length(intersect(ids_entrenamiento, ids_prueba)) == 0,
nrow(data.trn) + nrow(data.tst) == nrow(datos_completos)
)
#Desde aquí se trabaja exclusivamente con entrenamiento
datos <- data.trn
#Correspondencia entre ID del análisis y fila original
referencia_ID_entrenamiento <- data.frame(
ID = seq_len(nrow(data.trn)),
Fila_Boston = ids_entrenamiento
)Interpretación: Las 404 observaciones (80%) de entrenamiento se utilizan para estimar el preprocesamiento, ajustar los modelos y seleccionar sus parámetros mediante validación interna. Las 102 observaciones (20%) de prueba se reservan para evaluar la predicción de medv fuera de muestra.
5 4. Analísis descrIptivo
#Resumen de los datos
summary(datos)## crim zn indus chas
## Min. : 0.00632 Min. : 0.00 Min. : 0.460 Min. :0.00000
## 1st Qu.: 0.08135 1st Qu.: 0.00 1st Qu.: 5.175 1st Qu.:0.00000
## Median : 0.25635 Median : 0.00 Median : 9.900 Median :0.00000
## Mean : 3.73323 Mean : 11.42 Mean :11.404 Mean :0.07673
## 3rd Qu.: 3.67481 3rd Qu.: 0.00 3rd Qu.:18.100 3rd Qu.:0.00000
## Max. :88.97620 Max. :100.00 Max. :27.740 Max. :1.00000
## nox rm age dis
## Min. :0.3850 Min. :3.561 Min. : 2.90 Min. : 1.130
## 1st Qu.:0.4530 1st Qu.:5.888 1st Qu.: 45.80 1st Qu.: 2.088
## Median :0.5380 Median :6.210 Median : 77.75 Median : 3.107
## Mean :0.5567 Mean :6.289 Mean : 69.23 Mean : 3.761
## 3rd Qu.:0.6240 3rd Qu.:6.629 3rd Qu.: 94.03 3rd Qu.: 5.104
## Max. :0.8710 Max. :8.780 Max. :100.00 Max. :12.127
## rad tax ptratio black
## Min. : 1.000 Min. :187.0 Min. :12.60 Min. : 0.32
## 1st Qu.: 4.000 1st Qu.:284.0 1st Qu.:17.00 1st Qu.:373.48
## Median : 5.000 Median :346.5 Median :19.00 Median :391.06
## Mean : 9.554 Mean :413.2 Mean :18.44 Mean :352.47
## 3rd Qu.:24.000 3rd Qu.:666.0 3rd Qu.:20.20 3rd Qu.:396.06
## Max. :24.000 Max. :711.0 Max. :22.00 Max. :396.90
## lstat medv
## Min. : 1.730 Min. : 5.00
## 1st Qu.: 6.915 1st Qu.:17.07
## Median :11.395 Median :21.20
## Mean :12.546 Mean :22.46
## 3rd Qu.:16.605 3rd Qu.:25.00
## Max. :37.970 Max. :50.00skim(datos)| Name | datos |
| Number of rows | 404 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| crim | 0 | 1 | 3.73 | 9.13 | 0.01 | 0.08 | 0.26 | 3.67 | 88.98 | ▇▁▁▁▁ |
| zn | 0 | 1 | 11.42 | 24.14 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | ▇▁▁▁▁ |
| indus | 0 | 1 | 11.40 | 7.01 | 0.46 | 5.18 | 9.90 | 18.10 | 27.74 | ▆▆▁▇▁ |
| chas | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| nox | 0 | 1 | 0.56 | 0.12 | 0.38 | 0.45 | 0.54 | 0.62 | 0.87 | ▇▇▆▅▁ |
| rm | 0 | 1 | 6.29 | 0.69 | 3.56 | 5.89 | 6.21 | 6.63 | 8.78 | ▁▁▇▂▁ |
| age | 0 | 1 | 69.23 | 27.66 | 2.90 | 45.80 | 77.75 | 94.03 | 100.00 | ▁▂▂▃▇ |
| dis | 0 | 1 | 3.76 | 2.11 | 1.13 | 2.09 | 3.11 | 5.10 | 12.13 | ▇▅▂▁▁ |
| rad | 0 | 1 | 9.55 | 8.74 | 1.00 | 4.00 | 5.00 | 24.00 | 24.00 | ▇▂▁▁▃ |
| tax | 0 | 1 | 413.20 | 167.78 | 187.00 | 284.00 | 346.50 | 666.00 | 711.00 | ▇▇▅▁▇ |
| ptratio | 0 | 1 | 18.44 | 2.17 | 12.60 | 17.00 | 19.00 | 20.20 | 22.00 | ▁▃▅▅▇ |
| black | 0 | 1 | 352.47 | 97.46 | 0.32 | 373.48 | 391.06 | 396.06 | 396.90 | ▁▁▁▁▇ |
| lstat | 0 | 1 | 12.55 | 7.04 | 1.73 | 6.91 | 11.39 | 16.60 | 37.97 | ▇▇▃▂▁ |
| medv | 0 | 1 | 22.46 | 9.07 | 5.00 | 17.08 | 21.20 | 25.00 | 50.00 | ▃▇▅▁▁ |
describe(datos)## datos
##
## 14 Variables 404 Observations
## --------------------------------------------------------------------------------
## crim
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 402 1 3.733 1.19 6.025 0.02505
## .10 .25 .50 .75 .90 .95
## 0.03628 0.08135 0.25635 3.67481 10.78548 15.53254
##
## lowest : 0.00632 0.00906 0.01096 0.01301 0.01311
## highest: 45.7461 51.1358 67.9208 73.5341 88.9762
## --------------------------------------------------------------------------------
## zn
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 26 0.574 11.42 0 19.1 0
## .10 .25 .50 .75 .90 .95
## 0 0 0 0 45 80
##
## lowest : 0 12.5 17.5 18 20 , highest: 82.5 85 90 95 100
## --------------------------------------------------------------------------------
## indus
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 75 0.981 11.4 11.52 7.883 2.030
## .10 .25 .50 .75 .90 .95
## 2.930 5.175 9.900 18.100 19.580 21.890
##
## lowest : 0.46 0.74 1.21 1.22 1.25 , highest: 18.1 19.58 21.89 25.65 27.74
## --------------------------------------------------------------------------------
## chas
## n missing distinct Info Sum Mean
## 404 0 2 0.213 31 0.07673
##
## --------------------------------------------------------------------------------
## nox
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 80 1 0.5567 0.5495 0.1312 0.4056
## .10 .25 .50 .75 .90 .95
## 0.4232 0.4530 0.5380 0.6240 0.7130 0.7700
##
## lowest : 0.385 0.389 0.392 0.394 0.398, highest: 0.713 0.718 0.74 0.77 0.871
## --------------------------------------------------------------------------------
## rm
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 365 1 6.289 6.253 0.7395 5.345
## .10 .25 .50 .75 .90 .95
## 5.603 5.888 6.210 6.629 7.144 7.515
##
## lowest : 3.561 3.863 4.138 4.519 4.628, highest: 8.337 8.375 8.398 8.704 8.78
## --------------------------------------------------------------------------------
## age
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 291 0.999 69.23 70.15 30.89 17.95
## .10 .25 .50 .75 .90 .95
## 28.52 45.80 77.75 94.03 98.40 100.00
##
## lowest : 2.9 6 6.2 6.6 6.8 , highest: 98.5 98.8 98.9 99.3 100
## --------------------------------------------------------------------------------
## dis
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 342 1 3.761 3.539 2.286 1.502
## .10 .25 .50 .75 .90 .95
## 1.651 2.088 3.107 5.104 6.817 7.828
##
## lowest : 1.1296 1.137 1.1691 1.1742 1.2024
## highest: 9.2203 9.2229 10.5857 10.7103 12.1265
## --------------------------------------------------------------------------------
## rad
## n missing distinct Info Mean pMedian Gmd
## 404 0 9 0.959 9.554 6 8.568
##
## Value 1 2 3 4 5 6 7 8 24
## Frequency 17 23 27 84 95 22 11 19 106
## Proportion 0.042 0.057 0.067 0.208 0.235 0.054 0.027 0.047 0.262
## --------------------------------------------------------------------------------
## tax
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 65 0.981 413.2 403 181.9 222.2
## .10 .25 .50 .75 .90 .95
## 242.0 284.0 346.5 666.0 666.0 666.0
##
## lowest : 187 188 193 198 216, highest: 432 437 469 666 711
## --------------------------------------------------------------------------------
## ptratio
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 46 0.977 18.44 18.6 2.393 14.7
## .10 .25 .50 .75 .90 .95
## 14.7 17.0 19.0 20.2 20.9 21.0
##
## lowest : 12.6 13 13.6 14.4 14.7, highest: 20.9 21 21.1 21.2 22
## --------------------------------------------------------------------------------
## black
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 293 0.988 352.5 385.4 71.91 61.95
## .10 .25 .50 .75 .90 .95
## 252.40 373.48 391.06 396.06 396.90 396.90
##
## lowest : 0.32 2.52 2.6 3.5 3.65 , highest: 396.24 396.3 396.33 396.42 396.9
## --------------------------------------------------------------------------------
## lstat
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 377 1 12.55 11.91 7.757 3.709
## .10 .25 .50 .75 .90 .95
## 4.676 6.915 11.395 16.605 22.950 26.417
##
## lowest : 1.73 1.92 2.47 2.87 2.94 , highest: 34.02 34.37 34.77 36.98 37.97
## --------------------------------------------------------------------------------
## medv
## n missing distinct Info Mean pMedian Gmd .05
## 404 0 208 1 22.46 21.5 9.625 10.40
## .10 .25 .50 .75 .90 .95
## 12.86 17.08 21.20 25.00 33.37 43.44
##
## lowest : 5 5.6 6.3 7 7.2 , highest: 46.7 48.3 48.5 48.8 50
## --------------------------------------------------------------------------------#Estructura de los datos
str(datos)## 'data.frame': 404 obs. of 14 variables:
## $ crim : num 0.00632 0.02731 0.02729 0.03237 0.02985 ...
## $ zn : num 18 0 0 0 0 12.5 12.5 12.5 12.5 12.5 ...
## $ indus : num 2.31 7.07 7.07 2.18 2.18 7.87 7.87 7.87 7.87 7.87 ...
## $ chas : int 0 0 0 0 0 0 0 0 0 0 ...
## $ nox : num 0.538 0.469 0.469 0.458 0.458 0.524 0.524 0.524 0.524 0.524 ...
## $ rm : num 6.58 6.42 7.18 7 6.43 ...
## $ age : num 65.2 78.9 61.1 45.8 58.7 96.1 100 85.9 94.3 82.9 ...
## $ dis : num 4.09 4.97 4.97 6.06 6.06 ...
## $ rad : int 1 2 2 3 3 5 5 5 5 5 ...
## $ tax : num 296 242 242 222 222 311 311 311 311 311 ...
## $ ptratio: num 15.3 17.8 17.8 18.7 18.7 15.2 15.2 15.2 15.2 15.2 ...
## $ black : num 397 397 393 395 394 ...
## $ lstat : num 4.98 9.14 4.03 2.94 5.21 ...
## $ medv : num 24 21.6 34.7 33.4 28.7 27.1 16.5 18.9 15 18.9 ...#Boxplot de los datos
boxplot(
datos,
las = 2,
col = "lightblue",
main = "Boston: datos de entrenamiento"
)
Interpretación: La data de entrenamiento contiene 404
observaciones, 14 variables y ningún dato perdido. El boxplot señala
valores extremos, pero mezcla variables con escalas muy distintas.
6 5. Analisis de datos perdidos
#Datos perdidos
vis_miss(datos)
#Suma de datos perdidos
sum(is.na(datos))## [1] 0#Datos perdidos por variables
sapply(datos, function(x) sum(is.na(x)))## crim zn indus chas nox rm age dis rad tax
## 0 0 0 0 0 0 0 0 0 0
## ptratio black lstat medv
## 0 0 0 0Interpretación: La data de entrenamiento no presenta valores perdidos.
7 6. Analizar las variables
#chas permanece en la base
table(datos$chas, useNA = "ifany")##
## 0 1
## 373 31#Revisar valores distintos de 0 y 1
ids_chas_invalidos <- which(
!is.na(datos$chas) &
!(datos$chas %in% c(0, 1))
)
if (length(ids_chas_invalidos) > 0) {
stop("chas contiene valores distintos de 0 y 1: revisar la fuente.")
}
#Gráfico apropiado para una variable binaria
grafico_chas <- ggplot(
datos,
aes(x = factor(chas))
) +
geom_bar(fill = "steelblue", color = "white") +
labs(
title = "Distribución de chas: entrenamiento",
x = "chas",
y = "Frecuencia"
) +
theme_minimal()
print(grafico_chas)
Interpretación: La varibles chas es una variable binaria: sus valores 1 no son datos atípicos por ser menos frecuentes. Ambas categorías son válidas y deben conservarse. En el conjunto de entrenamiento, el 92,33 % de las zonas no limita con el río Charles, mientras que el 7,67 % sí lo hace. La variable presenta una mayor frecuencia de la categoría 0. Esta diferencia corresponde a la distribución de una característica geográfica y no implica errores ni necesidad de imputación.
8 7. Análisis de datos outliers
datos_originales <- datos
#medv: respuesta, no se reemplaza.
#chas: binaria, no se analiza con Z, IQR o MAD.
#Ambas permanecen como columnas de la base.
variables <- setdiff(
names(datos_originales),
c("medv", "chas")
)
#rad se analiza como índice discreto.
#Sus resultados requieren interpretación cautelosa.
resultados <- list()
resumenes <- list()
graficos <- list()
paneles <- list()
parametros_deteccion <- list()
mostrar_graficos <- TRUE9 7.1 Métodos para detectar outliers
for (variable in variables) {
datos_variable <- data.frame(
ID = seq_len(nrow(datos_originales)),
Valor = datos_originales[[variable]]
)
valido <- is.finite(datos_variable$Valor)
x <- datos_variable$Valor[valido]
if (length(x) < 2) {
warning(paste("Observaciones insuficientes en", variable))
next
}
media <- mean(x)
desv <- sd(x)
mediana <- median(x)
Q1 <- unname(quantile(x, 0.25))
Q3 <- unname(quantile(x, 0.75))
IQR_variable <- Q3 - Q1
MAD_variable <- mad(x, constant = 1)
MAD_ajustada <- 1.4826 * MAD_variable
Z_disponible <- is.finite(desv) && desv > 0
IQR_disponible <- is.finite(IQR_variable) &&
IQR_variable > 0
MAD_disponible <- is.finite(MAD_ajustada) &&
MAD_ajustada > 0
datos_variable$Z_score <- NA_real_
datos_variable$outlier_Z <- NA
datos_variable$outlier_IQR <- NA
datos_variable$outlier_extremo <- NA
datos_variable$Z_robusto <- NA_real_
datos_variable$outlier_MAD <- NA
cat(
"\n========================================\n",
"VARIABLE:", variable,
"\n========================================\n"
)
# ------------------------------------------------------------
# MÉTODO Z-SCORE
# ------------------------------------------------------------
limite_inferior_Z <- NA_real_
limite_superior_Z <- NA_real_
if (Z_disponible) {
datos_variable$Z_score[valido] <- (x - media) / desv
datos_variable$outlier_Z[valido] <-
abs(datos_variable$Z_score[valido]) > 3
limite_inferior_Z <- media - 3 * desv
limite_superior_Z <- media + 3 * desv
} else {
cat("Z-score no disponible: desviación nula o indefinida.\n")
}
cat("\n================ MÉTODO Z ================\n")
cat("Límite inferior =", limite_inferior_Z, "\n")
cat("Límite superior =", limite_superior_Z, "\n")
cat(
"Número de outliers =",
sum(datos_variable$outlier_Z, na.rm = TRUE),
"\n"
)
print(
datos_variable %>%
filter(outlier_Z %in% TRUE) %>%
dplyr::select(ID, Valor, Z_score)
)
# ------------------------------------------------------------
# MÉTODO BOX PLOT / IQR
# ------------------------------------------------------------
limite_inferior_IQR <- Q1 - 1.5 * IQR_variable
limite_superior_IQR <- Q3 + 1.5 * IQR_variable
if (IQR_disponible) {
datos_variable$outlier_IQR[valido] <-
x < limite_inferior_IQR |
x > limite_superior_IQR
} else {
cat("\nIQR nulo o indefinido: criterio no evaluado.\n")
}
cat("\n================ MÉTODO IQR ================\n")
cat("Q1 =", Q1, "\n")
cat("Q3 =", Q3, "\n")
cat("IQR =", IQR_variable, "\n")
cat("Límite inferior =", limite_inferior_IQR, "\n")
cat("Límite superior =", limite_superior_IQR, "\n")
cat(
"Número de outliers =",
sum(datos_variable$outlier_IQR, na.rm = TRUE),
"\n"
)
print(
datos_variable %>%
filter(outlier_IQR %in% TRUE) %>%
dplyr::select(ID, Valor)
)
# ------------------------------------------------------------
# OUTLIERS EXTREMOS SEGÚN TUKEY
# ------------------------------------------------------------
limite_inferior_extremo <- Q1 - 3 * IQR_variable
limite_superior_extremo <- Q3 + 3 * IQR_variable
if (IQR_disponible) {
datos_variable$outlier_extremo[valido] <-
x < limite_inferior_extremo |
x > limite_superior_extremo
}
cat("\n=========== OUTLIERS EXTREMOS TUKEY ===========\n")
cat("Límite inferior =", limite_inferior_extremo, "\n")
cat("Límite superior =", limite_superior_extremo, "\n")
cat(
"Número de outliers extremos =",
sum(datos_variable$outlier_extremo, na.rm = TRUE),
"\n"
)
print(
datos_variable %>%
filter(outlier_extremo %in% TRUE) %>%
dplyr::select(ID, Valor)
)
# ------------------------------------------------------------
# MÉTODO ROBUSTO MAD
# ------------------------------------------------------------
if (MAD_disponible) {
datos_variable$Z_robusto[valido] <-
(x - mediana) / MAD_ajustada
datos_variable$outlier_MAD[valido] <-
abs(datos_variable$Z_robusto[valido]) > 3.5
} else {
cat("\nMAD nula o indefinida: criterio no evaluado.\n")
}
cat("\n================ MÉTODO MAD ================\n")
cat("Mediana =", mediana, "\n")
cat("MAD =", MAD_variable, "\n")
cat("MAD ajustada =", MAD_ajustada, "\n")
cat(
"Número de outliers =",
sum(datos_variable$outlier_MAD, na.rm = TRUE),
"\n"
)
print(
datos_variable %>%
filter(outlier_MAD %in% TRUE) %>%
dplyr::select(ID, Valor, Z_robusto)
)
# ------------------------------------------------------------
# COMPARAR LOS CUATRO CRITERIOS
# ------------------------------------------------------------
comparacion <- datos_variable %>%
dplyr::select(
ID, Valor,
Z_score, outlier_Z,
outlier_IQR, outlier_extremo,
Z_robusto, outlier_MAD
)
cat("\n================ COMPARACIÓN ================\n")
print(comparacion)
#Coincidencias: Z-score, IQR y MAD.
#Tukey extremo no agrega un voto: está incluido en IQR.
indicadores <- datos_variable[, c(
"outlier_Z", "outlier_IQR", "outlier_MAD"
)]
datos_variable$metodos_evaluados <-
rowSums(!is.na(indicadores))
datos_variable$numero_metodos <-
rowSums(indicadores, na.rm = TRUE)
datos_variable$numero_metodos[
datos_variable$metodos_evaluados == 0
] <- NA_real_
# ------------------------------------------------------------
# AL MENOS UN MÉTODO
# ------------------------------------------------------------
cat("\n===== AL MENOS UN MÉTODO =====\n")
print(
datos_variable %>%
filter(numero_metodos >= 1) %>%
dplyr::select(
ID, Valor, Z_score, outlier_Z,
outlier_IQR, Z_robusto, outlier_MAD,
metodos_evaluados, numero_metodos
)
)
# ------------------------------------------------------------
# AL MENOS DOS MÉTODOS
# ------------------------------------------------------------
cat("\n===== AL MENOS DOS MÉTODOS =====\n")
print(
datos_variable %>%
filter(numero_metodos >= 2) %>%
dplyr::select(
ID, Valor, Z_score, outlier_Z,
outlier_IQR, Z_robusto, outlier_MAD,
metodos_evaluados, numero_metodos
)
)
# ------------------------------------------------------------
# LOS TRES MÉTODOS
# ------------------------------------------------------------
cat("\n===== LOS TRES MÉTODOS =====\n")
print(
datos_variable %>%
filter(numero_metodos == 3) %>%
dplyr::select(ID, Valor, Z_score, Z_robusto)
)
# ------------------------------------------------------------
# RESUMEN DEL NÚMERO DE OUTLIERS
# ------------------------------------------------------------
columnas <- c(
"outlier_Z", "outlier_IQR",
"outlier_extremo", "outlier_MAD"
)
resumen_variable <- data.frame(
Variable = variable,
Metodo = c("Z-score", "IQR", "Tukey extremo", "MAD"),
N_evaluados = vapply(
datos_variable[columnas],
function(z) as.integer(sum(!is.na(z))),
integer(1)
),
N_outliers = vapply(
datos_variable[columnas],
function(z) as.integer(sum(z, na.rm = TRUE)),
integer(1)
)
)
resumen_variable$Porcentaje <- NA_real_
evaluados <- resumen_variable$N_evaluados > 0
resumen_variable$Porcentaje[evaluados] <- round(
100 * resumen_variable$N_outliers[evaluados] /
resumen_variable$N_evaluados[evaluados],
2
)
cat("\n================ RESUMEN ================\n")
print(resumen_variable, row.names = FALSE)
# ------------------------------------------------------------
# GRÁFICOS
# ------------------------------------------------------------
datos_grafico <- datos_variable[valido, , drop = FALSE]
g <- list()
#Histograma
g$histograma <- ggplot(
datos_grafico,
aes(x = Valor)
) +
geom_histogram(
bins = 20,
fill = "steelblue",
color = "white"
) +
geom_vline(
xintercept = media,
linetype = "dashed",
color = "red"
) +
labs(
title = paste("Histograma:", variable),
subtitle = "Línea roja: media",
x = variable,
y = "Frecuencia"
) +
theme_minimal()
#Boxplot
g$boxplot <- ggplot(
datos_grafico,
aes(x = "", y = Valor)
) +
geom_boxplot(fill = "lightblue") +
labs(
title = paste("Boxplot:", variable),
x = NULL,
y = variable
) +
theme_minimal()
#Boxplot con ID
g$boxplot_ID <- ggplot(
datos_grafico,
aes(x = "", y = Valor)
) +
geom_boxplot(
fill = "lightblue",
outlier.shape = NA
) +
geom_point(
data = filter(datos_grafico, outlier_IQR %in% TRUE),
color = "red"
) +
geom_text(
data = filter(datos_grafico, outlier_IQR %in% TRUE),
aes(label = ID),
hjust = -0.2,
size = 3,
check_overlap = TRUE
) +
labs(
title = paste("Atípicos IQR con ID:", variable),
subtitle = "ID dentro del entrenamiento",
x = NULL,
y = variable
) +
theme_minimal()
#Z-score
if (Z_disponible) {
g$Z_score <- ggplot(
datos_grafico,
aes(x = ID, y = Z_score, color = outlier_Z)
) +
geom_point(size = 1) +
geom_hline(
yintercept = c(-3, 3),
linetype = "dashed"
) +
scale_color_manual(
values = c("NO" = "steelblue", "SI" = "red")
) +
labs(
title = paste("Z-score:", variable),
x = "ID",
y = "Z-score",
color = "Atípico"
) +
theme_minimal()
}
#IQR
if (IQR_disponible) {
g$IQR <- ggplot(
datos_grafico,
aes(x = ID, y = Valor, color = outlier_IQR)
) +
geom_point(size = 1) +
geom_hline(
yintercept = c(
limite_inferior_IQR,
limite_superior_IQR
),
linetype = "dashed"
) +
scale_color_manual(
values = c("NO" = "steelblue", "SI" = "red")
) +
labs(
title = paste("Método IQR:", variable),
x = "ID",
y = variable,
color = "Atípico"
) +
theme_minimal()
}
#MAD
if (MAD_disponible) {
g$MAD <- ggplot(
datos_grafico,
aes(x = ID, y = Z_robusto, color = outlier_MAD)
) +
geom_point(size = 1) +
geom_hline(
yintercept = c(-3.5, 3.5),
linetype = "dashed"
) +
scale_color_manual(
values = c("NO" = "steelblue", "SI" = "red")
) +
labs(
title = paste("Método MAD:", variable),
x = "ID",
y = "Z robusto",
color = "Atípico"
) +
theme_minimal()
}
# ------------------------------------------------------------
# SEIS GRÁFICOS: TRES FILAS Y DOS COLUMNAS
# ------------------------------------------------------------
graficos_panel <- unname(g)
while (length(graficos_panel) < 6) {
graficos_panel[[length(graficos_panel) + 1]] <-
patchwork::plot_spacer()
}
panel <- patchwork::wrap_plots(
graficos_panel,
ncol = 2,
nrow = 3
) +
patchwork::plot_annotation(
title = paste("Entrenamiento Boston:", variable)
)
if (mostrar_graficos) {
print(panel)
}
# ------------------------------------------------------------
# CLASIFICACIÓN FINAL
# ------------------------------------------------------------
datos_variable$clasificacion <- case_when(
datos_variable$metodos_evaluados == 0 ~
"No evaluado",
datos_variable$numero_metodos == 0 ~
"No señalado como atípico",
datos_variable$numero_metodos == 1 ~
"Outlier según 1 método",
datos_variable$numero_metodos == 2 ~
"Outlier según 2 métodos",
datos_variable$numero_metodos == 3 ~
"Outlier según 3 métodos"
)
cat("\n================ CLASIFICACIÓN FINAL ================\n")
print(
datos_variable %>%
filter(numero_metodos > 0) %>%
dplyr::select(
ID, Valor, Z_score, Z_robusto,
metodos_evaluados, numero_metodos,
clasificacion
)
)
#Conservar resultados
datos_variable$Variable <- variable
resultados[[variable]] <- datos_variable
resumenes[[variable]] <- resumen_variable
graficos[[variable]] <- g
paneles[[variable]] <- panel
#Guardar parámetros estimados exclusivamente en entrenamiento
parametros_deteccion[[variable]] <- data.frame(
Variable = variable,
Media = media,
Desviacion = desv,
Mediana = mediana,
MAD_ajustada = MAD_ajustada,
LI_Z = limite_inferior_Z,
LS_Z = limite_superior_Z,
LI_IQR = limite_inferior_IQR,
LS_IQR = limite_superior_IQR,
LI_extremo = limite_inferior_extremo,
LS_extremo = limite_superior_extremo,
Z_disponible = Z_disponible,
IQR_disponible = IQR_disponible,
MAD_disponible = MAD_disponible
)
}##
## ========================================
## VARIABLE: crim
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -23.65842
## Límite superior = 31.12487
## Número de outliers = 7
## ID Valor Z_score
## 1 305 88.9762 9.336019
## 2 321 41.5292 4.139508
## 3 322 67.9208 7.029980
## 4 324 51.1358 5.191646
## 5 328 45.7461 4.601353
## 6 332 73.5341 7.644763
## 7 341 37.6619 3.715951
##
## ================ MÉTODO IQR ================
## Q1 = 0.0813475
## Q3 = 3.674808
## IQR = 3.59346
## Límite inferior = -5.308842
## Límite superior = 9.064998
## Número de outliers = 53
## ID Valor
## 1 294 13.52220
## 2 297 9.23230
## 3 299 11.10810
## 4 300 18.49820
## 5 301 19.60910
## 6 302 15.28800
## 7 303 9.82349
## 8 304 23.64820
## 9 305 88.97620
## 10 306 9.18702
## 11 308 24.39380
## 12 309 22.59710
## 13 310 14.33370
## 14 312 11.57790
## 15 314 13.35980
## 16 317 9.91655
## 17 318 25.04610
## 18 319 9.59571
## 19 320 24.80170
## 20 321 41.52920
## 21 322 67.92080
## 22 324 51.13580
## 23 325 14.05070
## 24 326 18.81100
## 25 327 28.65580
## 26 328 45.74610
## 27 329 18.08460
## 28 330 10.83420
## 29 331 25.94060
## 30 332 73.53410
## 31 333 11.81230
## 32 334 11.08740
## 33 336 12.04820
## 34 339 15.86030
## 35 340 12.24720
## 36 341 37.66190
## 37 342 9.33889
## 38 344 10.06230
## 39 347 13.91340
## 40 348 11.16040
## 41 349 14.42080
## 42 350 13.67810
## 43 351 9.39063
## 44 352 22.05110
## 45 354 12.80230
## 46 355 10.67180
## 47 357 9.92485
## 48 362 9.51363
## 49 371 15.57570
## 50 372 13.07510
## 51 380 15.02340
## 52 381 10.23300
## 53 382 14.33370
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -10.69903
## Límite superior = 14.45519
## Número de outliers extremos = 23
## ID Valor
## 1 300 18.4982
## 2 301 19.6091
## 3 302 15.2880
## 4 304 23.6482
## 5 305 88.9762
## 6 308 24.3938
## 7 309 22.5971
## 8 318 25.0461
## 9 320 24.8017
## 10 321 41.5292
## 11 322 67.9208
## 12 324 51.1358
## 13 326 18.8110
## 14 327 28.6558
## 15 328 45.7461
## 16 329 18.0846
## 17 331 25.9406
## 18 332 73.5341
## 19 339 15.8603
## 20 341 37.6619
## 21 352 22.0511
## 22 371 15.5757
## 23 380 15.0234
##
## ================ MÉTODO MAD ================
## Mediana = 0.256355
## MAD = 0.221995
## MAD ajustada = 0.3291298
## Número de outliers = 130
## ID Valor Z_robusto
## 1 24 1.61282 4.121368
## 2 109 3.32105 9.311509
## 3 110 4.09740 11.670305
## 4 111 2.77974 7.666839
## 5 112 2.37934 6.450297
## 6 113 2.15505 5.768834
## 7 114 2.36862 6.417727
## 8 115 2.73397 7.527775
## 9 116 1.65660 4.254386
## 10 117 1.49632 3.767404
## 11 119 2.14918 5.750999
## 12 120 1.41385 3.516835
## 13 121 3.53501 9.961587
## 14 122 2.44668 6.654898
## 15 125 1.42502 3.550773
## 16 127 1.46336 3.667262
## 17 128 1.83377 4.792684
## 18 129 1.51902 3.836374
## 19 130 2.24236 6.034109
## 20 131 2.92400 8.105146
## 21 132 2.01019 5.328703
## 22 133 1.80028 4.690931
## 23 134 2.30040 6.210453
## 24 136 2.31390 6.251470
## 25 284 8.98296 26.514176
## 26 285 3.84970 10.917714
## 27 286 5.20177 15.025729
## 28 287 4.54192 13.020897
## 29 288 3.83684 10.878642
## 30 289 3.67822 10.396704
## 31 290 4.22239 12.050064
## 32 291 3.47428 9.777070
## 33 292 4.55587 13.063281
## 34 293 3.69695 10.453612
## 35 294 13.52220 40.305817
## 36 295 4.89822 14.103448
## 37 296 6.53876 19.087926
## 38 297 9.23230 27.271749
## 39 298 8.26725 24.339623
## 40 299 11.10810 32.971021
## 41 300 18.49820 55.424473
## 42 301 19.60910 58.799737
## 43 302 15.28800 45.670874
## 44 303 9.82349 29.067971
## 45 304 23.64820 71.071796
## 46 305 88.97620 269.558844
## 47 306 9.18702 27.134174
## 48 307 7.99248 23.504785
## 49 308 24.39380 73.337163
## 50 309 22.59710 67.878223
## 51 310 14.33370 42.771410
## 52 311 5.29305 15.303066
## 53 312 11.57790 34.398421
## 54 313 8.64476 25.486618
## 55 314 13.35980 39.812395
## 56 315 8.71675 25.705346
## 57 316 5.87205 17.062251
## 58 317 9.91655 29.350716
## 59 318 25.04610 75.319056
## 60 319 9.59571 28.375903
## 61 320 24.80170 74.576492
## 62 321 41.52920 125.399908
## 63 322 67.92080 205.585905
## 64 323 7.40389 21.716463
## 65 324 51.13580 154.587786
## 66 325 14.05070 41.911567
## 67 326 18.81100 56.374858
## 68 327 28.65580 86.286462
## 69 328 45.74610 138.212179
## 70 329 18.08460 54.167826
## 71 330 10.83420 32.138826
## 72 331 25.94060 78.036829
## 73 332 73.53410 222.640879
## 74 333 11.81230 35.110602
## 75 334 11.08740 32.908128
## 76 335 7.02259 20.557954
## 77 336 12.04820 35.827341
## 78 337 7.05042 20.642510
## 79 338 8.79212 25.934344
## 80 339 15.86030 47.409702
## 81 340 12.24720 36.431965
## 82 341 37.66190 113.649832
## 83 342 9.33889 27.595603
## 84 343 8.49213 25.022880
## 85 344 10.06230 29.793551
## 86 345 6.44405 18.800167
## 87 346 5.58107 16.178162
## 88 347 13.91340 41.494406
## 89 348 11.16040 33.129925
## 90 349 14.42080 43.036047
## 91 350 13.67810 40.779490
## 92 351 9.39063 27.752806
## 93 352 22.05110 66.219303
## 94 353 5.66637 16.437330
## 95 354 12.80230 38.118534
## 96 355 10.67180 31.645404
## 97 356 6.28807 18.326251
## 98 357 9.92485 29.375934
## 99 358 7.52601 22.087503
## 100 359 6.71772 19.631663
## 101 360 5.44114 15.753011
## 102 361 8.24809 24.281409
## 103 362 9.51363 28.126518
## 104 363 4.75237 13.660310
## 105 364 4.66883 13.406489
## 106 365 8.20058 24.137059
## 107 366 6.80117 19.885210
## 108 367 6.65492 19.440857
## 109 368 7.83932 23.039437
## 110 369 3.16360 8.833126
## 111 370 3.77498 10.690691
## 112 371 15.57570 46.544997
## 113 372 13.07510 38.947386
## 114 373 4.34879 12.434107
## 115 374 4.03841 11.491075
## 116 375 3.56868 10.063887
## 117 376 4.64689 13.339829
## 118 377 8.05579 23.697141
## 119 378 6.39312 18.645426
## 120 379 4.87141 14.021991
## 121 380 15.02340 44.866936
## 122 381 10.23300 30.312191
## 123 382 14.33370 42.771410
## 124 383 5.82401 16.916290
## 125 384 5.70818 16.564362
## 126 385 5.73116 16.634183
## 127 386 2.81838 7.784239
## 128 387 2.37857 6.447958
## 129 388 3.67367 10.382880
## 130 389 4.83567 13.913402
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo
## 1 1 0.00632 -0.408179933 FALSE FALSE FALSE
## 2 2 0.02731 -0.405881056 FALSE FALSE FALSE
## 3 3 0.02729 -0.405883247 FALSE FALSE FALSE
## 4 4 0.03237 -0.405326873 FALSE FALSE FALSE
## 5 5 0.02985 -0.405602869 FALSE FALSE FALSE
## 6 6 0.14455 -0.393040644 FALSE FALSE FALSE
## 7 7 0.21124 -0.385736592 FALSE FALSE FALSE
## 8 8 0.17004 -0.390248917 FALSE FALSE FALSE
## 9 9 0.22489 -0.384241611 FALSE FALSE FALSE
## 10 10 0.11747 -0.396006512 FALSE FALSE FALSE
## 11 11 0.62976 -0.339899257 FALSE FALSE FALSE
## 12 12 0.63796 -0.339001173 FALSE FALSE FALSE
## 13 13 0.78420 -0.322984610 FALSE FALSE FALSE
## 14 14 0.72580 -0.329380721 FALSE FALSE FALSE
## 15 15 0.85204 -0.315554607 FALSE FALSE FALSE
## 16 16 0.98843 -0.300616840 FALSE FALSE FALSE
## 17 17 0.75026 -0.326701802 FALSE FALSE FALSE
## 18 18 0.84054 -0.316814115 FALSE FALSE FALSE
## 19 19 0.95577 -0.304193843 FALSE FALSE FALSE
## 20 20 1.00245 -0.299081335 FALSE FALSE FALSE
## 21 21 1.13081 -0.285023033 FALSE FALSE FALSE
## 22 22 1.35472 -0.260499862 FALSE FALSE FALSE
## 23 23 1.15172 -0.282732919 FALSE FALSE FALSE
## 24 24 1.61282 -0.232232118 FALSE FALSE FALSE
## 25 25 0.06417 -0.401844059 FALSE FALSE FALSE
## 26 26 0.09744 -0.398200247 FALSE FALSE FALSE
## 27 27 0.17505 -0.389700210 FALSE FALSE FALSE
## 28 28 0.12744 -0.394914573 FALSE FALSE FALSE
## 29 29 0.14150 -0.393374688 FALSE FALSE FALSE
## 30 30 0.17142 -0.390097776 FALSE FALSE FALSE
## 31 31 0.18836 -0.388242466 FALSE FALSE FALSE
## 32 32 0.21977 -0.384802366 FALSE FALSE FALSE
## 33 33 0.08873 -0.399154187 FALSE FALSE FALSE
## 34 34 0.01360 -0.407382609 FALSE FALSE FALSE
## 35 35 0.01311 -0.407436275 FALSE FALSE FALSE
## 36 36 0.02055 -0.406621428 FALSE FALSE FALSE
## 37 37 0.01432 -0.407303753 FALSE FALSE FALSE
## 38 38 0.15445 -0.391956372 FALSE FALSE FALSE
## 39 39 0.17171 -0.390066015 FALSE FALSE FALSE
## 40 40 0.11027 -0.396795074 FALSE FALSE FALSE
## 41 41 0.01951 -0.406735331 FALSE FALSE FALSE
## 42 42 0.03584 -0.404946830 FALSE FALSE FALSE
## 43 43 0.04379 -0.404076126 FALSE FALSE FALSE
## 44 44 0.05789 -0.402531860 FALSE FALSE FALSE
## 45 45 0.12816 -0.394835717 FALSE FALSE FALSE
## 46 46 0.08826 -0.399205663 FALSE FALSE FALSE
## 47 47 0.09164 -0.398835477 FALSE FALSE FALSE
## 48 48 0.19539 -0.387472523 FALSE FALSE FALSE
## 49 49 0.07896 -0.400224222 FALSE FALSE FALSE
## 50 50 0.09512 -0.398454339 FALSE FALSE FALSE
## 51 51 0.10153 -0.397752300 FALSE FALSE FALSE
## 52 52 0.08707 -0.399335995 FALSE FALSE FALSE
## 53 53 0.08387 -0.399686467 FALSE FALSE FALSE
## 54 54 0.04113 -0.404367456 FALSE FALSE FALSE
## 55 55 0.04462 -0.403985223 FALSE FALSE FALSE
## 56 56 0.03659 -0.404864688 FALSE FALSE FALSE
## 57 57 0.05059 -0.403331374 FALSE FALSE FALSE
## 58 58 0.05735 -0.402591002 FALSE FALSE FALSE
## 59 59 0.05188 -0.403190090 FALSE FALSE FALSE
## 60 60 0.07151 -0.401040164 FALSE FALSE FALSE
## 61 61 0.05660 -0.402673144 FALSE FALSE FALSE
## 62 62 0.05302 -0.403065234 FALSE FALSE FALSE
## 63 63 0.04684 -0.403742083 FALSE FALSE FALSE
## 64 64 0.03932 -0.404565692 FALSE FALSE FALSE
## 65 65 0.04203 -0.404268886 FALSE FALSE FALSE
## 66 66 0.02875 -0.405723344 FALSE FALSE FALSE
## 67 67 0.04294 -0.404169220 FALSE FALSE FALSE
## 68 68 0.12204 -0.395505995 FALSE FALSE FALSE
## 69 69 0.11504 -0.396272652 FALSE FALSE FALSE
## 70 70 0.12083 -0.395638517 FALSE FALSE FALSE
## 71 71 0.08187 -0.399905511 FALSE FALSE FALSE
## 72 72 0.06860 -0.401358874 FALSE FALSE FALSE
## 73 73 0.14866 -0.392590507 FALSE FALSE FALSE
## 74 74 0.11432 -0.396351508 FALSE FALSE FALSE
## 75 75 0.22876 -0.383817759 FALSE FALSE FALSE
## 76 76 0.21161 -0.385696069 FALSE FALSE FALSE
## 77 77 0.13960 -0.393582780 FALSE FALSE FALSE
## 78 78 0.13262 -0.394347247 FALSE FALSE FALSE
## 79 79 0.13117 -0.394506055 FALSE FALSE FALSE
## 80 80 0.10793 -0.397051356 FALSE FALSE FALSE
## 81 81 0.10084 -0.397827871 FALSE FALSE FALSE
## 82 82 0.12329 -0.395369092 FALSE FALSE FALSE
## 83 83 0.22212 -0.384544988 FALSE FALSE FALSE
## 84 84 0.14231 -0.393285975 FALSE FALSE FALSE
## 85 85 0.17134 -0.390106538 FALSE FALSE FALSE
## 86 86 0.13158 -0.394461151 FALSE FALSE FALSE
## 87 87 0.15098 -0.392336415 FALSE FALSE FALSE
## 88 88 0.14476 -0.393017645 FALSE FALSE FALSE
## 89 89 0.06899 -0.401316161 FALSE FALSE FALSE
## 90 90 0.07165 -0.401024831 FALSE FALSE FALSE
## 91 91 0.09299 -0.398687622 FALSE FALSE FALSE
## 92 92 0.15038 -0.392402128 FALSE FALSE FALSE
## 93 93 0.09849 -0.398085248 FALSE FALSE FALSE
## 94 94 0.16902 -0.390360630 FALSE FALSE FALSE
## 95 95 0.25915 -0.380489372 FALSE FALSE FALSE
## 96 96 0.32543 -0.373230224 FALSE FALSE FALSE
## 97 97 0.88125 -0.312355456 FALSE FALSE FALSE
## 98 98 0.34006 -0.371627910 FALSE FALSE FALSE
## 99 99 1.19294 -0.278218404 FALSE FALSE FALSE
## 100 100 0.59005 -0.344248393 FALSE FALSE FALSE
## 101 101 0.32982 -0.372749420 FALSE FALSE FALSE
## 102 102 0.97617 -0.301959585 FALSE FALSE FALSE
## 103 103 0.55778 -0.347782683 FALSE FALSE FALSE
## 104 104 0.32264 -0.373535791 FALSE FALSE FALSE
## 105 105 0.35233 -0.370284070 FALSE FALSE FALSE
## 106 106 0.24980 -0.381513407 FALSE FALSE FALSE
## 107 107 0.54452 -0.349234950 FALSE FALSE FALSE
## 108 108 0.29090 -0.377012034 FALSE FALSE FALSE
## 109 109 3.32105 -0.045142584 FALSE FALSE FALSE
## 110 110 4.09740 0.039885170 FALSE FALSE FALSE
## 111 111 2.77974 -0.104428181 FALSE FALSE FALSE
## 112 112 2.37934 -0.148280970 FALSE FALSE FALSE
## 113 113 2.15505 -0.172845760 FALSE FALSE FALSE
## 114 114 2.36862 -0.149455050 FALSE FALSE FALSE
## 115 115 2.73397 -0.109441023 FALSE FALSE FALSE
## 116 116 1.65660 -0.227437225 FALSE FALSE FALSE
## 117 117 1.49632 -0.244991483 FALSE FALSE FALSE
## 118 118 1.12658 -0.285486313 FALSE FALSE FALSE
## 119 119 2.14918 -0.173488657 FALSE FALSE FALSE
## 120 120 1.41385 -0.254023799 FALSE FALSE FALSE
## 121 121 3.53501 -0.021709160 FALSE FALSE FALSE
## 122 122 2.44668 -0.140905728 FALSE FALSE FALSE
## 123 123 1.22358 -0.274862636 FALSE FALSE FALSE
## 124 124 1.34284 -0.261800988 FALSE FALSE FALSE
## 125 125 1.42502 -0.252800434 FALSE FALSE FALSE
## 126 126 1.27346 -0.269399656 FALSE FALSE FALSE
## 127 127 1.46336 -0.248601343 FALSE FALSE FALSE
## 128 128 1.83377 -0.208033132 FALSE FALSE FALSE
## 129 129 1.51902 -0.242505323 FALSE FALSE FALSE
## 130 130 2.24236 -0.163283355 FALSE FALSE FALSE
## 131 131 2.92400 -0.088628472 FALSE FALSE FALSE
## 132 132 2.01019 -0.188711182 FALSE FALSE FALSE
## 133 133 1.80028 -0.211701039 FALSE FALSE FALSE
## 134 134 2.30040 -0.156926672 FALSE FALSE FALSE
## 135 135 1.20742 -0.276632519 FALSE FALSE FALSE
## 136 136 2.31390 -0.155448119 FALSE FALSE FALSE
## 137 137 0.13914 -0.393633161 FALSE FALSE FALSE
## 138 138 0.09178 -0.398820144 FALSE FALSE FALSE
## 139 139 0.08447 -0.399620753 FALSE FALSE FALSE
## 140 140 0.06664 -0.401573538 FALSE FALSE FALSE
## 141 141 0.07022 -0.401181448 FALSE FALSE FALSE
## 142 142 0.06642 -0.401597633 FALSE FALSE FALSE
## 143 143 0.05780 -0.402541717 FALSE FALSE FALSE
## 144 144 0.06588 -0.401656775 FALSE FALSE FALSE
## 145 145 0.10008 -0.397911108 FALSE FALSE FALSE
## 146 146 0.08308 -0.399772989 FALSE FALSE FALSE
## 147 147 0.06047 -0.402249292 FALSE FALSE FALSE
## 148 148 0.07875 -0.400247222 FALSE FALSE FALSE
## 149 149 0.12579 -0.395095286 FALSE FALSE FALSE
## 150 150 0.08370 -0.399705085 FALSE FALSE FALSE
## 151 151 0.09068 -0.398940619 FALSE FALSE FALSE
## 152 152 0.06911 -0.401303018 FALSE FALSE FALSE
## 153 153 0.08664 -0.399383089 FALSE FALSE FALSE
## 154 154 0.02187 -0.406476858 FALSE FALSE FALSE
## 155 155 0.01439 -0.407296086 FALSE FALSE FALSE
## 156 156 0.01381 -0.407359609 FALSE FALSE FALSE
## 157 157 0.04011 -0.404479169 FALSE FALSE FALSE
## 158 158 0.04666 -0.403761797 FALSE FALSE FALSE
## 159 159 0.03768 -0.404745309 FALSE FALSE FALSE
## 160 160 0.03150 -0.405422157 FALSE FALSE FALSE
## 161 161 0.01778 -0.406924805 FALSE FALSE FALSE
## 162 162 0.03445 -0.405099066 FALSE FALSE FALSE
## 163 163 0.02177 -0.406487811 FALSE FALSE FALSE
## 164 164 0.03510 -0.405027876 FALSE FALSE FALSE
## 165 165 0.25199 -0.381273552 FALSE FALSE FALSE
## 166 166 0.13587 -0.393991299 FALSE FALSE FALSE
## 167 167 0.43571 -0.361152088 FALSE FALSE FALSE
## 168 168 0.17446 -0.389764828 FALSE FALSE FALSE
## 169 169 0.21719 -0.385084934 FALSE FALSE FALSE
## 170 170 0.14052 -0.393482020 FALSE FALSE FALSE
## 171 171 0.28955 -0.377159889 FALSE FALSE FALSE
## 172 172 0.19802 -0.387184479 FALSE FALSE FALSE
## 173 173 0.04560 -0.403877891 FALSE FALSE FALSE
## 174 174 0.11069 -0.396749074 FALSE FALSE FALSE
## 175 175 0.11425 -0.396359175 FALSE FALSE FALSE
## 176 176 0.35809 -0.369653221 FALSE FALSE FALSE
## 177 177 0.40771 -0.364218717 FALSE FALSE FALSE
## 178 178 0.62356 -0.340578296 FALSE FALSE FALSE
## 179 179 0.61470 -0.341548665 FALSE FALSE FALSE
## 180 180 0.31533 -0.374336400 FALSE FALSE FALSE
## 181 181 0.38214 -0.367019206 FALSE FALSE FALSE
## 182 182 0.41238 -0.363707247 FALSE FALSE FALSE
## 183 183 0.29819 -0.376213615 FALSE FALSE FALSE
## 184 184 0.53700 -0.350058559 FALSE FALSE FALSE
## 185 185 0.46296 -0.358167601 FALSE FALSE FALSE
## 186 186 0.57529 -0.345864944 FALSE FALSE FALSE
## 187 187 0.33147 -0.372568708 FALSE FALSE FALSE
## 188 188 0.44791 -0.359815914 FALSE FALSE FALSE
## 189 189 0.33045 -0.372680421 FALSE FALSE FALSE
## 190 190 0.52058 -0.351856918 FALSE FALSE FALSE
## 191 191 0.51183 -0.352815239 FALSE FALSE FALSE
## 192 192 0.09252 -0.398739097 FALSE FALSE FALSE
## 193 193 0.11329 -0.396464316 FALSE FALSE FALSE
## 194 194 0.10612 -0.397249592 FALSE FALSE FALSE
## 195 195 0.10290 -0.397602254 FALSE FALSE FALSE
## 196 196 0.12757 -0.394900336 FALSE FALSE FALSE
## 197 197 0.20608 -0.386301728 FALSE FALSE FALSE
## 198 198 0.33983 -0.371653100 FALSE FALSE FALSE
## 199 199 0.19657 -0.387343287 FALSE FALSE FALSE
## 200 200 0.16439 -0.390867719 FALSE FALSE FALSE
## 201 201 0.19073 -0.387982898 FALSE FALSE FALSE
## 202 202 0.21409 -0.385424453 FALSE FALSE FALSE
## 203 203 0.08221 -0.399868274 FALSE FALSE FALSE
## 204 204 0.04819 -0.403594228 FALSE FALSE FALSE
## 205 205 0.03548 -0.404986258 FALSE FALSE FALSE
## 206 206 0.01538 -0.407187659 FALSE FALSE FALSE
## 207 207 0.61154 -0.341894756 FALSE FALSE FALSE
## 208 208 0.65665 -0.336954198 FALSE FALSE FALSE
## 209 209 0.54011 -0.349717944 FALSE FALSE FALSE
## 210 210 0.53412 -0.350373984 FALSE FALSE FALSE
## 211 211 0.52014 -0.351905108 FALSE FALSE FALSE
## 212 212 0.78570 -0.322820326 FALSE FALSE FALSE
## 213 213 0.54050 -0.349675230 FALSE FALSE FALSE
## 214 214 0.09065 -0.398943904 FALSE FALSE FALSE
## 215 215 0.29916 -0.376107378 FALSE FALSE FALSE
## 216 216 0.16211 -0.391117430 FALSE FALSE FALSE
## 217 217 0.11460 -0.396320842 FALSE FALSE FALSE
## 218 218 0.10469 -0.397406209 FALSE FALSE FALSE
## 219 219 0.06127 -0.402161674 FALSE FALSE FALSE
## 220 220 0.07978 -0.400134413 FALSE FALSE FALSE
## 221 221 0.03578 -0.404953401 FALSE FALSE FALSE
## 222 222 0.03705 -0.404814308 FALSE FALSE FALSE
## 223 223 0.06129 -0.402159483 FALSE FALSE FALSE
## 224 224 0.01501 -0.407228182 FALSE FALSE FALSE
## 225 225 0.00906 -0.407879841 FALSE FALSE FALSE
## 226 226 0.01096 -0.407671748 FALSE FALSE FALSE
## 227 227 0.01965 -0.406719998 FALSE FALSE FALSE
## 228 228 0.03871 -0.404632500 FALSE FALSE FALSE
## 229 229 0.04590 -0.403845034 FALSE FALSE FALSE
## 230 230 0.04297 -0.404165935 FALSE FALSE FALSE
## 231 231 0.03502 -0.405036638 FALSE FALSE FALSE
## 232 232 0.07886 -0.400235174 FALSE FALSE FALSE
## 233 233 0.03615 -0.404912878 FALSE FALSE FALSE
## 234 234 0.12932 -0.394708671 FALSE FALSE FALSE
## 235 235 0.05372 -0.402988568 FALSE FALSE FALSE
## 236 236 0.14103 -0.393426163 FALSE FALSE FALSE
## 237 237 0.06466 -0.401790393 FALSE FALSE FALSE
## 238 238 0.03537 -0.404998305 FALSE FALSE FALSE
## 239 239 0.10000 -0.397919869 FALSE FALSE FALSE
## 240 240 0.05515 -0.402831951 FALSE FALSE FALSE
## 241 241 0.04932 -0.403470467 FALSE FALSE FALSE
## 242 242 0.49298 -0.354879737 FALSE FALSE FALSE
## 243 243 0.34940 -0.370604971 FALSE FALSE FALSE
## 244 244 0.79041 -0.322304475 FALSE FALSE FALSE
## 245 245 0.26169 -0.380211185 FALSE FALSE FALSE
## 246 246 0.26938 -0.379368957 FALSE FALSE FALSE
## 247 247 0.36920 -0.368436426 FALSE FALSE FALSE
## 248 248 0.25356 -0.381101602 FALSE FALSE FALSE
## 249 249 0.24522 -0.382015019 FALSE FALSE FALSE
## 250 250 0.40202 -0.364841899 FALSE FALSE FALSE
## 251 251 0.47547 -0.356797475 FALSE FALSE FALSE
## 252 252 0.16760 -0.390516152 FALSE FALSE FALSE
## 253 253 0.18159 -0.388983933 FALSE FALSE FALSE
## 254 254 0.35114 -0.370414402 FALSE FALSE FALSE
## 255 255 0.28392 -0.377776501 FALSE FALSE FALSE
## 256 256 0.34109 -0.371515102 FALSE FALSE FALSE
## 257 257 0.19186 -0.387859137 FALSE FALSE FALSE
## 258 258 0.30347 -0.375635337 FALSE FALSE FALSE
## 259 259 0.24103 -0.382473918 FALSE FALSE FALSE
## 260 260 0.06617 -0.401625014 FALSE FALSE FALSE
## 261 261 0.06724 -0.401507825 FALSE FALSE FALSE
## 262 262 0.04544 -0.403895414 FALSE FALSE FALSE
## 263 263 0.05023 -0.403370802 FALSE FALSE FALSE
## 264 264 0.05083 -0.403305088 FALSE FALSE FALSE
## 265 265 0.03738 -0.404778165 FALSE FALSE FALSE
## 266 266 0.03961 -0.404533930 FALSE FALSE FALSE
## 267 267 0.03427 -0.405118780 FALSE FALSE FALSE
## 268 268 0.03041 -0.405541537 FALSE FALSE FALSE
## 269 269 0.05497 -0.402851665 FALSE FALSE FALSE
## 270 270 0.06151 -0.402135389 FALSE FALSE FALSE
## 271 271 0.01301 -0.407447227 FALSE FALSE FALSE
## 272 272 0.02498 -0.406136244 FALSE FALSE FALSE
## 273 273 0.02543 -0.406086959 FALSE FALSE FALSE
## 274 274 0.03049 -0.405532775 FALSE FALSE FALSE
## 275 275 0.03113 -0.405462681 FALSE FALSE FALSE
## 276 276 0.06162 -0.402123341 FALSE FALSE FALSE
## 277 277 0.01870 -0.406824045 FALSE FALSE FALSE
## 278 278 0.01501 -0.407228182 FALSE FALSE FALSE
## 279 279 0.02899 -0.405697059 FALSE FALSE FALSE
## 280 280 0.07950 -0.400165080 FALSE FALSE FALSE
## 281 281 0.01709 -0.407000376 FALSE FALSE FALSE
## 282 282 0.04301 -0.404161554 FALSE FALSE FALSE
## 283 283 0.10659 -0.397198117 FALSE FALSE FALSE
## 284 284 8.98296 0.574963668 FALSE FALSE FALSE
## 285 285 3.84970 0.012756459 FALSE FALSE FALSE
## 286 286 5.20177 0.160838477 FALSE FALSE FALSE
## 287 287 4.54192 0.088570089 FALSE FALSE FALSE
## 288 288 3.83684 0.011348001 FALSE FALSE FALSE
## 289 289 3.67822 -0.006024450 FALSE FALSE FALSE
## 290 290 4.22239 0.053574381 FALSE FALSE FALSE
## 291 291 3.47428 -0.028360459 FALSE FALSE FALSE
## 292 292 4.55587 0.090097927 FALSE FALSE FALSE
## 293 293 3.69695 -0.003973095 FALSE FALSE FALSE
## 294 294 13.52220 1.072112352 FALSE TRUE FALSE
## 295 295 4.89822 0.127592938 FALSE FALSE FALSE
## 296 296 6.53876 0.307268897 FALSE FALSE FALSE
## 297 297 9.23230 0.602271996 FALSE TRUE FALSE
## 298 298 8.26725 0.496577356 FALSE FALSE FALSE
## 299 299 11.10810 0.807714206 FALSE TRUE FALSE
## 300 300 18.49820 1.617096060 FALSE TRUE TRUE
## 301 301 19.60910 1.738764549 FALSE TRUE TRUE
## 302 302 15.28800 1.265507093 FALSE TRUE TRUE
## 303 303 9.82349 0.667020573 FALSE TRUE FALSE
## 304 304 23.64820 2.181136675 FALSE TRUE TRUE
## 305 305 88.97620 9.336019252 TRUE TRUE TRUE
## 306 306 9.18702 0.597312819 FALSE TRUE FALSE
## 307 307 7.99248 0.466483872 FALSE FALSE FALSE
## 308 308 24.39380 2.262796613 FALSE TRUE TRUE
## 309 309 22.59710 2.066017628 FALSE TRUE TRUE
## 310 310 14.33370 1.160989819 FALSE TRUE FALSE
## 311 311 5.29305 0.170835687 FALSE FALSE FALSE
## 312 312 11.57790 0.859167853 FALSE TRUE FALSE
## 313 313 8.64476 0.537923176 FALSE FALSE FALSE
## 314 314 13.35980 1.054325906 FALSE TRUE FALSE
## 315 315 8.71675 0.545807697 FALSE FALSE FALSE
## 316 316 5.87205 0.234249185 FALSE FALSE FALSE
## 317 317 9.91655 0.677212732 FALSE TRUE FALSE
## 318 318 25.04610 2.334238107 FALSE TRUE TRUE
## 319 319 9.59571 0.642073549 FALSE TRUE FALSE
## 320 320 24.80170 2.307470820 FALSE TRUE TRUE
## 321 321 41.52920 4.139507593 TRUE TRUE TRUE
## 322 322 67.92080 7.029980270 TRUE TRUE TRUE
## 323 323 7.40389 0.402020054 FALSE FALSE FALSE
## 324 324 51.13580 5.191645956 TRUE TRUE TRUE
## 325 325 14.05070 1.129994966 FALSE TRUE FALSE
## 326 326 18.81100 1.651354682 FALSE TRUE TRUE
## 327 327 28.65580 2.729581293 FALSE TRUE TRUE
## 328 328 45.74610 4.601352810 TRUE TRUE TRUE
## 329 329 18.08460 1.571797575 FALSE TRUE TRUE
## 330 330 10.83420 0.777716007 FALSE TRUE FALSE
## 331 331 25.94060 2.432205938 FALSE TRUE TRUE
## 332 332 73.53410 7.644762636 TRUE TRUE TRUE
## 333 333 11.81230 0.884839915 FALSE TRUE FALSE
## 334 334 11.08740 0.805447092 FALSE TRUE FALSE
## 335 335 7.02259 0.360259144 FALSE FALSE FALSE
## 336 336 12.04820 0.910676261 FALSE TRUE FALSE
## 337 337 7.05042 0.363307154 FALSE FALSE FALSE
## 338 338 8.79212 0.554062404 FALSE FALSE FALSE
## 339 339 15.86030 1.328186791 FALSE TRUE TRUE
## 340 340 12.24720 0.932471228 FALSE TRUE FALSE
## 341 341 37.66190 3.715951425 TRUE TRUE TRUE
## 342 342 9.33889 0.613945994 FALSE TRUE FALSE
## 343 343 8.49213 0.521206764 FALSE FALSE FALSE
## 344 344 10.06230 0.693175629 FALSE TRUE FALSE
## 345 345 6.44405 0.296896026 FALSE FALSE FALSE
## 346 346 5.58107 0.202380343 FALSE FALSE FALSE
## 347 347 13.91340 1.114957534 FALSE TRUE FALSE
## 348 348 11.16040 0.813442230 FALSE TRUE FALSE
## 349 349 14.42080 1.170529225 FALSE TRUE FALSE
## 350 350 13.67810 1.089186901 FALSE TRUE FALSE
## 351 351 9.39063 0.619612685 FALSE TRUE FALSE
## 352 352 22.05110 2.006218371 FALSE TRUE TRUE
## 353 353 5.66637 0.211722607 FALSE FALSE FALSE
## 354 354 12.80230 0.993267140 FALSE TRUE FALSE
## 355 355 10.67180 0.759929562 FALSE TRUE FALSE
## 356 356 6.28807 0.279812714 FALSE FALSE FALSE
## 357 357 9.92485 0.678121768 FALSE TRUE FALSE
## 358 358 7.52601 0.415394935 FALSE FALSE FALSE
## 359 359 6.71772 0.326869035 FALSE FALSE FALSE
## 360 360 5.44114 0.187054866 FALSE FALSE FALSE
## 361 361 8.24809 0.494478906 FALSE FALSE FALSE
## 362 362 9.51363 0.633083946 FALSE TRUE FALSE
## 363 363 4.75237 0.111619089 FALSE FALSE FALSE
## 364 364 4.66883 0.102469583 FALSE FALSE FALSE
## 365 365 8.20058 0.489275494 FALSE FALSE FALSE
## 366 366 6.80117 0.336008683 FALSE FALSE FALSE
## 367 367 6.65492 0.319991025 FALSE FALSE FALSE
## 368 368 7.83932 0.449709414 FALSE FALSE FALSE
## 369 369 3.16360 -0.062386893 FALSE FALSE FALSE
## 370 370 3.77498 0.004572942 FALSE FALSE FALSE
## 371 371 15.57570 1.297016701 FALSE TRUE TRUE
## 372 372 13.07510 1.023144864 FALSE TRUE FALSE
## 373 373 4.34879 0.067418019 FALSE FALSE FALSE
## 374 374 4.03841 0.033424441 FALSE FALSE FALSE
## 375 375 3.56868 -0.018021540 FALSE FALSE FALSE
## 376 376 4.64689 0.100066661 FALSE FALSE FALSE
## 377 377 8.05579 0.473417739 FALSE FALSE FALSE
## 378 378 6.39312 0.291318048 FALSE FALSE FALSE
## 379 379 4.87141 0.124656641 FALSE FALSE FALSE
## 380 380 15.02340 1.236527453 FALSE TRUE TRUE
## 381 381 10.23300 0.711871111 FALSE TRUE FALSE
## 382 382 14.33370 1.160989819 FALSE TRUE FALSE
## 383 383 5.82401 0.228987726 FALSE FALSE FALSE
## 384 384 5.70818 0.216301741 FALSE FALSE FALSE
## 385 385 5.73116 0.218818567 FALSE FALSE FALSE
## 386 386 2.81838 -0.100196233 FALSE FALSE FALSE
## 387 387 2.37857 -0.148365302 FALSE FALSE FALSE
## 388 388 3.67367 -0.006522778 FALSE FALSE FALSE
## 389 389 4.83567 0.120742309 FALSE FALSE FALSE
## 390 390 0.15086 -0.392349558 FALSE FALSE FALSE
## 391 391 0.18337 -0.388788983 FALSE FALSE FALSE
## 392 392 0.20746 -0.386150587 FALSE FALSE FALSE
## 393 393 0.10574 -0.397291211 FALSE FALSE FALSE
## 394 394 0.11132 -0.396680075 FALSE FALSE FALSE
## 395 395 0.27957 -0.378252923 FALSE FALSE FALSE
## 396 396 0.17899 -0.389268691 FALSE FALSE FALSE
## 397 397 0.28960 -0.377154413 FALSE FALSE FALSE
## 398 398 0.23912 -0.382683106 FALSE FALSE FALSE
## 399 399 0.17783 -0.389395737 FALSE FALSE FALSE
## 400 400 0.22438 -0.384297467 FALSE FALSE FALSE
## 401 401 0.06263 -0.402012723 FALSE FALSE FALSE
## 402 402 0.04527 -0.403914033 FALSE FALSE FALSE
## 403 403 0.10959 -0.396869549 FALSE FALSE FALSE
## 404 404 0.04741 -0.403679655 FALSE FALSE FALSE
## Z_robusto outlier_MAD
## 1 -0.759685115 FALSE
## 2 -0.695910881 FALSE
## 3 -0.695971647 FALSE
## 4 -0.680537007 FALSE
## 5 -0.688193561 FALSE
## 6 -0.339698819 FALSE
## 7 -0.137073586 FALSE
## 8 -0.262252167 FALSE
## 9 -0.095600584 FALSE
## 10 -0.421976392 FALSE
## 11 1.134522048 FALSE
## 12 1.159436232 FALSE
## 13 1.603759431 FALSE
## 14 1.426321830 FALSE
## 15 1.809878727 FALSE
## 16 2.224274523 FALSE
## 17 1.500639017 FALSE
## 18 1.774938104 FALSE
## 19 2.125043152 FALSE
## 20 2.266871701 FALSE
## 21 2.656869826 FALSE
## 22 3.337178959 FALSE
## 23 2.720400995 FALSE
## 24 4.121368085 TRUE
## 25 -0.583918586 FALSE
## 26 -0.482833843 FALSE
## 27 -0.247030209 FALSE
## 28 -0.391684390 FALSE
## 29 -0.348965680 FALSE
## 30 -0.258059293 FALSE
## 31 -0.206590235 FALSE
## 32 -0.111156758 FALSE
## 33 -0.509297568 FALSE
## 34 -0.737566181 FALSE
## 35 -0.739054955 FALSE
## 36 -0.716449891 FALSE
## 37 -0.735378594 FALSE
## 38 -0.309619500 FALSE
## 39 -0.257178181 FALSE
## 40 -0.443852261 FALSE
## 41 -0.719609739 FALSE
## 42 -0.669994053 FALSE
## 43 -0.645839448 FALSE
## 44 -0.602999205 FALSE
## 45 -0.389496804 FALSE
## 46 -0.510725576 FALSE
## 47 -0.500456071 FALSE
## 48 -0.185230880 FALSE
## 49 -0.538981906 FALSE
## 50 -0.489882734 FALSE
## 51 -0.470407135 FALSE
## 52 -0.514341171 FALSE
## 53 -0.524063779 FALSE
## 54 -0.653921366 FALSE
## 55 -0.643317647 FALSE
## 56 -0.667715317 FALSE
## 57 -0.625178905 FALSE
## 58 -0.604639895 FALSE
## 59 -0.621259479 FALSE
## 60 -0.561617354 FALSE
## 61 -0.606918632 FALSE
## 62 -0.617795800 FALSE
## 63 -0.636572587 FALSE
## 64 -0.659420717 FALSE
## 65 -0.651186883 FALSE
## 66 -0.691535707 FALSE
## 67 -0.648422016 FALSE
## 68 -0.408091292 FALSE
## 69 -0.429359498 FALSE
## 70 -0.411767653 FALSE
## 71 -0.530140409 FALSE
## 72 -0.570458851 FALSE
## 73 -0.327211344 FALSE
## 74 -0.431547084 FALSE
## 75 -0.083842305 FALSE
## 76 -0.135949409 FALSE
## 77 -0.354738479 FALSE
## 78 -0.375945918 FALSE
## 79 -0.380351475 FALSE
## 80 -0.450961918 FALSE
## 81 -0.472503572 FALSE
## 82 -0.404293398 FALSE
## 83 -0.104016717 FALSE
## 84 -0.346504645 FALSE
## 85 -0.258302358 FALSE
## 86 -0.379105766 FALSE
## 87 -0.320162453 FALSE
## 88 -0.339060773 FALSE
## 89 -0.569273908 FALSE
## 90 -0.561191990 FALSE
## 91 -0.496354345 FALSE
## 92 -0.321985442 FALSE
## 93 -0.479643612 FALSE
## 94 -0.265351249 FALSE
## 95 0.008492091 FALSE
## 96 0.209871615 FALSE
## 97 1.898627911 FALSE
## 98 0.254322165 FALSE
## 99 2.845640343 FALSE
## 100 1.013870556 FALSE
## 101 0.223209818 FALSE
## 102 2.187024780 FALSE
## 103 0.915824127 FALSE
## 104 0.201394716 FALSE
## 105 0.291602291 FALSE
## 106 -0.019916155 FALSE
## 107 0.875536069 FALSE
## 108 0.104958595 FALSE
## 109 9.311509080 TRUE
## 110 11.670305003 TRUE
## 111 7.666838736 TRUE
## 112 6.450297372 TRUE
## 113 5.768833679 TRUE
## 114 6.417726634 TRUE
## 115 7.527775054 TRUE
## 116 4.254385520 TRUE
## 117 3.767404377 TRUE
## 118 2.644017753 FALSE
## 119 5.750998769 TRUE
## 120 3.516834531 TRUE
## 121 9.961586977 TRUE
## 122 6.654897510 TRUE
## 123 2.938734318 FALSE
## 124 3.301083776 FALSE
## 125 3.550772510 TRUE
## 126 3.090285475 FALSE
## 127 3.667261511 TRUE
## 128 4.792683805 TRUE
## 129 3.836374129 TRUE
## 130 6.034108970 TRUE
## 131 8.105146071 TRUE
## 132 5.328703354 TRUE
## 133 4.690930633 TRUE
## 134 6.210452778 TRUE
## 135 2.889635146 FALSE
## 136 6.251470032 TRUE
## 137 -0.356136104 FALSE
## 138 -0.500030707 FALSE
## 139 -0.522240790 FALSE
## 140 -0.576413948 FALSE
## 141 -0.565536780 FALSE
## 142 -0.577082378 FALSE
## 143 -0.603272654 FALSE
## 144 -0.578723068 FALSE
## 145 -0.474812691 FALSE
## 146 -0.526464048 FALSE
## 147 -0.595160352 FALSE
## 148 -0.539619952 FALSE
## 149 -0.396697610 FALSE
## 150 -0.524580293 FALSE
## 151 -0.503372853 FALSE
## 152 -0.568909310 FALSE
## 153 -0.515647646 FALSE
## 154 -0.712439315 FALSE
## 155 -0.735165912 FALSE
## 156 -0.736928135 FALSE
## 157 -0.657020448 FALSE
## 158 -0.637119484 FALSE
## 159 -0.664403553 FALSE
## 160 -0.683180341 FALSE
## 161 -0.724866024 FALSE
## 162 -0.674217311 FALSE
## 163 -0.712743147 FALSE
## 164 -0.672242406 FALSE
## 165 -0.013262245 FALSE
## 166 -0.366071394 FALSE
## 167 0.544937004 FALSE
## 168 -0.248822815 FALSE
## 169 -0.118995611 FALSE
## 170 -0.351943229 FALSE
## 171 0.100856870 FALSE
## 172 -0.177240111 FALSE
## 173 -0.640340098 FALSE
## 174 -0.442576168 FALSE
## 175 -0.431759767 FALSE
## 176 0.309102986 FALSE
## 177 0.459864181 FALSE
## 178 1.115684494 FALSE
## 179 1.088765023 FALSE
## 180 0.179184633 FALSE
## 181 0.382174464 FALSE
## 182 0.474053113 FALSE
## 183 0.127107912 FALSE
## 184 0.852687940 FALSE
## 185 0.627731090 FALSE
## 186 0.969025025 FALSE
## 187 0.228223038 FALSE
## 188 0.582004448 FALSE
## 189 0.225123957 FALSE
## 190 0.802798806 FALSE
## 191 0.776213549 FALSE
## 192 -0.497782354 FALSE
## 193 -0.434676549 FALSE
## 194 -0.456461268 FALSE
## 195 -0.466244643 FALSE
## 196 -0.391289409 FALSE
## 197 -0.152751291 FALSE
## 198 0.253623353 FALSE
## 199 -0.181645668 FALSE
## 200 -0.279418648 FALSE
## 201 -0.199389428 FALSE
## 202 -0.128414387 FALSE
## 203 -0.529107382 FALSE
## 204 -0.632470862 FALSE
## 205 -0.671087847 FALSE
## 206 -0.732157980 FALSE
## 207 1.079163947 FALSE
## 208 1.216222341 FALSE
## 209 0.862137100 FALSE
## 210 0.843937592 FALSE
## 211 0.801461947 FALSE
## 212 1.608316904 FALSE
## 213 0.863322043 FALSE
## 214 -0.503464003 FALSE
## 215 0.130055078 FALSE
## 216 -0.286346006 FALSE
## 217 -0.430696356 FALSE
## 218 -0.460806059 FALSE
## 219 -0.592729700 FALSE
## 220 -0.536490488 FALSE
## 221 -0.670176352 FALSE
## 222 -0.666317692 FALSE
## 223 -0.592668934 FALSE
## 224 -0.733282157 FALSE
## 225 -0.751360131 FALSE
## 226 -0.745587333 FALSE
## 227 -0.719184375 FALSE
## 228 -0.661274089 FALSE
## 229 -0.639428603 FALSE
## 230 -0.648330867 FALSE
## 231 -0.672485472 FALSE
## 232 -0.539285738 FALSE
## 233 -0.669052175 FALSE
## 234 -0.385972358 FALSE
## 235 -0.615668979 FALSE
## 236 -0.350393688 FALSE
## 237 -0.582429812 FALSE
## 238 -0.671422061 FALSE
## 239 -0.475055757 FALSE
## 240 -0.611324189 FALSE
## 241 -0.629037566 FALSE
## 242 0.718941309 FALSE
## 243 0.282700028 FALSE
## 244 1.622627368 FALSE
## 245 0.016209411 FALSE
## 246 0.039574054 FALSE
## 247 0.342858667 FALSE
## 248 -0.008492091 FALSE
## 249 -0.033831639 FALSE
## 250 0.442576168 FALSE
## 251 0.665740412 FALSE
## 252 -0.269665656 FALSE
## 253 -0.227159628 FALSE
## 254 0.287986696 FALSE
## 255 0.083751156 FALSE
## 256 0.257451630 FALSE
## 257 -0.195956132 FALSE
## 258 0.143150216 FALSE
## 259 -0.046562179 FALSE
## 260 -0.577841956 FALSE
## 261 -0.574590959 FALSE
## 262 -0.640826228 FALSE
## 263 -0.626272699 FALSE
## 264 -0.624449710 FALSE
## 265 -0.665315048 FALSE
## 266 -0.658539605 FALSE
## 267 -0.674764208 FALSE
## 268 -0.686492104 FALSE
## 269 -0.611871085 FALSE
## 270 -0.592000505 FALSE
## 271 -0.739358787 FALSE
## 272 -0.702990155 FALSE
## 273 -0.701622913 FALSE
## 274 -0.686249039 FALSE
## 275 -0.684304517 FALSE
## 276 -0.591666290 FALSE
## 277 -0.722070774 FALSE
## 278 -0.733282157 FALSE
## 279 -0.690806512 FALSE
## 280 -0.537341216 FALSE
## 281 -0.726962461 FALSE
## 282 -0.648209334 FALSE
## 283 -0.455033260 FALSE
## 284 26.514175698 TRUE
## 285 10.917714354 TRUE
## 286 15.025729045 TRUE
## 287 13.020896829 TRUE
## 288 10.878641622 TRUE
## 289 10.396704082 TRUE
## 290 12.050064007 TRUE
## 291 9.777070102 TRUE
## 292 13.063281325 TRUE
## 293 10.453611724 TRUE
## 294 40.305817109 TRUE
## 295 14.103448498 TRUE
## 296 19.087925943 TRUE
## 297 27.271749184 TRUE
## 298 24.339623202 TRUE
## 299 32.971020639 TRUE
## 300 55.424473021 TRUE
## 301 58.799737260 TRUE
## 302 45.670873904 TRUE
## 303 29.067970685 TRUE
## 304 71.071795759 TRUE
## 305 269.558844274 TRUE
## 306 27.134174276 TRUE
## 307 23.504785363 TRUE
## 308 73.337163494 TRUE
## 309 67.878222763 TRUE
## 310 42.771409809 TRUE
## 311 15.303066447 TRUE
## 312 34.398421070 TRUE
## 313 25.486617533 TRUE
## 314 39.812394738 TRUE
## 315 25.705345837 TRUE
## 316 17.062250886 TRUE
## 317 29.350716287 TRUE
## 318 75.319056430 TRUE
## 319 28.375903272 TRUE
## 320 74.576492221 TRUE
## 321 125.399907970 TRUE
## 322 205.585904627 TRUE
## 323 21.716463481 TRUE
## 324 154.587785760 TRUE
## 325 41.911566637 TRUE
## 326 56.374857983 TRUE
## 327 86.286462428 TRUE
## 328 138.212178893 TRUE
## 329 54.167825898 TRUE
## 330 32.138826134 TRUE
## 331 78.036829283 TRUE
## 332 222.640878749 TRUE
## 333 35.110602129 TRUE
## 334 32.908127516 TRUE
## 335 20.557953936 TRUE
## 336 35.827340659 TRUE
## 337 20.642510245 TRUE
## 338 25.934343645 TRUE
## 339 47.409701632 TRUE
## 340 36.431965363 TRUE
## 341 113.649832004 TRUE
## 342 27.595603190 TRUE
## 343 25.022879500 TRUE
## 344 29.793550713 TRUE
## 345 18.800167121 TRUE
## 346 16.178161960 TRUE
## 347 41.494405974 TRUE
## 348 33.129924518 TRUE
## 349 43.036047053 TRUE
## 350 40.779490432 TRUE
## 351 27.752805613 TRUE
## 352 66.219302721 TRUE
## 353 16.437330238 TRUE
## 354 38.118534072 TRUE
## 355 31.645403763 TRUE
## 356 18.326250732 TRUE
## 357 29.375934303 TRUE
## 358 22.087502521 TRUE
## 359 19.631662813 TRUE
## 360 15.753010529 TRUE
## 361 24.281409084 TRUE
## 362 28.126518369 TRUE
## 363 13.660310241 TRUE
## 364 13.406489398 TRUE
## 365 24.137058734 TRUE
## 366 19.885210207 TRUE
## 367 19.440856625 TRUE
## 368 23.039437023 TRUE
## 369 8.833126368 TRUE
## 370 10.690691451 TRUE
## 371 46.544997156 TRUE
## 372 38.947386430 TRUE
## 373 12.434107035 TRUE
## 374 11.491074796 TRUE
## 375 10.063887046 TRUE
## 376 13.339828765 TRUE
## 377 23.697141092 TRUE
## 378 18.645425733 TRUE
## 379 14.021991270 TRUE
## 380 44.866935730 TRUE
## 381 30.312191099 TRUE
## 382 42.771409809 TRUE
## 383 16.916290229 TRUE
## 384 16.564362192 TRUE
## 385 16.634182673 TRUE
## 386 7.784239231 TRUE
## 387 6.447957869 TRUE
## 388 10.382879748 TRUE
## 389 13.913401888 TRUE
## 390 -0.320527051 FALSE
## 391 -0.221751427 FALSE
## 392 -0.148558417 FALSE
## 393 -0.457615828 FALSE
## 394 -0.440662030 FALSE
## 395 0.070534485 FALSE
## 396 -0.235059247 FALSE
## 397 0.101008785 FALSE
## 398 -0.052365361 FALSE
## 399 -0.238583693 FALSE
## 400 -0.097150125 FALSE
## 401 -0.588597592 FALSE
## 402 -0.641342742 FALSE
## 403 -0.445918315 FALSE
## 404 -0.634840747 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 24 1.61282 -0.232232118 FALSE FALSE 4.121368 TRUE
## 2 109 3.32105 -0.045142584 FALSE FALSE 9.311509 TRUE
## 3 110 4.09740 0.039885170 FALSE FALSE 11.670305 TRUE
## 4 111 2.77974 -0.104428181 FALSE FALSE 7.666839 TRUE
## 5 112 2.37934 -0.148280970 FALSE FALSE 6.450297 TRUE
## 6 113 2.15505 -0.172845760 FALSE FALSE 5.768834 TRUE
## 7 114 2.36862 -0.149455050 FALSE FALSE 6.417727 TRUE
## 8 115 2.73397 -0.109441023 FALSE FALSE 7.527775 TRUE
## 9 116 1.65660 -0.227437225 FALSE FALSE 4.254386 TRUE
## 10 117 1.49632 -0.244991483 FALSE FALSE 3.767404 TRUE
## 11 119 2.14918 -0.173488657 FALSE FALSE 5.750999 TRUE
## 12 120 1.41385 -0.254023799 FALSE FALSE 3.516835 TRUE
## 13 121 3.53501 -0.021709160 FALSE FALSE 9.961587 TRUE
## 14 122 2.44668 -0.140905728 FALSE FALSE 6.654898 TRUE
## 15 125 1.42502 -0.252800434 FALSE FALSE 3.550773 TRUE
## 16 127 1.46336 -0.248601343 FALSE FALSE 3.667262 TRUE
## 17 128 1.83377 -0.208033132 FALSE FALSE 4.792684 TRUE
## 18 129 1.51902 -0.242505323 FALSE FALSE 3.836374 TRUE
## 19 130 2.24236 -0.163283355 FALSE FALSE 6.034109 TRUE
## 20 131 2.92400 -0.088628472 FALSE FALSE 8.105146 TRUE
## 21 132 2.01019 -0.188711182 FALSE FALSE 5.328703 TRUE
## 22 133 1.80028 -0.211701039 FALSE FALSE 4.690931 TRUE
## 23 134 2.30040 -0.156926672 FALSE FALSE 6.210453 TRUE
## 24 136 2.31390 -0.155448119 FALSE FALSE 6.251470 TRUE
## 25 284 8.98296 0.574963668 FALSE FALSE 26.514176 TRUE
## 26 285 3.84970 0.012756459 FALSE FALSE 10.917714 TRUE
## 27 286 5.20177 0.160838477 FALSE FALSE 15.025729 TRUE
## 28 287 4.54192 0.088570089 FALSE FALSE 13.020897 TRUE
## 29 288 3.83684 0.011348001 FALSE FALSE 10.878642 TRUE
## 30 289 3.67822 -0.006024450 FALSE FALSE 10.396704 TRUE
## 31 290 4.22239 0.053574381 FALSE FALSE 12.050064 TRUE
## 32 291 3.47428 -0.028360459 FALSE FALSE 9.777070 TRUE
## 33 292 4.55587 0.090097927 FALSE FALSE 13.063281 TRUE
## 34 293 3.69695 -0.003973095 FALSE FALSE 10.453612 TRUE
## 35 294 13.52220 1.072112352 FALSE TRUE 40.305817 TRUE
## 36 295 4.89822 0.127592938 FALSE FALSE 14.103448 TRUE
## 37 296 6.53876 0.307268897 FALSE FALSE 19.087926 TRUE
## 38 297 9.23230 0.602271996 FALSE TRUE 27.271749 TRUE
## 39 298 8.26725 0.496577356 FALSE FALSE 24.339623 TRUE
## 40 299 11.10810 0.807714206 FALSE TRUE 32.971021 TRUE
## 41 300 18.49820 1.617096060 FALSE TRUE 55.424473 TRUE
## 42 301 19.60910 1.738764549 FALSE TRUE 58.799737 TRUE
## 43 302 15.28800 1.265507093 FALSE TRUE 45.670874 TRUE
## 44 303 9.82349 0.667020573 FALSE TRUE 29.067971 TRUE
## 45 304 23.64820 2.181136675 FALSE TRUE 71.071796 TRUE
## 46 305 88.97620 9.336019252 TRUE TRUE 269.558844 TRUE
## 47 306 9.18702 0.597312819 FALSE TRUE 27.134174 TRUE
## 48 307 7.99248 0.466483872 FALSE FALSE 23.504785 TRUE
## 49 308 24.39380 2.262796613 FALSE TRUE 73.337163 TRUE
## 50 309 22.59710 2.066017628 FALSE TRUE 67.878223 TRUE
## 51 310 14.33370 1.160989819 FALSE TRUE 42.771410 TRUE
## 52 311 5.29305 0.170835687 FALSE FALSE 15.303066 TRUE
## 53 312 11.57790 0.859167853 FALSE TRUE 34.398421 TRUE
## 54 313 8.64476 0.537923176 FALSE FALSE 25.486618 TRUE
## 55 314 13.35980 1.054325906 FALSE TRUE 39.812395 TRUE
## 56 315 8.71675 0.545807697 FALSE FALSE 25.705346 TRUE
## 57 316 5.87205 0.234249185 FALSE FALSE 17.062251 TRUE
## 58 317 9.91655 0.677212732 FALSE TRUE 29.350716 TRUE
## 59 318 25.04610 2.334238107 FALSE TRUE 75.319056 TRUE
## 60 319 9.59571 0.642073549 FALSE TRUE 28.375903 TRUE
## 61 320 24.80170 2.307470820 FALSE TRUE 74.576492 TRUE
## 62 321 41.52920 4.139507593 TRUE TRUE 125.399908 TRUE
## 63 322 67.92080 7.029980270 TRUE TRUE 205.585905 TRUE
## 64 323 7.40389 0.402020054 FALSE FALSE 21.716463 TRUE
## 65 324 51.13580 5.191645956 TRUE TRUE 154.587786 TRUE
## 66 325 14.05070 1.129994966 FALSE TRUE 41.911567 TRUE
## 67 326 18.81100 1.651354682 FALSE TRUE 56.374858 TRUE
## 68 327 28.65580 2.729581293 FALSE TRUE 86.286462 TRUE
## 69 328 45.74610 4.601352810 TRUE TRUE 138.212179 TRUE
## 70 329 18.08460 1.571797575 FALSE TRUE 54.167826 TRUE
## 71 330 10.83420 0.777716007 FALSE TRUE 32.138826 TRUE
## 72 331 25.94060 2.432205938 FALSE TRUE 78.036829 TRUE
## 73 332 73.53410 7.644762636 TRUE TRUE 222.640879 TRUE
## 74 333 11.81230 0.884839915 FALSE TRUE 35.110602 TRUE
## 75 334 11.08740 0.805447092 FALSE TRUE 32.908128 TRUE
## 76 335 7.02259 0.360259144 FALSE FALSE 20.557954 TRUE
## 77 336 12.04820 0.910676261 FALSE TRUE 35.827341 TRUE
## 78 337 7.05042 0.363307154 FALSE FALSE 20.642510 TRUE
## 79 338 8.79212 0.554062404 FALSE FALSE 25.934344 TRUE
## 80 339 15.86030 1.328186791 FALSE TRUE 47.409702 TRUE
## 81 340 12.24720 0.932471228 FALSE TRUE 36.431965 TRUE
## 82 341 37.66190 3.715951425 TRUE TRUE 113.649832 TRUE
## 83 342 9.33889 0.613945994 FALSE TRUE 27.595603 TRUE
## 84 343 8.49213 0.521206764 FALSE FALSE 25.022880 TRUE
## 85 344 10.06230 0.693175629 FALSE TRUE 29.793551 TRUE
## 86 345 6.44405 0.296896026 FALSE FALSE 18.800167 TRUE
## 87 346 5.58107 0.202380343 FALSE FALSE 16.178162 TRUE
## 88 347 13.91340 1.114957534 FALSE TRUE 41.494406 TRUE
## 89 348 11.16040 0.813442230 FALSE TRUE 33.129925 TRUE
## 90 349 14.42080 1.170529225 FALSE TRUE 43.036047 TRUE
## 91 350 13.67810 1.089186901 FALSE TRUE 40.779490 TRUE
## 92 351 9.39063 0.619612685 FALSE TRUE 27.752806 TRUE
## 93 352 22.05110 2.006218371 FALSE TRUE 66.219303 TRUE
## 94 353 5.66637 0.211722607 FALSE FALSE 16.437330 TRUE
## 95 354 12.80230 0.993267140 FALSE TRUE 38.118534 TRUE
## 96 355 10.67180 0.759929562 FALSE TRUE 31.645404 TRUE
## 97 356 6.28807 0.279812714 FALSE FALSE 18.326251 TRUE
## 98 357 9.92485 0.678121768 FALSE TRUE 29.375934 TRUE
## 99 358 7.52601 0.415394935 FALSE FALSE 22.087503 TRUE
## 100 359 6.71772 0.326869035 FALSE FALSE 19.631663 TRUE
## 101 360 5.44114 0.187054866 FALSE FALSE 15.753011 TRUE
## 102 361 8.24809 0.494478906 FALSE FALSE 24.281409 TRUE
## 103 362 9.51363 0.633083946 FALSE TRUE 28.126518 TRUE
## 104 363 4.75237 0.111619089 FALSE FALSE 13.660310 TRUE
## 105 364 4.66883 0.102469583 FALSE FALSE 13.406489 TRUE
## 106 365 8.20058 0.489275494 FALSE FALSE 24.137059 TRUE
## 107 366 6.80117 0.336008683 FALSE FALSE 19.885210 TRUE
## 108 367 6.65492 0.319991025 FALSE FALSE 19.440857 TRUE
## 109 368 7.83932 0.449709414 FALSE FALSE 23.039437 TRUE
## 110 369 3.16360 -0.062386893 FALSE FALSE 8.833126 TRUE
## 111 370 3.77498 0.004572942 FALSE FALSE 10.690691 TRUE
## 112 371 15.57570 1.297016701 FALSE TRUE 46.544997 TRUE
## 113 372 13.07510 1.023144864 FALSE TRUE 38.947386 TRUE
## 114 373 4.34879 0.067418019 FALSE FALSE 12.434107 TRUE
## 115 374 4.03841 0.033424441 FALSE FALSE 11.491075 TRUE
## 116 375 3.56868 -0.018021540 FALSE FALSE 10.063887 TRUE
## 117 376 4.64689 0.100066661 FALSE FALSE 13.339829 TRUE
## 118 377 8.05579 0.473417739 FALSE FALSE 23.697141 TRUE
## 119 378 6.39312 0.291318048 FALSE FALSE 18.645426 TRUE
## 120 379 4.87141 0.124656641 FALSE FALSE 14.021991 TRUE
## 121 380 15.02340 1.236527453 FALSE TRUE 44.866936 TRUE
## 122 381 10.23300 0.711871111 FALSE TRUE 30.312191 TRUE
## 123 382 14.33370 1.160989819 FALSE TRUE 42.771410 TRUE
## 124 383 5.82401 0.228987726 FALSE FALSE 16.916290 TRUE
## 125 384 5.70818 0.216301741 FALSE FALSE 16.564362 TRUE
## 126 385 5.73116 0.218818567 FALSE FALSE 16.634183 TRUE
## 127 386 2.81838 -0.100196233 FALSE FALSE 7.784239 TRUE
## 128 387 2.37857 -0.148365302 FALSE FALSE 6.447958 TRUE
## 129 388 3.67367 -0.006522778 FALSE FALSE 10.382880 TRUE
## 130 389 4.83567 0.120742309 FALSE FALSE 13.913402 TRUE
## metodos_evaluados numero_metodos
## 1 3 1
## 2 3 1
## 3 3 1
## 4 3 1
## 5 3 1
## 6 3 1
## 7 3 1
## 8 3 1
## 9 3 1
## 10 3 1
## 11 3 1
## 12 3 1
## 13 3 1
## 14 3 1
## 15 3 1
## 16 3 1
## 17 3 1
## 18 3 1
## 19 3 1
## 20 3 1
## 21 3 1
## 22 3 1
## 23 3 1
## 24 3 1
## 25 3 1
## 26 3 1
## 27 3 1
## 28 3 1
## 29 3 1
## 30 3 1
## 31 3 1
## 32 3 1
## 33 3 1
## 34 3 1
## 35 3 2
## 36 3 1
## 37 3 1
## 38 3 2
## 39 3 1
## 40 3 2
## 41 3 2
## 42 3 2
## 43 3 2
## 44 3 2
## 45 3 2
## 46 3 3
## 47 3 2
## 48 3 1
## 49 3 2
## 50 3 2
## 51 3 2
## 52 3 1
## 53 3 2
## 54 3 1
## 55 3 2
## 56 3 1
## 57 3 1
## 58 3 2
## 59 3 2
## 60 3 2
## 61 3 2
## 62 3 3
## 63 3 3
## 64 3 1
## 65 3 3
## 66 3 2
## 67 3 2
## 68 3 2
## 69 3 3
## 70 3 2
## 71 3 2
## 72 3 2
## 73 3 3
## 74 3 2
## 75 3 2
## 76 3 1
## 77 3 2
## 78 3 1
## 79 3 1
## 80 3 2
## 81 3 2
## 82 3 3
## 83 3 2
## 84 3 1
## 85 3 2
## 86 3 1
## 87 3 1
## 88 3 2
## 89 3 2
## 90 3 2
## 91 3 2
## 92 3 2
## 93 3 2
## 94 3 1
## 95 3 2
## 96 3 2
## 97 3 1
## 98 3 2
## 99 3 1
## 100 3 1
## 101 3 1
## 102 3 1
## 103 3 2
## 104 3 1
## 105 3 1
## 106 3 1
## 107 3 1
## 108 3 1
## 109 3 1
## 110 3 1
## 111 3 1
## 112 3 2
## 113 3 2
## 114 3 1
## 115 3 1
## 116 3 1
## 117 3 1
## 118 3 1
## 119 3 1
## 120 3 1
## 121 3 2
## 122 3 2
## 123 3 2
## 124 3 1
## 125 3 1
## 126 3 1
## 127 3 1
## 128 3 1
## 129 3 1
## 130 3 1
##
## ===== AL MENOS DOS MÉTODOS =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 294 13.52220 1.0721124 FALSE TRUE 40.30582 TRUE
## 2 297 9.23230 0.6022720 FALSE TRUE 27.27175 TRUE
## 3 299 11.10810 0.8077142 FALSE TRUE 32.97102 TRUE
## 4 300 18.49820 1.6170961 FALSE TRUE 55.42447 TRUE
## 5 301 19.60910 1.7387645 FALSE TRUE 58.79974 TRUE
## 6 302 15.28800 1.2655071 FALSE TRUE 45.67087 TRUE
## 7 303 9.82349 0.6670206 FALSE TRUE 29.06797 TRUE
## 8 304 23.64820 2.1811367 FALSE TRUE 71.07180 TRUE
## 9 305 88.97620 9.3360193 TRUE TRUE 269.55884 TRUE
## 10 306 9.18702 0.5973128 FALSE TRUE 27.13417 TRUE
## 11 308 24.39380 2.2627966 FALSE TRUE 73.33716 TRUE
## 12 309 22.59710 2.0660176 FALSE TRUE 67.87822 TRUE
## 13 310 14.33370 1.1609898 FALSE TRUE 42.77141 TRUE
## 14 312 11.57790 0.8591679 FALSE TRUE 34.39842 TRUE
## 15 314 13.35980 1.0543259 FALSE TRUE 39.81239 TRUE
## 16 317 9.91655 0.6772127 FALSE TRUE 29.35072 TRUE
## 17 318 25.04610 2.3342381 FALSE TRUE 75.31906 TRUE
## 18 319 9.59571 0.6420735 FALSE TRUE 28.37590 TRUE
## 19 320 24.80170 2.3074708 FALSE TRUE 74.57649 TRUE
## 20 321 41.52920 4.1395076 TRUE TRUE 125.39991 TRUE
## 21 322 67.92080 7.0299803 TRUE TRUE 205.58590 TRUE
## 22 324 51.13580 5.1916460 TRUE TRUE 154.58779 TRUE
## 23 325 14.05070 1.1299950 FALSE TRUE 41.91157 TRUE
## 24 326 18.81100 1.6513547 FALSE TRUE 56.37486 TRUE
## 25 327 28.65580 2.7295813 FALSE TRUE 86.28646 TRUE
## 26 328 45.74610 4.6013528 TRUE TRUE 138.21218 TRUE
## 27 329 18.08460 1.5717976 FALSE TRUE 54.16783 TRUE
## 28 330 10.83420 0.7777160 FALSE TRUE 32.13883 TRUE
## 29 331 25.94060 2.4322059 FALSE TRUE 78.03683 TRUE
## 30 332 73.53410 7.6447626 TRUE TRUE 222.64088 TRUE
## 31 333 11.81230 0.8848399 FALSE TRUE 35.11060 TRUE
## 32 334 11.08740 0.8054471 FALSE TRUE 32.90813 TRUE
## 33 336 12.04820 0.9106763 FALSE TRUE 35.82734 TRUE
## 34 339 15.86030 1.3281868 FALSE TRUE 47.40970 TRUE
## 35 340 12.24720 0.9324712 FALSE TRUE 36.43197 TRUE
## 36 341 37.66190 3.7159514 TRUE TRUE 113.64983 TRUE
## 37 342 9.33889 0.6139460 FALSE TRUE 27.59560 TRUE
## 38 344 10.06230 0.6931756 FALSE TRUE 29.79355 TRUE
## 39 347 13.91340 1.1149575 FALSE TRUE 41.49441 TRUE
## 40 348 11.16040 0.8134422 FALSE TRUE 33.12992 TRUE
## 41 349 14.42080 1.1705292 FALSE TRUE 43.03605 TRUE
## 42 350 13.67810 1.0891869 FALSE TRUE 40.77949 TRUE
## 43 351 9.39063 0.6196127 FALSE TRUE 27.75281 TRUE
## 44 352 22.05110 2.0062184 FALSE TRUE 66.21930 TRUE
## 45 354 12.80230 0.9932671 FALSE TRUE 38.11853 TRUE
## 46 355 10.67180 0.7599296 FALSE TRUE 31.64540 TRUE
## 47 357 9.92485 0.6781218 FALSE TRUE 29.37593 TRUE
## 48 362 9.51363 0.6330839 FALSE TRUE 28.12652 TRUE
## 49 371 15.57570 1.2970167 FALSE TRUE 46.54500 TRUE
## 50 372 13.07510 1.0231449 FALSE TRUE 38.94739 TRUE
## 51 380 15.02340 1.2365275 FALSE TRUE 44.86694 TRUE
## 52 381 10.23300 0.7118711 FALSE TRUE 30.31219 TRUE
## 53 382 14.33370 1.1609898 FALSE TRUE 42.77141 TRUE
## metodos_evaluados numero_metodos
## 1 3 2
## 2 3 2
## 3 3 2
## 4 3 2
## 5 3 2
## 6 3 2
## 7 3 2
## 8 3 2
## 9 3 3
## 10 3 2
## 11 3 2
## 12 3 2
## 13 3 2
## 14 3 2
## 15 3 2
## 16 3 2
## 17 3 2
## 18 3 2
## 19 3 2
## 20 3 3
## 21 3 3
## 22 3 3
## 23 3 2
## 24 3 2
## 25 3 2
## 26 3 3
## 27 3 2
## 28 3 2
## 29 3 2
## 30 3 3
## 31 3 2
## 32 3 2
## 33 3 2
## 34 3 2
## 35 3 2
## 36 3 3
## 37 3 2
## 38 3 2
## 39 3 2
## 40 3 2
## 41 3 2
## 42 3 2
## 43 3 2
## 44 3 2
## 45 3 2
## 46 3 2
## 47 3 2
## 48 3 2
## 49 3 2
## 50 3 2
## 51 3 2
## 52 3 2
## 53 3 2
##
## ===== LOS TRES MÉTODOS =====
## ID Valor Z_score Z_robusto
## 1 305 88.9762 9.336019 269.5588
## 2 321 41.5292 4.139508 125.3999
## 3 322 67.9208 7.029980 205.5859
## 4 324 51.1358 5.191646 154.5878
## 5 328 45.7461 4.601353 138.2122
## 6 332 73.5341 7.644763 222.6409
## 7 341 37.6619 3.715951 113.6498
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## crim Z-score 404 7 1.73
## crim IQR 404 53 13.12
## crim Tukey extremo 404 23 5.69
## crim MAD 404 130 32.18## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 24 1.61282 -0.232232118 4.121368 3 1
## 2 109 3.32105 -0.045142584 9.311509 3 1
## 3 110 4.09740 0.039885170 11.670305 3 1
## 4 111 2.77974 -0.104428181 7.666839 3 1
## 5 112 2.37934 -0.148280970 6.450297 3 1
## 6 113 2.15505 -0.172845760 5.768834 3 1
## 7 114 2.36862 -0.149455050 6.417727 3 1
## 8 115 2.73397 -0.109441023 7.527775 3 1
## 9 116 1.65660 -0.227437225 4.254386 3 1
## 10 117 1.49632 -0.244991483 3.767404 3 1
## 11 119 2.14918 -0.173488657 5.750999 3 1
## 12 120 1.41385 -0.254023799 3.516835 3 1
## 13 121 3.53501 -0.021709160 9.961587 3 1
## 14 122 2.44668 -0.140905728 6.654898 3 1
## 15 125 1.42502 -0.252800434 3.550773 3 1
## 16 127 1.46336 -0.248601343 3.667262 3 1
## 17 128 1.83377 -0.208033132 4.792684 3 1
## 18 129 1.51902 -0.242505323 3.836374 3 1
## 19 130 2.24236 -0.163283355 6.034109 3 1
## 20 131 2.92400 -0.088628472 8.105146 3 1
## 21 132 2.01019 -0.188711182 5.328703 3 1
## 22 133 1.80028 -0.211701039 4.690931 3 1
## 23 134 2.30040 -0.156926672 6.210453 3 1
## 24 136 2.31390 -0.155448119 6.251470 3 1
## 25 284 8.98296 0.574963668 26.514176 3 1
## 26 285 3.84970 0.012756459 10.917714 3 1
## 27 286 5.20177 0.160838477 15.025729 3 1
## 28 287 4.54192 0.088570089 13.020897 3 1
## 29 288 3.83684 0.011348001 10.878642 3 1
## 30 289 3.67822 -0.006024450 10.396704 3 1
## 31 290 4.22239 0.053574381 12.050064 3 1
## 32 291 3.47428 -0.028360459 9.777070 3 1
## 33 292 4.55587 0.090097927 13.063281 3 1
## 34 293 3.69695 -0.003973095 10.453612 3 1
## 35 294 13.52220 1.072112352 40.305817 3 2
## 36 295 4.89822 0.127592938 14.103448 3 1
## 37 296 6.53876 0.307268897 19.087926 3 1
## 38 297 9.23230 0.602271996 27.271749 3 2
## 39 298 8.26725 0.496577356 24.339623 3 1
## 40 299 11.10810 0.807714206 32.971021 3 2
## 41 300 18.49820 1.617096060 55.424473 3 2
## 42 301 19.60910 1.738764549 58.799737 3 2
## 43 302 15.28800 1.265507093 45.670874 3 2
## 44 303 9.82349 0.667020573 29.067971 3 2
## 45 304 23.64820 2.181136675 71.071796 3 2
## 46 305 88.97620 9.336019252 269.558844 3 3
## 47 306 9.18702 0.597312819 27.134174 3 2
## 48 307 7.99248 0.466483872 23.504785 3 1
## 49 308 24.39380 2.262796613 73.337163 3 2
## 50 309 22.59710 2.066017628 67.878223 3 2
## 51 310 14.33370 1.160989819 42.771410 3 2
## 52 311 5.29305 0.170835687 15.303066 3 1
## 53 312 11.57790 0.859167853 34.398421 3 2
## 54 313 8.64476 0.537923176 25.486618 3 1
## 55 314 13.35980 1.054325906 39.812395 3 2
## 56 315 8.71675 0.545807697 25.705346 3 1
## 57 316 5.87205 0.234249185 17.062251 3 1
## 58 317 9.91655 0.677212732 29.350716 3 2
## 59 318 25.04610 2.334238107 75.319056 3 2
## 60 319 9.59571 0.642073549 28.375903 3 2
## 61 320 24.80170 2.307470820 74.576492 3 2
## 62 321 41.52920 4.139507593 125.399908 3 3
## 63 322 67.92080 7.029980270 205.585905 3 3
## 64 323 7.40389 0.402020054 21.716463 3 1
## 65 324 51.13580 5.191645956 154.587786 3 3
## 66 325 14.05070 1.129994966 41.911567 3 2
## 67 326 18.81100 1.651354682 56.374858 3 2
## 68 327 28.65580 2.729581293 86.286462 3 2
## 69 328 45.74610 4.601352810 138.212179 3 3
## 70 329 18.08460 1.571797575 54.167826 3 2
## 71 330 10.83420 0.777716007 32.138826 3 2
## 72 331 25.94060 2.432205938 78.036829 3 2
## 73 332 73.53410 7.644762636 222.640879 3 3
## 74 333 11.81230 0.884839915 35.110602 3 2
## 75 334 11.08740 0.805447092 32.908128 3 2
## 76 335 7.02259 0.360259144 20.557954 3 1
## 77 336 12.04820 0.910676261 35.827341 3 2
## 78 337 7.05042 0.363307154 20.642510 3 1
## 79 338 8.79212 0.554062404 25.934344 3 1
## 80 339 15.86030 1.328186791 47.409702 3 2
## 81 340 12.24720 0.932471228 36.431965 3 2
## 82 341 37.66190 3.715951425 113.649832 3 3
## 83 342 9.33889 0.613945994 27.595603 3 2
## 84 343 8.49213 0.521206764 25.022880 3 1
## 85 344 10.06230 0.693175629 29.793551 3 2
## 86 345 6.44405 0.296896026 18.800167 3 1
## 87 346 5.58107 0.202380343 16.178162 3 1
## 88 347 13.91340 1.114957534 41.494406 3 2
## 89 348 11.16040 0.813442230 33.129925 3 2
## 90 349 14.42080 1.170529225 43.036047 3 2
## 91 350 13.67810 1.089186901 40.779490 3 2
## 92 351 9.39063 0.619612685 27.752806 3 2
## 93 352 22.05110 2.006218371 66.219303 3 2
## 94 353 5.66637 0.211722607 16.437330 3 1
## 95 354 12.80230 0.993267140 38.118534 3 2
## 96 355 10.67180 0.759929562 31.645404 3 2
## 97 356 6.28807 0.279812714 18.326251 3 1
## 98 357 9.92485 0.678121768 29.375934 3 2
## 99 358 7.52601 0.415394935 22.087503 3 1
## 100 359 6.71772 0.326869035 19.631663 3 1
## 101 360 5.44114 0.187054866 15.753011 3 1
## 102 361 8.24809 0.494478906 24.281409 3 1
## 103 362 9.51363 0.633083946 28.126518 3 2
## 104 363 4.75237 0.111619089 13.660310 3 1
## 105 364 4.66883 0.102469583 13.406489 3 1
## 106 365 8.20058 0.489275494 24.137059 3 1
## 107 366 6.80117 0.336008683 19.885210 3 1
## 108 367 6.65492 0.319991025 19.440857 3 1
## 109 368 7.83932 0.449709414 23.039437 3 1
## 110 369 3.16360 -0.062386893 8.833126 3 1
## 111 370 3.77498 0.004572942 10.690691 3 1
## 112 371 15.57570 1.297016701 46.544997 3 2
## 113 372 13.07510 1.023144864 38.947386 3 2
## 114 373 4.34879 0.067418019 12.434107 3 1
## 115 374 4.03841 0.033424441 11.491075 3 1
## 116 375 3.56868 -0.018021540 10.063887 3 1
## 117 376 4.64689 0.100066661 13.339829 3 1
## 118 377 8.05579 0.473417739 23.697141 3 1
## 119 378 6.39312 0.291318048 18.645426 3 1
## 120 379 4.87141 0.124656641 14.021991 3 1
## 121 380 15.02340 1.236527453 44.866936 3 2
## 122 381 10.23300 0.711871111 30.312191 3 2
## 123 382 14.33370 1.160989819 42.771410 3 2
## 124 383 5.82401 0.228987726 16.916290 3 1
## 125 384 5.70818 0.216301741 16.564362 3 1
## 126 385 5.73116 0.218818567 16.634183 3 1
## 127 386 2.81838 -0.100196233 7.784239 3 1
## 128 387 2.37857 -0.148365302 6.447958 3 1
## 129 388 3.67367 -0.006522778 10.382880 3 1
## 130 389 4.83567 0.120742309 13.913402 3 1
## clasificacion
## 1 Outlier según 1 método
## 2 Outlier según 1 método
## 3 Outlier según 1 método
## 4 Outlier según 1 método
## 5 Outlier según 1 método
## 6 Outlier según 1 método
## 7 Outlier según 1 método
## 8 Outlier según 1 método
## 9 Outlier según 1 método
## 10 Outlier según 1 método
## 11 Outlier según 1 método
## 12 Outlier según 1 método
## 13 Outlier según 1 método
## 14 Outlier según 1 método
## 15 Outlier según 1 método
## 16 Outlier según 1 método
## 17 Outlier según 1 método
## 18 Outlier según 1 método
## 19 Outlier según 1 método
## 20 Outlier según 1 método
## 21 Outlier según 1 método
## 22 Outlier según 1 método
## 23 Outlier según 1 método
## 24 Outlier según 1 método
## 25 Outlier según 1 método
## 26 Outlier según 1 método
## 27 Outlier según 1 método
## 28 Outlier según 1 método
## 29 Outlier según 1 método
## 30 Outlier según 1 método
## 31 Outlier según 1 método
## 32 Outlier según 1 método
## 33 Outlier según 1 método
## 34 Outlier según 1 método
## 35 Outlier según 2 métodos
## 36 Outlier según 1 método
## 37 Outlier según 1 método
## 38 Outlier según 2 métodos
## 39 Outlier según 1 método
## 40 Outlier según 2 métodos
## 41 Outlier según 2 métodos
## 42 Outlier según 2 métodos
## 43 Outlier según 2 métodos
## 44 Outlier según 2 métodos
## 45 Outlier según 2 métodos
## 46 Outlier según 3 métodos
## 47 Outlier según 2 métodos
## 48 Outlier según 1 método
## 49 Outlier según 2 métodos
## 50 Outlier según 2 métodos
## 51 Outlier según 2 métodos
## 52 Outlier según 1 método
## 53 Outlier según 2 métodos
## 54 Outlier según 1 método
## 55 Outlier según 2 métodos
## 56 Outlier según 1 método
## 57 Outlier según 1 método
## 58 Outlier según 2 métodos
## 59 Outlier según 2 métodos
## 60 Outlier según 2 métodos
## 61 Outlier según 2 métodos
## 62 Outlier según 3 métodos
## 63 Outlier según 3 métodos
## 64 Outlier según 1 método
## 65 Outlier según 3 métodos
## 66 Outlier según 2 métodos
## 67 Outlier según 2 métodos
## 68 Outlier según 2 métodos
## 69 Outlier según 3 métodos
## 70 Outlier según 2 métodos
## 71 Outlier según 2 métodos
## 72 Outlier según 2 métodos
## 73 Outlier según 3 métodos
## 74 Outlier según 2 métodos
## 75 Outlier según 2 métodos
## 76 Outlier según 1 método
## 77 Outlier según 2 métodos
## 78 Outlier según 1 método
## 79 Outlier según 1 método
## 80 Outlier según 2 métodos
## 81 Outlier según 2 métodos
## 82 Outlier según 3 métodos
## 83 Outlier según 2 métodos
## 84 Outlier según 1 método
## 85 Outlier según 2 métodos
## 86 Outlier según 1 método
## 87 Outlier según 1 método
## 88 Outlier según 2 métodos
## 89 Outlier según 2 métodos
## 90 Outlier según 2 métodos
## 91 Outlier según 2 métodos
## 92 Outlier según 2 métodos
## 93 Outlier según 2 métodos
## 94 Outlier según 1 método
## 95 Outlier según 2 métodos
## 96 Outlier según 2 métodos
## 97 Outlier según 1 método
## 98 Outlier según 2 métodos
## 99 Outlier según 1 método
## 100 Outlier según 1 método
## 101 Outlier según 1 método
## 102 Outlier según 1 método
## 103 Outlier según 2 métodos
## 104 Outlier según 1 método
## 105 Outlier según 1 método
## 106 Outlier según 1 método
## 107 Outlier según 1 método
## 108 Outlier según 1 método
## 109 Outlier según 1 método
## 110 Outlier según 1 método
## 111 Outlier según 1 método
## 112 Outlier según 2 métodos
## 113 Outlier según 2 métodos
## 114 Outlier según 1 método
## 115 Outlier según 1 método
## 116 Outlier según 1 método
## 117 Outlier según 1 método
## 118 Outlier según 1 método
## 119 Outlier según 1 método
## 120 Outlier según 1 método
## 121 Outlier según 2 métodos
## 122 Outlier según 2 métodos
## 123 Outlier según 2 métodos
## 124 Outlier según 1 método
## 125 Outlier según 1 método
## 126 Outlier según 1 método
## 127 Outlier según 1 método
## 128 Outlier según 1 método
## 129 Outlier según 1 método
## 130 Outlier según 1 método
##
## ========================================
## VARIABLE: zn
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -61.004
## Límite superior = 83.84311
## Número de outliers = 11
## ID Valor Z_score
## 1 35 90 3.255037
## 2 36 85 3.047922
## 3 37 100 3.669267
## 4 160 95 3.462152
## 5 161 95 3.462152
## 6 164 95 3.462152
## 7 206 90 3.255037
## 8 224 90 3.255037
## 9 225 90 3.255037
## 10 277 85 3.047922
## 11 281 90 3.255037
##
## IQR nulo o indefinido: criterio no evaluado.
##
## ================ MÉTODO IQR ================
## Q1 = 0
## Q3 = 0
## IQR = 0
## Límite inferior = 0
## Límite superior = 0
## Número de outliers = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = 0
## Límite superior = 0
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## MAD nula o indefinida: criterio no evaluado.
##
## ================ MÉTODO MAD ================
## Mediana = 0
## MAD = 0
## MAD ajustada = 0
## Número de outliers = 0
## [1] ID Valor Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 18.0 0.27258170 FALSE NA NA NA
## 2 2 0.0 -0.47303204 FALSE NA NA NA
## 3 3 0.0 -0.47303204 FALSE NA NA NA
## 4 4 0.0 -0.47303204 FALSE NA NA NA
## 5 5 0.0 -0.47303204 FALSE NA NA NA
## 6 6 12.5 0.04475528 FALSE NA NA NA
## 7 7 12.5 0.04475528 FALSE NA NA NA
## 8 8 12.5 0.04475528 FALSE NA NA NA
## 9 9 12.5 0.04475528 FALSE NA NA NA
## 10 10 12.5 0.04475528 FALSE NA NA NA
## 11 11 0.0 -0.47303204 FALSE NA NA NA
## 12 12 0.0 -0.47303204 FALSE NA NA NA
## 13 13 0.0 -0.47303204 FALSE NA NA NA
## 14 14 0.0 -0.47303204 FALSE NA NA NA
## 15 15 0.0 -0.47303204 FALSE NA NA NA
## 16 16 0.0 -0.47303204 FALSE NA NA NA
## 17 17 0.0 -0.47303204 FALSE NA NA NA
## 18 18 0.0 -0.47303204 FALSE NA NA NA
## 19 19 0.0 -0.47303204 FALSE NA NA NA
## 20 20 0.0 -0.47303204 FALSE NA NA NA
## 21 21 0.0 -0.47303204 FALSE NA NA NA
## 22 22 0.0 -0.47303204 FALSE NA NA NA
## 23 23 0.0 -0.47303204 FALSE NA NA NA
## 24 24 0.0 -0.47303204 FALSE NA NA NA
## 25 25 0.0 -0.47303204 FALSE NA NA NA
## 26 26 0.0 -0.47303204 FALSE NA NA NA
## 27 27 0.0 -0.47303204 FALSE NA NA NA
## 28 28 0.0 -0.47303204 FALSE NA NA NA
## 29 29 0.0 -0.47303204 FALSE NA NA NA
## 30 30 0.0 -0.47303204 FALSE NA NA NA
## 31 31 0.0 -0.47303204 FALSE NA NA NA
## 32 32 0.0 -0.47303204 FALSE NA NA NA
## 33 33 21.0 0.39685066 FALSE NA NA NA
## 34 34 75.0 2.63369188 FALSE NA NA NA
## 35 35 90.0 3.25503666 TRUE NA NA NA
## 36 36 85.0 3.04792173 TRUE NA NA NA
## 37 37 100.0 3.66926652 TRUE NA NA NA
## 38 38 25.0 0.56254260 FALSE NA NA NA
## 39 39 25.0 0.56254260 FALSE NA NA NA
## 40 40 25.0 0.56254260 FALSE NA NA NA
## 41 41 17.5 0.25187021 FALSE NA NA NA
## 42 42 80.0 2.84080681 FALSE NA NA NA
## 43 43 80.0 2.84080681 FALSE NA NA NA
## 44 44 12.5 0.04475528 FALSE NA NA NA
## 45 45 12.5 0.04475528 FALSE NA NA NA
## 46 46 0.0 -0.47303204 FALSE NA NA NA
## 47 47 0.0 -0.47303204 FALSE NA NA NA
## 48 48 0.0 -0.47303204 FALSE NA NA NA
## 49 49 0.0 -0.47303204 FALSE NA NA NA
## 50 50 0.0 -0.47303204 FALSE NA NA NA
## 51 51 0.0 -0.47303204 FALSE NA NA NA
## 52 52 0.0 -0.47303204 FALSE NA NA NA
## 53 53 0.0 -0.47303204 FALSE NA NA NA
## 54 54 25.0 0.56254260 FALSE NA NA NA
## 55 55 25.0 0.56254260 FALSE NA NA NA
## 56 56 25.0 0.56254260 FALSE NA NA NA
## 57 57 0.0 -0.47303204 FALSE NA NA NA
## 58 58 0.0 -0.47303204 FALSE NA NA NA
## 59 59 0.0 -0.47303204 FALSE NA NA NA
## 60 60 0.0 -0.47303204 FALSE NA NA NA
## 61 61 0.0 -0.47303204 FALSE NA NA NA
## 62 62 0.0 -0.47303204 FALSE NA NA NA
## 63 63 0.0 -0.47303204 FALSE NA NA NA
## 64 64 0.0 -0.47303204 FALSE NA NA NA
## 65 65 28.0 0.68681156 FALSE NA NA NA
## 66 66 28.0 0.68681156 FALSE NA NA NA
## 67 67 28.0 0.68681156 FALSE NA NA NA
## 68 68 0.0 -0.47303204 FALSE NA NA NA
## 69 69 0.0 -0.47303204 FALSE NA NA NA
## 70 70 0.0 -0.47303204 FALSE NA NA NA
## 71 71 0.0 -0.47303204 FALSE NA NA NA
## 72 72 0.0 -0.47303204 FALSE NA NA NA
## 73 73 0.0 -0.47303204 FALSE NA NA NA
## 74 74 0.0 -0.47303204 FALSE NA NA NA
## 75 75 0.0 -0.47303204 FALSE NA NA NA
## 76 76 0.0 -0.47303204 FALSE NA NA NA
## 77 77 0.0 -0.47303204 FALSE NA NA NA
## 78 78 0.0 -0.47303204 FALSE NA NA NA
## 79 79 0.0 -0.47303204 FALSE NA NA NA
## 80 80 0.0 -0.47303204 FALSE NA NA NA
## 81 81 0.0 -0.47303204 FALSE NA NA NA
## 82 82 0.0 -0.47303204 FALSE NA NA NA
## 83 83 0.0 -0.47303204 FALSE NA NA NA
## 84 84 0.0 -0.47303204 FALSE NA NA NA
## 85 85 0.0 -0.47303204 FALSE NA NA NA
## 86 86 0.0 -0.47303204 FALSE NA NA NA
## 87 87 0.0 -0.47303204 FALSE NA NA NA
## 88 88 0.0 -0.47303204 FALSE NA NA NA
## 89 89 0.0 -0.47303204 FALSE NA NA NA
## 90 90 0.0 -0.47303204 FALSE NA NA NA
## 91 91 0.0 -0.47303204 FALSE NA NA NA
## 92 92 0.0 -0.47303204 FALSE NA NA NA
## 93 93 0.0 -0.47303204 FALSE NA NA NA
## 94 94 0.0 -0.47303204 FALSE NA NA NA
## 95 95 0.0 -0.47303204 FALSE NA NA NA
## 96 96 0.0 -0.47303204 FALSE NA NA NA
## 97 97 0.0 -0.47303204 FALSE NA NA NA
## 98 98 0.0 -0.47303204 FALSE NA NA NA
## 99 99 0.0 -0.47303204 FALSE NA NA NA
## 100 100 0.0 -0.47303204 FALSE NA NA NA
## 101 101 0.0 -0.47303204 FALSE NA NA NA
## 102 102 0.0 -0.47303204 FALSE NA NA NA
## 103 103 0.0 -0.47303204 FALSE NA NA NA
## 104 104 0.0 -0.47303204 FALSE NA NA NA
## 105 105 0.0 -0.47303204 FALSE NA NA NA
## 106 106 0.0 -0.47303204 FALSE NA NA NA
## 107 107 0.0 -0.47303204 FALSE NA NA NA
## 108 108 0.0 -0.47303204 FALSE NA NA NA
## 109 109 0.0 -0.47303204 FALSE NA NA NA
## 110 110 0.0 -0.47303204 FALSE NA NA NA
## 111 111 0.0 -0.47303204 FALSE NA NA NA
## 112 112 0.0 -0.47303204 FALSE NA NA NA
## 113 113 0.0 -0.47303204 FALSE NA NA NA
## 114 114 0.0 -0.47303204 FALSE NA NA NA
## 115 115 0.0 -0.47303204 FALSE NA NA NA
## 116 116 0.0 -0.47303204 FALSE NA NA NA
## 117 117 0.0 -0.47303204 FALSE NA NA NA
## 118 118 0.0 -0.47303204 FALSE NA NA NA
## 119 119 0.0 -0.47303204 FALSE NA NA NA
## 120 120 0.0 -0.47303204 FALSE NA NA NA
## 121 121 0.0 -0.47303204 FALSE NA NA NA
## 122 122 0.0 -0.47303204 FALSE NA NA NA
## 123 123 0.0 -0.47303204 FALSE NA NA NA
## 124 124 0.0 -0.47303204 FALSE NA NA NA
## 125 125 0.0 -0.47303204 FALSE NA NA NA
## 126 126 0.0 -0.47303204 FALSE NA NA NA
## 127 127 0.0 -0.47303204 FALSE NA NA NA
## 128 128 0.0 -0.47303204 FALSE NA NA NA
## 129 129 0.0 -0.47303204 FALSE NA NA NA
## 130 130 0.0 -0.47303204 FALSE NA NA NA
## 131 131 0.0 -0.47303204 FALSE NA NA NA
## 132 132 0.0 -0.47303204 FALSE NA NA NA
## 133 133 0.0 -0.47303204 FALSE NA NA NA
## 134 134 0.0 -0.47303204 FALSE NA NA NA
## 135 135 0.0 -0.47303204 FALSE NA NA NA
## 136 136 0.0 -0.47303204 FALSE NA NA NA
## 137 137 0.0 -0.47303204 FALSE NA NA NA
## 138 138 0.0 -0.47303204 FALSE NA NA NA
## 139 139 0.0 -0.47303204 FALSE NA NA NA
## 140 140 0.0 -0.47303204 FALSE NA NA NA
## 141 141 0.0 -0.47303204 FALSE NA NA NA
## 142 142 0.0 -0.47303204 FALSE NA NA NA
## 143 143 0.0 -0.47303204 FALSE NA NA NA
## 144 144 0.0 -0.47303204 FALSE NA NA NA
## 145 145 0.0 -0.47303204 FALSE NA NA NA
## 146 146 0.0 -0.47303204 FALSE NA NA NA
## 147 147 0.0 -0.47303204 FALSE NA NA NA
## 148 148 45.0 1.39100231 FALSE NA NA NA
## 149 149 45.0 1.39100231 FALSE NA NA NA
## 150 150 45.0 1.39100231 FALSE NA NA NA
## 151 151 45.0 1.39100231 FALSE NA NA NA
## 152 152 45.0 1.39100231 FALSE NA NA NA
## 153 153 45.0 1.39100231 FALSE NA NA NA
## 154 154 60.0 2.01234709 FALSE NA NA NA
## 155 155 60.0 2.01234709 FALSE NA NA NA
## 156 156 80.0 2.84080681 FALSE NA NA NA
## 157 157 80.0 2.84080681 FALSE NA NA NA
## 158 158 80.0 2.84080681 FALSE NA NA NA
## 159 159 80.0 2.84080681 FALSE NA NA NA
## 160 160 95.0 3.46215159 TRUE NA NA NA
## 161 161 95.0 3.46215159 TRUE NA NA NA
## 162 162 82.5 2.94436427 FALSE NA NA NA
## 163 163 82.5 2.94436427 FALSE NA NA NA
## 164 164 95.0 3.46215159 TRUE NA NA NA
## 165 165 0.0 -0.47303204 FALSE NA NA NA
## 166 166 0.0 -0.47303204 FALSE NA NA NA
## 167 167 0.0 -0.47303204 FALSE NA NA NA
## 168 168 0.0 -0.47303204 FALSE NA NA NA
## 169 169 0.0 -0.47303204 FALSE NA NA NA
## 170 170 0.0 -0.47303204 FALSE NA NA NA
## 171 171 0.0 -0.47303204 FALSE NA NA NA
## 172 172 0.0 -0.47303204 FALSE NA NA NA
## 173 173 0.0 -0.47303204 FALSE NA NA NA
## 174 174 0.0 -0.47303204 FALSE NA NA NA
## 175 175 0.0 -0.47303204 FALSE NA NA NA
## 176 176 0.0 -0.47303204 FALSE NA NA NA
## 177 177 0.0 -0.47303204 FALSE NA NA NA
## 178 178 0.0 -0.47303204 FALSE NA NA NA
## 179 179 0.0 -0.47303204 FALSE NA NA NA
## 180 180 0.0 -0.47303204 FALSE NA NA NA
## 181 181 0.0 -0.47303204 FALSE NA NA NA
## 182 182 0.0 -0.47303204 FALSE NA NA NA
## 183 183 0.0 -0.47303204 FALSE NA NA NA
## 184 184 0.0 -0.47303204 FALSE NA NA NA
## 185 185 0.0 -0.47303204 FALSE NA NA NA
## 186 186 0.0 -0.47303204 FALSE NA NA NA
## 187 187 0.0 -0.47303204 FALSE NA NA NA
## 188 188 0.0 -0.47303204 FALSE NA NA NA
## 189 189 0.0 -0.47303204 FALSE NA NA NA
## 190 190 0.0 -0.47303204 FALSE NA NA NA
## 191 191 0.0 -0.47303204 FALSE NA NA NA
## 192 192 30.0 0.76965753 FALSE NA NA NA
## 193 193 30.0 0.76965753 FALSE NA NA NA
## 194 194 30.0 0.76965753 FALSE NA NA NA
## 195 195 30.0 0.76965753 FALSE NA NA NA
## 196 196 30.0 0.76965753 FALSE NA NA NA
## 197 197 22.0 0.43827364 FALSE NA NA NA
## 198 198 22.0 0.43827364 FALSE NA NA NA
## 199 199 22.0 0.43827364 FALSE NA NA NA
## 200 200 22.0 0.43827364 FALSE NA NA NA
## 201 201 22.0 0.43827364 FALSE NA NA NA
## 202 202 22.0 0.43827364 FALSE NA NA NA
## 203 203 22.0 0.43827364 FALSE NA NA NA
## 204 204 80.0 2.84080681 FALSE NA NA NA
## 205 205 80.0 2.84080681 FALSE NA NA NA
## 206 206 90.0 3.25503666 TRUE NA NA NA
## 207 207 20.0 0.35542767 FALSE NA NA NA
## 208 208 20.0 0.35542767 FALSE NA NA NA
## 209 209 20.0 0.35542767 FALSE NA NA NA
## 210 210 20.0 0.35542767 FALSE NA NA NA
## 211 211 20.0 0.35542767 FALSE NA NA NA
## 212 212 20.0 0.35542767 FALSE NA NA NA
## 213 213 20.0 0.35542767 FALSE NA NA NA
## 214 214 20.0 0.35542767 FALSE NA NA NA
## 215 215 20.0 0.35542767 FALSE NA NA NA
## 216 216 20.0 0.35542767 FALSE NA NA NA
## 217 217 20.0 0.35542767 FALSE NA NA NA
## 218 218 40.0 1.18388738 FALSE NA NA NA
## 219 219 40.0 1.18388738 FALSE NA NA NA
## 220 220 40.0 1.18388738 FALSE NA NA NA
## 221 221 20.0 0.35542767 FALSE NA NA NA
## 222 222 20.0 0.35542767 FALSE NA NA NA
## 223 223 20.0 0.35542767 FALSE NA NA NA
## 224 224 90.0 3.25503666 TRUE NA NA NA
## 225 225 90.0 3.25503666 TRUE NA NA NA
## 226 226 55.0 1.80523217 FALSE NA NA NA
## 227 227 80.0 2.84080681 FALSE NA NA NA
## 228 228 52.5 1.70167470 FALSE NA NA NA
## 229 229 52.5 1.70167470 FALSE NA NA NA
## 230 230 52.5 1.70167470 FALSE NA NA NA
## 231 231 80.0 2.84080681 FALSE NA NA NA
## 232 232 80.0 2.84080681 FALSE NA NA NA
## 233 233 80.0 2.84080681 FALSE NA NA NA
## 234 234 0.0 -0.47303204 FALSE NA NA NA
## 235 235 0.0 -0.47303204 FALSE NA NA NA
## 236 236 0.0 -0.47303204 FALSE NA NA NA
## 237 237 70.0 2.42657695 FALSE NA NA NA
## 238 238 34.0 0.93534947 FALSE NA NA NA
## 239 239 34.0 0.93534947 FALSE NA NA NA
## 240 240 33.0 0.89392648 FALSE NA NA NA
## 241 241 33.0 0.89392648 FALSE NA NA NA
## 242 242 0.0 -0.47303204 FALSE NA NA NA
## 243 243 0.0 -0.47303204 FALSE NA NA NA
## 244 244 0.0 -0.47303204 FALSE NA NA NA
## 245 245 0.0 -0.47303204 FALSE NA NA NA
## 246 246 0.0 -0.47303204 FALSE NA NA NA
## 247 247 0.0 -0.47303204 FALSE NA NA NA
## 248 248 0.0 -0.47303204 FALSE NA NA NA
## 249 249 0.0 -0.47303204 FALSE NA NA NA
## 250 250 0.0 -0.47303204 FALSE NA NA NA
## 251 251 0.0 -0.47303204 FALSE NA NA NA
## 252 252 0.0 -0.47303204 FALSE NA NA NA
## 253 253 0.0 -0.47303204 FALSE NA NA NA
## 254 254 0.0 -0.47303204 FALSE NA NA NA
## 255 255 0.0 -0.47303204 FALSE NA NA NA
## 256 256 0.0 -0.47303204 FALSE NA NA NA
## 257 257 0.0 -0.47303204 FALSE NA NA NA
## 258 258 0.0 -0.47303204 FALSE NA NA NA
## 259 259 0.0 -0.47303204 FALSE NA NA NA
## 260 260 0.0 -0.47303204 FALSE NA NA NA
## 261 261 0.0 -0.47303204 FALSE NA NA NA
## 262 262 0.0 -0.47303204 FALSE NA NA NA
## 263 263 35.0 0.97677246 FALSE NA NA NA
## 264 264 0.0 -0.47303204 FALSE NA NA NA
## 265 265 0.0 -0.47303204 FALSE NA NA NA
## 266 266 0.0 -0.47303204 FALSE NA NA NA
## 267 267 0.0 -0.47303204 FALSE NA NA NA
## 268 268 0.0 -0.47303204 FALSE NA NA NA
## 269 269 0.0 -0.47303204 FALSE NA NA NA
## 270 270 0.0 -0.47303204 FALSE NA NA NA
## 271 271 35.0 0.97677246 FALSE NA NA NA
## 272 272 0.0 -0.47303204 FALSE NA NA NA
## 273 273 55.0 1.80523217 FALSE NA NA NA
## 274 274 55.0 1.80523217 FALSE NA NA NA
## 275 275 0.0 -0.47303204 FALSE NA NA NA
## 276 276 0.0 -0.47303204 FALSE NA NA NA
## 277 277 85.0 3.04792173 TRUE NA NA NA
## 278 278 80.0 2.84080681 FALSE NA NA NA
## 279 279 40.0 1.18388738 FALSE NA NA NA
## 280 280 60.0 2.01234709 FALSE NA NA NA
## 281 281 90.0 3.25503666 TRUE NA NA NA
## 282 282 80.0 2.84080681 FALSE NA NA NA
## 283 283 80.0 2.84080681 FALSE NA NA NA
## 284 284 0.0 -0.47303204 FALSE NA NA NA
## 285 285 0.0 -0.47303204 FALSE NA NA NA
## 286 286 0.0 -0.47303204 FALSE NA NA NA
## 287 287 0.0 -0.47303204 FALSE NA NA NA
## 288 288 0.0 -0.47303204 FALSE NA NA NA
## 289 289 0.0 -0.47303204 FALSE NA NA NA
## 290 290 0.0 -0.47303204 FALSE NA NA NA
## 291 291 0.0 -0.47303204 FALSE NA NA NA
## 292 292 0.0 -0.47303204 FALSE NA NA NA
## 293 293 0.0 -0.47303204 FALSE NA NA NA
## 294 294 0.0 -0.47303204 FALSE NA NA NA
## 295 295 0.0 -0.47303204 FALSE NA NA NA
## 296 296 0.0 -0.47303204 FALSE NA NA NA
## 297 297 0.0 -0.47303204 FALSE NA NA NA
## 298 298 0.0 -0.47303204 FALSE NA NA NA
## 299 299 0.0 -0.47303204 FALSE NA NA NA
## 300 300 0.0 -0.47303204 FALSE NA NA NA
## 301 301 0.0 -0.47303204 FALSE NA NA NA
## 302 302 0.0 -0.47303204 FALSE NA NA NA
## 303 303 0.0 -0.47303204 FALSE NA NA NA
## 304 304 0.0 -0.47303204 FALSE NA NA NA
## 305 305 0.0 -0.47303204 FALSE NA NA NA
## 306 306 0.0 -0.47303204 FALSE NA NA NA
## 307 307 0.0 -0.47303204 FALSE NA NA NA
## 308 308 0.0 -0.47303204 FALSE NA NA NA
## 309 309 0.0 -0.47303204 FALSE NA NA NA
## 310 310 0.0 -0.47303204 FALSE NA NA NA
## 311 311 0.0 -0.47303204 FALSE NA NA NA
## 312 312 0.0 -0.47303204 FALSE NA NA NA
## 313 313 0.0 -0.47303204 FALSE NA NA NA
## 314 314 0.0 -0.47303204 FALSE NA NA NA
## 315 315 0.0 -0.47303204 FALSE NA NA NA
## 316 316 0.0 -0.47303204 FALSE NA NA NA
## 317 317 0.0 -0.47303204 FALSE NA NA NA
## 318 318 0.0 -0.47303204 FALSE NA NA NA
## 319 319 0.0 -0.47303204 FALSE NA NA NA
## 320 320 0.0 -0.47303204 FALSE NA NA NA
## 321 321 0.0 -0.47303204 FALSE NA NA NA
## 322 322 0.0 -0.47303204 FALSE NA NA NA
## 323 323 0.0 -0.47303204 FALSE NA NA NA
## 324 324 0.0 -0.47303204 FALSE NA NA NA
## 325 325 0.0 -0.47303204 FALSE NA NA NA
## 326 326 0.0 -0.47303204 FALSE NA NA NA
## 327 327 0.0 -0.47303204 FALSE NA NA NA
## 328 328 0.0 -0.47303204 FALSE NA NA NA
## 329 329 0.0 -0.47303204 FALSE NA NA NA
## 330 330 0.0 -0.47303204 FALSE NA NA NA
## 331 331 0.0 -0.47303204 FALSE NA NA NA
## 332 332 0.0 -0.47303204 FALSE NA NA NA
## 333 333 0.0 -0.47303204 FALSE NA NA NA
## 334 334 0.0 -0.47303204 FALSE NA NA NA
## 335 335 0.0 -0.47303204 FALSE NA NA NA
## 336 336 0.0 -0.47303204 FALSE NA NA NA
## 337 337 0.0 -0.47303204 FALSE NA NA NA
## 338 338 0.0 -0.47303204 FALSE NA NA NA
## 339 339 0.0 -0.47303204 FALSE NA NA NA
## 340 340 0.0 -0.47303204 FALSE NA NA NA
## 341 341 0.0 -0.47303204 FALSE NA NA NA
## 342 342 0.0 -0.47303204 FALSE NA NA NA
## 343 343 0.0 -0.47303204 FALSE NA NA NA
## 344 344 0.0 -0.47303204 FALSE NA NA NA
## 345 345 0.0 -0.47303204 FALSE NA NA NA
## 346 346 0.0 -0.47303204 FALSE NA NA NA
## 347 347 0.0 -0.47303204 FALSE NA NA NA
## 348 348 0.0 -0.47303204 FALSE NA NA NA
## 349 349 0.0 -0.47303204 FALSE NA NA NA
## 350 350 0.0 -0.47303204 FALSE NA NA NA
## 351 351 0.0 -0.47303204 FALSE NA NA NA
## 352 352 0.0 -0.47303204 FALSE NA NA NA
## 353 353 0.0 -0.47303204 FALSE NA NA NA
## 354 354 0.0 -0.47303204 FALSE NA NA NA
## 355 355 0.0 -0.47303204 FALSE NA NA NA
## 356 356 0.0 -0.47303204 FALSE NA NA NA
## 357 357 0.0 -0.47303204 FALSE NA NA NA
## 358 358 0.0 -0.47303204 FALSE NA NA NA
## 359 359 0.0 -0.47303204 FALSE NA NA NA
## 360 360 0.0 -0.47303204 FALSE NA NA NA
## 361 361 0.0 -0.47303204 FALSE NA NA NA
## 362 362 0.0 -0.47303204 FALSE NA NA NA
## 363 363 0.0 -0.47303204 FALSE NA NA NA
## 364 364 0.0 -0.47303204 FALSE NA NA NA
## 365 365 0.0 -0.47303204 FALSE NA NA NA
## 366 366 0.0 -0.47303204 FALSE NA NA NA
## 367 367 0.0 -0.47303204 FALSE NA NA NA
## 368 368 0.0 -0.47303204 FALSE NA NA NA
## 369 369 0.0 -0.47303204 FALSE NA NA NA
## 370 370 0.0 -0.47303204 FALSE NA NA NA
## 371 371 0.0 -0.47303204 FALSE NA NA NA
## 372 372 0.0 -0.47303204 FALSE NA NA NA
## 373 373 0.0 -0.47303204 FALSE NA NA NA
## 374 374 0.0 -0.47303204 FALSE NA NA NA
## 375 375 0.0 -0.47303204 FALSE NA NA NA
## 376 376 0.0 -0.47303204 FALSE NA NA NA
## 377 377 0.0 -0.47303204 FALSE NA NA NA
## 378 378 0.0 -0.47303204 FALSE NA NA NA
## 379 379 0.0 -0.47303204 FALSE NA NA NA
## 380 380 0.0 -0.47303204 FALSE NA NA NA
## 381 381 0.0 -0.47303204 FALSE NA NA NA
## 382 382 0.0 -0.47303204 FALSE NA NA NA
## 383 383 0.0 -0.47303204 FALSE NA NA NA
## 384 384 0.0 -0.47303204 FALSE NA NA NA
## 385 385 0.0 -0.47303204 FALSE NA NA NA
## 386 386 0.0 -0.47303204 FALSE NA NA NA
## 387 387 0.0 -0.47303204 FALSE NA NA NA
## 388 388 0.0 -0.47303204 FALSE NA NA NA
## 389 389 0.0 -0.47303204 FALSE NA NA NA
## 390 390 0.0 -0.47303204 FALSE NA NA NA
## 391 391 0.0 -0.47303204 FALSE NA NA NA
## 392 392 0.0 -0.47303204 FALSE NA NA NA
## 393 393 0.0 -0.47303204 FALSE NA NA NA
## 394 394 0.0 -0.47303204 FALSE NA NA NA
## 395 395 0.0 -0.47303204 FALSE NA NA NA
## 396 396 0.0 -0.47303204 FALSE NA NA NA
## 397 397 0.0 -0.47303204 FALSE NA NA NA
## 398 398 0.0 -0.47303204 FALSE NA NA NA
## 399 399 0.0 -0.47303204 FALSE NA NA NA
## 400 400 0.0 -0.47303204 FALSE NA NA NA
## 401 401 0.0 -0.47303204 FALSE NA NA NA
## 402 402 0.0 -0.47303204 FALSE NA NA NA
## 403 403 0.0 -0.47303204 FALSE NA NA NA
## 404 404 0.0 -0.47303204 FALSE NA NA NA
## outlier_MAD
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## 7 NA
## 8 NA
## 9 NA
## 10 NA
## 11 NA
## 12 NA
## 13 NA
## 14 NA
## 15 NA
## 16 NA
## 17 NA
## 18 NA
## 19 NA
## 20 NA
## 21 NA
## 22 NA
## 23 NA
## 24 NA
## 25 NA
## 26 NA
## 27 NA
## 28 NA
## 29 NA
## 30 NA
## 31 NA
## 32 NA
## 33 NA
## 34 NA
## 35 NA
## 36 NA
## 37 NA
## 38 NA
## 39 NA
## 40 NA
## 41 NA
## 42 NA
## 43 NA
## 44 NA
## 45 NA
## 46 NA
## 47 NA
## 48 NA
## 49 NA
## 50 NA
## 51 NA
## 52 NA
## 53 NA
## 54 NA
## 55 NA
## 56 NA
## 57 NA
## 58 NA
## 59 NA
## 60 NA
## 61 NA
## 62 NA
## 63 NA
## 64 NA
## 65 NA
## 66 NA
## 67 NA
## 68 NA
## 69 NA
## 70 NA
## 71 NA
## 72 NA
## 73 NA
## 74 NA
## 75 NA
## 76 NA
## 77 NA
## 78 NA
## 79 NA
## 80 NA
## 81 NA
## 82 NA
## 83 NA
## 84 NA
## 85 NA
## 86 NA
## 87 NA
## 88 NA
## 89 NA
## 90 NA
## 91 NA
## 92 NA
## 93 NA
## 94 NA
## 95 NA
## 96 NA
## 97 NA
## 98 NA
## 99 NA
## 100 NA
## 101 NA
## 102 NA
## 103 NA
## 104 NA
## 105 NA
## 106 NA
## 107 NA
## 108 NA
## 109 NA
## 110 NA
## 111 NA
## 112 NA
## 113 NA
## 114 NA
## 115 NA
## 116 NA
## 117 NA
## 118 NA
## 119 NA
## 120 NA
## 121 NA
## 122 NA
## 123 NA
## 124 NA
## 125 NA
## 126 NA
## 127 NA
## 128 NA
## 129 NA
## 130 NA
## 131 NA
## 132 NA
## 133 NA
## 134 NA
## 135 NA
## 136 NA
## 137 NA
## 138 NA
## 139 NA
## 140 NA
## 141 NA
## 142 NA
## 143 NA
## 144 NA
## 145 NA
## 146 NA
## 147 NA
## 148 NA
## 149 NA
## 150 NA
## 151 NA
## 152 NA
## 153 NA
## 154 NA
## 155 NA
## 156 NA
## 157 NA
## 158 NA
## 159 NA
## 160 NA
## 161 NA
## 162 NA
## 163 NA
## 164 NA
## 165 NA
## 166 NA
## 167 NA
## 168 NA
## 169 NA
## 170 NA
## 171 NA
## 172 NA
## 173 NA
## 174 NA
## 175 NA
## 176 NA
## 177 NA
## 178 NA
## 179 NA
## 180 NA
## 181 NA
## 182 NA
## 183 NA
## 184 NA
## 185 NA
## 186 NA
## 187 NA
## 188 NA
## 189 NA
## 190 NA
## 191 NA
## 192 NA
## 193 NA
## 194 NA
## 195 NA
## 196 NA
## 197 NA
## 198 NA
## 199 NA
## 200 NA
## 201 NA
## 202 NA
## 203 NA
## 204 NA
## 205 NA
## 206 NA
## 207 NA
## 208 NA
## 209 NA
## 210 NA
## 211 NA
## 212 NA
## 213 NA
## 214 NA
## 215 NA
## 216 NA
## 217 NA
## 218 NA
## 219 NA
## 220 NA
## 221 NA
## 222 NA
## 223 NA
## 224 NA
## 225 NA
## 226 NA
## 227 NA
## 228 NA
## 229 NA
## 230 NA
## 231 NA
## 232 NA
## 233 NA
## 234 NA
## 235 NA
## 236 NA
## 237 NA
## 238 NA
## 239 NA
## 240 NA
## 241 NA
## 242 NA
## 243 NA
## 244 NA
## 245 NA
## 246 NA
## 247 NA
## 248 NA
## 249 NA
## 250 NA
## 251 NA
## 252 NA
## 253 NA
## 254 NA
## 255 NA
## 256 NA
## 257 NA
## 258 NA
## 259 NA
## 260 NA
## 261 NA
## 262 NA
## 263 NA
## 264 NA
## 265 NA
## 266 NA
## 267 NA
## 268 NA
## 269 NA
## 270 NA
## 271 NA
## 272 NA
## 273 NA
## 274 NA
## 275 NA
## 276 NA
## 277 NA
## 278 NA
## 279 NA
## 280 NA
## 281 NA
## 282 NA
## 283 NA
## 284 NA
## 285 NA
## 286 NA
## 287 NA
## 288 NA
## 289 NA
## 290 NA
## 291 NA
## 292 NA
## 293 NA
## 294 NA
## 295 NA
## 296 NA
## 297 NA
## 298 NA
## 299 NA
## 300 NA
## 301 NA
## 302 NA
## 303 NA
## 304 NA
## 305 NA
## 306 NA
## 307 NA
## 308 NA
## 309 NA
## 310 NA
## 311 NA
## 312 NA
## 313 NA
## 314 NA
## 315 NA
## 316 NA
## 317 NA
## 318 NA
## 319 NA
## 320 NA
## 321 NA
## 322 NA
## 323 NA
## 324 NA
## 325 NA
## 326 NA
## 327 NA
## 328 NA
## 329 NA
## 330 NA
## 331 NA
## 332 NA
## 333 NA
## 334 NA
## 335 NA
## 336 NA
## 337 NA
## 338 NA
## 339 NA
## 340 NA
## 341 NA
## 342 NA
## 343 NA
## 344 NA
## 345 NA
## 346 NA
## 347 NA
## 348 NA
## 349 NA
## 350 NA
## 351 NA
## 352 NA
## 353 NA
## 354 NA
## 355 NA
## 356 NA
## 357 NA
## 358 NA
## 359 NA
## 360 NA
## 361 NA
## 362 NA
## 363 NA
## 364 NA
## 365 NA
## 366 NA
## 367 NA
## 368 NA
## 369 NA
## 370 NA
## 371 NA
## 372 NA
## 373 NA
## 374 NA
## 375 NA
## 376 NA
## 377 NA
## 378 NA
## 379 NA
## 380 NA
## 381 NA
## 382 NA
## 383 NA
## 384 NA
## 385 NA
## 386 NA
## 387 NA
## 388 NA
## 389 NA
## 390 NA
## 391 NA
## 392 NA
## 393 NA
## 394 NA
## 395 NA
## 396 NA
## 397 NA
## 398 NA
## 399 NA
## 400 NA
## 401 NA
## 402 NA
## 403 NA
## 404 NA
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 35 90 3.255037 TRUE NA NA NA
## 2 36 85 3.047922 TRUE NA NA NA
## 3 37 100 3.669267 TRUE NA NA NA
## 4 160 95 3.462152 TRUE NA NA NA
## 5 161 95 3.462152 TRUE NA NA NA
## 6 164 95 3.462152 TRUE NA NA NA
## 7 206 90 3.255037 TRUE NA NA NA
## 8 224 90 3.255037 TRUE NA NA NA
## 9 225 90 3.255037 TRUE NA NA NA
## 10 277 85 3.047922 TRUE NA NA NA
## 11 281 90 3.255037 TRUE NA NA NA
## metodos_evaluados numero_metodos
## 1 1 1
## 2 1 1
## 3 1 1
## 4 1 1
## 5 1 1
## 6 1 1
## 7 1 1
## 8 1 1
## 9 1 1
## 10 1 1
## 11 1 1
##
## ===== AL MENOS DOS MÉTODOS =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== LOS TRES MÉTODOS =====
## [1] ID Valor Z_score Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## zn Z-score 404 11 2.72
## zn IQR 0 0 NA
## zn Tukey extremo 0 0 NA
## zn MAD 0 0 NA## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 35 90 3.255037 NA 1 1
## 2 36 85 3.047922 NA 1 1
## 3 37 100 3.669267 NA 1 1
## 4 160 95 3.462152 NA 1 1
## 5 161 95 3.462152 NA 1 1
## 6 164 95 3.462152 NA 1 1
## 7 206 90 3.255037 NA 1 1
## 8 224 90 3.255037 NA 1 1
## 9 225 90 3.255037 NA 1 1
## 10 277 85 3.047922 NA 1 1
## 11 281 90 3.255037 NA 1 1
## clasificacion
## 1 Outlier según 1 método
## 2 Outlier según 1 método
## 3 Outlier según 1 método
## 4 Outlier según 1 método
## 5 Outlier según 1 método
## 6 Outlier según 1 método
## 7 Outlier según 1 método
## 8 Outlier según 1 método
## 9 Outlier según 1 método
## 10 Outlier según 1 método
## 11 Outlier según 1 método
##
## ========================================
## VARIABLE: indus
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -9.620728
## Límite superior = 32.4281
## Número de outliers = 0
## [1] ID Valor Z_score
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO IQR ================
## Q1 = 5.175
## Q3 = 18.1
## IQR = 12.925
## Límite inferior = -14.2125
## Límite superior = 37.4875
## Número de outliers = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -33.6
## Límite superior = 56.875
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 9.9
## MAD = 6.795
## MAD ajustada = 10.07427
## Número de outliers = 0
## [1] ID Valor Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 2.31 -1.29758961 FALSE FALSE FALSE -0.75340469
## 2 2 7.07 -0.61837932 FALSE FALSE FALSE -0.28091374
## 3 3 7.07 -0.61837932 FALSE FALSE FALSE -0.28091374
## 4 4 2.18 -1.31613947 FALSE FALSE FALSE -0.76630885
## 5 5 2.18 -1.31613947 FALSE FALSE FALSE -0.76630885
## 6 6 7.87 -0.50422633 FALSE FALSE FALSE -0.20150349
## 7 7 7.87 -0.50422633 FALSE FALSE FALSE -0.20150349
## 8 8 7.87 -0.50422633 FALSE FALSE FALSE -0.20150349
## 9 9 7.87 -0.50422633 FALSE FALSE FALSE -0.20150349
## 10 10 7.87 -0.50422633 FALSE FALSE FALSE -0.20150349
## 11 11 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 12 12 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 13 13 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 14 14 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 15 15 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 16 16 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 17 17 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 18 18 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 19 19 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 20 20 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 21 21 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 22 22 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 23 23 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 24 24 8.14 -0.46569970 FALSE FALSE FALSE -0.17470254
## 25 25 5.96 -0.77676660 FALSE FALSE FALSE -0.39109545
## 26 26 5.96 -0.77676660 FALSE FALSE FALSE -0.39109545
## 27 27 5.96 -0.77676660 FALSE FALSE FALSE -0.39109545
## 28 28 6.91 -0.64120992 FALSE FALSE FALSE -0.29679579
## 29 29 6.91 -0.64120992 FALSE FALSE FALSE -0.29679579
## 30 30 6.91 -0.64120992 FALSE FALSE FALSE -0.29679579
## 31 31 6.91 -0.64120992 FALSE FALSE FALSE -0.29679579
## 32 32 6.91 -0.64120992 FALSE FALSE FALSE -0.29679579
## 33 33 5.64 -0.82242779 FALSE FALSE FALSE -0.42285955
## 34 34 4.00 -1.05644142 FALSE FALSE FALSE -0.58565055
## 35 35 1.22 -1.45312306 FALSE FALSE FALSE -0.86160115
## 36 36 0.74 -1.52161486 FALSE FALSE FALSE -0.90924729
## 37 37 1.32 -1.43885394 FALSE FALSE FALSE -0.85167487
## 38 38 5.13 -0.89520032 FALSE FALSE FALSE -0.47348358
## 39 39 5.13 -0.89520032 FALSE FALSE FALSE -0.47348358
## 40 40 5.13 -0.89520032 FALSE FALSE FALSE -0.47348358
## 41 41 1.38 -1.43029246 FALSE FALSE FALSE -0.84571910
## 42 42 3.37 -1.14633690 FALSE FALSE FALSE -0.64818612
## 43 43 3.37 -1.14633690 FALSE FALSE FALSE -0.64818612
## 44 44 6.07 -0.76107056 FALSE FALSE FALSE -0.38017654
## 45 45 6.07 -0.76107056 FALSE FALSE FALSE -0.38017654
## 46 46 10.81 -0.08471409 FALSE FALSE FALSE 0.09032915
## 47 47 10.81 -0.08471409 FALSE FALSE FALSE 0.09032915
## 48 48 10.81 -0.08471409 FALSE FALSE FALSE 0.09032915
## 49 49 12.83 0.20352221 FALSE FALSE FALSE 0.29084002
## 50 50 12.83 0.20352221 FALSE FALSE FALSE 0.29084002
## 51 51 12.83 0.20352221 FALSE FALSE FALSE 0.29084002
## 52 52 12.83 0.20352221 FALSE FALSE FALSE 0.29084002
## 53 53 12.83 0.20352221 FALSE FALSE FALSE 0.29084002
## 54 54 4.86 -0.93372696 FALSE FALSE FALSE -0.50028454
## 55 55 4.86 -0.93372696 FALSE FALSE FALSE -0.50028454
## 56 56 4.86 -0.93372696 FALSE FALSE FALSE -0.50028454
## 57 57 4.49 -0.98652272 FALSE FALSE FALSE -0.53701177
## 58 58 4.49 -0.98652272 FALSE FALSE FALSE -0.53701177
## 59 59 4.49 -0.98652272 FALSE FALSE FALSE -0.53701177
## 60 60 4.49 -0.98652272 FALSE FALSE FALSE -0.53701177
## 61 61 3.41 -1.14062925 FALSE FALSE FALSE -0.64421560
## 62 62 3.41 -1.14062925 FALSE FALSE FALSE -0.64421560
## 63 63 3.41 -1.14062925 FALSE FALSE FALSE -0.64421560
## 64 64 3.41 -1.14062925 FALSE FALSE FALSE -0.64421560
## 65 65 15.04 0.51886984 FALSE FALSE FALSE 0.51021082
## 66 66 15.04 0.51886984 FALSE FALSE FALSE 0.51021082
## 67 67 15.04 0.51886984 FALSE FALSE FALSE 0.51021082
## 68 68 2.89 -1.21482870 FALSE FALSE FALSE -0.69583226
## 69 69 2.89 -1.21482870 FALSE FALSE FALSE -0.69583226
## 70 70 2.89 -1.21482870 FALSE FALSE FALSE -0.69583226
## 71 71 2.89 -1.21482870 FALSE FALSE FALSE -0.69583226
## 72 72 2.89 -1.21482870 FALSE FALSE FALSE -0.69583226
## 73 73 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 74 74 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 75 75 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 76 76 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 77 77 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 78 78 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 79 79 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 80 80 8.56 -0.40576938 FALSE FALSE FALSE -0.13301216
## 81 81 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 82 82 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 83 83 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 84 84 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 85 85 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 86 86 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 87 87 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 88 88 10.01 -0.19886708 FALSE FALSE FALSE 0.01091891
## 89 89 25.65 2.03282388 FALSE FALSE FALSE 1.56338918
## 90 90 25.65 2.03282388 FALSE FALSE FALSE 1.56338918
## 91 91 25.65 2.03282388 FALSE FALSE FALSE 1.56338918
## 92 92 25.65 2.03282388 FALSE FALSE FALSE 1.56338918
## 93 93 25.65 2.03282388 FALSE FALSE FALSE 1.56338918
## 94 94 25.65 2.03282388 FALSE FALSE FALSE 1.56338918
## 95 95 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 96 96 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 97 97 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 98 98 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 99 99 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 100 100 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 101 101 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 102 102 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 103 103 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 104 104 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 105 105 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 106 106 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 107 107 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 108 108 21.89 1.49630482 FALSE FALSE FALSE 1.19016103
## 109 109 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 110 110 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 111 111 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 112 112 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 113 113 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 114 114 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 115 115 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 116 116 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 117 117 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 118 118 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 119 119 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 120 120 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 121 121 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 122 122 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 123 123 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 124 124 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 125 125 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 126 126 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 127 127 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 128 128 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 129 129 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 130 130 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 131 131 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 132 132 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 133 133 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 134 134 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 135 135 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 136 136 19.58 1.16668806 FALSE FALSE FALSE 0.96086395
## 137 137 4.05 -1.04930686 FALSE FALSE FALSE -0.58068741
## 138 138 4.05 -1.04930686 FALSE FALSE FALSE -0.58068741
## 139 139 4.05 -1.04930686 FALSE FALSE FALSE -0.58068741
## 140 140 4.05 -1.04930686 FALSE FALSE FALSE -0.58068741
## 141 141 4.05 -1.04930686 FALSE FALSE FALSE -0.58068741
## 142 142 4.05 -1.04930686 FALSE FALSE FALSE -0.58068741
## 143 143 2.46 -1.27618593 FALSE FALSE FALSE -0.73851527
## 144 144 2.46 -1.27618593 FALSE FALSE FALSE -0.73851527
## 145 145 2.46 -1.27618593 FALSE FALSE FALSE -0.73851527
## 146 146 2.46 -1.27618593 FALSE FALSE FALSE -0.73851527
## 147 147 2.46 -1.27618593 FALSE FALSE FALSE -0.73851527
## 148 148 3.44 -1.13634852 FALSE FALSE FALSE -0.64123772
## 149 149 3.44 -1.13634852 FALSE FALSE FALSE -0.64123772
## 150 150 3.44 -1.13634852 FALSE FALSE FALSE -0.64123772
## 151 151 3.44 -1.13634852 FALSE FALSE FALSE -0.64123772
## 152 152 3.44 -1.13634852 FALSE FALSE FALSE -0.64123772
## 153 153 3.44 -1.13634852 FALSE FALSE FALSE -0.64123772
## 154 154 2.93 -1.20912105 FALSE FALSE FALSE -0.69186175
## 155 155 2.93 -1.20912105 FALSE FALSE FALSE -0.69186175
## 156 156 0.46 -1.56156840 FALSE FALSE FALSE -0.93704088
## 157 157 1.52 -1.41031569 FALSE FALSE FALSE -0.83182231
## 158 158 1.52 -1.41031569 FALSE FALSE FALSE -0.83182231
## 159 159 1.52 -1.41031569 FALSE FALSE FALSE -0.83182231
## 160 160 1.47 -1.41745025 FALSE FALSE FALSE -0.83678545
## 161 161 1.47 -1.41745025 FALSE FALSE FALSE -0.83678545
## 162 162 2.03 -1.33754316 FALSE FALSE FALSE -0.78119827
## 163 163 2.03 -1.33754316 FALSE FALSE FALSE -0.78119827
## 164 164 2.68 -1.24479386 FALSE FALSE FALSE -0.71667745
## 165 165 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 166 166 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 167 167 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 168 168 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 169 169 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 170 170 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 171 171 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 172 172 10.59 -0.11610616 FALSE FALSE FALSE 0.06849134
## 173 173 13.89 0.35477492 FALSE FALSE FALSE 0.39605859
## 174 174 13.89 0.35477492 FALSE FALSE FALSE 0.39605859
## 175 175 13.89 0.35477492 FALSE FALSE FALSE 0.39605859
## 176 176 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 177 177 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 178 178 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 179 179 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 180 180 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 181 181 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 182 182 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 183 183 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 184 184 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 185 185 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 186 186 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 187 187 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 188 188 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 189 189 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 190 190 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 191 191 6.20 -0.74252070 FALSE FALSE FALSE -0.36727238
## 192 192 4.93 -0.92373857 FALSE FALSE FALSE -0.49333614
## 193 193 4.93 -0.92373857 FALSE FALSE FALSE -0.49333614
## 194 194 4.93 -0.92373857 FALSE FALSE FALSE -0.49333614
## 195 195 4.93 -0.92373857 FALSE FALSE FALSE -0.49333614
## 196 196 4.93 -0.92373857 FALSE FALSE FALSE -0.49333614
## 197 197 5.86 -0.79103572 FALSE FALSE FALSE -0.40102173
## 198 198 5.86 -0.79103572 FALSE FALSE FALSE -0.40102173
## 199 199 5.86 -0.79103572 FALSE FALSE FALSE -0.40102173
## 200 200 5.86 -0.79103572 FALSE FALSE FALSE -0.40102173
## 201 201 5.86 -0.79103572 FALSE FALSE FALSE -0.40102173
## 202 202 5.86 -0.79103572 FALSE FALSE FALSE -0.40102173
## 203 203 5.86 -0.79103572 FALSE FALSE FALSE -0.40102173
## 204 204 3.64 -1.10781027 FALSE FALSE FALSE -0.62138516
## 205 205 3.64 -1.10781027 FALSE FALSE FALSE -0.62138516
## 206 206 3.75 -1.09211423 FALSE FALSE FALSE -0.61046625
## 207 207 3.97 -1.06072216 FALSE FALSE FALSE -0.58862843
## 208 208 3.97 -1.06072216 FALSE FALSE FALSE -0.58862843
## 209 209 3.97 -1.06072216 FALSE FALSE FALSE -0.58862843
## 210 210 3.97 -1.06072216 FALSE FALSE FALSE -0.58862843
## 211 211 3.97 -1.06072216 FALSE FALSE FALSE -0.58862843
## 212 212 3.97 -1.06072216 FALSE FALSE FALSE -0.58862843
## 213 213 3.97 -1.06072216 FALSE FALSE FALSE -0.58862843
## 214 214 6.96 -0.63407536 FALSE FALSE FALSE -0.29183265
## 215 215 6.96 -0.63407536 FALSE FALSE FALSE -0.29183265
## 216 216 6.96 -0.63407536 FALSE FALSE FALSE -0.29183265
## 217 217 6.96 -0.63407536 FALSE FALSE FALSE -0.29183265
## 218 218 6.41 -0.71255554 FALSE FALSE FALSE -0.34642719
## 219 219 6.41 -0.71255554 FALSE FALSE FALSE -0.34642719
## 220 220 6.41 -0.71255554 FALSE FALSE FALSE -0.34642719
## 221 221 3.33 -1.15204455 FALSE FALSE FALSE -0.65215663
## 222 222 3.33 -1.15204455 FALSE FALSE FALSE -0.65215663
## 223 223 3.33 -1.15204455 FALSE FALSE FALSE -0.65215663
## 224 224 1.21 -1.45454998 FALSE FALSE FALSE -0.86259377
## 225 225 2.97 -1.20341340 FALSE FALSE FALSE -0.68789124
## 226 226 2.25 -1.30615109 FALSE FALSE FALSE -0.75936046
## 227 227 1.76 -1.37606979 FALSE FALSE FALSE -0.80799923
## 228 228 5.32 -0.86808899 FALSE FALSE FALSE -0.45462365
## 229 229 5.32 -0.86808899 FALSE FALSE FALSE -0.45462365
## 230 230 5.32 -0.86808899 FALSE FALSE FALSE -0.45462365
## 231 231 4.95 -0.92088475 FALSE FALSE FALSE -0.49135088
## 232 232 4.95 -0.92088475 FALSE FALSE FALSE -0.49135088
## 233 233 4.95 -0.92088475 FALSE FALSE FALSE -0.49135088
## 234 234 13.92 0.35905566 FALSE FALSE FALSE 0.39903648
## 235 235 13.92 0.35905566 FALSE FALSE FALSE 0.39903648
## 236 236 13.92 0.35905566 FALSE FALSE FALSE 0.39903648
## 237 237 2.24 -1.30757800 FALSE FALSE FALSE -0.76035309
## 238 238 6.09 -0.75821673 FALSE FALSE FALSE -0.37819129
## 239 239 6.09 -0.75821673 FALSE FALSE FALSE -0.37819129
## 240 240 2.18 -1.31613947 FALSE FALSE FALSE -0.76630885
## 241 241 2.18 -1.31613947 FALSE FALSE FALSE -0.76630885
## 242 242 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 243 243 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 244 244 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 245 245 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 246 246 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 247 247 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 248 248 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 249 249 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 250 250 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 251 251 9.90 -0.21456312 FALSE FALSE FALSE 0.00000000
## 252 252 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 253 253 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 254 254 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 255 255 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 256 256 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 257 257 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 258 258 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 259 259 7.38 -0.57414504 FALSE FALSE FALSE -0.25014227
## 260 260 3.24 -1.16488676 FALSE FALSE FALSE -0.66109028
## 261 261 3.24 -1.16488676 FALSE FALSE FALSE -0.66109028
## 262 262 3.24 -1.16488676 FALSE FALSE FALSE -0.66109028
## 263 263 6.06 -0.76249747 FALSE FALSE FALSE -0.38116917
## 264 264 5.19 -0.88663885 FALSE FALSE FALSE -0.46752781
## 265 265 5.19 -0.88663885 FALSE FALSE FALSE -0.46752781
## 266 266 5.19 -0.88663885 FALSE FALSE FALSE -0.46752781
## 267 267 5.19 -0.88663885 FALSE FALSE FALSE -0.46752781
## 268 268 5.19 -0.88663885 FALSE FALSE FALSE -0.46752781
## 269 269 5.19 -0.88663885 FALSE FALSE FALSE -0.46752781
## 270 270 5.19 -0.88663885 FALSE FALSE FALSE -0.46752781
## 271 271 1.52 -1.41031569 FALSE FALSE FALSE -0.83182231
## 272 272 1.89 -1.35751993 FALSE FALSE FALSE -0.79509507
## 273 273 3.78 -1.08783349 FALSE FALSE FALSE -0.60748837
## 274 274 3.78 -1.08783349 FALSE FALSE FALSE -0.60748837
## 275 275 4.39 -1.00079184 FALSE FALSE FALSE -0.54693806
## 276 276 4.39 -1.00079184 FALSE FALSE FALSE -0.54693806
## 277 277 4.15 -1.03503774 FALSE FALSE FALSE -0.57076113
## 278 278 2.01 -1.34039699 FALSE FALSE FALSE -0.78318353
## 279 279 1.25 -1.44884233 FALSE FALSE FALSE -0.85862326
## 280 280 1.69 -1.38605818 FALSE FALSE FALSE -0.81494763
## 281 281 2.02 -1.33897007 FALSE FALSE FALSE -0.78219090
## 282 282 1.91 -1.35466611 FALSE FALSE FALSE -0.79310981
## 283 283 1.91 -1.35466611 FALSE FALSE FALSE -0.79310981
## 284 284 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 285 285 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 286 286 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 287 287 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 288 288 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 289 289 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 290 290 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 291 291 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 292 292 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 293 293 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 294 294 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 295 295 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 296 296 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 297 297 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 298 298 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 299 299 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 300 300 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 301 301 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 302 302 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 303 303 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 304 304 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 305 305 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 306 306 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 307 307 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 308 308 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 309 309 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 310 310 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 311 311 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 312 312 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 313 313 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 314 314 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 315 315 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 316 316 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 317 317 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 318 318 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 319 319 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 320 320 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 321 321 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 322 322 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 323 323 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 324 324 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 325 325 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 326 326 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 327 327 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 328 328 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 329 329 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 330 330 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 331 331 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 332 332 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 333 333 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 334 334 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 335 335 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 336 336 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 337 337 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 338 338 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 339 339 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 340 340 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 341 341 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 342 342 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 343 343 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 344 344 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 345 345 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 346 346 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 347 347 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 348 348 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 349 349 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 350 350 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 351 351 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 352 352 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 353 353 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 354 354 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 355 355 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 356 356 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 357 357 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 358 358 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 359 359 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 360 360 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 361 361 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 362 362 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 363 363 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 364 364 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 365 365 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 366 366 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 367 367 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 368 368 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 369 369 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 370 370 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 371 371 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 372 372 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 373 373 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 374 374 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 375 375 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 376 376 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 377 377 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 378 378 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 379 379 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 380 380 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 381 381 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 382 382 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 383 383 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 384 384 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 385 385 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 386 386 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 387 387 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 388 388 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 389 389 18.10 0.95550503 FALSE FALSE FALSE 0.81395500
## 390 390 27.74 2.33104856 FALSE FALSE FALSE 1.77084844
## 391 391 27.74 2.33104856 FALSE FALSE FALSE 1.77084844
## 392 392 27.74 2.33104856 FALSE FALSE FALSE 1.77084844
## 393 393 27.74 2.33104856 FALSE FALSE FALSE 1.77084844
## 394 394 27.74 2.33104856 FALSE FALSE FALSE 1.77084844
## 395 395 9.69 -0.24452828 FALSE FALSE FALSE -0.02084519
## 396 396 9.69 -0.24452828 FALSE FALSE FALSE -0.02084519
## 397 397 9.69 -0.24452828 FALSE FALSE FALSE -0.02084519
## 398 398 9.69 -0.24452828 FALSE FALSE FALSE -0.02084519
## 399 399 9.69 -0.24452828 FALSE FALSE FALSE -0.02084519
## 400 400 9.69 -0.24452828 FALSE FALSE FALSE -0.02084519
## 401 401 11.93 0.07510009 FALSE FALSE FALSE 0.20150349
## 402 402 11.93 0.07510009 FALSE FALSE FALSE 0.20150349
## 403 403 11.93 0.07510009 FALSE FALSE FALSE 0.20150349
## 404 404 11.93 0.07510009 FALSE FALSE FALSE 0.20150349
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 FALSE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 FALSE
## 289 FALSE
## 290 FALSE
## 291 FALSE
## 292 FALSE
## 293 FALSE
## 294 FALSE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 FALSE
## 299 FALSE
## 300 FALSE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 FALSE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 FALSE
## 322 FALSE
## 323 FALSE
## 324 FALSE
## 325 FALSE
## 326 FALSE
## 327 FALSE
## 328 FALSE
## 329 FALSE
## 330 FALSE
## 331 FALSE
## 332 FALSE
## 333 FALSE
## 334 FALSE
## 335 FALSE
## 336 FALSE
## 337 FALSE
## 338 FALSE
## 339 FALSE
## 340 FALSE
## 341 FALSE
## 342 FALSE
## 343 FALSE
## 344 FALSE
## 345 FALSE
## 346 FALSE
## 347 FALSE
## 348 FALSE
## 349 FALSE
## 350 FALSE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 FALSE
## 355 FALSE
## 356 FALSE
## 357 FALSE
## 358 FALSE
## 359 FALSE
## 360 FALSE
## 361 FALSE
## 362 FALSE
## 363 FALSE
## 364 FALSE
## 365 FALSE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 FALSE
## 370 FALSE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 FALSE
## 378 FALSE
## 379 FALSE
## 380 FALSE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== AL MENOS DOS MÉTODOS =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== LOS TRES MÉTODOS =====
## [1] ID Valor Z_score Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## indus Z-score 404 0 0
## indus IQR 404 0 0
## indus Tukey extremo 404 0 0
## indus MAD 404 0 0## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## [1] ID Valor Z_score Z_robusto
## [5] metodos_evaluados numero_metodos clasificacion
## <0 rows> (o 0- extensión row.names)
##
## ========================================
## VARIABLE: nox
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = 0.2042084
## Límite superior = 0.9092263
## Número de outliers = 0
## [1] ID Valor Z_score
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO IQR ================
## Q1 = 0.453
## Q3 = 0.624
## IQR = 0.171
## Límite inferior = 0.1965
## Límite superior = 0.8805
## Número de outliers = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -0.06
## Límite superior = 1.137
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 0.538
## MAD = 0.086
## MAD ajustada = 0.1275036
## Número de outliers = 0
## [1] ID Valor Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 2 2 0.4690 -0.74651147 FALSE FALSE FALSE -0.54116119
## 3 3 0.4690 -0.74651147 FALSE FALSE FALSE -0.54116119
## 4 4 0.4580 -0.84012611 FALSE FALSE FALSE -0.62743326
## 5 5 0.4580 -0.84012611 FALSE FALSE FALSE -0.62743326
## 6 6 0.5240 -0.27843826 FALSE FALSE FALSE -0.10980082
## 7 7 0.5240 -0.27843826 FALSE FALSE FALSE -0.10980082
## 8 8 0.5240 -0.27843826 FALSE FALSE FALSE -0.10980082
## 9 9 0.5240 -0.27843826 FALSE FALSE FALSE -0.10980082
## 10 10 0.5240 -0.27843826 FALSE FALSE FALSE -0.10980082
## 11 11 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 12 12 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 13 13 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 14 14 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 15 15 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 16 16 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 17 17 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 18 18 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 19 19 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 20 20 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 21 21 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 22 22 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 23 23 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 24 24 0.5380 -0.15929235 FALSE FALSE FALSE 0.00000000
## 25 25 0.4990 -0.49119881 FALSE FALSE FALSE -0.30587372
## 26 26 0.4990 -0.49119881 FALSE FALSE FALSE -0.30587372
## 27 27 0.4990 -0.49119881 FALSE FALSE FALSE -0.30587372
## 28 28 0.4480 -0.92523033 FALSE FALSE FALSE -0.70586242
## 29 29 0.4480 -0.92523033 FALSE FALSE FALSE -0.70586242
## 30 30 0.4480 -0.92523033 FALSE FALSE FALSE -0.70586242
## 31 31 0.4480 -0.92523033 FALSE FALSE FALSE -0.70586242
## 32 32 0.4480 -0.92523033 FALSE FALSE FALSE -0.70586242
## 33 33 0.4390 -1.00182413 FALSE FALSE FALSE -0.77644866
## 34 34 0.4100 -1.24862636 FALSE FALSE FALSE -1.00389322
## 35 35 0.4030 -1.30819932 FALSE FALSE FALSE -1.05879363
## 36 36 0.4100 -1.24862636 FALSE FALSE FALSE -1.00389322
## 37 37 0.4110 -1.24011594 FALSE FALSE FALSE -0.99605031
## 38 38 0.4530 -0.88267822 FALSE FALSE FALSE -0.66664784
## 39 39 0.4530 -0.88267822 FALSE FALSE FALSE -0.66664784
## 40 40 0.4530 -0.88267822 FALSE FALSE FALSE -0.66664784
## 41 41 0.4161 -1.19671279 FALSE FALSE FALSE -0.95605144
## 42 42 0.3980 -1.35075143 FALSE FALSE FALSE -1.09800821
## 43 43 0.3980 -1.35075143 FALSE FALSE FALSE -1.09800821
## 44 44 0.4090 -1.25713679 FALSE FALSE FALSE -1.01173614
## 45 45 0.4090 -1.25713679 FALSE FALSE FALSE -1.01173614
## 46 46 0.4130 -1.22309510 FALSE FALSE FALSE -0.98036448
## 47 47 0.4130 -1.22309510 FALSE FALSE FALSE -0.98036448
## 48 48 0.4130 -1.22309510 FALSE FALSE FALSE -0.98036448
## 49 49 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 50 50 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 51 51 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 52 52 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 53 53 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 54 54 0.4260 -1.11245961 FALSE FALSE FALSE -0.87840657
## 55 55 0.4260 -1.11245961 FALSE FALSE FALSE -0.87840657
## 56 56 0.4260 -1.11245961 FALSE FALSE FALSE -0.87840657
## 57 57 0.4490 -0.91671991 FALSE FALSE FALSE -0.69801951
## 58 58 0.4490 -0.91671991 FALSE FALSE FALSE -0.69801951
## 59 59 0.4490 -0.91671991 FALSE FALSE FALSE -0.69801951
## 60 60 0.4490 -0.91671991 FALSE FALSE FALSE -0.69801951
## 61 61 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 62 62 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 63 63 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 64 64 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 65 65 0.4640 -0.78906358 FALSE FALSE FALSE -0.58037577
## 66 66 0.4640 -0.78906358 FALSE FALSE FALSE -0.58037577
## 67 67 0.4640 -0.78906358 FALSE FALSE FALSE -0.58037577
## 68 68 0.4450 -0.95076159 FALSE FALSE FALSE -0.72939117
## 69 69 0.4450 -0.95076159 FALSE FALSE FALSE -0.72939117
## 70 70 0.4450 -0.95076159 FALSE FALSE FALSE -0.72939117
## 71 71 0.4450 -0.95076159 FALSE FALSE FALSE -0.72939117
## 72 72 0.4450 -0.95076159 FALSE FALSE FALSE -0.72939117
## 73 73 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 74 74 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 75 75 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 76 76 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 77 77 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 78 78 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 79 79 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 80 80 0.5200 -0.31247995 FALSE FALSE FALSE -0.14117248
## 81 81 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 82 82 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 83 83 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 84 84 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 85 85 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 86 86 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 87 87 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 88 88 0.5470 -0.08269855 FALSE FALSE FALSE 0.07058624
## 89 89 0.5810 0.20665580 FALSE FALSE FALSE 0.33724538
## 90 90 0.5810 0.20665580 FALSE FALSE FALSE 0.33724538
## 91 91 0.5810 0.20665580 FALSE FALSE FALSE 0.33724538
## 92 92 0.5810 0.20665580 FALSE FALSE FALSE 0.33724538
## 93 93 0.5810 0.20665580 FALSE FALSE FALSE 0.33724538
## 94 94 0.5810 0.20665580 FALSE FALSE FALSE 0.33724538
## 95 95 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 96 96 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 97 97 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 98 98 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 99 99 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 100 100 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 101 101 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 102 102 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 103 103 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 104 104 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 105 105 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 106 106 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 107 107 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 108 108 0.6240 0.57260394 FALSE FALSE FALSE 0.67449076
## 109 109 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 110 110 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 111 111 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 112 112 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 113 113 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 114 114 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 115 115 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 116 116 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 117 117 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 118 118 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 119 119 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 120 120 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 121 121 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 122 122 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 123 123 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 124 124 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 125 125 0.8710 2.67467818 FALSE FALSE FALSE 2.61169096
## 126 126 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 127 127 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 128 128 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 129 129 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 130 130 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 131 131 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 132 132 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 133 133 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 134 134 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 135 135 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 136 136 0.6050 0.41090592 FALSE FALSE FALSE 0.52547536
## 137 137 0.5100 -0.39758417 FALSE FALSE FALSE -0.21960164
## 138 138 0.5100 -0.39758417 FALSE FALSE FALSE -0.21960164
## 139 139 0.5100 -0.39758417 FALSE FALSE FALSE -0.21960164
## 140 140 0.5100 -0.39758417 FALSE FALSE FALSE -0.21960164
## 141 141 0.5100 -0.39758417 FALSE FALSE FALSE -0.21960164
## 142 142 0.5100 -0.39758417 FALSE FALSE FALSE -0.21960164
## 143 143 0.4880 -0.58481345 FALSE FALSE FALSE -0.39214579
## 144 144 0.4880 -0.58481345 FALSE FALSE FALSE -0.39214579
## 145 145 0.4880 -0.58481345 FALSE FALSE FALSE -0.39214579
## 146 146 0.4880 -0.58481345 FALSE FALSE FALSE -0.39214579
## 147 147 0.4880 -0.58481345 FALSE FALSE FALSE -0.39214579
## 148 148 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 149 149 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 150 150 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 151 151 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 152 152 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 153 153 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 154 154 0.4010 -1.32522016 FALSE FALSE FALSE -1.07447947
## 155 155 0.4010 -1.32522016 FALSE FALSE FALSE -1.07447947
## 156 156 0.4220 -1.14650130 FALSE FALSE FALSE -0.90977823
## 157 157 0.4040 -1.29968890 FALSE FALSE FALSE -1.05095072
## 158 158 0.4040 -1.29968890 FALSE FALSE FALSE -1.05095072
## 159 159 0.4040 -1.29968890 FALSE FALSE FALSE -1.05095072
## 160 160 0.4030 -1.30819932 FALSE FALSE FALSE -1.05879363
## 161 161 0.4030 -1.30819932 FALSE FALSE FALSE -1.05879363
## 162 162 0.4150 -1.20607425 FALSE FALSE FALSE -0.96467864
## 163 163 0.4150 -1.20607425 FALSE FALSE FALSE -0.96467864
## 164 164 0.4161 -1.19671279 FALSE FALSE FALSE -0.95605144
## 165 165 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 166 166 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 167 167 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 168 168 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 169 169 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 170 170 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 171 171 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 172 172 0.4890 -0.57630303 FALSE FALSE FALSE -0.38430287
## 173 173 0.5500 -0.05716729 FALSE FALSE FALSE 0.09411499
## 174 174 0.5500 -0.05716729 FALSE FALSE FALSE 0.09411499
## 175 175 0.5500 -0.05716729 FALSE FALSE FALSE 0.09411499
## 176 176 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 177 177 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 178 178 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 179 179 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 180 180 0.5040 -0.44864670 FALSE FALSE FALSE -0.26665914
## 181 181 0.5040 -0.44864670 FALSE FALSE FALSE -0.26665914
## 182 182 0.5040 -0.44864670 FALSE FALSE FALSE -0.26665914
## 183 183 0.5040 -0.44864670 FALSE FALSE FALSE -0.26665914
## 184 184 0.5040 -0.44864670 FALSE FALSE FALSE -0.26665914
## 185 185 0.5040 -0.44864670 FALSE FALSE FALSE -0.26665914
## 186 186 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 187 187 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 188 188 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 189 189 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 190 190 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 191 191 0.5070 -0.42311543 FALSE FALSE FALSE -0.24313039
## 192 192 0.4280 -1.09543877 FALSE FALSE FALSE -0.86272074
## 193 193 0.4280 -1.09543877 FALSE FALSE FALSE -0.86272074
## 194 194 0.4280 -1.09543877 FALSE FALSE FALSE -0.86272074
## 195 195 0.4280 -1.09543877 FALSE FALSE FALSE -0.86272074
## 196 196 0.4280 -1.09543877 FALSE FALSE FALSE -0.86272074
## 197 197 0.4310 -1.06990750 FALSE FALSE FALSE -0.83919199
## 198 198 0.4310 -1.06990750 FALSE FALSE FALSE -0.83919199
## 199 199 0.4310 -1.06990750 FALSE FALSE FALSE -0.83919199
## 200 200 0.4310 -1.06990750 FALSE FALSE FALSE -0.83919199
## 201 201 0.4310 -1.06990750 FALSE FALSE FALSE -0.83919199
## 202 202 0.4310 -1.06990750 FALSE FALSE FALSE -0.83919199
## 203 203 0.4310 -1.06990750 FALSE FALSE FALSE -0.83919199
## 204 204 0.3920 -1.40181396 FALSE FALSE FALSE -1.14506571
## 205 205 0.3920 -1.40181396 FALSE FALSE FALSE -1.14506571
## 206 206 0.3940 -1.38479312 FALSE FALSE FALSE -1.12937988
## 207 207 0.6470 0.76834365 FALSE FALSE FALSE 0.85487782
## 208 208 0.6470 0.76834365 FALSE FALSE FALSE 0.85487782
## 209 209 0.6470 0.76834365 FALSE FALSE FALSE 0.85487782
## 210 210 0.6470 0.76834365 FALSE FALSE FALSE 0.85487782
## 211 211 0.6470 0.76834365 FALSE FALSE FALSE 0.85487782
## 212 212 0.6470 0.76834365 FALSE FALSE FALSE 0.85487782
## 213 213 0.5750 0.15559326 FALSE FALSE FALSE 0.29018788
## 214 214 0.4640 -0.78906358 FALSE FALSE FALSE -0.58037577
## 215 215 0.4640 -0.78906358 FALSE FALSE FALSE -0.58037577
## 216 216 0.4640 -0.78906358 FALSE FALSE FALSE -0.58037577
## 217 217 0.4640 -0.78906358 FALSE FALSE FALSE -0.58037577
## 218 218 0.4470 -0.93374075 FALSE FALSE FALSE -0.71370534
## 219 219 0.4470 -0.93374075 FALSE FALSE FALSE -0.71370534
## 220 220 0.4470 -0.93374075 FALSE FALSE FALSE -0.71370534
## 221 221 0.4429 -0.96863348 FALSE FALSE FALSE -0.74586129
## 222 222 0.4429 -0.96863348 FALSE FALSE FALSE -0.74586129
## 223 223 0.4429 -0.96863348 FALSE FALSE FALSE -0.74586129
## 224 224 0.4010 -1.32522016 FALSE FALSE FALSE -1.07447947
## 225 225 0.4000 -1.33373058 FALSE FALSE FALSE -1.08232238
## 226 226 0.3890 -1.42734523 FALSE FALSE FALSE -1.16859446
## 227 227 0.3850 -1.46138691 FALSE FALSE FALSE -1.19996612
## 228 228 0.4050 -1.29117847 FALSE FALSE FALSE -1.04310780
## 229 229 0.4050 -1.29117847 FALSE FALSE FALSE -1.04310780
## 230 230 0.4050 -1.29117847 FALSE FALSE FALSE -1.04310780
## 231 231 0.4110 -1.24011594 FALSE FALSE FALSE -0.99605031
## 232 232 0.4110 -1.24011594 FALSE FALSE FALSE -0.99605031
## 233 233 0.4110 -1.24011594 FALSE FALSE FALSE -0.99605031
## 234 234 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 235 235 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 236 236 0.4370 -1.01884497 FALSE FALSE FALSE -0.79213450
## 237 237 0.4000 -1.33373058 FALSE FALSE FALSE -1.08232238
## 238 238 0.4330 -1.05288666 FALSE FALSE FALSE -0.82350616
## 239 239 0.4330 -1.05288666 FALSE FALSE FALSE -0.82350616
## 240 240 0.4720 -0.72098020 FALSE FALSE FALSE -0.51763244
## 241 241 0.4720 -0.72098020 FALSE FALSE FALSE -0.51763244
## 242 242 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 243 243 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 244 244 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 245 245 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 246 246 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 247 247 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 248 248 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 249 249 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 250 250 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 251 251 0.5440 -0.10822982 FALSE FALSE FALSE 0.04705749
## 252 252 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 253 253 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 254 254 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 255 255 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 256 256 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 257 257 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 258 258 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 259 259 0.4930 -0.54226134 FALSE FALSE FALSE -0.35293121
## 260 260 0.4600 -0.82310526 FALSE FALSE FALSE -0.61174743
## 261 261 0.4600 -0.82310526 FALSE FALSE FALSE -0.61174743
## 262 262 0.4600 -0.82310526 FALSE FALSE FALSE -0.61174743
## 263 263 0.4379 -1.01118559 FALSE FALSE FALSE -0.78507587
## 264 264 0.5150 -0.35503206 FALSE FALSE FALSE -0.18038706
## 265 265 0.5150 -0.35503206 FALSE FALSE FALSE -0.18038706
## 266 266 0.5150 -0.35503206 FALSE FALSE FALSE -0.18038706
## 267 267 0.5150 -0.35503206 FALSE FALSE FALSE -0.18038706
## 268 268 0.5150 -0.35503206 FALSE FALSE FALSE -0.18038706
## 269 269 0.5150 -0.35503206 FALSE FALSE FALSE -0.18038706
## 270 270 0.5150 -0.35503206 FALSE FALSE FALSE -0.18038706
## 271 271 0.4420 -0.97629286 FALSE FALSE FALSE -0.75291992
## 272 272 0.5180 -0.32950079 FALSE FALSE FALSE -0.15685832
## 273 273 0.4840 -0.61885514 FALSE FALSE FALSE -0.42351745
## 274 274 0.4840 -0.61885514 FALSE FALSE FALSE -0.42351745
## 275 275 0.4420 -0.97629286 FALSE FALSE FALSE -0.75291992
## 276 276 0.4420 -0.97629286 FALSE FALSE FALSE -0.75291992
## 277 277 0.4290 -1.08692835 FALSE FALSE FALSE -0.85487782
## 278 278 0.4350 -1.03586581 FALSE FALSE FALSE -0.80782033
## 279 279 0.4290 -1.08692835 FALSE FALSE FALSE -0.85487782
## 280 280 0.4110 -1.24011594 FALSE FALSE FALSE -0.99605031
## 281 281 0.4100 -1.24862636 FALSE FALSE FALSE -1.00389322
## 282 282 0.4130 -1.22309510 FALSE FALSE FALSE -0.98036448
## 283 283 0.4130 -1.22309510 FALSE FALSE FALSE -0.98036448
## 284 284 0.7700 1.81512555 FALSE FALSE FALSE 1.81955647
## 285 285 0.7700 1.81512555 FALSE FALSE FALSE 1.81955647
## 286 286 0.7700 1.81512555 FALSE FALSE FALSE 1.81955647
## 287 287 0.7700 1.81512555 FALSE FALSE FALSE 1.81955647
## 288 288 0.7700 1.81512555 FALSE FALSE FALSE 1.81955647
## 289 289 0.7700 1.81512555 FALSE FALSE FALSE 1.81955647
## 290 290 0.7700 1.81512555 FALSE FALSE FALSE 1.81955647
## 291 291 0.7180 1.37258361 FALSE FALSE FALSE 1.41172485
## 292 292 0.7180 1.37258361 FALSE FALSE FALSE 1.41172485
## 293 293 0.7180 1.37258361 FALSE FALSE FALSE 1.41172485
## 294 294 0.6310 0.63217690 FALSE FALSE FALSE 0.72939117
## 295 295 0.6310 0.63217690 FALSE FALSE FALSE 0.72939117
## 296 296 0.6310 0.63217690 FALSE FALSE FALSE 0.72939117
## 297 297 0.6310 0.63217690 FALSE FALSE FALSE 0.72939117
## 298 298 0.6680 0.94706251 FALSE FALSE FALSE 1.01957906
## 299 299 0.6680 0.94706251 FALSE FALSE FALSE 1.01957906
## 300 300 0.6680 0.94706251 FALSE FALSE FALSE 1.01957906
## 301 301 0.6710 0.97259378 FALSE FALSE FALSE 1.04310780
## 302 302 0.6710 0.97259378 FALSE FALSE FALSE 1.04310780
## 303 303 0.6710 0.97259378 FALSE FALSE FALSE 1.04310780
## 304 304 0.6710 0.97259378 FALSE FALSE FALSE 1.04310780
## 305 305 0.6710 0.97259378 FALSE FALSE FALSE 1.04310780
## 306 306 0.7000 1.21939601 FALSE FALSE FALSE 1.27055236
## 307 307 0.7000 1.21939601 FALSE FALSE FALSE 1.27055236
## 308 308 0.7000 1.21939601 FALSE FALSE FALSE 1.27055236
## 309 309 0.7000 1.21939601 FALSE FALSE FALSE 1.27055236
## 310 310 0.7000 1.21939601 FALSE FALSE FALSE 1.27055236
## 311 311 0.7000 1.21939601 FALSE FALSE FALSE 1.27055236
## 312 312 0.7000 1.21939601 FALSE FALSE FALSE 1.27055236
## 313 313 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 314 314 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 315 315 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 316 316 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 317 317 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 318 318 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 319 319 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 320 320 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 321 321 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 322 322 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 323 323 0.5970 0.34282255 FALSE FALSE FALSE 0.46273203
## 324 324 0.5970 0.34282255 FALSE FALSE FALSE 0.46273203
## 325 325 0.5970 0.34282255 FALSE FALSE FALSE 0.46273203
## 326 326 0.5970 0.34282255 FALSE FALSE FALSE 0.46273203
## 327 327 0.5970 0.34282255 FALSE FALSE FALSE 0.46273203
## 328 328 0.6930 1.15982306 FALSE FALSE FALSE 1.21565195
## 329 329 0.6790 1.04067715 FALSE FALSE FALSE 1.10585113
## 330 330 0.6790 1.04067715 FALSE FALSE FALSE 1.10585113
## 331 331 0.6790 1.04067715 FALSE FALSE FALSE 1.10585113
## 332 332 0.6790 1.04067715 FALSE FALSE FALSE 1.10585113
## 333 333 0.7180 1.37258361 FALSE FALSE FALSE 1.41172485
## 334 334 0.7180 1.37258361 FALSE FALSE FALSE 1.41172485
## 335 335 0.7180 1.37258361 FALSE FALSE FALSE 1.41172485
## 336 336 0.6140 0.48749972 FALSE FALSE FALSE 0.59606160
## 337 337 0.6140 0.48749972 FALSE FALSE FALSE 0.59606160
## 338 338 0.5840 0.23218706 FALSE FALSE FALSE 0.36077413
## 339 339 0.6790 1.04067715 FALSE FALSE FALSE 1.10585113
## 340 340 0.5840 0.23218706 FALSE FALSE FALSE 0.36077413
## 341 341 0.6790 1.04067715 FALSE FALSE FALSE 1.10585113
## 342 342 0.6790 1.04067715 FALSE FALSE FALSE 1.10585113
## 343 343 0.5840 0.23218706 FALSE FALSE FALSE 0.36077413
## 344 344 0.5840 0.23218706 FALSE FALSE FALSE 0.36077413
## 345 345 0.5840 0.23218706 FALSE FALSE FALSE 0.36077413
## 346 346 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 347 347 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 348 348 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 349 349 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 350 350 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 351 351 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 352 352 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 353 353 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 354 354 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 355 355 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 356 356 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 357 357 0.7400 1.55981289 FALSE FALSE FALSE 1.58426899
## 358 358 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 359 359 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 360 360 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 361 361 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 362 362 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 363 363 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 364 364 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 365 365 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 366 366 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 367 367 0.7130 1.33003150 FALSE FALSE FALSE 1.37251027
## 368 368 0.6550 0.83642702 FALSE FALSE FALSE 0.91762115
## 369 369 0.6550 0.83642702 FALSE FALSE FALSE 0.91762115
## 370 370 0.6550 0.83642702 FALSE FALSE FALSE 0.91762115
## 371 371 0.5800 0.19814537 FALSE FALSE FALSE 0.32940246
## 372 372 0.5800 0.19814537 FALSE FALSE FALSE 0.32940246
## 373 373 0.5800 0.19814537 FALSE FALSE FALSE 0.32940246
## 374 374 0.5320 -0.21035488 FALSE FALSE FALSE -0.04705749
## 375 375 0.5800 0.19814537 FALSE FALSE FALSE 0.32940246
## 376 376 0.6140 0.48749972 FALSE FALSE FALSE 0.59606160
## 377 377 0.5840 0.23218706 FALSE FALSE FALSE 0.36077413
## 378 378 0.5840 0.23218706 FALSE FALSE FALSE 0.36077413
## 379 379 0.6140 0.48749972 FALSE FALSE FALSE 0.59606160
## 380 380 0.6140 0.48749972 FALSE FALSE FALSE 0.59606160
## 381 381 0.6140 0.48749972 FALSE FALSE FALSE 0.59606160
## 382 382 0.6140 0.48749972 FALSE FALSE FALSE 0.59606160
## 383 383 0.5320 -0.21035488 FALSE FALSE FALSE -0.04705749
## 384 384 0.5320 -0.21035488 FALSE FALSE FALSE -0.04705749
## 385 385 0.5320 -0.21035488 FALSE FALSE FALSE -0.04705749
## 386 386 0.5320 -0.21035488 FALSE FALSE FALSE -0.04705749
## 387 387 0.5830 0.22367664 FALSE FALSE FALSE 0.35293121
## 388 388 0.5830 0.22367664 FALSE FALSE FALSE 0.35293121
## 389 389 0.5830 0.22367664 FALSE FALSE FALSE 0.35293121
## 390 390 0.6090 0.44494761 FALSE FALSE FALSE 0.55684702
## 391 391 0.6090 0.44494761 FALSE FALSE FALSE 0.55684702
## 392 392 0.6090 0.44494761 FALSE FALSE FALSE 0.55684702
## 393 393 0.6090 0.44494761 FALSE FALSE FALSE 0.55684702
## 394 394 0.6090 0.44494761 FALSE FALSE FALSE 0.55684702
## 395 395 0.5850 0.24069748 FALSE FALSE FALSE 0.36861704
## 396 396 0.5850 0.24069748 FALSE FALSE FALSE 0.36861704
## 397 397 0.5850 0.24069748 FALSE FALSE FALSE 0.36861704
## 398 398 0.5850 0.24069748 FALSE FALSE FALSE 0.36861704
## 399 399 0.5850 0.24069748 FALSE FALSE FALSE 0.36861704
## 400 400 0.5850 0.24069748 FALSE FALSE FALSE 0.36861704
## 401 401 0.5730 0.13857242 FALSE FALSE FALSE 0.27450205
## 402 402 0.5730 0.13857242 FALSE FALSE FALSE 0.27450205
## 403 403 0.5730 0.13857242 FALSE FALSE FALSE 0.27450205
## 404 404 0.5730 0.13857242 FALSE FALSE FALSE 0.27450205
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 FALSE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 FALSE
## 289 FALSE
## 290 FALSE
## 291 FALSE
## 292 FALSE
## 293 FALSE
## 294 FALSE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 FALSE
## 299 FALSE
## 300 FALSE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 FALSE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 FALSE
## 322 FALSE
## 323 FALSE
## 324 FALSE
## 325 FALSE
## 326 FALSE
## 327 FALSE
## 328 FALSE
## 329 FALSE
## 330 FALSE
## 331 FALSE
## 332 FALSE
## 333 FALSE
## 334 FALSE
## 335 FALSE
## 336 FALSE
## 337 FALSE
## 338 FALSE
## 339 FALSE
## 340 FALSE
## 341 FALSE
## 342 FALSE
## 343 FALSE
## 344 FALSE
## 345 FALSE
## 346 FALSE
## 347 FALSE
## 348 FALSE
## 349 FALSE
## 350 FALSE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 FALSE
## 355 FALSE
## 356 FALSE
## 357 FALSE
## 358 FALSE
## 359 FALSE
## 360 FALSE
## 361 FALSE
## 362 FALSE
## 363 FALSE
## 364 FALSE
## 365 FALSE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 FALSE
## 370 FALSE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 FALSE
## 378 FALSE
## 379 FALSE
## 380 FALSE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== AL MENOS DOS MÉTODOS =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== LOS TRES MÉTODOS =====
## [1] ID Valor Z_score Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## nox Z-score 404 0 0
## nox IQR 404 0 0
## nox Tukey extremo 404 0 0
## nox MAD 404 0 0## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## [1] ID Valor Z_score Z_robusto
## [5] metodos_evaluados numero_metodos clasificacion
## <0 rows> (o 0- extensión row.names)
##
## ========================================
## VARIABLE: rm
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = 4.213644
## Límite superior = 8.364035
## Número de outliers = 7
## ID Valor Z_score
## 1 129 8.375 3.015852
## 2 207 8.704 3.491470
## 3 211 8.398 3.049102
## 4 291 8.780 3.601339
## 5 292 3.561 -3.943492
## 6 294 3.863 -3.506907
## 7 300 4.138 -3.109354
##
## ================ MÉTODO IQR ================
## Q1 = 5.888
## Q3 = 6.62925
## IQR = 0.74125
## Límite inferior = 4.776125
## Límite superior = 7.741125
## Número de outliers = 23
## ID Valor
## 1 70 8.069
## 2 71 7.820
## 3 128 7.802
## 4 129 8.375
## 5 132 7.929
## 6 144 7.765
## 7 156 7.875
## 8 164 7.853
## 9 180 8.266
## 10 181 8.040
## 11 186 8.337
## 12 187 8.247
## 13 207 8.704
## 14 211 8.398
## 15 221 7.820
## 16 224 7.923
## 17 291 8.780
## 18 292 3.561
## 19 294 3.863
## 20 300 4.138
## 21 308 4.652
## 22 326 4.628
## 23 328 4.519
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = 3.66425
## Límite superior = 8.853
## Número de outliers extremos = 1
## ID Valor
## 1 292 3.561
##
## ================ MÉTODO MAD ================
## Mediana = 6.21
## MAD = 0.3415
## MAD ajustada = 0.5063079
## Número de outliers = 12
## ID Valor Z_robusto
## 1 70 8.069 3.671679
## 2 129 8.375 4.276054
## 3 180 8.266 4.060770
## 4 181 8.040 3.614401
## 5 186 8.337 4.201001
## 6 187 8.247 4.023244
## 7 207 8.704 4.925856
## 8 211 8.398 4.321481
## 9 291 8.780 5.075963
## 10 292 3.561 -5.231994
## 11 294 3.863 -4.635519
## 12 300 4.138 -4.092371
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 6.575 0.413687635 FALSE FALSE FALSE 0.720905204
## 2 2 6.421 0.191057996 FALSE FALSE FALSE 0.416742460
## 3 3 7.185 1.295532306 FALSE FALSE FALSE 1.925705682
## 4 4 6.998 1.025196317 FALSE FALSE FALSE 1.556365208
## 5 5 6.430 0.204068819 FALSE FALSE FALSE 0.434518205
## 6 6 6.172 -0.168908108 FALSE FALSE FALSE -0.075053145
## 7 7 5.631 -0.951003136 FALSE FALSE FALSE -1.143572913
## 8 8 6.004 -0.411776804 FALSE FALSE FALSE -0.406867047
## 9 9 6.377 0.127449528 FALSE FALSE FALSE 0.329838819
## 10 10 6.009 -0.404548569 FALSE FALSE FALSE -0.396991633
## 11 11 5.949 -0.491287389 FALSE FALSE FALSE -0.515496598
## 12 12 6.096 -0.278777280 FALSE FALSE FALSE -0.225159434
## 13 13 5.990 -0.432015862 FALSE FALSE FALSE -0.434518205
## 14 14 5.727 -0.812221024 FALSE FALSE FALSE -0.953964969
## 15 15 5.965 -0.468157037 FALSE FALSE FALSE -0.483895274
## 16 16 5.813 -0.687895382 FALSE FALSE FALSE -0.784107852
## 17 17 5.924 -0.527428564 FALSE FALSE FALSE -0.564873667
## 18 18 5.599 -0.997263840 FALSE FALSE FALSE -1.206775561
## 19 19 6.047 -0.349613983 FALSE FALSE FALSE -0.321938488
## 20 20 6.674 0.556806688 FALSE FALSE FALSE 0.916438396
## 21 21 5.713 -0.832460082 FALSE FALSE FALSE -0.981616127
## 22 22 6.072 -0.313472808 FALSE FALSE FALSE -0.272561420
## 23 23 5.701 -0.849807846 FALSE FALSE FALSE -1.005317120
## 24 24 6.096 -0.278777280 FALSE FALSE FALSE -0.225159434
## 25 25 5.933 -0.514417741 FALSE FALSE FALSE -0.547097922
## 26 26 5.841 -0.647417266 FALSE FALSE FALSE -0.728805535
## 27 27 5.966 -0.466711390 FALSE FALSE FALSE -0.481920191
## 28 28 6.770 0.695588800 FALSE FALSE FALSE 1.106046341
## 29 29 6.169 -0.173245049 FALSE FALSE FALSE -0.080978393
## 30 30 5.682 -0.877275139 FALSE FALSE FALSE -1.042843693
## 31 31 5.786 -0.726927851 FALSE FALSE FALSE -0.837435086
## 32 32 5.602 -0.992926899 FALSE FALSE FALSE -1.200850313
## 33 33 5.963 -0.471048331 FALSE FALSE FALSE -0.487845440
## 34 34 5.888 -0.579471856 FALSE FALSE FALSE -0.635976646
## 35 35 7.249 1.388053715 FALSE FALSE FALSE 2.052110978
## 36 36 6.383 0.136123410 FALSE FALSE FALSE 0.341689316
## 37 37 6.816 0.762088562 FALSE FALSE FALSE 1.196900147
## 38 38 6.145 -0.207940577 FALSE FALSE FALSE -0.128380379
## 39 39 5.966 -0.466711390 FALSE FALSE FALSE -0.481920191
## 40 40 6.456 0.241655641 FALSE FALSE FALSE 0.485870357
## 41 41 7.104 1.178434899 FALSE FALSE FALSE 1.765723979
## 42 42 6.290 0.001678239 FALSE FALSE FALSE 0.158006620
## 43 43 5.787 -0.725482204 FALSE FALSE FALSE -0.835460004
## 44 44 5.878 -0.593928326 FALSE FALSE FALSE -0.655727473
## 45 45 5.885 -0.583808797 FALSE FALSE FALSE -0.641901894
## 46 46 6.417 0.185275408 FALSE FALSE FALSE 0.408842129
## 47 47 6.065 -0.323592337 FALSE FALSE FALSE -0.286386999
## 48 48 6.245 -0.063375876 FALSE FALSE FALSE 0.069127896
## 49 49 6.273 -0.022897760 FALSE FALSE FALSE 0.124430213
## 50 50 6.286 -0.004104349 FALSE FALSE FALSE 0.150106289
## 51 51 6.279 -0.014223878 FALSE FALSE FALSE 0.136280710
## 52 52 6.140 -0.215168812 FALSE FALSE FALSE -0.138255793
## 53 53 5.874 -0.599710915 FALSE FALSE FALSE -0.663627804
## 54 54 6.727 0.633425979 FALSE FALSE FALSE 1.021117782
## 55 55 6.619 0.477296103 FALSE FALSE FALSE 0.807808845
## 56 56 6.302 0.019026003 FALSE FALSE FALSE 0.181707613
## 57 57 6.389 0.144797292 FALSE FALSE FALSE 0.353539812
## 58 58 6.630 0.493198220 FALSE FALSE FALSE 0.829534755
## 59 59 6.015 -0.395874687 FALSE FALSE FALSE -0.385141136
## 60 60 6.121 -0.242636105 FALSE FALSE FALSE -0.175782365
## 61 61 7.007 1.038207140 FALSE FALSE FALSE 1.574140953
## 62 62 7.079 1.142293724 FALSE FALSE FALSE 1.716346911
## 63 63 6.417 0.185275408 FALSE FALSE FALSE 0.408842129
## 64 64 6.405 0.167927644 FALSE FALSE FALSE 0.385141136
## 65 65 6.442 0.221416583 FALSE FALSE FALSE 0.458219198
## 66 66 6.211 -0.112527875 FALSE FALSE FALSE 0.001975083
## 67 67 6.249 -0.057593288 FALSE FALSE FALSE 0.077028227
## 68 68 6.625 0.485969985 FALSE FALSE FALSE 0.819659342
## 69 69 6.163 -0.181918931 FALSE FALSE FALSE -0.092828889
## 70 70 8.069 2.573484257 FALSE TRUE FALSE 3.671678834
## 71 71 7.820 2.213518153 FALSE TRUE FALSE 3.179883229
## 72 72 7.416 1.629476764 FALSE FALSE FALSE 2.381949798
## 73 73 6.727 0.633425979 FALSE FALSE FALSE 1.021117782
## 74 74 6.781 0.711490917 FALSE FALSE FALSE 1.127772251
## 75 75 6.405 0.167927644 FALSE FALSE FALSE 0.385141136
## 76 76 6.137 -0.219505753 FALSE FALSE FALSE -0.144181041
## 77 77 6.167 -0.176136343 FALSE FALSE FALSE -0.084928558
## 78 78 5.851 -0.632960796 FALSE FALSE FALSE -0.709054708
## 79 79 6.127 -0.233962223 FALSE FALSE FALSE -0.163931868
## 80 80 6.195 -0.135658227 FALSE FALSE FALSE -0.029626241
## 81 81 6.715 0.616078215 FALSE FALSE FALSE 0.997416789
## 82 82 5.913 -0.543330681 FALSE FALSE FALSE -0.586599577
## 83 83 6.092 -0.284559868 FALSE FALSE FALSE -0.233059765
## 84 84 6.254 -0.050365053 FALSE FALSE FALSE 0.086903641
## 85 85 5.928 -0.521645976 FALSE FALSE FALSE -0.556973336
## 86 86 6.176 -0.163125520 FALSE FALSE FALSE -0.067152814
## 87 87 6.021 -0.387200805 FALSE FALSE FALSE -0.373290640
## 88 88 5.731 -0.806438436 FALSE FALSE FALSE -0.946064638
## 89 89 5.870 -0.605493503 FALSE FALSE FALSE -0.671528135
## 90 90 6.004 -0.411776804 FALSE FALSE FALSE -0.406867047
## 91 91 5.961 -0.473939625 FALSE FALSE FALSE -0.491795605
## 92 92 5.856 -0.625732561 FALSE FALSE FALSE -0.699179294
## 93 93 5.879 -0.592482679 FALSE FALSE FALSE -0.653752391
## 94 94 5.986 -0.437798450 FALSE FALSE FALSE -0.442418536
## 95 95 5.693 -0.861373022 FALSE FALSE FALSE -1.021117782
## 96 96 6.431 0.205514466 FALSE FALSE FALSE 0.436493288
## 97 97 5.637 -0.942329254 FALSE FALSE FALSE -1.131722416
## 98 98 6.458 0.244546935 FALSE FALSE FALSE 0.489820522
## 99 99 6.326 0.053721531 FALSE FALSE FALSE 0.229109599
## 100 100 6.372 0.120221293 FALSE FALSE FALSE 0.319963406
## 101 101 5.822 -0.674884559 FALSE FALSE FALSE -0.766332107
## 102 102 5.757 -0.768851614 FALSE FALSE FALSE -0.894712486
## 103 103 6.335 0.066732354 FALSE FALSE FALSE 0.246885344
## 104 104 5.942 -0.501406918 FALSE FALSE FALSE -0.529322177
## 105 105 6.454 0.238764347 FALSE FALSE FALSE 0.481920191
## 106 106 5.857 -0.624286914 FALSE FALSE FALSE -0.697204211
## 107 107 6.151 -0.199266695 FALSE FALSE FALSE -0.116529882
## 108 108 6.174 -0.166016814 FALSE FALSE FALSE -0.071102979
## 109 109 5.403 -1.280610653 FALSE FALSE FALSE -1.593891780
## 110 110 5.468 -1.186643598 FALSE FALSE FALSE -1.465511401
## 111 111 4.903 -2.003434154 FALSE FALSE FALSE -2.581433156
## 112 112 6.130 -0.229625282 FALSE FALSE FALSE -0.158006620
## 113 113 5.628 -0.955340077 FALSE FALSE FALSE -1.149498161
## 114 114 4.926 -1.970184273 FALSE FALSE FALSE -2.536006252
## 115 115 5.597 -1.000155134 FALSE FALSE FALSE -1.210725726
## 116 116 6.122 -0.241190458 FALSE FALSE FALSE -0.173807282
## 117 117 5.404 -1.279165006 FALSE FALSE FALSE -1.591916697
## 118 118 5.012 -1.845858631 FALSE FALSE FALSE -2.366149136
## 119 119 5.709 -0.838242670 FALSE FALSE FALSE -0.989516458
## 120 120 6.129 -0.231070929 FALSE FALSE FALSE -0.159981703
## 121 121 6.152 -0.197821048 FALSE FALSE FALSE -0.114554800
## 122 122 5.272 -1.469990410 FALSE FALSE FALSE -1.852627620
## 123 123 6.943 0.945685732 FALSE FALSE FALSE 1.447735657
## 124 124 6.066 -0.322146690 FALSE FALSE FALSE -0.284411916
## 125 125 6.510 0.319720579 FALSE FALSE FALSE 0.592524825
## 126 126 6.250 -0.056147641 FALSE FALSE FALSE 0.079003310
## 127 127 7.489 1.735008995 FALSE FALSE FALSE 2.526130839
## 128 128 7.802 2.187496507 FALSE TRUE FALSE 3.144331740
## 129 129 8.375 3.015852240 TRUE TRUE FALSE 4.276054156
## 130 130 5.854 -0.628623855 FALSE FALSE FALSE -0.703129459
## 131 131 6.101 -0.271549045 FALSE FALSE FALSE -0.215284020
## 132 132 7.929 2.371093677 FALSE TRUE FALSE 3.395167249
## 133 133 5.877 -0.595373974 FALSE FALSE FALSE -0.657702556
## 134 134 6.319 0.043602002 FALSE FALSE FALSE 0.215284020
## 135 135 5.875 -0.598265268 FALSE FALSE FALSE -0.661652722
## 136 136 5.880 -0.591037032 FALSE FALSE FALSE -0.651777308
## 137 137 5.572 -1.036296309 FALSE FALSE FALSE -1.260102795
## 138 138 6.416 0.183829761 FALSE FALSE FALSE 0.406867047
## 139 139 5.859 -0.621395620 FALSE FALSE FALSE -0.693254046
## 140 140 6.546 0.371763871 FALSE FALSE FALSE 0.663627804
## 141 141 6.020 -0.388646452 FALSE FALSE FALSE -0.375265723
## 142 142 6.860 0.825697030 FALSE FALSE FALSE 1.283803788
## 143 143 6.980 0.999174671 FALSE FALSE FALSE 1.520813718
## 144 144 7.765 2.134007568 FALSE TRUE FALSE 3.071253678
## 145 145 6.563 0.396339871 FALSE FALSE FALSE 0.697204211
## 146 146 5.604 -0.990035605 FALSE FALSE FALSE -1.196900147
## 147 147 6.153 -0.196375401 FALSE FALSE FALSE -0.112579717
## 148 148 6.782 0.712936564 FALSE FALSE FALSE 1.129747334
## 149 149 6.556 0.386220342 FALSE FALSE FALSE 0.683378632
## 150 150 7.185 1.295532306 FALSE FALSE FALSE 1.925705682
## 151 151 6.951 0.957250908 FALSE FALSE FALSE 1.463536319
## 152 152 6.739 0.650773743 FALSE FALSE FALSE 1.044818775
## 153 153 7.178 1.285412777 FALSE FALSE FALSE 1.911880103
## 154 154 6.800 0.738958210 FALSE FALSE FALSE 1.165298823
## 155 155 6.604 0.455611398 FALSE FALSE FALSE 0.778182604
## 156 156 7.875 2.293028738 FALSE TRUE FALSE 3.288512780
## 157 157 7.287 1.442988301 FALSE FALSE FALSE 2.127164123
## 158 158 7.107 1.182771840 FALSE FALSE FALSE 1.771649228
## 159 159 7.274 1.424194890 FALSE FALSE FALSE 2.101488047
## 160 160 6.975 0.991946436 FALSE FALSE FALSE 1.510938305
## 161 161 7.135 1.223249956 FALSE FALSE FALSE 1.826951545
## 162 162 6.162 -0.183364578 FALSE FALSE FALSE -0.094803972
## 163 163 7.610 1.909932283 FALSE FALSE FALSE 2.765115851
## 164 164 7.853 2.261224504 FALSE TRUE FALSE 3.245060960
## 165 165 5.783 -0.731264792 FALSE FALSE FALSE -0.843360335
## 166 166 6.064 -0.325037984 FALSE FALSE FALSE -0.288362082
## 167 167 5.344 -1.365903826 FALSE FALSE FALSE -1.710421662
## 168 168 5.960 -0.475385272 FALSE FALSE FALSE -0.493770688
## 169 169 5.807 -0.696569264 FALSE FALSE FALSE -0.795958349
## 170 170 6.375 0.124558234 FALSE FALSE FALSE 0.325888654
## 171 171 5.412 -1.267599830 FALSE FALSE FALSE -1.576116035
## 172 172 6.182 -0.154451638 FALSE FALSE FALSE -0.055302317
## 173 173 5.888 -0.579471856 FALSE FALSE FALSE -0.635976646
## 174 174 5.951 -0.488396095 FALSE FALSE FALSE -0.511546433
## 175 175 6.373 0.121666940 FALSE FALSE FALSE 0.321938488
## 176 176 6.951 0.957250908 FALSE FALSE FALSE 1.463536319
## 177 177 6.164 -0.180473284 FALSE FALSE FALSE -0.090853807
## 178 178 6.879 0.853164323 FALSE FALSE FALSE 1.321330360
## 179 179 6.618 0.475850456 FALSE FALSE FALSE 0.805833762
## 180 180 8.266 2.858276717 FALSE TRUE FALSE 4.060770136
## 181 181 8.040 2.531560494 FALSE TRUE FALSE 3.614401434
## 182 182 7.163 1.263728072 FALSE FALSE FALSE 1.882253862
## 183 183 7.686 2.019801455 FALSE FALSE FALSE 2.915222141
## 184 184 5.981 -0.445026685 FALSE FALSE FALSE -0.452293950
## 185 185 7.412 1.623694176 FALSE FALSE FALSE 2.374049467
## 186 186 8.337 2.960917654 FALSE TRUE FALSE 4.201001011
## 187 187 8.247 2.830809424 FALSE TRUE FALSE 4.023243564
## 188 188 6.726 0.631980332 FALSE FALSE FALSE 1.019142700
## 189 189 6.086 -0.293233750 FALSE FALSE FALSE -0.244910261
## 190 190 6.631 0.494643867 FALSE FALSE FALSE 0.831509838
## 191 191 7.358 1.545629238 FALSE FALSE FALSE 2.267394998
## 192 192 6.606 0.458502692 FALSE FALSE FALSE 0.782132769
## 193 193 6.897 0.879185970 FALSE FALSE FALSE 1.356881850
## 194 194 6.095 -0.280222927 FALSE FALSE FALSE -0.227134516
## 195 195 6.358 0.099982235 FALSE FALSE FALSE 0.292312247
## 196 196 6.393 0.150579880 FALSE FALSE FALSE 0.361440143
## 197 197 5.593 -1.005937722 FALSE FALSE FALSE -1.218626057
## 198 198 6.108 -0.261429516 FALSE FALSE FALSE -0.201458441
## 199 199 6.226 -0.090843169 FALSE FALSE FALSE 0.031601324
## 200 200 6.433 0.208405760 FALSE FALSE FALSE 0.440443453
## 201 201 6.718 0.620415156 FALSE FALSE FALSE 1.003342038
## 202 202 6.438 0.215633995 FALSE FALSE FALSE 0.450318867
## 203 203 6.957 0.965924790 FALSE FALSE FALSE 1.475386815
## 204 204 6.108 -0.261429516 FALSE FALSE FALSE -0.201458441
## 205 205 5.876 -0.596819621 FALSE FALSE FALSE -0.659677639
## 206 206 7.454 1.684411350 FALSE FALSE FALSE 2.457002942
## 207 207 8.704 3.491470104 TRUE TRUE FALSE 4.925856381
## 208 208 6.842 0.799675384 FALSE FALSE FALSE 1.248252299
## 209 209 7.203 1.321553952 FALSE FALSE FALSE 1.961257172
## 210 210 7.520 1.779824052 FALSE FALSE FALSE 2.587358404
## 211 211 8.398 3.049102121 TRUE TRUE FALSE 4.321481059
## 212 212 7.014 1.048326669 FALSE FALSE FALSE 1.587966532
## 213 213 7.470 1.707541702 FALSE FALSE FALSE 2.488604266
## 214 214 5.920 -0.533211152 FALSE FALSE FALSE -0.572773998
## 215 215 5.856 -0.625732561 FALSE FALSE FALSE -0.699179294
## 216 216 6.240 -0.070604111 FALSE FALSE FALSE 0.059252483
## 217 217 6.538 0.360198695 FALSE FALSE FALSE 0.647827142
## 218 218 7.267 1.414075361 FALSE FALSE FALSE 2.087662468
## 219 219 6.826 0.776545032 FALSE FALSE FALSE 1.216650975
## 220 220 6.482 0.279242463 FALSE FALSE FALSE 0.537222508
## 221 221 7.820 2.213518153 FALSE TRUE FALSE 3.179883229
## 222 222 6.968 0.981826907 FALSE FALSE FALSE 1.497112725
## 223 223 7.645 1.960529928 FALSE FALSE FALSE 2.834243748
## 224 224 7.923 2.362419795 FALSE TRUE FALSE 3.383316753
## 225 225 7.088 1.155304547 FALSE FALSE FALSE 1.734122655
## 226 226 6.453 0.237318700 FALSE FALSE FALSE 0.479945109
## 227 227 6.230 -0.085060581 FALSE FALSE FALSE 0.039501655
## 228 228 6.209 -0.115419169 FALSE FALSE FALSE -0.001975083
## 229 229 6.315 0.037819414 FALSE FALSE FALSE 0.207383689
## 230 230 6.565 0.399231165 FALSE FALSE FALSE 0.701154377
## 231 231 6.861 0.827142677 FALSE FALSE FALSE 1.285778871
## 232 232 7.148 1.242043367 FALSE FALSE FALSE 1.852627620
## 233 233 6.630 0.493198220 FALSE FALSE FALSE 0.829534755
## 234 234 6.678 0.562589276 FALSE FALSE FALSE 0.924338727
## 235 235 6.549 0.376100812 FALSE FALSE FALSE 0.669553053
## 236 236 5.790 -0.721145263 FALSE FALSE FALSE -0.829534755
## 237 237 6.345 0.081188824 FALSE FALSE FALSE 0.266636171
## 238 238 6.590 0.435372340 FALSE FALSE FALSE 0.750531445
## 239 239 6.982 1.002065965 FALSE FALSE FALSE 1.524763884
## 240 240 7.236 1.369260304 FALSE FALSE FALSE 2.026434903
## 241 241 6.849 0.809794913 FALSE FALSE FALSE 1.262077878
## 242 242 6.635 0.500426455 FALSE FALSE FALSE 0.839410169
## 243 243 5.972 -0.458037508 FALSE FALSE FALSE -0.470069695
## 244 244 6.122 -0.241190458 FALSE FALSE FALSE -0.173807282
## 245 245 6.023 -0.384309511 FALSE FALSE FALSE -0.369340474
## 246 246 6.266 -0.033017289 FALSE FALSE FALSE 0.110604634
## 247 247 6.567 0.402122459 FALSE FALSE FALSE 0.705104542
## 248 248 5.705 -0.844025258 FALSE FALSE FALSE -0.997416789
## 249 249 5.782 -0.732710439 FALSE FALSE FALSE -0.845335417
## 250 250 6.382 0.134677763 FALSE FALSE FALSE 0.339714233
## 251 251 6.113 -0.254201281 FALSE FALSE FALSE -0.191583027
## 252 252 6.426 0.198286231 FALSE FALSE FALSE 0.426617874
## 253 253 6.376 0.126003881 FALSE FALSE FALSE 0.327863737
## 254 254 6.041 -0.358287865 FALSE FALSE FALSE -0.333788985
## 255 255 5.708 -0.839688317 FALSE FALSE FALSE -0.991491541
## 256 256 6.415 0.182384114 FALSE FALSE FALSE 0.404891964
## 257 257 6.431 0.205514466 FALSE FALSE FALSE 0.436493288
## 258 258 6.312 0.033482473 FALSE FALSE FALSE 0.201458441
## 259 259 6.083 -0.297570691 FALSE FALSE FALSE -0.250835509
## 260 260 5.868 -0.608384797 FALSE FALSE FALSE -0.675478301
## 261 261 6.333 0.063841060 FALSE FALSE FALSE 0.242935178
## 262 262 6.144 -0.209386224 FALSE FALSE FALSE -0.130355462
## 263 263 5.706 -0.842579611 FALSE FALSE FALSE -0.995441707
## 264 264 6.316 0.039265061 FALSE FALSE FALSE 0.209358772
## 265 265 6.310 0.030591179 FALSE FALSE FALSE 0.197508275
## 266 266 6.037 -0.364070453 FALSE FALSE FALSE -0.341689316
## 267 267 5.869 -0.606939150 FALSE FALSE FALSE -0.673503218
## 268 268 5.895 -0.569352327 FALSE FALSE FALSE -0.622151067
## 269 269 5.985 -0.439244097 FALSE FALSE FALSE -0.444393619
## 270 270 5.968 -0.463820096 FALSE FALSE FALSE -0.477970026
## 271 271 7.241 1.376488539 FALSE FALSE FALSE 2.036310316
## 272 272 6.540 0.363089989 FALSE FALSE FALSE 0.651777308
## 273 273 6.696 0.588610922 FALSE FALSE FALSE 0.959890217
## 274 274 6.874 0.845936088 FALSE FALSE FALSE 1.311454947
## 275 275 6.014 -0.397320334 FALSE FALSE FALSE -0.387116219
## 276 276 5.898 -0.565015386 FALSE FALSE FALSE -0.616225818
## 277 277 6.516 0.328394461 FALSE FALSE FALSE 0.604375322
## 278 278 6.635 0.500426455 FALSE FALSE FALSE 0.839410169
## 279 279 6.939 0.939903144 FALSE FALSE FALSE 1.439835326
## 280 280 6.579 0.419470223 FALSE FALSE FALSE 0.728805535
## 281 281 6.728 0.634871626 FALSE FALSE FALSE 1.023092865
## 282 282 5.663 -0.904742432 FALSE FALSE FALSE -1.080370265
## 283 283 5.936 -0.510080800 FALSE FALSE FALSE -0.541172674
## 284 284 6.212 -0.111082228 FALSE FALSE FALSE 0.003950166
## 285 285 6.395 0.153471174 FALSE FALSE FALSE 0.365390309
## 286 286 6.127 -0.233962223 FALSE FALSE FALSE -0.163931868
## 287 287 6.398 0.157808115 FALSE FALSE FALSE 0.371315557
## 288 288 6.251 -0.054701994 FALSE FALSE FALSE 0.080978393
## 289 289 5.362 -1.339882180 FALSE FALSE FALSE -1.674870173
## 290 290 5.803 -0.702351852 FALSE FALSE FALSE -0.803858680
## 291 291 8.780 3.601339276 TRUE TRUE FALSE 5.075962670
## 292 292 3.561 -3.943492432 TRUE TRUE TRUE -5.231994207
## 293 293 4.963 -1.916695334 FALSE FALSE FALSE -2.462928191
## 294 294 3.863 -3.506907037 TRUE TRUE FALSE -4.635519217
## 295 295 4.970 -1.906575805 FALSE FALSE FALSE -2.449102611
## 296 296 7.016 1.051217963 FALSE FALSE FALSE 1.591916697
## 297 297 6.216 -0.105299639 FALSE FALSE FALSE 0.011850497
## 298 298 5.875 -0.598265268 FALSE FALSE FALSE -0.661652722
## 299 299 4.906 -1.999097213 FALSE FALSE FALSE -2.575507907
## 300 300 4.138 -3.109354112 TRUE TRUE FALSE -4.092371460
## 301 301 7.313 1.480575123 FALSE FALSE FALSE 2.178516274
## 302 302 6.649 0.520665513 FALSE FALSE FALSE 0.867061328
## 303 303 6.794 0.730284328 FALSE FALSE FALSE 1.153448327
## 304 304 6.380 0.131786469 FALSE FALSE FALSE 0.335764068
## 305 305 6.968 0.981826907 FALSE FALSE FALSE 1.497112725
## 306 306 5.536 -1.088339602 FALSE FALSE FALSE -1.331205774
## 307 307 5.520 -1.111469954 FALSE FALSE FALSE -1.362807098
## 308 308 4.652 -2.366291552 FALSE TRUE FALSE -3.077178926
## 309 309 5.000 -1.863206395 FALSE FALSE FALSE -2.389850129
## 310 310 4.880 -2.036684035 FALSE FALSE FALSE -2.626860059
## 311 311 6.051 -0.343831395 FALSE FALSE FALSE -0.314038157
## 312 312 5.036 -1.811163103 FALSE FALSE FALSE -2.318747150
## 313 313 6.193 -0.138549521 FALSE FALSE FALSE -0.033576407
## 314 314 5.887 -0.580917503 FALSE FALSE FALSE -0.637951729
## 315 315 6.471 0.263340346 FALSE FALSE FALSE 0.515496598
## 316 316 6.405 0.167927644 FALSE FALSE FALSE 0.385141136
## 317 317 5.852 -0.631515149 FALSE FALSE FALSE -0.707079625
## 318 318 5.987 -0.436352803 FALSE FALSE FALSE -0.440443453
## 319 319 6.404 0.166481997 FALSE FALSE FALSE 0.383166054
## 320 320 5.349 -1.358675591 FALSE FALSE FALSE -1.700546249
## 321 321 5.531 -1.095567837 FALSE FALSE FALSE -1.341081188
## 322 322 5.683 -0.875829492 FALSE FALSE FALSE -1.040868610
## 323 323 5.617 -0.971242194 FALSE FALSE FALSE -1.171224071
## 324 324 5.757 -0.768851614 FALSE FALSE FALSE -0.894712486
## 325 325 6.657 0.532230689 FALSE FALSE FALSE 0.882861990
## 326 326 4.628 -2.400987080 FALSE TRUE FALSE -3.124580912
## 327 327 5.155 -1.639131110 FALSE FALSE FALSE -2.083712302
## 328 328 4.519 -2.558562604 FALSE TRUE FALSE -3.339864932
## 329 329 6.434 0.209851407 FALSE FALSE FALSE 0.442418536
## 330 330 6.782 0.712936564 FALSE FALSE FALSE 1.129747334
## 331 331 5.304 -1.423729706 FALSE FALSE FALSE -1.789424972
## 332 332 5.957 -0.479722213 FALSE FALSE FALSE -0.499695936
## 333 333 6.824 0.773653738 FALSE FALSE FALSE 1.212700809
## 334 334 6.411 0.176601526 FALSE FALSE FALSE 0.396991633
## 335 335 6.006 -0.408885510 FALSE FALSE FALSE -0.402916881
## 336 336 5.648 -0.926427137 FALSE FALSE FALSE -1.109996506
## 337 337 6.103 -0.268657751 FALSE FALSE FALSE -0.211333854
## 338 338 5.565 -1.046415838 FALSE FALSE FALSE -1.273928374
## 339 339 5.896 -0.567906680 FALSE FALSE FALSE -0.620175984
## 340 340 5.837 -0.653199854 FALSE FALSE FALSE -0.736705866
## 341 341 6.202 -0.125538698 FALSE FALSE FALSE -0.015800662
## 342 342 6.380 0.131786469 FALSE FALSE FALSE 0.335764068
## 343 343 6.348 0.085525765 FALSE FALSE FALSE 0.272561420
## 344 344 6.833 0.786664561 FALSE FALSE FALSE 1.230476554
## 345 345 6.425 0.196840584 FALSE FALSE FALSE 0.424642791
## 346 346 6.436 0.212742701 FALSE FALSE FALSE 0.446368702
## 347 347 6.208 -0.116864816 FALSE FALSE FALSE -0.003950166
## 348 348 6.629 0.491752573 FALSE FALSE FALSE 0.827559673
## 349 349 6.461 0.248883876 FALSE FALSE FALSE 0.495745771
## 350 350 5.935 -0.511526447 FALSE FALSE FALSE -0.543147757
## 351 351 5.627 -0.956785724 FALSE FALSE FALSE -1.151473244
## 352 352 5.818 -0.680667147 FALSE FALSE FALSE -0.774232438
## 353 353 6.219 -0.100962698 FALSE FALSE FALSE 0.017775745
## 354 354 5.854 -0.628623855 FALSE FALSE FALSE -0.703129459
## 355 355 6.459 0.245992582 FALSE FALSE FALSE 0.491795605
## 356 356 6.341 0.075406236 FALSE FALSE FALSE 0.258735840
## 357 357 6.251 -0.054701994 FALSE FALSE FALSE 0.080978393
## 358 358 6.417 0.185275408 FALSE FALSE FALSE 0.408842129
## 359 359 6.749 0.665230213 FALSE FALSE FALSE 1.064569603
## 360 360 6.655 0.529339395 FALSE FALSE FALSE 0.878911824
## 361 361 7.393 1.596226883 FALSE FALSE FALSE 2.336522894
## 362 362 6.728 0.634871626 FALSE FALSE FALSE 1.023092865
## 363 363 6.525 0.341405284 FALSE FALSE FALSE 0.622151067
## 364 364 5.976 -0.452254920 FALSE FALSE FALSE -0.462169364
## 365 365 5.936 -0.510080800 FALSE FALSE FALSE -0.541172674
## 366 366 6.081 -0.300461985 FALSE FALSE FALSE -0.254785675
## 367 367 6.317 0.040710708 FALSE FALSE FALSE 0.211333854
## 368 368 6.209 -0.115419169 FALSE FALSE FALSE -0.001975083
## 369 369 5.759 -0.765960320 FALSE FALSE FALSE -0.890762321
## 370 370 5.952 -0.486950448 FALSE FALSE FALSE -0.509571350
## 371 371 5.926 -0.524537270 FALSE FALSE FALSE -0.560923501
## 372 372 5.713 -0.832460082 FALSE FALSE FALSE -0.981616127
## 373 373 6.167 -0.176136343 FALSE FALSE FALSE -0.084928558
## 374 374 6.229 -0.086506228 FALSE FALSE FALSE 0.037526572
## 375 375 6.437 0.214188348 FALSE FALSE FALSE 0.448343784
## 376 376 6.980 0.999174671 FALSE FALSE FALSE 1.520813718
## 377 377 5.427 -1.245915125 FALSE FALSE FALSE -1.546489794
## 378 378 6.162 -0.183364578 FALSE FALSE FALSE -0.094803972
## 379 379 6.484 0.282133757 FALSE FALSE FALSE 0.541172674
## 380 380 5.304 -1.423729706 FALSE FALSE FALSE -1.789424972
## 381 381 6.185 -0.150114697 FALSE FALSE FALSE -0.049377069
## 382 382 6.229 -0.086506228 FALSE FALSE FALSE 0.037526572
## 383 383 6.242 -0.067712817 FALSE FALSE FALSE 0.063202648
## 384 384 6.750 0.666675860 FALSE FALSE FALSE 1.066544686
## 385 385 7.061 1.116272078 FALSE FALSE FALSE 1.680795421
## 386 386 5.762 -0.761623379 FALSE FALSE FALSE -0.884837072
## 387 387 5.871 -0.604047856 FALSE FALSE FALSE -0.669553053
## 388 388 6.312 0.033482473 FALSE FALSE FALSE 0.201458441
## 389 389 5.905 -0.554895857 FALSE FALSE FALSE -0.602400239
## 390 390 5.454 -1.206882656 FALSE FALSE FALSE -1.493162560
## 391 391 5.414 -1.264708536 FALSE FALSE FALSE -1.572165870
## 392 392 5.093 -1.728761224 FALSE FALSE FALSE -2.206167433
## 393 393 5.983 -0.442135391 FALSE FALSE FALSE -0.448343784
## 394 394 5.983 -0.442135391 FALSE FALSE FALSE -0.448343784
## 395 395 5.926 -0.524537270 FALSE FALSE FALSE -0.560923501
## 396 396 5.670 -0.894622903 FALSE FALSE FALSE -1.066544686
## 397 397 5.390 -1.299404064 FALSE FALSE FALSE -1.619567856
## 398 398 6.019 -0.390092099 FALSE FALSE FALSE -0.377240805
## 399 399 5.569 -1.040633250 FALSE FALSE FALSE -1.266028043
## 400 400 6.027 -0.378526923 FALSE FALSE FALSE -0.361440143
## 401 401 6.593 0.439709281 FALSE FALSE FALSE 0.756456694
## 402 402 6.120 -0.244081752 FALSE FALSE FALSE -0.177757448
## 403 403 6.794 0.730284328 FALSE FALSE FALSE 1.153448327
## 404 404 6.030 -0.374189982 FALSE FALSE FALSE -0.355514895
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 TRUE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 TRUE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 FALSE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 TRUE
## 181 TRUE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 TRUE
## 187 TRUE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 TRUE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 TRUE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 FALSE
## 289 FALSE
## 290 FALSE
## 291 TRUE
## 292 TRUE
## 293 FALSE
## 294 TRUE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 FALSE
## 299 FALSE
## 300 TRUE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 FALSE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 FALSE
## 322 FALSE
## 323 FALSE
## 324 FALSE
## 325 FALSE
## 326 FALSE
## 327 FALSE
## 328 FALSE
## 329 FALSE
## 330 FALSE
## 331 FALSE
## 332 FALSE
## 333 FALSE
## 334 FALSE
## 335 FALSE
## 336 FALSE
## 337 FALSE
## 338 FALSE
## 339 FALSE
## 340 FALSE
## 341 FALSE
## 342 FALSE
## 343 FALSE
## 344 FALSE
## 345 FALSE
## 346 FALSE
## 347 FALSE
## 348 FALSE
## 349 FALSE
## 350 FALSE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 FALSE
## 355 FALSE
## 356 FALSE
## 357 FALSE
## 358 FALSE
## 359 FALSE
## 360 FALSE
## 361 FALSE
## 362 FALSE
## 363 FALSE
## 364 FALSE
## 365 FALSE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 FALSE
## 370 FALSE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 FALSE
## 378 FALSE
## 379 FALSE
## 380 FALSE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 70 8.069 2.573484 FALSE TRUE 3.671679 TRUE
## 2 71 7.820 2.213518 FALSE TRUE 3.179883 FALSE
## 3 128 7.802 2.187497 FALSE TRUE 3.144332 FALSE
## 4 129 8.375 3.015852 TRUE TRUE 4.276054 TRUE
## 5 132 7.929 2.371094 FALSE TRUE 3.395167 FALSE
## 6 144 7.765 2.134008 FALSE TRUE 3.071254 FALSE
## 7 156 7.875 2.293029 FALSE TRUE 3.288513 FALSE
## 8 164 7.853 2.261225 FALSE TRUE 3.245061 FALSE
## 9 180 8.266 2.858277 FALSE TRUE 4.060770 TRUE
## 10 181 8.040 2.531560 FALSE TRUE 3.614401 TRUE
## 11 186 8.337 2.960918 FALSE TRUE 4.201001 TRUE
## 12 187 8.247 2.830809 FALSE TRUE 4.023244 TRUE
## 13 207 8.704 3.491470 TRUE TRUE 4.925856 TRUE
## 14 211 8.398 3.049102 TRUE TRUE 4.321481 TRUE
## 15 221 7.820 2.213518 FALSE TRUE 3.179883 FALSE
## 16 224 7.923 2.362420 FALSE TRUE 3.383317 FALSE
## 17 291 8.780 3.601339 TRUE TRUE 5.075963 TRUE
## 18 292 3.561 -3.943492 TRUE TRUE -5.231994 TRUE
## 19 294 3.863 -3.506907 TRUE TRUE -4.635519 TRUE
## 20 300 4.138 -3.109354 TRUE TRUE -4.092371 TRUE
## 21 308 4.652 -2.366292 FALSE TRUE -3.077179 FALSE
## 22 326 4.628 -2.400987 FALSE TRUE -3.124581 FALSE
## 23 328 4.519 -2.558563 FALSE TRUE -3.339865 FALSE
## metodos_evaluados numero_metodos
## 1 3 2
## 2 3 1
## 3 3 1
## 4 3 3
## 5 3 1
## 6 3 1
## 7 3 1
## 8 3 1
## 9 3 2
## 10 3 2
## 11 3 2
## 12 3 2
## 13 3 3
## 14 3 3
## 15 3 1
## 16 3 1
## 17 3 3
## 18 3 3
## 19 3 3
## 20 3 3
## 21 3 1
## 22 3 1
## 23 3 1
##
## ===== AL MENOS DOS MÉTODOS =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 70 8.069 2.573484 FALSE TRUE 3.671679 TRUE
## 2 129 8.375 3.015852 TRUE TRUE 4.276054 TRUE
## 3 180 8.266 2.858277 FALSE TRUE 4.060770 TRUE
## 4 181 8.040 2.531560 FALSE TRUE 3.614401 TRUE
## 5 186 8.337 2.960918 FALSE TRUE 4.201001 TRUE
## 6 187 8.247 2.830809 FALSE TRUE 4.023244 TRUE
## 7 207 8.704 3.491470 TRUE TRUE 4.925856 TRUE
## 8 211 8.398 3.049102 TRUE TRUE 4.321481 TRUE
## 9 291 8.780 3.601339 TRUE TRUE 5.075963 TRUE
## 10 292 3.561 -3.943492 TRUE TRUE -5.231994 TRUE
## 11 294 3.863 -3.506907 TRUE TRUE -4.635519 TRUE
## 12 300 4.138 -3.109354 TRUE TRUE -4.092371 TRUE
## metodos_evaluados numero_metodos
## 1 3 2
## 2 3 3
## 3 3 2
## 4 3 2
## 5 3 2
## 6 3 2
## 7 3 3
## 8 3 3
## 9 3 3
## 10 3 3
## 11 3 3
## 12 3 3
##
## ===== LOS TRES MÉTODOS =====
## ID Valor Z_score Z_robusto
## 1 129 8.375 3.015852 4.276054
## 2 207 8.704 3.491470 4.925856
## 3 211 8.398 3.049102 4.321481
## 4 291 8.780 3.601339 5.075963
## 5 292 3.561 -3.943492 -5.231994
## 6 294 3.863 -3.506907 -4.635519
## 7 300 4.138 -3.109354 -4.092371
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## rm Z-score 404 7 1.73
## rm IQR 404 23 5.69
## rm Tukey extremo 404 1 0.25
## rm MAD 404 12 2.97## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 70 8.069 2.573484 3.671679 3 2
## 2 71 7.820 2.213518 3.179883 3 1
## 3 128 7.802 2.187497 3.144332 3 1
## 4 129 8.375 3.015852 4.276054 3 3
## 5 132 7.929 2.371094 3.395167 3 1
## 6 144 7.765 2.134008 3.071254 3 1
## 7 156 7.875 2.293029 3.288513 3 1
## 8 164 7.853 2.261225 3.245061 3 1
## 9 180 8.266 2.858277 4.060770 3 2
## 10 181 8.040 2.531560 3.614401 3 2
## 11 186 8.337 2.960918 4.201001 3 2
## 12 187 8.247 2.830809 4.023244 3 2
## 13 207 8.704 3.491470 4.925856 3 3
## 14 211 8.398 3.049102 4.321481 3 3
## 15 221 7.820 2.213518 3.179883 3 1
## 16 224 7.923 2.362420 3.383317 3 1
## 17 291 8.780 3.601339 5.075963 3 3
## 18 292 3.561 -3.943492 -5.231994 3 3
## 19 294 3.863 -3.506907 -4.635519 3 3
## 20 300 4.138 -3.109354 -4.092371 3 3
## 21 308 4.652 -2.366292 -3.077179 3 1
## 22 326 4.628 -2.400987 -3.124581 3 1
## 23 328 4.519 -2.558563 -3.339865 3 1
## clasificacion
## 1 Outlier según 2 métodos
## 2 Outlier según 1 método
## 3 Outlier según 1 método
## 4 Outlier según 3 métodos
## 5 Outlier según 1 método
## 6 Outlier según 1 método
## 7 Outlier según 1 método
## 8 Outlier según 1 método
## 9 Outlier según 2 métodos
## 10 Outlier según 2 métodos
## 11 Outlier según 2 métodos
## 12 Outlier según 2 métodos
## 13 Outlier según 3 métodos
## 14 Outlier según 3 métodos
## 15 Outlier según 1 método
## 16 Outlier según 1 método
## 17 Outlier según 3 métodos
## 18 Outlier según 3 métodos
## 19 Outlier según 3 métodos
## 20 Outlier según 3 métodos
## 21 Outlier según 1 método
## 22 Outlier según 1 método
## 23 Outlier según 1 método
##
## ========================================
## VARIABLE: age
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -13.7473
## Límite superior = 152.1978
## Número de outliers = 0
## [1] ID Valor Z_score
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO IQR ================
## Q1 = 45.8
## Q3 = 94.025
## IQR = 48.225
## Límite inferior = -26.5375
## Límite superior = 166.3625
## Número de outliers = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -98.875
## Límite superior = 238.7
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 77.75
## MAD = 19
## MAD ajustada = 28.1694
## Número de outliers = 0
## [1] ID Valor Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 65.2 -0.145539008 FALSE FALSE FALSE -0.445518896
## 2 2 78.9 0.349805539 FALSE FALSE FALSE 0.040824441
## 3 3 61.1 -0.293780807 FALSE FALSE FALSE -0.591066902
## 4 4 45.8 -0.846975813 FALSE FALSE FALSE -1.134209461
## 5 5 58.7 -0.380556494 FALSE FALSE FALSE -0.676265735
## 6 6 96.1 0.971697964 FALSE FALSE FALSE 0.651416076
## 7 7 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 8 8 85.9 0.602901293 FALSE FALSE FALSE 0.289321036
## 9 9 94.3 0.906616198 FALSE FALSE FALSE 0.587516951
## 10 10 82.9 0.494431684 FALSE FALSE FALSE 0.182822495
## 11 11 61.8 -0.268471232 FALSE FALSE FALSE -0.566217243
## 12 12 84.5 0.552282143 FALSE FALSE FALSE 0.239621717
## 13 13 81.7 0.451043841 FALSE FALSE FALSE 0.140223079
## 14 14 69.5 0.009934098 FALSE FALSE FALSE -0.292870988
## 15 15 89.2 0.722217863 FALSE FALSE FALSE 0.406469431
## 16 16 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 17 17 94.1 0.899384891 FALSE FALSE FALSE 0.580417048
## 18 18 85.7 0.595669986 FALSE FALSE FALSE 0.282221134
## 19 19 88.8 0.707755249 FALSE FALSE FALSE 0.392269626
## 20 20 87.3 0.653520444 FALSE FALSE FALSE 0.339020355
## 21 21 94.1 0.899384891 FALSE FALSE FALSE 0.580417048
## 22 22 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 23 23 95.0 0.931925774 FALSE FALSE FALSE 0.612366611
## 24 24 96.9 1.000623193 FALSE FALSE FALSE 0.679815687
## 25 25 68.2 -0.037069399 FALSE FALSE FALSE -0.339020355
## 26 26 61.4 -0.282933846 FALSE FALSE FALSE -0.580417048
## 27 27 30.2 -1.411017779 FALSE FALSE FALSE -1.688001874
## 28 28 2.9 -2.398091220 FALSE FALSE FALSE -2.657138597
## 29 29 6.6 -2.264312036 FALSE FALSE FALSE -2.525790397
## 30 30 33.8 -1.280854248 FALSE FALSE FALSE -1.560203625
## 31 31 33.3 -1.298932517 FALSE FALSE FALSE -1.577953382
## 32 32 62.0 -0.261239924 FALSE FALSE FALSE -0.559117340
## 33 33 45.7 -0.850591466 FALSE FALSE FALSE -1.137759413
## 34 34 47.6 -0.781894047 FALSE FALSE FALSE -1.070310337
## 35 35 21.9 -1.711117031 FALSE FALSE FALSE -1.982647838
## 36 36 35.7 -1.212156829 FALSE FALSE FALSE -1.492754549
## 37 37 40.5 -1.038605455 FALSE FALSE FALSE -1.322356884
## 38 38 29.2 -1.447174315 FALSE FALSE FALSE -1.723501388
## 39 39 93.4 0.874075316 FALSE FALSE FALSE 0.555567389
## 40 40 67.8 -0.051532014 FALSE FALSE FALSE -0.353220161
## 41 41 59.5 -0.351631265 FALSE FALSE FALSE -0.647866124
## 42 42 17.8 -1.859358829 FALSE FALSE FALSE -2.128195844
## 43 43 31.1 -1.378476897 FALSE FALSE FALSE -1.656052312
## 44 44 21.4 -1.729195299 FALSE FALSE FALSE -2.000397595
## 45 45 33.0 -1.309779478 FALSE FALSE FALSE -1.588603236
## 46 46 6.6 -2.264312036 FALSE FALSE FALSE -2.525790397
## 47 47 7.8 -2.220924193 FALSE FALSE FALSE -2.483190980
## 48 48 6.2 -2.278774651 FALSE FALSE FALSE -2.539990202
## 49 49 6.0 -2.286005958 FALSE FALSE FALSE -2.547090105
## 50 50 45.0 -0.875901042 FALSE FALSE FALSE -1.162609072
## 51 51 74.5 0.190716779 FALSE FALSE FALSE -0.115373419
## 52 52 45.8 -0.846975813 FALSE FALSE FALSE -1.134209461
## 53 53 36.6 -1.179615947 FALSE FALSE FALSE -1.460804987
## 54 54 33.5 -1.291701209 FALSE FALSE FALSE -1.570853479
## 55 55 70.4 0.042474981 FALSE FALSE FALSE -0.260921425
## 56 56 32.2 -1.338704707 FALSE FALSE FALSE -1.617002847
## 57 57 48.0 -0.767431433 FALSE FALSE FALSE -1.056110531
## 58 58 56.1 -0.474563489 FALSE FALSE FALSE -0.768564471
## 59 59 45.1 -0.872285388 FALSE FALSE FALSE -1.159059121
## 60 60 56.8 -0.449253913 FALSE FALSE FALSE -0.743714811
## 61 61 86.3 0.617363908 FALSE FALSE FALSE 0.303520842
## 62 62 63.1 -0.221467735 FALSE FALSE FALSE -0.520067875
## 63 63 66.1 -0.112998126 FALSE FALSE FALSE -0.413569334
## 64 64 73.9 0.169022858 FALSE FALSE FALSE -0.136673128
## 65 65 53.6 -0.564954830 FALSE FALSE FALSE -0.857313255
## 66 66 28.9 -1.458021276 FALSE FALSE FALSE -1.734151242
## 67 67 77.3 0.291955081 FALSE FALSE FALSE -0.015974781
## 68 68 57.8 -0.413097377 FALSE FALSE FALSE -0.708215297
## 69 69 69.6 0.013549751 FALSE FALSE FALSE -0.289321036
## 70 70 76.0 0.244951584 FALSE FALSE FALSE -0.062124149
## 71 71 36.9 -1.168768986 FALSE FALSE FALSE -1.450155133
## 72 72 62.5 -0.243161656 FALSE FALSE FALSE -0.541367583
## 73 73 79.9 0.385962075 FALSE FALSE FALSE 0.076323954
## 74 74 71.3 0.075015863 FALSE FALSE FALSE -0.228971863
## 75 75 85.4 0.584823025 FALSE FALSE FALSE 0.271571279
## 76 76 87.4 0.657136098 FALSE FALSE FALSE 0.342570307
## 77 77 90.0 0.751143092 FALSE FALSE FALSE 0.434869042
## 78 78 96.7 0.993391886 FALSE FALSE FALSE 0.672715784
## 79 79 85.2 0.577591718 FALSE FALSE FALSE 0.264471377
## 80 80 54.4 -0.536029600 FALSE FALSE FALSE -0.828913644
## 81 81 81.6 0.447428187 FALSE FALSE FALSE 0.136673128
## 82 82 92.9 0.855997048 FALSE FALSE FALSE 0.537817632
## 83 83 95.4 0.946388388 FALSE FALSE FALSE 0.626566416
## 84 84 84.2 0.541435182 FALSE FALSE FALSE 0.228971863
## 85 85 88.2 0.686061327 FALSE FALSE FALSE 0.370969918
## 86 86 72.5 0.118403707 FALSE FALSE FALSE -0.186372447
## 87 87 82.6 0.483584724 FALSE FALSE FALSE 0.172172641
## 88 88 65.2 -0.145539008 FALSE FALSE FALSE -0.445518896
## 89 89 69.7 0.017165405 FALSE FALSE FALSE -0.285771085
## 90 90 84.1 0.537819528 FALSE FALSE FALSE 0.225421912
## 91 91 92.9 0.855997048 FALSE FALSE FALSE 0.537817632
## 92 92 97.0 1.004238846 FALSE FALSE FALSE 0.683365638
## 93 93 95.8 0.960851003 FALSE FALSE FALSE 0.640766222
## 94 94 88.4 0.693292634 FALSE FALSE FALSE 0.378069820
## 95 95 96.0 0.968082310 FALSE FALSE FALSE 0.647866124
## 96 96 98.8 1.069320612 FALSE FALSE FALSE 0.747264762
## 97 97 94.7 0.921078813 FALSE FALSE FALSE 0.601716756
## 98 98 98.9 1.072936265 FALSE FALSE FALSE 0.750814714
## 99 99 97.7 1.029548422 FALSE FALSE FALSE 0.708215297
## 100 100 97.9 1.036779729 FALSE FALSE FALSE 0.715315200
## 101 101 95.4 0.946388388 FALSE FALSE FALSE 0.626566416
## 102 102 98.4 1.054857997 FALSE FALSE FALSE 0.733064957
## 103 103 98.2 1.047626690 FALSE FALSE FALSE 0.725965054
## 104 104 93.5 0.877690969 FALSE FALSE FALSE 0.559117340
## 105 105 98.4 1.054857997 FALSE FALSE FALSE 0.733064957
## 106 106 98.2 1.047626690 FALSE FALSE FALSE 0.725965054
## 107 107 97.9 1.036779729 FALSE FALSE FALSE 0.715315200
## 108 108 93.6 0.881306623 FALSE FALSE FALSE 0.562667291
## 109 109 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 110 110 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 111 111 97.8 1.033164075 FALSE FALSE FALSE 0.711765249
## 112 112 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 113 113 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 114 114 95.7 0.957235349 FALSE FALSE FALSE 0.637216270
## 115 115 94.9 0.928310120 FALSE FALSE FALSE 0.608816659
## 116 116 97.3 1.015085807 FALSE FALSE FALSE 0.694015492
## 117 117 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 118 118 88.0 0.678830020 FALSE FALSE FALSE 0.363870015
## 119 119 98.5 1.058473651 FALSE FALSE FALSE 0.736614908
## 120 120 96.0 0.968082310 FALSE FALSE FALSE 0.647866124
## 121 121 82.6 0.483584724 FALSE FALSE FALSE 0.172172641
## 122 122 94.0 0.895769237 FALSE FALSE FALSE 0.576867097
## 123 123 97.4 1.018701461 FALSE FALSE FALSE 0.697565443
## 124 124 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 125 125 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 126 126 92.6 0.845150087 FALSE FALSE FALSE 0.527167778
## 127 127 90.8 0.780068321 FALSE FALSE FALSE 0.463268653
## 128 128 98.2 1.047626690 FALSE FALSE FALSE 0.725965054
## 129 129 93.9 0.892153584 FALSE FALSE FALSE 0.573317146
## 130 130 91.8 0.816224858 FALSE FALSE FALSE 0.498768167
## 131 131 93.0 0.859612701 FALSE FALSE FALSE 0.541367583
## 132 132 96.2 0.975313617 FALSE FALSE FALSE 0.654966027
## 133 133 79.2 0.360652500 FALSE FALSE FALSE 0.051474295
## 134 134 96.1 0.971697964 FALSE FALSE FALSE 0.651416076
## 135 135 94.6 0.917463159 FALSE FALSE FALSE 0.598166805
## 136 136 97.3 1.015085807 FALSE FALSE FALSE 0.694015492
## 137 137 88.5 0.696908288 FALSE FALSE FALSE 0.381619772
## 138 138 84.1 0.537819528 FALSE FALSE FALSE 0.225421912
## 139 139 68.7 -0.018991131 FALSE FALSE FALSE -0.321270599
## 140 140 33.1 -1.306163824 FALSE FALSE FALSE -1.585053285
## 141 141 47.2 -0.796356662 FALSE FALSE FALSE -1.084510142
## 142 142 74.4 0.187101126 FALSE FALSE FALSE -0.118923371
## 143 143 58.4 -0.391403455 FALSE FALSE FALSE -0.686915589
## 144 144 83.3 0.508894299 FALSE FALSE FALSE 0.197022301
## 145 145 95.6 0.953619696 FALSE FALSE FALSE 0.633666319
## 146 146 89.8 0.743911785 FALSE FALSE FALSE 0.427769140
## 147 147 68.8 -0.015375478 FALSE FALSE FALSE -0.317720647
## 148 148 41.1 -1.016911533 FALSE FALSE FALSE -1.301057176
## 149 149 29.1 -1.450789969 FALSE FALSE FALSE -1.727051339
## 150 150 38.9 -1.096455913 FALSE FALSE FALSE -1.379156106
## 151 151 21.5 -1.725579645 FALSE FALSE FALSE -1.996847643
## 152 152 30.8 -1.389323857 FALSE FALSE FALSE -1.666702166
## 153 153 26.3 -1.552028271 FALSE FALSE FALSE -1.826449978
## 154 154 9.9 -2.144995466 FALSE FALSE FALSE -2.408642002
## 155 155 18.8 -1.823202293 FALSE FALSE FALSE -2.092696330
## 156 156 32.0 -1.345936014 FALSE FALSE FALSE -1.624102750
## 157 157 34.1 -1.270007288 FALSE FALSE FALSE -1.549553771
## 158 158 36.6 -1.179615947 FALSE FALSE FALSE -1.460804987
## 159 159 38.3 -1.118149835 FALSE FALSE FALSE -1.400455814
## 160 160 15.3 -1.949750170 FALSE FALSE FALSE -2.216944628
## 161 161 13.9 -2.000369321 FALSE FALSE FALSE -2.266643947
## 162 162 38.4 -1.114534181 FALSE FALSE FALSE -1.396905862
## 163 163 15.7 -1.935287556 FALSE FALSE FALSE -2.202744822
## 164 164 33.2 -1.302548170 FALSE FALSE FALSE -1.581503333
## 165 165 72.7 0.125635014 FALSE FALSE FALSE -0.179272544
## 166 166 59.1 -0.366093880 FALSE FALSE FALSE -0.662065930
## 167 167 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 168 168 92.1 0.827071818 FALSE FALSE FALSE 0.509418021
## 169 169 53.8 -0.557723522 FALSE FALSE FALSE -0.850213352
## 170 170 32.3 -1.335089053 FALSE FALSE FALSE -1.613452896
## 171 171 9.8 -2.148611120 FALSE FALSE FALSE -2.412191953
## 172 172 42.4 -0.969908036 FALSE FALSE FALSE -1.254907808
## 173 173 56.0 -0.478179142 FALSE FALSE FALSE -0.772114422
## 174 174 93.8 0.888537930 FALSE FALSE FALSE 0.569767194
## 175 175 92.4 0.837918779 FALSE FALSE FALSE 0.520067875
## 176 176 88.5 0.696908288 FALSE FALSE FALSE 0.381619772
## 177 177 91.3 0.798146589 FALSE FALSE FALSE 0.481018410
## 178 178 77.7 0.306417696 FALSE FALSE FALSE -0.001774976
## 179 179 80.8 0.418502958 FALSE FALSE FALSE 0.108273517
## 180 180 78.3 0.328111617 FALSE FALSE FALSE 0.019524733
## 181 181 86.5 0.624595215 FALSE FALSE FALSE 0.310620744
## 182 182 79.9 0.385962075 FALSE FALSE FALSE 0.076323954
## 183 183 17.0 -1.888284058 FALSE FALSE FALSE -2.156595455
## 184 184 68.1 -0.040685053 FALSE FALSE FALSE -0.342570307
## 185 185 76.9 0.277492467 FALSE FALSE FALSE -0.030174587
## 186 186 73.3 0.147328936 FALSE FALSE FALSE -0.157972836
## 187 187 70.4 0.042474981 FALSE FALSE FALSE -0.260921425
## 188 188 66.5 -0.098535511 FALSE FALSE FALSE -0.399369529
## 189 189 61.5 -0.279318193 FALSE FALSE FALSE -0.576867097
## 190 190 76.5 0.263029852 FALSE FALSE FALSE -0.044374392
## 191 191 71.6 0.085862824 FALSE FALSE FALSE -0.218322009
## 192 192 42.2 -0.977139343 FALSE FALSE FALSE -1.262007710
## 193 193 54.3 -0.539645254 FALSE FALSE FALSE -0.832463595
## 194 194 65.1 -0.149154662 FALSE FALSE FALSE -0.449068848
## 195 195 52.9 -0.590264405 FALSE FALSE FALSE -0.882162914
## 196 196 7.8 -2.220924193 FALSE FALSE FALSE -2.483190980
## 197 197 76.5 0.263029852 FALSE FALSE FALSE -0.044374392
## 198 198 34.9 -1.241082059 FALSE FALSE FALSE -1.521154160
## 199 199 79.2 0.360652500 FALSE FALSE FALSE 0.051474295
## 200 200 49.1 -0.727659243 FALSE FALSE FALSE -1.017061066
## 201 201 17.5 -1.870205790 FALSE FALSE FALSE -2.138845698
## 202 202 8.9 -2.181152003 FALSE FALSE FALSE -2.444141515
## 203 203 6.8 -2.257080729 FALSE FALSE FALSE -2.518690494
## 204 204 32.0 -1.345936014 FALSE FALSE FALSE -1.624102750
## 205 205 19.1 -1.812355332 FALSE FALSE FALSE -2.082046476
## 206 206 34.2 -1.266391634 FALSE FALSE FALSE -1.546003820
## 207 207 86.9 0.639057830 FALSE FALSE FALSE 0.324820550
## 208 208 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 209 209 81.8 0.454659494 FALSE FALSE FALSE 0.143773030
## 210 210 89.4 0.729449170 FALSE FALSE FALSE 0.413569334
## 211 211 91.5 0.805377897 FALSE FALSE FALSE 0.488118313
## 212 212 84.6 0.555897796 FALSE FALSE FALSE 0.243171669
## 213 213 52.6 -0.601111366 FALSE FALSE FALSE -0.892812768
## 214 214 61.5 -0.279318193 FALSE FALSE FALSE -0.576867097
## 215 215 42.1 -0.980754997 FALSE FALSE FALSE -1.265557662
## 216 216 16.3 -1.913593634 FALSE FALSE FALSE -2.181445114
## 217 217 58.7 -0.380556494 FALSE FALSE FALSE -0.676265735
## 218 218 49.0 -0.731274897 FALSE FALSE FALSE -1.020611018
## 219 219 27.6 -1.505024774 FALSE FALSE FALSE -1.780300610
## 220 220 32.1 -1.342320360 FALSE FALSE FALSE -1.620552798
## 221 221 64.5 -0.170848584 FALSE FALSE FALSE -0.470368556
## 222 222 37.2 -1.157922025 FALSE FALSE FALSE -1.439505279
## 223 223 49.7 -0.705965321 FALSE FALSE FALSE -0.995761358
## 224 224 24.8 -1.606263075 FALSE FALSE FALSE -1.879699248
## 225 225 20.8 -1.750889221 FALSE FALSE FALSE -2.021697303
## 226 226 31.9 -1.349551667 FALSE FALSE FALSE -1.627652701
## 227 227 31.5 -1.364014282 FALSE FALSE FALSE -1.641852507
## 228 228 31.3 -1.371245589 FALSE FALSE FALSE -1.648952409
## 229 229 45.6 -0.854207120 FALSE FALSE FALSE -1.141309364
## 230 230 22.9 -1.674960494 FALSE FALSE FALSE -1.947148324
## 231 231 27.9 -1.494177813 FALSE FALSE FALSE -1.769650756
## 232 232 27.7 -1.501409120 FALSE FALSE FALSE -1.776750659
## 233 233 23.4 -1.656882226 FALSE FALSE FALSE -1.929398567
## 234 234 31.1 -1.378476897 FALSE FALSE FALSE -1.656052312
## 235 235 51.0 -0.658961824 FALSE FALSE FALSE -0.949611990
## 236 236 58.0 -0.405866070 FALSE FALSE FALSE -0.701115395
## 237 237 20.1 -1.776198796 FALSE FALSE FALSE -2.046546962
## 238 238 40.4 -1.042221109 FALSE FALSE FALSE -1.325906835
## 239 239 17.7 -1.862974483 FALSE FALSE FALSE -2.131745795
## 240 240 41.1 -1.016911533 FALSE FALSE FALSE -1.301057176
## 241 241 70.3 0.038859327 FALSE FALSE FALSE -0.264471377
## 242 242 82.5 0.479969070 FALSE FALSE FALSE 0.168622690
## 243 243 76.7 0.270261159 FALSE FALSE FALSE -0.037274489
## 244 244 52.8 -0.593880059 FALSE FALSE FALSE -0.885712866
## 245 245 90.4 0.765605707 FALSE FALSE FALSE 0.449068848
## 246 246 82.8 0.490816031 FALSE FALSE FALSE 0.179272544
## 247 247 87.3 0.653520444 FALSE FALSE FALSE 0.339020355
## 248 248 77.7 0.306417696 FALSE FALSE FALSE -0.001774976
## 249 249 71.7 0.089478478 FALSE FALSE FALSE -0.214772058
## 250 250 67.2 -0.073225936 FALSE FALSE FALSE -0.374519869
## 251 251 58.8 -0.376940841 FALSE FALSE FALSE -0.672715784
## 252 252 52.3 -0.611958327 FALSE FALSE FALSE -0.903462623
## 253 253 54.3 -0.539645254 FALSE FALSE FALSE -0.832463595
## 254 254 49.9 -0.698734014 FALSE FALSE FALSE -0.988661455
## 255 255 74.3 0.183485472 FALSE FALSE FALSE -0.122473322
## 256 256 40.1 -1.053068070 FALSE FALSE FALSE -1.336556689
## 257 257 14.7 -1.971444092 FALSE FALSE FALSE -2.238244336
## 258 258 28.9 -1.458021276 FALSE FALSE FALSE -1.734151242
## 259 259 43.7 -0.922904539 FALSE FALSE FALSE -1.208758440
## 260 260 25.8 -1.570106539 FALSE FALSE FALSE -1.844199734
## 261 261 17.2 -1.881052751 FALSE FALSE FALSE -2.149495552
## 262 262 32.2 -1.338704707 FALSE FALSE FALSE -1.617002847
## 263 263 28.4 -1.476099545 FALSE FALSE FALSE -1.751900999
## 264 264 38.1 -1.125381142 FALSE FALSE FALSE -1.407555716
## 265 265 38.5 -1.110918528 FALSE FALSE FALSE -1.393355911
## 266 266 34.5 -1.255544673 FALSE FALSE FALSE -1.535353966
## 267 267 46.3 -0.828897545 FALSE FALSE FALSE -1.116459705
## 268 268 59.6 -0.348015612 FALSE FALSE FALSE -0.644316173
## 269 269 45.4 -0.861438427 FALSE FALSE FALSE -1.148409267
## 270 270 58.5 -0.387787802 FALSE FALSE FALSE -0.683365638
## 271 271 49.3 -0.720427936 FALSE FALSE FALSE -1.009961164
## 272 272 59.7 -0.344399958 FALSE FALSE FALSE -0.640766222
## 273 273 56.4 -0.463716528 FALSE FALSE FALSE -0.757914617
## 274 274 28.1 -1.486946505 FALSE FALSE FALSE -1.762550853
## 275 275 48.5 -0.749353165 FALSE FALSE FALSE -1.038360774
## 276 276 52.3 -0.611958327 FALSE FALSE FALSE -0.903462623
## 277 277 27.7 -1.501409120 FALSE FALSE FALSE -1.776750659
## 278 278 29.7 -1.429096047 FALSE FALSE FALSE -1.705751631
## 279 279 34.5 -1.255544673 FALSE FALSE FALSE -1.535353966
## 280 280 35.9 -1.204925522 FALSE FALSE FALSE -1.485654647
## 281 281 36.1 -1.197694215 FALSE FALSE FALSE -1.478554744
## 282 282 21.9 -1.711117031 FALSE FALSE FALSE -1.982647838
## 283 283 19.5 -1.797892718 FALSE FALSE FALSE -2.067846671
## 284 284 97.4 1.018701461 FALSE FALSE FALSE 0.697565443
## 285 285 91.0 0.787299629 FALSE FALSE FALSE 0.470368556
## 286 286 83.4 0.512509953 FALSE FALSE FALSE 0.200572252
## 287 287 88.0 0.678830020 FALSE FALSE FALSE 0.363870015
## 288 288 91.1 0.790915282 FALSE FALSE FALSE 0.473918507
## 289 289 96.2 0.975313617 FALSE FALSE FALSE 0.654966027
## 290 290 89.0 0.714986556 FALSE FALSE FALSE 0.399369529
## 291 291 82.9 0.494431684 FALSE FALSE FALSE 0.182822495
## 292 292 87.9 0.675214366 FALSE FALSE FALSE 0.360320064
## 293 293 91.4 0.801762243 FALSE FALSE FALSE 0.484568361
## 294 294 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 295 295 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 296 296 97.5 1.022317115 FALSE FALSE FALSE 0.701115395
## 297 297 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 298 298 89.6 0.736680478 FALSE FALSE FALSE 0.420669237
## 299 299 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 300 300 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 301 301 97.9 1.036779729 FALSE FALSE FALSE 0.715315200
## 302 302 93.3 0.870459662 FALSE FALSE FALSE 0.552017437
## 303 303 98.8 1.069320612 FALSE FALSE FALSE 0.747264762
## 304 304 96.2 0.975313617 FALSE FALSE FALSE 0.654966027
## 305 305 91.9 0.819840511 FALSE FALSE FALSE 0.502318118
## 306 306 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 307 307 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 308 308 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 309 309 89.5 0.733064824 FALSE FALSE FALSE 0.417119285
## 310 310 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 311 311 82.5 0.479969070 FALSE FALSE FALSE 0.168622690
## 312 312 97.0 1.004238846 FALSE FALSE FALSE 0.683365638
## 313 313 92.6 0.845150087 FALSE FALSE FALSE 0.527167778
## 314 314 94.7 0.921078813 FALSE FALSE FALSE 0.601716756
## 315 315 98.8 1.069320612 FALSE FALSE FALSE 0.747264762
## 316 316 96.0 0.968082310 FALSE FALSE FALSE 0.647866124
## 317 317 77.8 0.310033349 FALSE FALSE FALSE 0.001774976
## 318 318 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 319 319 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 320 320 96.0 0.968082310 FALSE FALSE FALSE 0.647866124
## 321 321 85.4 0.584823025 FALSE FALSE FALSE 0.271571279
## 322 322 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 323 323 97.9 1.036779729 FALSE FALSE FALSE 0.715315200
## 324 324 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 325 325 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 326 326 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 327 327 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 328 328 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 329 329 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 330 330 90.8 0.780068321 FALSE FALSE FALSE 0.463268653
## 331 331 89.1 0.718602210 FALSE FALSE FALSE 0.402919480
## 332 332 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 333 333 76.5 0.263029852 FALSE FALSE FALSE -0.044374392
## 334 334 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 335 335 95.3 0.942772735 FALSE FALSE FALSE 0.623016465
## 336 336 87.6 0.664367405 FALSE FALSE FALSE 0.349670210
## 337 337 85.1 0.573976064 FALSE FALSE FALSE 0.260921425
## 338 338 70.6 0.049706288 FALSE FALSE FALSE -0.253821523
## 339 339 95.4 0.946388388 FALSE FALSE FALSE 0.626566416
## 340 340 59.7 -0.344399958 FALSE FALSE FALSE -0.640766222
## 341 341 78.7 0.342574232 FALSE FALSE FALSE 0.033724538
## 342 342 95.6 0.953619696 FALSE FALSE FALSE 0.633666319
## 343 343 86.1 0.610132601 FALSE FALSE FALSE 0.296420939
## 344 344 94.3 0.906616198 FALSE FALSE FALSE 0.587516951
## 345 345 74.8 0.201563740 FALSE FALSE FALSE -0.104723565
## 346 346 87.9 0.675214366 FALSE FALSE FALSE 0.360320064
## 347 347 95.0 0.931925774 FALSE FALSE FALSE 0.612366611
## 348 348 94.6 0.917463159 FALSE FALSE FALSE 0.598166805
## 349 349 93.3 0.870459662 FALSE FALSE FALSE 0.552017437
## 350 350 87.9 0.675214366 FALSE FALSE FALSE 0.360320064
## 351 351 93.9 0.892153584 FALSE FALSE FALSE 0.573317146
## 352 352 92.4 0.837918779 FALSE FALSE FALSE 0.520067875
## 353 353 100.0 1.112708455 FALSE FALSE FALSE 0.789864179
## 354 354 96.6 0.989776232 FALSE FALSE FALSE 0.669165832
## 355 355 94.8 0.924694467 FALSE FALSE FALSE 0.605266708
## 356 356 96.4 0.982544925 FALSE FALSE FALSE 0.662065930
## 357 357 96.6 0.989776232 FALSE FALSE FALSE 0.669165832
## 358 358 98.3 1.051242344 FALSE FALSE FALSE 0.729515006
## 359 359 92.6 0.845150087 FALSE FALSE FALSE 0.527167778
## 360 360 98.2 1.047626690 FALSE FALSE FALSE 0.725965054
## 361 361 99.3 1.087398880 FALSE FALSE FALSE 0.765014519
## 362 362 94.1 0.899384891 FALSE FALSE FALSE 0.580417048
## 363 363 86.5 0.624595215 FALSE FALSE FALSE 0.310620744
## 364 364 87.9 0.675214366 FALSE FALSE FALSE 0.360320064
## 365 365 80.3 0.400424690 FALSE FALSE FALSE 0.090523760
## 366 366 84.4 0.548666489 FALSE FALSE FALSE 0.236071766
## 367 367 83.0 0.498047338 FALSE FALSE FALSE 0.186372447
## 368 368 65.4 -0.138307701 FALSE FALSE FALSE -0.438418994
## 369 369 48.2 -0.760200126 FALSE FALSE FALSE -1.049010629
## 370 370 84.7 0.559513450 FALSE FALSE FALSE 0.246721620
## 371 371 71.0 0.064168902 FALSE FALSE FALSE -0.239621717
## 372 372 56.7 -0.452869567 FALSE FALSE FALSE -0.747264762
## 373 373 84.0 0.534203874 FALSE FALSE FALSE 0.221871960
## 374 374 90.7 0.776452668 FALSE FALSE FALSE 0.459718702
## 375 375 75.0 0.208795048 FALSE FALSE FALSE -0.097623663
## 376 376 67.6 -0.058763321 FALSE FALSE FALSE -0.360320064
## 377 377 95.4 0.946388388 FALSE FALSE FALSE 0.626566416
## 378 378 97.4 1.018701461 FALSE FALSE FALSE 0.697565443
## 379 379 93.6 0.881306623 FALSE FALSE FALSE 0.562667291
## 380 380 97.3 1.015085807 FALSE FALSE FALSE 0.694015492
## 381 381 96.7 0.993391886 FALSE FALSE FALSE 0.672715784
## 382 382 88.0 0.678830020 FALSE FALSE FALSE 0.363870015
## 383 383 64.7 -0.163617276 FALSE FALSE FALSE -0.463268653
## 384 384 74.9 0.205179394 FALSE FALSE FALSE -0.101173614
## 385 385 77.0 0.281108120 FALSE FALSE FALSE -0.026624635
## 386 386 40.3 -1.045836762 FALSE FALSE FALSE -1.329456786
## 387 387 41.9 -0.987986304 FALSE FALSE FALSE -1.272657565
## 388 388 51.9 -0.626420941 FALSE FALSE FALSE -0.917662428
## 389 389 53.2 -0.579417444 FALSE FALSE FALSE -0.871513060
## 390 390 92.7 0.848765740 FALSE FALSE FALSE 0.530717729
## 391 391 98.3 1.051242344 FALSE FALSE FALSE 0.729515006
## 392 392 98.0 1.040395383 FALSE FALSE FALSE 0.718865152
## 393 393 98.8 1.069320612 FALSE FALSE FALSE 0.747264762
## 394 394 83.5 0.516125606 FALSE FALSE FALSE 0.204122204
## 395 395 42.6 -0.962676729 FALSE FALSE FALSE -1.247807905
## 396 396 28.8 -1.461636930 FALSE FALSE FALSE -1.737701193
## 397 397 72.9 0.132866321 FALSE FALSE FALSE -0.172172641
## 398 398 65.3 -0.141923355 FALSE FALSE FALSE -0.441968945
## 399 399 73.5 0.154560243 FALSE FALSE FALSE -0.150872933
## 400 400 79.7 0.378730768 FALSE FALSE FALSE 0.069224052
## 401 401 69.1 -0.004528517 FALSE FALSE FALSE -0.307070793
## 402 402 76.7 0.270261159 FALSE FALSE FALSE -0.037274489
## 403 403 89.3 0.725833517 FALSE FALSE FALSE 0.410019383
## 404 404 80.8 0.418502958 FALSE FALSE FALSE 0.108273517
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 FALSE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 FALSE
## 289 FALSE
## 290 FALSE
## 291 FALSE
## 292 FALSE
## 293 FALSE
## 294 FALSE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 FALSE
## 299 FALSE
## 300 FALSE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 FALSE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 FALSE
## 322 FALSE
## 323 FALSE
## 324 FALSE
## 325 FALSE
## 326 FALSE
## 327 FALSE
## 328 FALSE
## 329 FALSE
## 330 FALSE
## 331 FALSE
## 332 FALSE
## 333 FALSE
## 334 FALSE
## 335 FALSE
## 336 FALSE
## 337 FALSE
## 338 FALSE
## 339 FALSE
## 340 FALSE
## 341 FALSE
## 342 FALSE
## 343 FALSE
## 344 FALSE
## 345 FALSE
## 346 FALSE
## 347 FALSE
## 348 FALSE
## 349 FALSE
## 350 FALSE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 FALSE
## 355 FALSE
## 356 FALSE
## 357 FALSE
## 358 FALSE
## 359 FALSE
## 360 FALSE
## 361 FALSE
## 362 FALSE
## 363 FALSE
## 364 FALSE
## 365 FALSE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 FALSE
## 370 FALSE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 FALSE
## 378 FALSE
## 379 FALSE
## 380 FALSE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== AL MENOS DOS MÉTODOS =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== LOS TRES MÉTODOS =====
## [1] ID Valor Z_score Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## age Z-score 404 0 0
## age IQR 404 0 0
## age Tukey extremo 404 0 0
## age MAD 404 0 0## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## [1] ID Valor Z_score Z_robusto
## [5] metodos_evaluados numero_metodos clasificacion
## <0 rows> (o 0- extensión row.names)
##
## ========================================
## VARIABLE: dis
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -2.560505
## Límite superior = 10.08216
## Número de outliers = 4
## ID Valor Z_score
## 1 280 10.7103 3.298107
## 2 281 12.1265 3.970212
## 3 282 10.5857 3.238974
## 4 283 10.5857 3.238974
##
## ================ MÉTODO IQR ================
## Q1 = 2.087875
## Q3 = 5.104475
## IQR = 3.0166
## Límite inferior = -2.437025
## Límite superior = 9.629375
## Número de outliers = 4
## ID Valor
## 1 280 10.7103
## 2 281 12.1265
## 3 282 10.5857
## 4 283 10.5857
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -6.961925
## Límite superior = 14.15427
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 3.1073
## MAD = 1.24275
## MAD ajustada = 1.842501
## Número de outliers = 4
## ID Valor Z_robusto
## 1 280 10.7103 4.126456
## 2 281 12.1265 4.895085
## 3 282 10.5857 4.058831
## 4 283 10.5857 4.058831
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo
## 1 1 4.0900 0.1562207116 FALSE FALSE FALSE
## 2 2 4.9671 0.5724780072 FALSE FALSE FALSE
## 3 3 4.9671 0.5724780072 FALSE FALSE FALSE
## 4 4 6.0622 1.0921945326 FALSE FALSE FALSE
## 5 5 6.0622 1.0921945326 FALSE FALSE FALSE
## 6 6 5.9505 1.0391835419 FALSE FALSE FALSE
## 7 7 6.0821 1.1016387467 FALSE FALSE FALSE
## 8 8 6.5921 1.3436763943 FALSE FALSE FALSE
## 9 9 6.3467 1.2272135733 FALSE FALSE FALSE
## 10 10 6.2267 1.1702635385 FALSE FALSE FALSE
## 11 11 4.7075 0.4492760987 FALSE FALSE FALSE
## 12 12 4.4619 0.3327183610 FALSE FALSE FALSE
## 13 13 4.2579 0.2359033019 FALSE FALSE FALSE
## 14 14 3.7965 0.0169304183 FALSE FALSE FALSE
## 15 15 4.0123 0.1193455641 FALSE FALSE FALSE
## 16 16 4.0952 0.1586885465 FALSE FALSE FALSE
## 17 17 4.3996 0.3031518013 FALSE FALSE FALSE
## 18 18 4.4546 0.3292539005 FALSE FALSE FALSE
## 19 19 4.4534 0.3286844002 FALSE FALSE FALSE
## 20 20 4.2390 0.2269336714 FALSE FALSE FALSE
## 21 21 4.2330 0.2240861697 FALSE FALSE FALSE
## 22 22 4.1750 0.1965603196 FALSE FALSE FALSE
## 23 23 3.7872 0.0125167906 FALSE FALSE FALSE
## 24 24 3.7598 -0.0004868006 FALSE FALSE FALSE
## 25 25 3.3603 -0.1900829579 FALSE FALSE FALSE
## 26 26 3.3779 -0.1817302862 FALSE FALSE FALSE
## 27 27 3.8473 0.0410392664 FALSE FALSE FALSE
## 28 28 5.7209 0.9302191421 FALSE FALSE FALSE
## 29 29 5.7209 0.9302191421 FALSE FALSE FALSE
## 30 30 5.1004 0.6357400041 FALSE FALSE FALSE
## 31 31 5.1004 0.6357400041 FALSE FALSE FALSE
## 32 32 6.0877 1.1042964150 FALSE FALSE FALSE
## 33 33 6.8147 1.4493187087 FALSE FALSE FALSE
## 34 34 7.3197 1.6889834383 FALSE FALSE FALSE
## 35 35 8.6966 2.3424376285 FALSE FALSE FALSE
## 36 36 9.1876 2.5754581874 FALSE FALSE FALSE
## 37 37 8.3248 2.1659874376 FALSE FALSE FALSE
## 38 38 7.8148 1.9239497899 FALSE FALSE FALSE
## 39 39 6.8185 1.4511221265 FALSE FALSE FALSE
## 40 40 7.2255 1.6442776610 FALSE FALSE FALSE
## 41 41 9.2229 2.5922109892 FALSE FALSE FALSE
## 42 42 6.6115 1.3528833166 FALSE FALSE FALSE
## 43 43 6.6115 1.3528833166 FALSE FALSE FALSE
## 44 44 6.4980 1.2990180754 FALSE FALSE FALSE
## 45 45 6.4980 1.2990180754 FALSE FALSE FALSE
## 46 46 5.2873 0.7244396832 FALSE FALSE FALSE
## 47 47 5.2873 0.7244396832 FALSE FALSE FALSE
## 48 48 5.2873 0.7244396832 FALSE FALSE FALSE
## 49 49 4.2515 0.2328659667 FALSE FALSE FALSE
## 50 50 4.5026 0.3520339144 FALSE FALSE FALSE
## 51 51 4.0522 0.1382814507 FALSE FALSE FALSE
## 52 52 4.0905 0.1564580034 FALSE FALSE FALSE
## 53 53 4.5026 0.3520339144 FALSE FALSE FALSE
## 54 54 5.4007 0.7782574661 FALSE FALSE FALSE
## 55 55 5.4007 0.7782574661 FALSE FALSE FALSE
## 56 56 5.4007 0.7782574661 FALSE FALSE FALSE
## 57 57 4.7794 0.4833986612 FALSE FALSE FALSE
## 58 58 4.4377 0.3212334373 FALSE FALSE FALSE
## 59 59 4.4272 0.3162503092 FALSE FALSE FALSE
## 60 60 3.7476 -0.0062767208 FALSE FALSE FALSE
## 61 61 3.4217 -0.1609435235 FALSE FALSE FALSE
## 62 62 3.4145 -0.1643605256 FALSE FALSE FALSE
## 63 63 3.0923 -0.3172713689 FALSE FALSE FALSE
## 64 64 3.0921 -0.3173662856 FALSE FALSE FALSE
## 65 65 3.6659 -0.0450502028 FALSE FALSE FALSE
## 66 66 3.6659 -0.0450502028 FALSE FALSE FALSE
## 67 67 3.6150 -0.0692065092 FALSE FALSE FALSE
## 68 68 3.4952 -0.1260616272 FALSE FALSE FALSE
## 69 69 3.4952 -0.1260616272 FALSE FALSE FALSE
## 70 70 3.4952 -0.1260616272 FALSE FALSE FALSE
## 71 71 3.4952 -0.1260616272 FALSE FALSE FALSE
## 72 72 3.4952 -0.1260616272 FALSE FALSE FALSE
## 73 73 2.7778 -0.4665279182 FALSE FALSE FALSE
## 74 74 2.8561 -0.4293680206 FALSE FALSE FALSE
## 75 75 2.7147 -0.4964741448 FALSE FALSE FALSE
## 76 76 2.7147 -0.4964741448 FALSE FALSE FALSE
## 77 77 2.4210 -0.6358593549 FALSE FALSE FALSE
## 78 78 2.1069 -0.7849260708 FALSE FALSE FALSE
## 79 79 2.1224 -0.7775700246 FALSE FALSE FALSE
## 80 80 2.7778 -0.4665279182 FALSE FALSE FALSE
## 81 81 2.6775 -0.5141286556 FALSE FALSE FALSE
## 82 82 2.3534 -0.6679412078 FALSE FALSE FALSE
## 83 83 2.5480 -0.5755872348 FALSE FALSE FALSE
## 84 84 2.2565 -0.7139283608 FALSE FALSE FALSE
## 85 85 2.4631 -0.6158793843 FALSE FALSE FALSE
## 86 86 2.7301 -0.4891655571 FALSE FALSE FALSE
## 87 87 2.7474 -0.4809552604 FALSE FALSE FALSE
## 88 88 2.7592 -0.4753551736 FALSE FALSE FALSE
## 89 89 2.2577 -0.7133588605 FALSE FALSE FALSE
## 90 90 2.1974 -0.7419762529 FALSE FALSE FALSE
## 91 91 2.0869 -0.7944177433 FALSE FALSE FALSE
## 92 92 1.9444 -0.8620459095 FALSE FALSE FALSE
## 93 93 2.0063 -0.8326691833 FALSE FALSE FALSE
## 94 94 1.9929 -0.8390286038 FALSE FALSE FALSE
## 95 95 1.7883 -0.9361284130 FALSE FALSE FALSE
## 96 96 1.8125 -0.9246434894 FALSE FALSE FALSE
## 97 97 1.9799 -0.8451981909 FALSE FALSE FALSE
## 98 98 2.1185 -0.7794209008 FALSE FALSE FALSE
## 99 99 2.2710 -0.7070468983 FALSE FALSE FALSE
## 100 100 2.3274 -0.6802803820 FALSE FALSE FALSE
## 101 101 2.4699 -0.6126522157 FALSE FALSE FALSE
## 102 102 2.3460 -0.6714531266 FALSE FALSE FALSE
## 103 103 2.1107 -0.7831226530 FALSE FALSE FALSE
## 104 104 1.9669 -0.8513677780 FALSE FALSE FALSE
## 105 105 1.8498 -0.9069415202 FALSE FALSE FALSE
## 106 106 1.6686 -0.9929360727 FALSE FALSE FALSE
## 107 107 1.6687 -0.9928886143 FALSE FALSE FALSE
## 108 108 1.6119 -1.0198449641 FALSE FALSE FALSE
## 109 109 1.3216 -1.1576165898 FALSE FALSE FALSE
## 110 110 1.4118 -1.1148091470 FALSE FALSE FALSE
## 111 111 1.3459 -1.1460842078 FALSE FALSE FALSE
## 112 112 1.4191 -1.1113446866 FALSE FALSE FALSE
## 113 113 1.5166 -1.0650727834 FALSE FALSE FALSE
## 114 114 1.4608 -1.0915545495 FALSE FALSE FALSE
## 115 115 1.5257 -1.0607540724 FALSE FALSE FALSE
## 116 116 1.6180 -1.0169500040 FALSE FALSE FALSE
## 117 117 1.5916 -1.0294790116 FALSE FALSE FALSE
## 118 118 1.6102 -1.0206517563 FALSE FALSE FALSE
## 119 119 1.6232 -1.0144821692 FALSE FALSE FALSE
## 120 120 1.7494 -0.9545897160 FALSE FALSE FALSE
## 121 121 1.7455 -0.9564405921 FALSE FALSE FALSE
## 122 122 1.7364 -0.9607593031 FALSE FALSE FALSE
## 123 123 1.8773 -0.8938904706 FALSE FALSE FALSE
## 124 124 1.7573 -0.9508405053 FALSE FALSE FALSE
## 125 125 1.7659 -0.9467590862 FALSE FALSE FALSE
## 126 126 1.7984 -0.9313351184 FALSE FALSE FALSE
## 127 127 1.9709 -0.8494694435 FALSE FALSE FALSE
## 128 128 2.0407 -0.8163435066 FALSE FALSE FALSE
## 129 129 2.1620 -0.7587765132 FALSE FALSE FALSE
## 130 130 2.4220 -0.6353847712 FALSE FALSE FALSE
## 131 131 2.2834 -0.7011620614 FALSE FALSE FALSE
## 132 132 2.0459 -0.8138756718 FALSE FALSE FALSE
## 133 133 2.4259 -0.6335338951 FALSE FALSE FALSE
## 134 134 2.1000 -0.7882006978 FALSE FALSE FALSE
## 135 135 2.4259 -0.6335338951 FALSE FALSE FALSE
## 136 136 2.3887 -0.6511884059 FALSE FALSE FALSE
## 137 137 2.5961 -0.5527597625 FALSE FALSE FALSE
## 138 138 2.6463 -0.5289356646 FALSE FALSE FALSE
## 139 139 2.7019 -0.5025488152 FALSE FALSE FALSE
## 140 140 3.1323 -0.2982880240 FALSE FALSE FALSE
## 141 141 3.5549 -0.0977289849 FALSE FALSE FALSE
## 142 142 2.9153 -0.4012726701 FALSE FALSE FALSE
## 143 143 2.8290 -0.4422292368 FALSE FALSE FALSE
## 144 144 2.7410 -0.4839925956 FALSE FALSE FALSE
## 145 145 2.8470 -0.4336867315 FALSE FALSE FALSE
## 146 146 2.9879 -0.3668178991 FALSE FALSE FALSE
## 147 147 3.2797 -0.2283343979 FALSE FALSE FALSE
## 148 148 3.7886 0.0131812077 FALSE FALSE FALSE
## 149 149 4.5667 0.3824547246 FALSE FALSE FALSE
## 150 150 4.5667 0.3824547246 FALSE FALSE FALSE
## 151 151 6.4798 1.2903806535 FALSE FALSE FALSE
## 152 152 6.4798 1.2903806535 FALSE FALSE FALSE
## 153 153 6.4798 1.2903806535 FALSE FALSE FALSE
## 154 154 6.2196 1.1668939948 FALSE FALSE FALSE
## 155 155 6.2196 1.1668939948 FALSE FALSE FALSE
## 156 156 5.6484 0.8958118294 FALSE FALSE FALSE
## 157 157 7.3090 1.6839053935 FALSE FALSE FALSE
## 158 158 7.3090 1.6839053935 FALSE FALSE FALSE
## 159 159 7.3090 1.6839053935 FALSE FALSE FALSE
## 160 160 7.6534 1.8473519932 FALSE FALSE FALSE
## 161 161 7.6534 1.8473519932 FALSE FALSE FALSE
## 162 162 6.2700 1.1908130094 FALSE FALSE FALSE
## 163 163 6.2700 1.1908130094 FALSE FALSE FALSE
## 164 164 5.1180 0.6440926759 FALSE FALSE FALSE
## 165 165 4.3549 0.2819379133 FALSE FALSE FALSE
## 166 166 4.2392 0.2270285882 FALSE FALSE FALSE
## 167 167 3.8750 0.0541852327 FALSE FALSE FALSE
## 168 168 3.8771 0.0551818583 FALSE FALSE FALSE
## 169 169 3.6526 -0.0513621650 FALSE FALSE FALSE
## 170 170 3.9454 0.0875959198 FALSE FALSE FALSE
## 171 171 3.5875 -0.0822575588 FALSE FALSE FALSE
## 172 172 3.9454 0.0875959198 FALSE FALSE FALSE
## 173 173 3.1121 -0.3078746131 FALSE FALSE FALSE
## 174 174 2.8893 -0.4136118443 FALSE FALSE FALSE
## 175 175 3.3633 -0.1886592071 FALSE FALSE FALSE
## 176 176 2.8617 -0.4267103523 FALSE FALSE FALSE
## 177 177 3.0480 -0.3382954234 FALSE FALSE FALSE
## 178 178 3.2721 -0.2319412335 FALSE FALSE FALSE
## 179 179 3.2721 -0.2319412335 FALSE FALSE FALSE
## 180 180 2.8944 -0.4111914678 FALSE FALSE FALSE
## 181 181 3.2157 -0.2587077498 FALSE FALSE FALSE
## 182 182 3.2157 -0.2587077498 FALSE FALSE FALSE
## 183 183 3.3751 -0.1830591203 FALSE FALSE FALSE
## 184 184 3.6715 -0.0423925345 FALSE FALSE FALSE
## 185 185 3.6715 -0.0423925345 FALSE FALSE FALSE
## 186 186 3.8384 0.0368154721 FALSE FALSE FALSE
## 187 187 3.6519 -0.0516943735 FALSE FALSE FALSE
## 188 188 3.6519 -0.0516943735 FALSE FALSE FALSE
## 189 189 3.6519 -0.0516943735 FALSE FALSE FALSE
## 190 190 4.1480 0.1837465618 FALSE FALSE FALSE
## 191 191 4.1480 0.1837465618 FALSE FALSE FALSE
## 192 192 6.1899 1.1527988612 FALSE FALSE FALSE
## 193 193 6.3361 1.2221829869 FALSE FALSE FALSE
## 194 194 6.3361 1.2221829869 FALSE FALSE FALSE
## 195 195 7.0355 1.5541067727 FALSE FALSE FALSE
## 196 196 7.0355 1.5541067727 FALSE FALSE FALSE
## 197 197 7.9549 1.9904389555 FALSE FALSE FALSE
## 198 198 8.0555 2.0381820680 FALSE FALSE FALSE
## 199 199 8.0555 2.0381820680 FALSE FALSE FALSE
## 200 200 7.8265 1.9295024183 FALSE FALSE FALSE
## 201 201 7.8265 1.9295024183 FALSE FALSE FALSE
## 202 202 7.3967 1.7255263772 FALSE FALSE FALSE
## 203 203 8.9067 2.4421476477 FALSE FALSE FALSE
## 204 204 9.2203 2.5909770718 FALSE FALSE FALSE
## 205 205 9.2203 2.5909770718 FALSE FALSE FALSE
## 206 206 6.3361 1.2221829869 FALSE FALSE FALSE
## 207 207 1.8010 -0.9301012010 FALSE FALSE FALSE
## 208 208 2.0107 -0.8305810153 FALSE FALSE FALSE
## 209 209 2.1121 -0.7824582360 FALSE FALSE FALSE
## 210 210 2.1398 -0.7693122696 FALSE FALSE FALSE
## 211 211 2.2885 -0.6987416849 FALSE FALSE FALSE
## 212 212 2.1329 -0.7725868966 FALSE FALSE FALSE
## 213 213 2.8720 -0.4218221410 FALSE FALSE FALSE
## 214 214 3.9175 0.0743550367 FALSE FALSE FALSE
## 215 215 4.4290 0.3171045598 FALSE FALSE FALSE
## 216 216 4.4290 0.3171045598 FALSE FALSE FALSE
## 217 217 3.9175 0.0743550367 FALSE FALSE FALSE
## 218 218 4.7872 0.4871004135 FALSE FALSE FALSE
## 219 219 4.8628 0.5229789354 FALSE FALSE FALSE
## 220 220 4.1403 0.1800922679 FALSE FALSE FALSE
## 221 221 4.6947 0.4432014284 FALSE FALSE FALSE
## 222 222 5.2447 0.7042224209 FALSE FALSE FALSE
## 223 223 5.2119 0.6886560781 FALSE FALSE FALSE
## 224 224 5.8850 1.0080983146 FALSE FALSE FALSE
## 225 225 7.3073 1.6830986014 FALSE FALSE FALSE
## 226 226 7.3073 1.6830986014 FALSE FALSE FALSE
## 227 227 9.0892 2.5287591589 FALSE FALSE FALSE
## 228 228 7.3172 1.6877969792 FALSE FALSE FALSE
## 229 229 7.3172 1.6877969792 FALSE FALSE FALSE
## 230 230 7.3172 1.6877969792 FALSE FALSE FALSE
## 231 231 5.1167 0.6434757172 FALSE FALSE FALSE
## 232 232 5.1167 0.6434757172 FALSE FALSE FALSE
## 233 233 5.1167 0.6434757172 FALSE FALSE FALSE
## 234 234 5.9604 1.0438819198 FALSE FALSE FALSE
## 235 235 5.9604 1.0438819198 FALSE FALSE FALSE
## 236 236 6.3200 1.2145421905 FALSE FALSE FALSE
## 237 237 7.8278 1.9301193770 FALSE FALSE FALSE
## 238 238 5.4917 0.8214445757 FALSE FALSE FALSE
## 239 239 5.4917 0.8214445757 FALSE FALSE FALSE
## 240 240 4.0220 0.1239490253 FALSE FALSE FALSE
## 241 241 3.1827 -0.2743690094 FALSE FALSE FALSE
## 242 242 3.3175 -0.2103951370 FALSE FALSE FALSE
## 243 243 3.1025 -0.3124306159 FALSE FALSE FALSE
## 244 244 2.6403 -0.5317831664 FALSE FALSE FALSE
## 245 245 2.8340 -0.4398563186 FALSE FALSE FALSE
## 246 246 3.2628 -0.2363548612 FALSE FALSE FALSE
## 247 247 3.6023 -0.0752337212 FALSE FALSE FALSE
## 248 248 3.9450 0.0874060863 FALSE FALSE FALSE
## 249 249 4.0317 0.1285524864 FALSE FALSE FALSE
## 250 250 3.5325 -0.1083596581 FALSE FALSE FALSE
## 251 251 4.0019 0.1144098945 FALSE FALSE FALSE
## 252 252 4.5404 0.3699731754 FALSE FALSE FALSE
## 253 253 4.5404 0.3699731754 FALSE FALSE FALSE
## 254 254 4.7211 0.4557304360 FALSE FALSE FALSE
## 255 255 4.7211 0.4557304360 FALSE FALSE FALSE
## 256 256 4.7211 0.4557304360 FALSE FALSE FALSE
## 257 257 5.4159 0.7854711371 FALSE FALSE FALSE
## 258 258 5.4159 0.7854711371 FALSE FALSE FALSE
## 259 259 5.4159 0.7854711371 FALSE FALSE FALSE
## 260 260 5.2146 0.6899374539 FALSE FALSE FALSE
## 261 261 5.2146 0.6899374539 FALSE FALSE FALSE
## 262 262 5.8736 1.0026880613 FALSE FALSE FALSE
## 263 263 6.6407 1.3667411584 FALSE FALSE FALSE
## 264 264 6.4584 1.2802245639 FALSE FALSE FALSE
## 265 265 6.4584 1.2802245639 FALSE FALSE FALSE
## 266 266 5.9853 1.0556990520 FALSE FALSE FALSE
## 267 267 5.2311 0.6977680836 FALSE FALSE FALSE
## 268 268 5.6150 0.8799607364 FALSE FALSE FALSE
## 269 269 4.8122 0.4989650040 FALSE FALSE FALSE
## 270 270 4.8122 0.4989650040 FALSE FALSE FALSE
## 271 271 7.0379 1.5552457734 FALSE FALSE FALSE
## 272 272 6.2669 1.1893418002 FALSE FALSE FALSE
## 273 273 5.7321 0.9355344787 FALSE FALSE FALSE
## 274 274 6.4654 1.2835466493 FALSE FALSE FALSE
## 275 275 8.0136 2.0182970142 FALSE FALSE FALSE
## 276 276 8.0136 2.0182970142 FALSE FALSE FALSE
## 277 277 8.5353 2.2658872902 FALSE FALSE FALSE
## 278 278 8.3440 2.1750994431 FALSE FALSE FALSE
## 279 279 8.7921 2.3877603645 FALSE FALSE FALSE
## 280 280 10.7103 3.2981066698 TRUE TRUE FALSE
## 281 281 12.1265 3.9702119965 TRUE TRUE FALSE
## 282 282 10.5857 3.2389735504 TRUE TRUE FALSE
## 283 283 10.5857 3.2389735504 TRUE TRUE FALSE
## 284 284 2.1222 -0.7776649414 FALSE FALSE FALSE
## 285 285 2.5052 -0.5958994138 FALSE FALSE FALSE
## 286 286 2.7227 -0.4926774759 FALSE FALSE FALSE
## 287 287 2.5182 -0.5897298267 FALSE FALSE FALSE
## 288 288 2.2955 -0.6954195995 FALSE FALSE FALSE
## 289 289 2.1036 -0.7864921968 FALSE FALSE FALSE
## 290 290 1.9047 -0.8808868793 FALSE FALSE FALSE
## 291 291 1.9047 -0.8808868793 FALSE FALSE FALSE
## 292 292 1.6132 -1.0192280054 FALSE FALSE FALSE
## 293 293 1.7523 -0.9532134235 FALSE FALSE FALSE
## 294 294 1.5106 -1.0679202851 FALSE FALSE FALSE
## 295 295 1.3325 -1.1524436283 FALSE FALSE FALSE
## 296 296 1.2024 -1.2141869576 FALSE FALSE FALSE
## 297 297 1.1691 -1.2299905923 FALSE FALSE FALSE
## 298 298 1.1296 -1.2487366454 FALSE FALSE FALSE
## 299 299 1.1742 -1.2275702158 FALSE FALSE FALSE
## 300 300 1.1370 -1.2452247266 FALSE FALSE FALSE
## 301 301 1.3163 -1.1601318830 FALSE FALSE FALSE
## 302 302 1.3449 -1.1465587914 FALSE FALSE FALSE
## 303 303 1.3580 -1.1403417459 FALSE FALSE FALSE
## 304 304 1.3861 -1.1270059461 FALSE FALSE FALSE
## 305 305 1.4165 -1.1125786040 FALSE FALSE FALSE
## 306 306 1.5804 -1.0347943482 FALSE FALSE FALSE
## 307 307 1.5331 -1.0572421536 FALSE FALSE FALSE
## 308 308 1.4672 -1.0885172143 FALSE FALSE FALSE
## 309 309 1.5184 -1.0642185328 FALSE FALSE FALSE
## 310 310 1.5895 -1.0304756372 FALSE FALSE FALSE
## 311 311 2.1678 -0.7560239282 FALSE FALSE FALSE
## 312 312 1.7700 -0.9448132933 FALSE FALSE FALSE
## 313 313 1.7912 -0.9347521205 FALSE FALSE FALSE
## 314 314 1.7821 -0.9390708315 FALSE FALSE FALSE
## 315 315 1.7257 -0.9658373478 FALSE FALSE FALSE
## 316 316 1.6768 -0.9890444870 FALSE FALSE FALSE
## 317 317 1.5004 -1.0727610380 FALSE FALSE FALSE
## 318 318 1.5888 -1.0308078458 FALSE FALSE FALSE
## 319 319 1.6390 -1.0069837479 FALSE FALSE FALSE
## 320 320 1.7028 -0.9767053128 FALSE FALSE FALSE
## 321 321 1.6074 -1.0219805904 FALSE FALSE FALSE
## 322 322 1.4254 -1.1083548098 FALSE FALSE FALSE
## 323 323 1.4547 -1.0944495096 FALSE FALSE FALSE
## 324 324 1.4130 -1.1142396467 FALSE FALSE FALSE
## 325 325 1.5275 -1.0598998219 FALSE FALSE FALSE
## 326 326 1.5539 -1.0473708142 FALSE FALSE FALSE
## 327 327 1.5894 -1.0305230956 FALSE FALSE FALSE
## 328 328 1.6582 -0.9978717424 FALSE FALSE FALSE
## 329 329 1.8347 -0.9141077329 FALSE FALSE FALSE
## 330 330 1.8195 -0.9213214040 FALSE FALSE FALSE
## 331 331 1.6475 -1.0029497871 FALSE FALSE FALSE
## 332 332 1.8026 -0.9293418672 FALSE FALSE FALSE
## 333 333 1.7940 -0.9334232864 FALSE FALSE FALSE
## 334 334 1.8589 -0.9026228093 FALSE FALSE FALSE
## 335 335 1.8746 -0.8951718464 FALSE FALSE FALSE
## 336 336 1.9512 -0.8588187409 FALSE FALSE FALSE
## 337 337 2.0218 -0.8253131371 FALSE FALSE FALSE
## 338 338 2.0635 -0.8055230000 FALSE FALSE FALSE
## 339 339 1.9096 -0.8785614196 FALSE FALSE FALSE
## 340 340 1.9976 -0.8367980608 FALSE FALSE FALSE
## 341 341 1.8629 -0.9007244748 FALSE FALSE FALSE
## 342 342 1.9682 -0.8507508193 FALSE FALSE FALSE
## 343 343 2.0527 -0.8106485032 FALSE FALSE FALSE
## 344 344 2.0882 -0.7938007845 FALSE FALSE FALSE
## 345 345 2.2004 -0.7405525021 FALSE FALSE FALSE
## 346 346 2.3158 -0.6857855520 FALSE FALSE FALSE
## 347 347 2.2222 -0.7302065791 FALSE FALSE FALSE
## 348 348 2.1247 -0.7764784823 FALSE FALSE FALSE
## 349 349 2.0026 -0.8344251427 FALSE FALSE FALSE
## 350 350 1.8206 -0.9207993620 FALSE FALSE FALSE
## 351 351 1.8172 -0.9224129463 FALSE FALSE FALSE
## 352 352 1.8662 -0.8991583488 FALSE FALSE FALSE
## 353 353 2.0048 -0.8333810587 FALSE FALSE FALSE
## 354 354 1.8956 -0.8852055903 FALSE FALSE FALSE
## 355 355 1.9879 -0.8414015219 FALSE FALSE FALSE
## 356 356 2.0720 -0.8014890392 FALSE FALSE FALSE
## 357 357 2.1980 -0.7416915028 FALSE FALSE FALSE
## 358 358 2.1850 -0.7478610899 FALSE FALSE FALSE
## 359 359 2.3236 -0.6820837997 FALSE FALSE FALSE
## 360 360 2.3552 -0.6670869573 FALSE FALSE FALSE
## 361 361 2.4527 -0.6208150540 FALSE FALSE FALSE
## 362 362 2.4961 -0.6002181248 FALSE FALSE FALSE
## 363 363 2.4358 -0.6288355173 FALSE FALSE FALSE
## 364 364 2.5806 -0.5601158087 FALSE FALSE FALSE
## 365 365 2.7792 -0.4658635012 FALSE FALSE FALSE
## 366 366 2.7175 -0.4951453107 FALSE FALSE FALSE
## 367 367 2.7344 -0.4871248475 FALSE FALSE FALSE
## 368 368 2.9634 -0.3784451978 FALSE FALSE FALSE
## 369 369 3.0665 -0.3295156263 FALSE FALSE FALSE
## 370 370 2.8715 -0.4220594328 FALSE FALSE FALSE
## 371 371 2.9084 -0.4045472971 FALSE FALSE FALSE
## 372 372 2.8237 -0.4447445300 FALSE FALSE FALSE
## 373 373 3.0334 -0.3452243442 FALSE FALSE FALSE
## 374 374 3.0993 -0.3139492835 FALSE FALSE FALSE
## 375 375 2.8965 -0.4101948422 FALSE FALSE FALSE
## 376 376 2.5329 -0.5827534475 FALSE FALSE FALSE
## 377 377 2.4298 -0.6316830190 FALSE FALSE FALSE
## 378 378 2.2060 -0.7378948338 FALSE FALSE FALSE
## 379 379 2.3053 -0.6907686800 FALSE FALSE FALSE
## 380 380 2.1007 -0.7878684893 FALSE FALSE FALSE
## 381 381 2.1705 -0.7547425524 FALSE FALSE FALSE
## 382 382 1.9512 -0.8588187409 FALSE FALSE FALSE
## 383 383 3.4242 -0.1597570644 FALSE FALSE FALSE
## 384 384 3.3317 -0.2036560496 FALSE FALSE FALSE
## 385 385 3.4106 -0.1662114017 FALSE FALSE FALSE
## 386 386 4.0983 0.1601597557 FALSE FALSE FALSE
## 387 387 3.7240 -0.0174768943 FALSE FALSE FALSE
## 388 388 3.9917 0.1095691415 FALSE FALSE FALSE
## 389 389 3.1523 -0.2887963515 FALSE FALSE FALSE
## 390 390 1.8209 -0.9206569869 FALSE FALSE FALSE
## 391 391 1.7554 -0.9517422142 FALSE FALSE FALSE
## 392 392 1.8226 -0.9198501948 FALSE FALSE FALSE
## 393 393 1.8681 -0.8982566399 FALSE FALSE FALSE
## 394 394 2.1099 -0.7835023199 FALSE FALSE FALSE
## 395 395 2.3817 -0.6545104912 FALSE FALSE FALSE
## 396 396 2.7986 -0.4566565789 FALSE FALSE FALSE
## 397 397 2.7986 -0.4566565789 FALSE FALSE FALSE
## 398 398 2.4091 -0.6415069000 FALSE FALSE FALSE
## 399 399 2.3999 -0.6458730693 FALSE FALSE FALSE
## 400 400 2.4982 -0.5992214992 FALSE FALSE FALSE
## 401 401 2.4786 -0.6085233382 FALSE FALSE FALSE
## 402 402 2.2875 -0.6992162685 FALSE FALSE FALSE
## 403 403 2.3889 -0.6510934892 FALSE FALSE FALSE
## 404 404 2.5050 -0.5959943306 FALSE FALSE FALSE
## Z_robusto outlier_MAD
## 1 0.533351092 FALSE
## 2 1.009388787 FALSE
## 3 1.009388787 FALSE
## 4 1.603743911 FALSE
## 5 1.603743911 FALSE
## 6 1.543119797 FALSE
## 7 1.614544447 FALSE
## 8 1.891342103 FALSE
## 9 1.758153584 FALSE
## 10 1.693024723 FALSE
## 11 0.868493352 FALSE
## 12 0.735196285 FALSE
## 13 0.624477222 FALSE
## 14 0.374056754 FALSE
## 15 0.491180155 FALSE
## 16 0.536173342 FALSE
## 17 0.701383551 FALSE
## 18 0.731234279 FALSE
## 19 0.730582990 FALSE
## 20 0.614219427 FALSE
## 21 0.610962984 FALSE
## 22 0.579484035 FALSE
## 23 0.369009268 FALSE
## 24 0.354138178 FALSE
## 25 0.137313347 FALSE
## 26 0.146865580 FALSE
## 27 0.401627972 FALSE
## 28 1.418506577 FALSE
## 29 1.418506577 FALSE
## 30 1.081736096 FALSE
## 31 1.081736096 FALSE
## 32 1.617583794 FALSE
## 33 2.012156139 FALSE
## 34 2.286240093 FALSE
## 35 3.033539491 FALSE
## 36 3.300025077 FALSE
## 37 2.831748572 FALSE
## 38 2.554950915 FALSE
## 39 2.014218553 FALSE
## 40 2.235113937 FALSE
## 41 3.319183817 FALSE
## 42 1.901871269 FALSE
## 43 1.901871269 FALSE
## 44 1.840270222 FALSE
## 45 1.840270222 FALSE
## 46 1.183174295 FALSE
## 47 1.183174295 FALSE
## 48 1.183174295 FALSE
## 49 0.621003683 FALSE
## 50 0.757285823 FALSE
## 51 0.512835501 FALSE
## 52 0.533622462 FALSE
## 53 0.757285823 FALSE
## 54 1.244721068 FALSE
## 55 1.244721068 FALSE
## 56 1.244721068 FALSE
## 57 0.907516394 FALSE
## 58 0.722061965 FALSE
## 59 0.716363189 FALSE
## 60 0.347516744 FALSE
## 61 0.170637614 FALSE
## 62 0.166729882 FALSE
## 63 -0.008141108 FALSE
## 64 -0.008249656 FALSE
## 65 0.303174845 FALSE
## 66 0.303174845 FALSE
## 67 0.275549353 FALSE
## 68 0.210529041 FALSE
## 69 0.210529041 FALSE
## 70 0.210529041 FALSE
## 71 0.210529041 FALSE
## 72 0.210529041 FALSE
## 73 -0.178832996 FALSE
## 74 -0.136336414 FALSE
## 75 -0.213079921 FALSE
## 76 -0.213079921 FALSE
## 77 -0.372482807 FALSE
## 78 -0.542957599 FALSE
## 79 -0.534545121 FALSE
## 80 -0.178832996 FALSE
## 81 -0.233269868 FALSE
## 82 -0.409172065 FALSE
## 83 -0.303554763 FALSE
## 84 -0.461763620 FALSE
## 85 -0.349633432 FALSE
## 86 -0.204721718 FALSE
## 87 -0.195332307 FALSE
## 88 -0.188927969 FALSE
## 89 -0.461112331 FALSE
## 90 -0.493839583 FALSE
## 91 -0.553812409 FALSE
## 92 -0.631152930 FALSE
## 93 -0.597557293 FALSE
## 94 -0.604830016 FALSE
## 95 -0.715874723 FALSE
## 96 -0.702740403 FALSE
## 97 -0.611885643 FALSE
## 98 -0.536661809 FALSE
## 99 -0.453893882 FALSE
## 100 -0.423283318 FALSE
## 101 -0.345942796 FALSE
## 102 -0.413188345 FALSE
## 103 -0.540895185 FALSE
## 104 -0.618941269 FALSE
## 105 -0.682496182 FALSE
## 106 -0.780840761 FALSE
## 107 -0.780786487 FALSE
## 108 -0.811614147 FALSE
## 109 -0.969171715 FALSE
## 110 -0.920216522 FALSE
## 111 -0.955983121 FALSE
## 112 -0.916254516 FALSE
## 113 -0.863337317 FALSE
## 114 -0.893622237 FALSE
## 115 -0.858398379 FALSE
## 116 -0.808303430 FALSE
## 117 -0.822631780 FALSE
## 118 -0.812536806 FALSE
## 119 -0.805481180 FALSE
## 120 -0.736987328 FALSE
## 121 -0.739104016 FALSE
## 122 -0.744042955 FALSE
## 123 -0.667570818 FALSE
## 124 -0.732699678 FALSE
## 125 -0.728032110 FALSE
## 126 -0.710393044 FALSE
## 127 -0.616770307 FALSE
## 128 -0.578887020 FALSE
## 129 -0.513052597 FALSE
## 130 -0.371940066 FALSE
## 131 -0.447163900 FALSE
## 132 -0.576064769 FALSE
## 133 -0.369823378 FALSE
## 134 -0.546702508 FALSE
## 135 -0.369823378 FALSE
## 136 -0.390013325 FALSE
## 137 -0.277448945 FALSE
## 138 -0.250203372 FALSE
## 139 -0.220027000 FALSE
## 140 0.013568513 FALSE
## 141 0.242930649 FALSE
## 142 -0.104206176 FALSE
## 143 -0.151044682 FALSE
## 144 -0.198805846 FALSE
## 145 -0.141275353 FALSE
## 146 -0.064803216 FALSE
## 147 0.093568463 FALSE
## 148 0.369769104 FALSE
## 149 0.792075489 FALSE
## 150 0.792075489 FALSE
## 151 1.830392345 FALSE
## 152 1.830392345 FALSE
## 153 1.830392345 FALSE
## 154 1.689171266 FALSE
## 155 1.689171266 FALSE
## 156 1.379157891 FALSE
## 157 2.280432769 FALSE
## 158 2.280432769 FALSE
## 159 2.280432769 FALSE
## 160 2.467352598 FALSE
## 161 2.467352598 FALSE
## 162 1.716525387 FALSE
## 163 1.716525387 FALSE
## 164 1.091288328 FALSE
## 165 0.677123051 FALSE
## 166 0.614327975 FALSE
## 167 0.416661884 FALSE
## 168 0.417801639 FALSE
## 169 0.295956396 FALSE
## 170 0.454870815 FALSE
## 171 0.260623989 FALSE
## 172 0.454870815 FALSE
## 173 0.002605154 FALSE
## 174 -0.118317430 FALSE
## 175 0.138941569 FALSE
## 176 -0.133297067 FALSE
## 177 -0.032184512 FALSE
## 178 0.089443635 FALSE
## 179 0.089443635 FALSE
## 180 -0.115549453 FALSE
## 181 0.058833070 FALSE
## 182 0.058833070 FALSE
## 183 0.145345907 FALSE
## 184 0.306214192 FALSE
## 185 0.306214192 FALSE
## 186 0.396797581 FALSE
## 187 0.295576478 FALSE
## 188 0.295576478 FALSE
## 189 0.295576478 FALSE
## 190 0.564830041 FALSE
## 191 0.564830041 FALSE
## 192 1.673051873 FALSE
## 193 1.752400534 FALSE
## 194 1.752400534 FALSE
## 195 2.131993242 FALSE
## 196 2.131993242 FALSE
## 197 2.630988860 FALSE
## 198 2.685588554 FALSE
## 199 2.685588554 FALSE
## 200 2.561300979 FALSE
## 201 2.561300979 FALSE
## 202 2.328031111 FALSE
## 203 3.147569270 FALSE
## 204 3.317772692 FALSE
## 205 3.317772692 FALSE
## 206 1.752400534 FALSE
## 207 -0.708981918 FALSE
## 208 -0.595169235 FALSE
## 209 -0.540135348 FALSE
## 210 -0.525101436 FALSE
## 211 -0.444395923 FALSE
## 212 -0.528846346 FALSE
## 213 -0.127706840 FALSE
## 214 0.439728355 FALSE
## 215 0.717340122 FALSE
## 216 0.717340122 FALSE
## 217 0.439728355 FALSE
## 218 0.911749770 FALSE
## 219 0.952780952 FALSE
## 220 0.560650939 FALSE
## 221 0.861546274 FALSE
## 222 1.160053550 FALSE
## 223 1.142251662 FALSE
## 224 1.507570294 FALSE
## 225 2.279510110 FALSE
## 226 2.279510110 FALSE
## 227 3.246619412 FALSE
## 228 2.284883241 FALSE
## 229 2.284883241 FALSE
## 230 2.284883241 FALSE
## 231 1.090582766 FALSE
## 232 1.090582766 FALSE
## 233 1.090582766 FALSE
## 234 1.548492928 FALSE
## 235 1.548492928 FALSE
## 236 1.743662412 FALSE
## 237 2.562006542 FALSE
## 238 1.294110454 FALSE
## 239 1.294110454 FALSE
## 240 0.496444738 FALSE
## 241 0.040922634 FALSE
## 242 0.114084054 FALSE
## 243 -0.002605154 FALSE
## 244 -0.253459815 FALSE
## 245 -0.148330979 FALSE
## 246 0.084396148 FALSE
## 247 0.268656549 FALSE
## 248 0.454653719 FALSE
## 249 0.501709321 FALSE
## 250 0.230773262 FALSE
## 251 0.485535654 FALSE
## 252 0.777801414 FALSE
## 253 0.777801414 FALSE
## 254 0.875874623 FALSE
## 255 0.875874623 FALSE
## 256 0.875874623 FALSE
## 257 1.252970724 FALSE
## 258 1.252970724 FALSE
## 259 1.252970724 FALSE
## 260 1.143717061 FALSE
## 261 1.143717061 FALSE
## 262 1.501383052 FALSE
## 263 1.917719292 FALSE
## 264 1.818777698 FALSE
## 265 1.818777698 FALSE
## 266 1.562007166 FALSE
## 267 1.152672279 FALSE
## 268 1.361030358 FALSE
## 269 0.925318283 FALSE
## 270 0.925318283 FALSE
## 271 2.133295819 FALSE
## 272 1.714842892 FALSE
## 273 1.424585271 FALSE
## 274 1.822576881 FALSE
## 275 2.662847727 FALSE
## 276 2.662847727 FALSE
## 277 2.945995448 FALSE
## 278 2.842169189 FALSE
## 279 3.085371209 FALSE
## 280 4.126456040 TRUE
## 281 4.895085140 TRUE
## 282 4.058830574 TRUE
## 283 4.058830574 TRUE
## 284 -0.534653669 FALSE
## 285 -0.326784057 FALSE
## 286 -0.208737997 FALSE
## 287 -0.319728430 FALSE
## 288 -0.440596740 FALSE
## 289 -0.544748642 FALSE
## 290 -0.652699728 FALSE
## 291 -0.652699728 FALSE
## 292 -0.810908585 FALSE
## 293 -0.735413381 FALSE
## 294 -0.866593760 FALSE
## 295 -0.963255844 FALSE
## 296 -1.033866383 FALSE
## 297 -1.051939642 FALSE
## 298 -1.073377892 FALSE
## 299 -1.049171665 FALSE
## 300 -1.069361612 FALSE
## 301 -0.972048240 FALSE
## 302 -0.956525862 FALSE
## 303 -0.949415961 FALSE
## 304 -0.934164953 FALSE
## 305 -0.917665642 FALSE
## 306 -0.828710473 FALSE
## 307 -0.854382099 FALSE
## 308 -0.890148698 FALSE
## 309 -0.862360384 FALSE
## 310 -0.823771535 FALSE
## 311 -0.509904702 FALSE
## 312 -0.725806874 FALSE
## 313 -0.714300775 FALSE
## 314 -0.719239714 FALSE
## 315 -0.749850278 FALSE
## 316 -0.776390289 FALSE
## 317 -0.872129713 FALSE
## 318 -0.824151453 FALSE
## 319 -0.796905880 FALSE
## 320 -0.762279036 FALSE
## 321 -0.814056480 FALSE
## 322 -0.912835251 FALSE
## 323 -0.896932954 FALSE
## 324 -0.919565233 FALSE
## 325 -0.857421446 FALSE
## 326 -0.843093097 FALSE
## 327 -0.823825809 FALSE
## 328 -0.786485262 FALSE
## 329 -0.690691563 FALSE
## 330 -0.698941219 FALSE
## 331 -0.792292586 FALSE
## 332 -0.708113534 FALSE
## 333 -0.712781102 FALSE
## 334 -0.677557243 FALSE
## 335 -0.669036217 FALSE
## 336 -0.627462295 FALSE
## 337 -0.589144815 FALSE
## 338 -0.566512537 FALSE
## 339 -0.650040300 FALSE
## 340 -0.602279136 FALSE
## 341 -0.675386281 FALSE
## 342 -0.618235706 FALSE
## 343 -0.572374134 FALSE
## 344 -0.553106846 FALSE
## 345 -0.492211362 FALSE
## 346 -0.429579108 FALSE
## 347 -0.480379619 FALSE
## 348 -0.533296818 FALSE
## 349 -0.599565433 FALSE
## 350 -0.698344205 FALSE
## 351 -0.700189522 FALSE
## 352 -0.673595238 FALSE
## 353 -0.598371404 FALSE
## 354 -0.657638667 FALSE
## 355 -0.607543718 FALSE
## 356 -0.561899242 FALSE
## 357 -0.493513939 FALSE
## 358 -0.500569565 FALSE
## 359 -0.425345732 FALSE
## 360 -0.408195132 FALSE
## 361 -0.355277933 FALSE
## 362 -0.331722995 FALSE
## 363 -0.364450247 FALSE
## 364 -0.285861423 FALSE
## 365 -0.178073159 FALSE
## 366 -0.211560248 FALSE
## 367 -0.202387933 FALSE
## 368 -0.078100358 FALSE
## 369 -0.022143813 FALSE
## 370 -0.127978210 FALSE
## 371 -0.107951086 FALSE
## 372 -0.153921207 FALSE
## 373 -0.040108523 FALSE
## 374 -0.004341924 FALSE
## 375 -0.114409698 FALSE
## 376 -0.311750145 FALSE
## 377 -0.367706690 FALSE
## 378 -0.489172015 FALSE
## 379 -0.435277883 FALSE
## 380 -0.546322590 FALSE
## 381 -0.508439303 FALSE
## 382 -0.627462295 FALSE
## 383 0.171994465 FALSE
## 384 0.121790969 FALSE
## 385 0.164613194 FALSE
## 386 0.537855838 FALSE
## 387 0.334708068 FALSE
## 388 0.479999700 FALSE
## 389 0.024423323 FALSE
## 390 -0.698181382 FALSE
## 391 -0.733730885 FALSE
## 392 -0.697258724 FALSE
## 393 -0.672564031 FALSE
## 394 -0.541329377 FALSE
## 395 -0.393812509 FALSE
## 396 -0.167543993 FALSE
## 397 -0.167543993 FALSE
## 398 -0.378941419 FALSE
## 399 -0.383934631 FALSE
## 400 -0.330583240 FALSE
## 401 -0.341220954 FALSE
## 402 -0.444938664 FALSE
## 403 -0.389904777 FALSE
## 404 -0.326892605 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 280 10.7103 3.298107 TRUE TRUE 4.126456 TRUE
## 2 281 12.1265 3.970212 TRUE TRUE 4.895085 TRUE
## 3 282 10.5857 3.238974 TRUE TRUE 4.058831 TRUE
## 4 283 10.5857 3.238974 TRUE TRUE 4.058831 TRUE
## metodos_evaluados numero_metodos
## 1 3 3
## 2 3 3
## 3 3 3
## 4 3 3
##
## ===== AL MENOS DOS MÉTODOS =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 280 10.7103 3.298107 TRUE TRUE 4.126456 TRUE
## 2 281 12.1265 3.970212 TRUE TRUE 4.895085 TRUE
## 3 282 10.5857 3.238974 TRUE TRUE 4.058831 TRUE
## 4 283 10.5857 3.238974 TRUE TRUE 4.058831 TRUE
## metodos_evaluados numero_metodos
## 1 3 3
## 2 3 3
## 3 3 3
## 4 3 3
##
## ===== LOS TRES MÉTODOS =====
## ID Valor Z_score Z_robusto
## 1 280 10.7103 3.298107 4.126456
## 2 281 12.1265 3.970212 4.895085
## 3 282 10.5857 3.238974 4.058831
## 4 283 10.5857 3.238974 4.058831
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## dis Z-score 404 4 0.99
## dis IQR 404 4 0.99
## dis Tukey extremo 404 0 0.00
## dis MAD 404 4 0.99## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 280 10.7103 3.298107 4.126456 3 3
## 2 281 12.1265 3.970212 4.895085 3 3
## 3 282 10.5857 3.238974 4.058831 3 3
## 4 283 10.5857 3.238974 4.058831 3 3
## clasificacion
## 1 Outlier según 3 métodos
## 2 Outlier según 3 métodos
## 3 Outlier según 3 métodos
## 4 Outlier según 3 métodos
##
## ========================================
## VARIABLE: rad
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -16.66963
## Límite superior = 35.77854
## Número de outliers = 0
## [1] ID Valor Z_score
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO IQR ================
## Q1 = 4
## Q3 = 24
## IQR = 20
## Límite inferior = -26
## Límite superior = 54
## Número de outliers = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -56
## Límite superior = 84
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 5
## MAD = 2
## MAD ajustada = 2.9652
## Número de outliers = 106
## ID Valor Z_robusto
## 1 284 24 6.407662
## 2 285 24 6.407662
## 3 286 24 6.407662
## 4 287 24 6.407662
## 5 288 24 6.407662
## 6 289 24 6.407662
## 7 290 24 6.407662
## 8 291 24 6.407662
## 9 292 24 6.407662
## 10 293 24 6.407662
## 11 294 24 6.407662
## 12 295 24 6.407662
## 13 296 24 6.407662
## 14 297 24 6.407662
## 15 298 24 6.407662
## 16 299 24 6.407662
## 17 300 24 6.407662
## 18 301 24 6.407662
## 19 302 24 6.407662
## 20 303 24 6.407662
## 21 304 24 6.407662
## 22 305 24 6.407662
## 23 306 24 6.407662
## 24 307 24 6.407662
## 25 308 24 6.407662
## 26 309 24 6.407662
## 27 310 24 6.407662
## 28 311 24 6.407662
## 29 312 24 6.407662
## 30 313 24 6.407662
## 31 314 24 6.407662
## 32 315 24 6.407662
## 33 316 24 6.407662
## 34 317 24 6.407662
## 35 318 24 6.407662
## 36 319 24 6.407662
## 37 320 24 6.407662
## 38 321 24 6.407662
## 39 322 24 6.407662
## 40 323 24 6.407662
## 41 324 24 6.407662
## 42 325 24 6.407662
## 43 326 24 6.407662
## 44 327 24 6.407662
## 45 328 24 6.407662
## 46 329 24 6.407662
## 47 330 24 6.407662
## 48 331 24 6.407662
## 49 332 24 6.407662
## 50 333 24 6.407662
## 51 334 24 6.407662
## 52 335 24 6.407662
## 53 336 24 6.407662
## 54 337 24 6.407662
## 55 338 24 6.407662
## 56 339 24 6.407662
## 57 340 24 6.407662
## 58 341 24 6.407662
## 59 342 24 6.407662
## 60 343 24 6.407662
## 61 344 24 6.407662
## 62 345 24 6.407662
## 63 346 24 6.407662
## 64 347 24 6.407662
## 65 348 24 6.407662
## 66 349 24 6.407662
## 67 350 24 6.407662
## 68 351 24 6.407662
## 69 352 24 6.407662
## 70 353 24 6.407662
## 71 354 24 6.407662
## 72 355 24 6.407662
## 73 356 24 6.407662
## 74 357 24 6.407662
## 75 358 24 6.407662
## 76 359 24 6.407662
## 77 360 24 6.407662
## 78 361 24 6.407662
## 79 362 24 6.407662
## 80 363 24 6.407662
## 81 364 24 6.407662
## 82 365 24 6.407662
## 83 366 24 6.407662
## 84 367 24 6.407662
## 85 368 24 6.407662
## 86 369 24 6.407662
## 87 370 24 6.407662
## 88 371 24 6.407662
## 89 372 24 6.407662
## 90 373 24 6.407662
## 91 374 24 6.407662
## 92 375 24 6.407662
## 93 376 24 6.407662
## 94 377 24 6.407662
## 95 378 24 6.407662
## 96 379 24 6.407662
## 97 380 24 6.407662
## 98 381 24 6.407662
## 99 382 24 6.407662
## 100 383 24 6.407662
## 101 384 24 6.407662
## 102 385 24 6.407662
## 103 386 24 6.407662
## 104 387 24 6.407662
## 105 388 24 6.407662
## 106 389 24 6.407662
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 2 2 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 3 3 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 4 4 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 5 5 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 6 6 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 7 7 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 8 8 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 9 9 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 10 10 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 11 11 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 12 12 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 13 13 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 14 14 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 15 15 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 16 16 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 17 17 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 18 18 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 19 19 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 20 20 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 21 21 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 22 22 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 23 23 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 24 24 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 25 25 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 26 26 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 27 27 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 28 28 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 29 29 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 30 30 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 31 31 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 32 32 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 33 33 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 34 34 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 35 35 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 36 36 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 37 37 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 38 38 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 39 39 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 40 40 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 41 41 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 42 42 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 43 43 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 44 44 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 45 45 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 46 46 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 47 47 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 48 48 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 49 49 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 50 50 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 51 51 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 52 52 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 53 53 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 54 54 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 55 55 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 56 56 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 57 57 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 58 58 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 59 59 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 60 60 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 61 61 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 62 62 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 63 63 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 64 64 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 65 65 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 66 66 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 67 67 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 68 68 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 69 69 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 70 70 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 71 71 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 72 72 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 73 73 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 74 74 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 75 75 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 76 76 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 77 77 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 78 78 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 79 79 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 80 80 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 81 81 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 82 82 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 83 83 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 84 84 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 85 85 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 86 86 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 87 87 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 88 88 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 89 89 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 90 90 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 91 91 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 92 92 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 93 93 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 94 94 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 95 95 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 96 96 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 97 97 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 98 98 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 99 99 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 100 100 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 101 101 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 102 102 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 103 103 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 104 104 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 105 105 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 106 106 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 107 107 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 108 108 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 109 109 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 110 110 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 111 111 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 112 112 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 113 113 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 114 114 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 115 115 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 116 116 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 117 117 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 118 118 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 119 119 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 120 120 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 121 121 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 122 122 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 123 123 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 124 124 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 125 125 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 126 126 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 127 127 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 128 128 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 129 129 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 130 130 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 131 131 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 132 132 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 133 133 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 134 134 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 135 135 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 136 136 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 137 137 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 138 138 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 139 139 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 140 140 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 141 141 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 142 142 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 143 143 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 144 144 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 145 145 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 146 146 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 147 147 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 148 148 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 149 149 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 150 150 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 151 151 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 152 152 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 153 153 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 154 154 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 155 155 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 156 156 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 157 157 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 158 158 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 159 159 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 160 160 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 161 161 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 162 162 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 163 163 2 -0.8642194 FALSE FALSE FALSE -1.0117361
## 164 164 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 165 165 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 166 166 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 167 167 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 168 168 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 169 169 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 170 170 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 171 171 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 172 172 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 173 173 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 174 174 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 175 175 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 176 176 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 177 177 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 178 178 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 179 179 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 180 180 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 181 181 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 182 182 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 183 183 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 184 184 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 185 185 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 186 186 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 187 187 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 188 188 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 189 189 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 190 190 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 191 191 8 -0.1778276 FALSE FALSE FALSE 1.0117361
## 192 192 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 193 193 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 194 194 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 195 195 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 196 196 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 197 197 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 198 198 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 199 199 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 200 200 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 201 201 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 202 202 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 203 203 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 204 204 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 205 205 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 206 206 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 207 207 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 208 208 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 209 209 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 210 210 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 211 211 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 212 212 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 213 213 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 214 214 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 215 215 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 216 216 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 217 217 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 218 218 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 219 219 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 220 220 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 221 221 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 222 222 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 223 223 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 224 224 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 225 225 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 226 226 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 227 227 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 228 228 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 229 229 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 230 230 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 231 231 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 232 232 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 233 233 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 234 234 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 235 235 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 236 236 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 237 237 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 238 238 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 239 239 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 240 240 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 241 241 7 -0.2922262 FALSE FALSE FALSE 0.6744908
## 242 242 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 243 243 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 244 244 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 245 245 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 246 246 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 247 247 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 248 248 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 249 249 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 250 250 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 251 251 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 252 252 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 253 253 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 254 254 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 255 255 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 256 256 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 257 257 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 258 258 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 259 259 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 260 260 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 261 261 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 262 262 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 263 263 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 264 264 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 265 265 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 266 266 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 267 267 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 268 268 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 269 269 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 270 270 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 271 271 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 272 272 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 273 273 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 274 274 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 275 275 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 276 276 3 -0.7498208 FALSE FALSE FALSE -0.6744908
## 277 277 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 278 278 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 279 279 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 280 280 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 281 281 5 -0.5210235 FALSE FALSE FALSE 0.0000000
## 282 282 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 283 283 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 284 284 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 285 285 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 286 286 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 287 287 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 288 288 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 289 289 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 290 290 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 291 291 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 292 292 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 293 293 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 294 294 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 295 295 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 296 296 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 297 297 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 298 298 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 299 299 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 300 300 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 301 301 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 302 302 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 303 303 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 304 304 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 305 305 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 306 306 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 307 307 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 308 308 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 309 309 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 310 310 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 311 311 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 312 312 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 313 313 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 314 314 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 315 315 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 316 316 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 317 317 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 318 318 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 319 319 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 320 320 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 321 321 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 322 322 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 323 323 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 324 324 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 325 325 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 326 326 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 327 327 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 328 328 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 329 329 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 330 330 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 331 331 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 332 332 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 333 333 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 334 334 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 335 335 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 336 336 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 337 337 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 338 338 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 339 339 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 340 340 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 341 341 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 342 342 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 343 343 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 344 344 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 345 345 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 346 346 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 347 347 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 348 348 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 349 349 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 350 350 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 351 351 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 352 352 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 353 353 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 354 354 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 355 355 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 356 356 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 357 357 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 358 358 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 359 359 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 360 360 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 361 361 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 362 362 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 363 363 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 364 364 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 365 365 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 366 366 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 367 367 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 368 368 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 369 369 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 370 370 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 371 371 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 372 372 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 373 373 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 374 374 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 375 375 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 376 376 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 377 377 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 378 378 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 379 379 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 380 380 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 381 381 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 382 382 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 383 383 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 384 384 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 385 385 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 386 386 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 387 387 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 388 388 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 389 389 24 1.6525507 FALSE FALSE FALSE 6.4076622
## 390 390 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 391 391 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 392 392 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 393 393 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 394 394 4 -0.6354222 FALSE FALSE FALSE -0.3372454
## 395 395 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 396 396 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 397 397 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 398 398 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 399 399 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 400 400 6 -0.4066249 FALSE FALSE FALSE 0.3372454
## 401 401 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 402 402 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 403 403 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## 404 404 1 -0.9786181 FALSE FALSE FALSE -1.3489815
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 FALSE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 TRUE
## 285 TRUE
## 286 TRUE
## 287 TRUE
## 288 TRUE
## 289 TRUE
## 290 TRUE
## 291 TRUE
## 292 TRUE
## 293 TRUE
## 294 TRUE
## 295 TRUE
## 296 TRUE
## 297 TRUE
## 298 TRUE
## 299 TRUE
## 300 TRUE
## 301 TRUE
## 302 TRUE
## 303 TRUE
## 304 TRUE
## 305 TRUE
## 306 TRUE
## 307 TRUE
## 308 TRUE
## 309 TRUE
## 310 TRUE
## 311 TRUE
## 312 TRUE
## 313 TRUE
## 314 TRUE
## 315 TRUE
## 316 TRUE
## 317 TRUE
## 318 TRUE
## 319 TRUE
## 320 TRUE
## 321 TRUE
## 322 TRUE
## 323 TRUE
## 324 TRUE
## 325 TRUE
## 326 TRUE
## 327 TRUE
## 328 TRUE
## 329 TRUE
## 330 TRUE
## 331 TRUE
## 332 TRUE
## 333 TRUE
## 334 TRUE
## 335 TRUE
## 336 TRUE
## 337 TRUE
## 338 TRUE
## 339 TRUE
## 340 TRUE
## 341 TRUE
## 342 TRUE
## 343 TRUE
## 344 TRUE
## 345 TRUE
## 346 TRUE
## 347 TRUE
## 348 TRUE
## 349 TRUE
## 350 TRUE
## 351 TRUE
## 352 TRUE
## 353 TRUE
## 354 TRUE
## 355 TRUE
## 356 TRUE
## 357 TRUE
## 358 TRUE
## 359 TRUE
## 360 TRUE
## 361 TRUE
## 362 TRUE
## 363 TRUE
## 364 TRUE
## 365 TRUE
## 366 TRUE
## 367 TRUE
## 368 TRUE
## 369 TRUE
## 370 TRUE
## 371 TRUE
## 372 TRUE
## 373 TRUE
## 374 TRUE
## 375 TRUE
## 376 TRUE
## 377 TRUE
## 378 TRUE
## 379 TRUE
## 380 TRUE
## 381 TRUE
## 382 TRUE
## 383 TRUE
## 384 TRUE
## 385 TRUE
## 386 TRUE
## 387 TRUE
## 388 TRUE
## 389 TRUE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 284 24 1.652551 FALSE FALSE 6.407662 TRUE
## 2 285 24 1.652551 FALSE FALSE 6.407662 TRUE
## 3 286 24 1.652551 FALSE FALSE 6.407662 TRUE
## 4 287 24 1.652551 FALSE FALSE 6.407662 TRUE
## 5 288 24 1.652551 FALSE FALSE 6.407662 TRUE
## 6 289 24 1.652551 FALSE FALSE 6.407662 TRUE
## 7 290 24 1.652551 FALSE FALSE 6.407662 TRUE
## 8 291 24 1.652551 FALSE FALSE 6.407662 TRUE
## 9 292 24 1.652551 FALSE FALSE 6.407662 TRUE
## 10 293 24 1.652551 FALSE FALSE 6.407662 TRUE
## 11 294 24 1.652551 FALSE FALSE 6.407662 TRUE
## 12 295 24 1.652551 FALSE FALSE 6.407662 TRUE
## 13 296 24 1.652551 FALSE FALSE 6.407662 TRUE
## 14 297 24 1.652551 FALSE FALSE 6.407662 TRUE
## 15 298 24 1.652551 FALSE FALSE 6.407662 TRUE
## 16 299 24 1.652551 FALSE FALSE 6.407662 TRUE
## 17 300 24 1.652551 FALSE FALSE 6.407662 TRUE
## 18 301 24 1.652551 FALSE FALSE 6.407662 TRUE
## 19 302 24 1.652551 FALSE FALSE 6.407662 TRUE
## 20 303 24 1.652551 FALSE FALSE 6.407662 TRUE
## 21 304 24 1.652551 FALSE FALSE 6.407662 TRUE
## 22 305 24 1.652551 FALSE FALSE 6.407662 TRUE
## 23 306 24 1.652551 FALSE FALSE 6.407662 TRUE
## 24 307 24 1.652551 FALSE FALSE 6.407662 TRUE
## 25 308 24 1.652551 FALSE FALSE 6.407662 TRUE
## 26 309 24 1.652551 FALSE FALSE 6.407662 TRUE
## 27 310 24 1.652551 FALSE FALSE 6.407662 TRUE
## 28 311 24 1.652551 FALSE FALSE 6.407662 TRUE
## 29 312 24 1.652551 FALSE FALSE 6.407662 TRUE
## 30 313 24 1.652551 FALSE FALSE 6.407662 TRUE
## 31 314 24 1.652551 FALSE FALSE 6.407662 TRUE
## 32 315 24 1.652551 FALSE FALSE 6.407662 TRUE
## 33 316 24 1.652551 FALSE FALSE 6.407662 TRUE
## 34 317 24 1.652551 FALSE FALSE 6.407662 TRUE
## 35 318 24 1.652551 FALSE FALSE 6.407662 TRUE
## 36 319 24 1.652551 FALSE FALSE 6.407662 TRUE
## 37 320 24 1.652551 FALSE FALSE 6.407662 TRUE
## 38 321 24 1.652551 FALSE FALSE 6.407662 TRUE
## 39 322 24 1.652551 FALSE FALSE 6.407662 TRUE
## 40 323 24 1.652551 FALSE FALSE 6.407662 TRUE
## 41 324 24 1.652551 FALSE FALSE 6.407662 TRUE
## 42 325 24 1.652551 FALSE FALSE 6.407662 TRUE
## 43 326 24 1.652551 FALSE FALSE 6.407662 TRUE
## 44 327 24 1.652551 FALSE FALSE 6.407662 TRUE
## 45 328 24 1.652551 FALSE FALSE 6.407662 TRUE
## 46 329 24 1.652551 FALSE FALSE 6.407662 TRUE
## 47 330 24 1.652551 FALSE FALSE 6.407662 TRUE
## 48 331 24 1.652551 FALSE FALSE 6.407662 TRUE
## 49 332 24 1.652551 FALSE FALSE 6.407662 TRUE
## 50 333 24 1.652551 FALSE FALSE 6.407662 TRUE
## 51 334 24 1.652551 FALSE FALSE 6.407662 TRUE
## 52 335 24 1.652551 FALSE FALSE 6.407662 TRUE
## 53 336 24 1.652551 FALSE FALSE 6.407662 TRUE
## 54 337 24 1.652551 FALSE FALSE 6.407662 TRUE
## 55 338 24 1.652551 FALSE FALSE 6.407662 TRUE
## 56 339 24 1.652551 FALSE FALSE 6.407662 TRUE
## 57 340 24 1.652551 FALSE FALSE 6.407662 TRUE
## 58 341 24 1.652551 FALSE FALSE 6.407662 TRUE
## 59 342 24 1.652551 FALSE FALSE 6.407662 TRUE
## 60 343 24 1.652551 FALSE FALSE 6.407662 TRUE
## 61 344 24 1.652551 FALSE FALSE 6.407662 TRUE
## 62 345 24 1.652551 FALSE FALSE 6.407662 TRUE
## 63 346 24 1.652551 FALSE FALSE 6.407662 TRUE
## 64 347 24 1.652551 FALSE FALSE 6.407662 TRUE
## 65 348 24 1.652551 FALSE FALSE 6.407662 TRUE
## 66 349 24 1.652551 FALSE FALSE 6.407662 TRUE
## 67 350 24 1.652551 FALSE FALSE 6.407662 TRUE
## 68 351 24 1.652551 FALSE FALSE 6.407662 TRUE
## 69 352 24 1.652551 FALSE FALSE 6.407662 TRUE
## 70 353 24 1.652551 FALSE FALSE 6.407662 TRUE
## 71 354 24 1.652551 FALSE FALSE 6.407662 TRUE
## 72 355 24 1.652551 FALSE FALSE 6.407662 TRUE
## 73 356 24 1.652551 FALSE FALSE 6.407662 TRUE
## 74 357 24 1.652551 FALSE FALSE 6.407662 TRUE
## 75 358 24 1.652551 FALSE FALSE 6.407662 TRUE
## 76 359 24 1.652551 FALSE FALSE 6.407662 TRUE
## 77 360 24 1.652551 FALSE FALSE 6.407662 TRUE
## 78 361 24 1.652551 FALSE FALSE 6.407662 TRUE
## 79 362 24 1.652551 FALSE FALSE 6.407662 TRUE
## 80 363 24 1.652551 FALSE FALSE 6.407662 TRUE
## 81 364 24 1.652551 FALSE FALSE 6.407662 TRUE
## 82 365 24 1.652551 FALSE FALSE 6.407662 TRUE
## 83 366 24 1.652551 FALSE FALSE 6.407662 TRUE
## 84 367 24 1.652551 FALSE FALSE 6.407662 TRUE
## 85 368 24 1.652551 FALSE FALSE 6.407662 TRUE
## 86 369 24 1.652551 FALSE FALSE 6.407662 TRUE
## 87 370 24 1.652551 FALSE FALSE 6.407662 TRUE
## 88 371 24 1.652551 FALSE FALSE 6.407662 TRUE
## 89 372 24 1.652551 FALSE FALSE 6.407662 TRUE
## 90 373 24 1.652551 FALSE FALSE 6.407662 TRUE
## 91 374 24 1.652551 FALSE FALSE 6.407662 TRUE
## 92 375 24 1.652551 FALSE FALSE 6.407662 TRUE
## 93 376 24 1.652551 FALSE FALSE 6.407662 TRUE
## 94 377 24 1.652551 FALSE FALSE 6.407662 TRUE
## 95 378 24 1.652551 FALSE FALSE 6.407662 TRUE
## 96 379 24 1.652551 FALSE FALSE 6.407662 TRUE
## 97 380 24 1.652551 FALSE FALSE 6.407662 TRUE
## 98 381 24 1.652551 FALSE FALSE 6.407662 TRUE
## 99 382 24 1.652551 FALSE FALSE 6.407662 TRUE
## 100 383 24 1.652551 FALSE FALSE 6.407662 TRUE
## 101 384 24 1.652551 FALSE FALSE 6.407662 TRUE
## 102 385 24 1.652551 FALSE FALSE 6.407662 TRUE
## 103 386 24 1.652551 FALSE FALSE 6.407662 TRUE
## 104 387 24 1.652551 FALSE FALSE 6.407662 TRUE
## 105 388 24 1.652551 FALSE FALSE 6.407662 TRUE
## 106 389 24 1.652551 FALSE FALSE 6.407662 TRUE
## metodos_evaluados numero_metodos
## 1 3 1
## 2 3 1
## 3 3 1
## 4 3 1
## 5 3 1
## 6 3 1
## 7 3 1
## 8 3 1
## 9 3 1
## 10 3 1
## 11 3 1
## 12 3 1
## 13 3 1
## 14 3 1
## 15 3 1
## 16 3 1
## 17 3 1
## 18 3 1
## 19 3 1
## 20 3 1
## 21 3 1
## 22 3 1
## 23 3 1
## 24 3 1
## 25 3 1
## 26 3 1
## 27 3 1
## 28 3 1
## 29 3 1
## 30 3 1
## 31 3 1
## 32 3 1
## 33 3 1
## 34 3 1
## 35 3 1
## 36 3 1
## 37 3 1
## 38 3 1
## 39 3 1
## 40 3 1
## 41 3 1
## 42 3 1
## 43 3 1
## 44 3 1
## 45 3 1
## 46 3 1
## 47 3 1
## 48 3 1
## 49 3 1
## 50 3 1
## 51 3 1
## 52 3 1
## 53 3 1
## 54 3 1
## 55 3 1
## 56 3 1
## 57 3 1
## 58 3 1
## 59 3 1
## 60 3 1
## 61 3 1
## 62 3 1
## 63 3 1
## 64 3 1
## 65 3 1
## 66 3 1
## 67 3 1
## 68 3 1
## 69 3 1
## 70 3 1
## 71 3 1
## 72 3 1
## 73 3 1
## 74 3 1
## 75 3 1
## 76 3 1
## 77 3 1
## 78 3 1
## 79 3 1
## 80 3 1
## 81 3 1
## 82 3 1
## 83 3 1
## 84 3 1
## 85 3 1
## 86 3 1
## 87 3 1
## 88 3 1
## 89 3 1
## 90 3 1
## 91 3 1
## 92 3 1
## 93 3 1
## 94 3 1
## 95 3 1
## 96 3 1
## 97 3 1
## 98 3 1
## 99 3 1
## 100 3 1
## 101 3 1
## 102 3 1
## 103 3 1
## 104 3 1
## 105 3 1
## 106 3 1
##
## ===== AL MENOS DOS MÉTODOS =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== LOS TRES MÉTODOS =====
## [1] ID Valor Z_score Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## rad Z-score 404 0 0.00
## rad IQR 404 0 0.00
## rad Tukey extremo 404 0 0.00
## rad MAD 404 106 26.24## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 284 24 1.652551 6.407662 3 1
## 2 285 24 1.652551 6.407662 3 1
## 3 286 24 1.652551 6.407662 3 1
## 4 287 24 1.652551 6.407662 3 1
## 5 288 24 1.652551 6.407662 3 1
## 6 289 24 1.652551 6.407662 3 1
## 7 290 24 1.652551 6.407662 3 1
## 8 291 24 1.652551 6.407662 3 1
## 9 292 24 1.652551 6.407662 3 1
## 10 293 24 1.652551 6.407662 3 1
## 11 294 24 1.652551 6.407662 3 1
## 12 295 24 1.652551 6.407662 3 1
## 13 296 24 1.652551 6.407662 3 1
## 14 297 24 1.652551 6.407662 3 1
## 15 298 24 1.652551 6.407662 3 1
## 16 299 24 1.652551 6.407662 3 1
## 17 300 24 1.652551 6.407662 3 1
## 18 301 24 1.652551 6.407662 3 1
## 19 302 24 1.652551 6.407662 3 1
## 20 303 24 1.652551 6.407662 3 1
## 21 304 24 1.652551 6.407662 3 1
## 22 305 24 1.652551 6.407662 3 1
## 23 306 24 1.652551 6.407662 3 1
## 24 307 24 1.652551 6.407662 3 1
## 25 308 24 1.652551 6.407662 3 1
## 26 309 24 1.652551 6.407662 3 1
## 27 310 24 1.652551 6.407662 3 1
## 28 311 24 1.652551 6.407662 3 1
## 29 312 24 1.652551 6.407662 3 1
## 30 313 24 1.652551 6.407662 3 1
## 31 314 24 1.652551 6.407662 3 1
## 32 315 24 1.652551 6.407662 3 1
## 33 316 24 1.652551 6.407662 3 1
## 34 317 24 1.652551 6.407662 3 1
## 35 318 24 1.652551 6.407662 3 1
## 36 319 24 1.652551 6.407662 3 1
## 37 320 24 1.652551 6.407662 3 1
## 38 321 24 1.652551 6.407662 3 1
## 39 322 24 1.652551 6.407662 3 1
## 40 323 24 1.652551 6.407662 3 1
## 41 324 24 1.652551 6.407662 3 1
## 42 325 24 1.652551 6.407662 3 1
## 43 326 24 1.652551 6.407662 3 1
## 44 327 24 1.652551 6.407662 3 1
## 45 328 24 1.652551 6.407662 3 1
## 46 329 24 1.652551 6.407662 3 1
## 47 330 24 1.652551 6.407662 3 1
## 48 331 24 1.652551 6.407662 3 1
## 49 332 24 1.652551 6.407662 3 1
## 50 333 24 1.652551 6.407662 3 1
## 51 334 24 1.652551 6.407662 3 1
## 52 335 24 1.652551 6.407662 3 1
## 53 336 24 1.652551 6.407662 3 1
## 54 337 24 1.652551 6.407662 3 1
## 55 338 24 1.652551 6.407662 3 1
## 56 339 24 1.652551 6.407662 3 1
## 57 340 24 1.652551 6.407662 3 1
## 58 341 24 1.652551 6.407662 3 1
## 59 342 24 1.652551 6.407662 3 1
## 60 343 24 1.652551 6.407662 3 1
## 61 344 24 1.652551 6.407662 3 1
## 62 345 24 1.652551 6.407662 3 1
## 63 346 24 1.652551 6.407662 3 1
## 64 347 24 1.652551 6.407662 3 1
## 65 348 24 1.652551 6.407662 3 1
## 66 349 24 1.652551 6.407662 3 1
## 67 350 24 1.652551 6.407662 3 1
## 68 351 24 1.652551 6.407662 3 1
## 69 352 24 1.652551 6.407662 3 1
## 70 353 24 1.652551 6.407662 3 1
## 71 354 24 1.652551 6.407662 3 1
## 72 355 24 1.652551 6.407662 3 1
## 73 356 24 1.652551 6.407662 3 1
## 74 357 24 1.652551 6.407662 3 1
## 75 358 24 1.652551 6.407662 3 1
## 76 359 24 1.652551 6.407662 3 1
## 77 360 24 1.652551 6.407662 3 1
## 78 361 24 1.652551 6.407662 3 1
## 79 362 24 1.652551 6.407662 3 1
## 80 363 24 1.652551 6.407662 3 1
## 81 364 24 1.652551 6.407662 3 1
## 82 365 24 1.652551 6.407662 3 1
## 83 366 24 1.652551 6.407662 3 1
## 84 367 24 1.652551 6.407662 3 1
## 85 368 24 1.652551 6.407662 3 1
## 86 369 24 1.652551 6.407662 3 1
## 87 370 24 1.652551 6.407662 3 1
## 88 371 24 1.652551 6.407662 3 1
## 89 372 24 1.652551 6.407662 3 1
## 90 373 24 1.652551 6.407662 3 1
## 91 374 24 1.652551 6.407662 3 1
## 92 375 24 1.652551 6.407662 3 1
## 93 376 24 1.652551 6.407662 3 1
## 94 377 24 1.652551 6.407662 3 1
## 95 378 24 1.652551 6.407662 3 1
## 96 379 24 1.652551 6.407662 3 1
## 97 380 24 1.652551 6.407662 3 1
## 98 381 24 1.652551 6.407662 3 1
## 99 382 24 1.652551 6.407662 3 1
## 100 383 24 1.652551 6.407662 3 1
## 101 384 24 1.652551 6.407662 3 1
## 102 385 24 1.652551 6.407662 3 1
## 103 386 24 1.652551 6.407662 3 1
## 104 387 24 1.652551 6.407662 3 1
## 105 388 24 1.652551 6.407662 3 1
## 106 389 24 1.652551 6.407662 3 1
## clasificacion
## 1 Outlier según 1 método
## 2 Outlier según 1 método
## 3 Outlier según 1 método
## 4 Outlier según 1 método
## 5 Outlier según 1 método
## 6 Outlier según 1 método
## 7 Outlier según 1 método
## 8 Outlier según 1 método
## 9 Outlier según 1 método
## 10 Outlier según 1 método
## 11 Outlier según 1 método
## 12 Outlier según 1 método
## 13 Outlier según 1 método
## 14 Outlier según 1 método
## 15 Outlier según 1 método
## 16 Outlier según 1 método
## 17 Outlier según 1 método
## 18 Outlier según 1 método
## 19 Outlier según 1 método
## 20 Outlier según 1 método
## 21 Outlier según 1 método
## 22 Outlier según 1 método
## 23 Outlier según 1 método
## 24 Outlier según 1 método
## 25 Outlier según 1 método
## 26 Outlier según 1 método
## 27 Outlier según 1 método
## 28 Outlier según 1 método
## 29 Outlier según 1 método
## 30 Outlier según 1 método
## 31 Outlier según 1 método
## 32 Outlier según 1 método
## 33 Outlier según 1 método
## 34 Outlier según 1 método
## 35 Outlier según 1 método
## 36 Outlier según 1 método
## 37 Outlier según 1 método
## 38 Outlier según 1 método
## 39 Outlier según 1 método
## 40 Outlier según 1 método
## 41 Outlier según 1 método
## 42 Outlier según 1 método
## 43 Outlier según 1 método
## 44 Outlier según 1 método
## 45 Outlier según 1 método
## 46 Outlier según 1 método
## 47 Outlier según 1 método
## 48 Outlier según 1 método
## 49 Outlier según 1 método
## 50 Outlier según 1 método
## 51 Outlier según 1 método
## 52 Outlier según 1 método
## 53 Outlier según 1 método
## 54 Outlier según 1 método
## 55 Outlier según 1 método
## 56 Outlier según 1 método
## 57 Outlier según 1 método
## 58 Outlier según 1 método
## 59 Outlier según 1 método
## 60 Outlier según 1 método
## 61 Outlier según 1 método
## 62 Outlier según 1 método
## 63 Outlier según 1 método
## 64 Outlier según 1 método
## 65 Outlier según 1 método
## 66 Outlier según 1 método
## 67 Outlier según 1 método
## 68 Outlier según 1 método
## 69 Outlier según 1 método
## 70 Outlier según 1 método
## 71 Outlier según 1 método
## 72 Outlier según 1 método
## 73 Outlier según 1 método
## 74 Outlier según 1 método
## 75 Outlier según 1 método
## 76 Outlier según 1 método
## 77 Outlier según 1 método
## 78 Outlier según 1 método
## 79 Outlier según 1 método
## 80 Outlier según 1 método
## 81 Outlier según 1 método
## 82 Outlier según 1 método
## 83 Outlier según 1 método
## 84 Outlier según 1 método
## 85 Outlier según 1 método
## 86 Outlier según 1 método
## 87 Outlier según 1 método
## 88 Outlier según 1 método
## 89 Outlier según 1 método
## 90 Outlier según 1 método
## 91 Outlier según 1 método
## 92 Outlier según 1 método
## 93 Outlier según 1 método
## 94 Outlier según 1 método
## 95 Outlier según 1 método
## 96 Outlier según 1 método
## 97 Outlier según 1 método
## 98 Outlier según 1 método
## 99 Outlier según 1 método
## 100 Outlier según 1 método
## 101 Outlier según 1 método
## 102 Outlier según 1 método
## 103 Outlier según 1 método
## 104 Outlier según 1 método
## 105 Outlier según 1 método
## 106 Outlier según 1 método
##
## ========================================
## VARIABLE: tax
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -90.13602
## Límite superior = 916.5271
## Número de outliers = 0
## [1] ID Valor Z_score
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO IQR ================
## Q1 = 284
## Q3 = 666
## IQR = 382
## Límite inferior = -289
## Límite superior = 1239
## Número de outliers = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -862
## Límite superior = 1812
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 346.5
## MAD = 79
## MAD ajustada = 117.1254
## Número de outliers = 0
## [1] ID Valor Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 296 -0.69851894 FALSE FALSE FALSE -0.43116181
## 2 2 242 -1.02037438 FALSE FALSE FALSE -0.89220613
## 3 3 242 -1.02037438 FALSE FALSE FALSE -0.89220613
## 4 4 222 -1.13958010 FALSE FALSE FALSE -1.06296329
## 5 5 222 -1.13958010 FALSE FALSE FALSE -1.06296329
## 6 6 311 -0.60911466 FALSE FALSE FALSE -0.30309395
## 7 7 311 -0.60911466 FALSE FALSE FALSE -0.30309395
## 8 8 311 -0.60911466 FALSE FALSE FALSE -0.30309395
## 9 9 311 -0.60911466 FALSE FALSE FALSE -0.30309395
## 10 10 311 -0.60911466 FALSE FALSE FALSE -0.30309395
## 11 11 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 12 12 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 13 13 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 14 14 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 15 15 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 16 16 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 17 17 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 18 18 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 19 19 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 20 20 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 21 21 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 22 22 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 23 23 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 24 24 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 25 25 279 -0.79984380 FALSE FALSE FALSE -0.57630540
## 26 26 279 -0.79984380 FALSE FALSE FALSE -0.57630540
## 27 27 279 -0.79984380 FALSE FALSE FALSE -0.57630540
## 28 28 233 -1.07401695 FALSE FALSE FALSE -0.96904685
## 29 29 233 -1.07401695 FALSE FALSE FALSE -0.96904685
## 30 30 233 -1.07401695 FALSE FALSE FALSE -0.96904685
## 31 31 233 -1.07401695 FALSE FALSE FALSE -0.96904685
## 32 32 233 -1.07401695 FALSE FALSE FALSE -0.96904685
## 33 33 243 -1.01441409 FALSE FALSE FALSE -0.88366827
## 34 34 469 0.33261051 FALSE FALSE FALSE 1.04588757
## 35 35 226 -1.11573895 FALSE FALSE FALSE -1.02881185
## 36 36 313 -0.59719408 FALSE FALSE FALSE -0.28601823
## 37 37 256 -0.93693038 FALSE FALSE FALSE -0.77267612
## 38 38 284 -0.77004237 FALSE FALSE FALSE -0.53361611
## 39 39 284 -0.77004237 FALSE FALSE FALSE -0.53361611
## 40 40 284 -0.77004237 FALSE FALSE FALSE -0.53361611
## 41 41 216 -1.17534181 FALSE FALSE FALSE -1.11419043
## 42 42 337 -0.45414722 FALSE FALSE FALSE -0.08110965
## 43 43 337 -0.45414722 FALSE FALSE FALSE -0.08110965
## 44 44 345 -0.40646494 FALSE FALSE FALSE -0.01280679
## 45 45 345 -0.40646494 FALSE FALSE FALSE -0.01280679
## 46 46 305 -0.64487637 FALSE FALSE FALSE -0.35432110
## 47 47 305 -0.64487637 FALSE FALSE FALSE -0.35432110
## 48 48 305 -0.64487637 FALSE FALSE FALSE -0.35432110
## 49 49 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 50 50 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 51 51 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 52 52 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 53 53 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 54 54 281 -0.78792323 FALSE FALSE FALSE -0.55922968
## 55 55 281 -0.78792323 FALSE FALSE FALSE -0.55922968
## 56 56 281 -0.78792323 FALSE FALSE FALSE -0.55922968
## 57 57 247 -0.99057295 FALSE FALSE FALSE -0.84951684
## 58 58 247 -0.99057295 FALSE FALSE FALSE -0.84951684
## 59 59 247 -0.99057295 FALSE FALSE FALSE -0.84951684
## 60 60 247 -0.99057295 FALSE FALSE FALSE -0.84951684
## 61 61 270 -0.85348638 FALSE FALSE FALSE -0.65314612
## 62 62 270 -0.85348638 FALSE FALSE FALSE -0.65314612
## 63 63 270 -0.85348638 FALSE FALSE FALSE -0.65314612
## 64 64 270 -0.85348638 FALSE FALSE FALSE -0.65314612
## 65 65 270 -0.85348638 FALSE FALSE FALSE -0.65314612
## 66 66 270 -0.85348638 FALSE FALSE FALSE -0.65314612
## 67 67 270 -0.85348638 FALSE FALSE FALSE -0.65314612
## 68 68 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 69 69 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 70 70 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 71 71 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 72 72 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 73 73 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 74 74 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 75 75 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 76 76 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 77 77 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 78 78 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 79 79 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 80 80 384 -0.17401379 FALSE FALSE FALSE 0.32016966
## 81 81 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 82 82 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 83 83 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 84 84 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 85 85 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 86 86 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 87 87 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 88 88 432 0.11207993 FALSE FALSE FALSE 0.72998683
## 89 89 188 -1.34222981 FALSE FALSE FALSE -1.35325045
## 90 90 188 -1.34222981 FALSE FALSE FALSE -1.35325045
## 91 91 188 -1.34222981 FALSE FALSE FALSE -1.35325045
## 92 92 188 -1.34222981 FALSE FALSE FALSE -1.35325045
## 93 93 188 -1.34222981 FALSE FALSE FALSE -1.35325045
## 94 94 188 -1.34222981 FALSE FALSE FALSE -1.35325045
## 95 95 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 96 96 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 97 97 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 98 98 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 99 99 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 100 100 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 101 101 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 102 102 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 103 103 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 104 104 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 105 105 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 106 106 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 107 107 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 108 108 437 0.14188136 FALSE FALSE FALSE 0.77267612
## 109 109 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 110 110 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 111 111 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 112 112 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 113 113 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 114 114 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 115 115 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 116 116 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 117 117 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 118 118 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 119 119 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 120 120 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 121 121 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 122 122 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 123 123 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 124 124 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 125 125 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 126 126 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 127 127 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 128 128 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 129 129 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 130 130 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 131 131 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 132 132 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 133 133 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 134 134 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 135 135 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 136 136 403 -0.06076836 FALSE FALSE FALSE 0.48238896
## 137 137 296 -0.69851894 FALSE FALSE FALSE -0.43116181
## 138 138 296 -0.69851894 FALSE FALSE FALSE -0.43116181
## 139 139 296 -0.69851894 FALSE FALSE FALSE -0.43116181
## 140 140 296 -0.69851894 FALSE FALSE FALSE -0.43116181
## 141 141 296 -0.69851894 FALSE FALSE FALSE -0.43116181
## 142 142 296 -0.69851894 FALSE FALSE FALSE -0.43116181
## 143 143 193 -1.31242838 FALSE FALSE FALSE -1.31056116
## 144 144 193 -1.31242838 FALSE FALSE FALSE -1.31056116
## 145 145 193 -1.31242838 FALSE FALSE FALSE -1.31056116
## 146 146 193 -1.31242838 FALSE FALSE FALSE -1.31056116
## 147 147 193 -1.31242838 FALSE FALSE FALSE -1.31056116
## 148 148 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 149 149 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 150 150 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 151 151 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 152 152 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 153 153 398 -0.09056979 FALSE FALSE FALSE 0.43969967
## 154 154 265 -0.88328780 FALSE FALSE FALSE -0.69583540
## 155 155 265 -0.88328780 FALSE FALSE FALSE -0.69583540
## 156 156 255 -0.94289066 FALSE FALSE FALSE -0.78121398
## 157 157 329 -0.50182951 FALSE FALSE FALSE -0.14941251
## 158 158 329 -0.50182951 FALSE FALSE FALSE -0.14941251
## 159 159 329 -0.50182951 FALSE FALSE FALSE -0.14941251
## 160 160 402 -0.06672865 FALSE FALSE FALSE 0.47385110
## 161 161 402 -0.06672865 FALSE FALSE FALSE 0.47385110
## 162 162 348 -0.38858408 FALSE FALSE FALSE 0.01280679
## 163 163 348 -0.38858408 FALSE FALSE FALSE 0.01280679
## 164 164 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 165 165 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 166 166 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 167 167 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 168 168 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 169 169 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 170 170 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 171 171 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 172 172 277 -0.81176437 FALSE FALSE FALSE -0.59338111
## 173 173 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 174 174 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 175 175 276 -0.81772466 FALSE FALSE FALSE -0.60191897
## 176 176 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 177 177 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 178 178 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 179 179 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 180 180 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 181 181 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 182 182 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 183 183 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 184 184 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 185 185 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 186 186 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 187 187 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 188 188 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 189 189 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 190 190 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 191 191 307 -0.63295580 FALSE FALSE FALSE -0.33724538
## 192 192 300 -0.67467780 FALSE FALSE FALSE -0.39701038
## 193 193 300 -0.67467780 FALSE FALSE FALSE -0.39701038
## 194 194 300 -0.67467780 FALSE FALSE FALSE -0.39701038
## 195 195 300 -0.67467780 FALSE FALSE FALSE -0.39701038
## 196 196 300 -0.67467780 FALSE FALSE FALSE -0.39701038
## 197 197 330 -0.49586923 FALSE FALSE FALSE -0.14087465
## 198 198 330 -0.49586923 FALSE FALSE FALSE -0.14087465
## 199 199 330 -0.49586923 FALSE FALSE FALSE -0.14087465
## 200 200 330 -0.49586923 FALSE FALSE FALSE -0.14087465
## 201 201 330 -0.49586923 FALSE FALSE FALSE -0.14087465
## 202 202 330 -0.49586923 FALSE FALSE FALSE -0.14087465
## 203 203 330 -0.49586923 FALSE FALSE FALSE -0.14087465
## 204 204 315 -0.58527351 FALSE FALSE FALSE -0.26894252
## 205 205 315 -0.58527351 FALSE FALSE FALSE -0.26894252
## 206 206 244 -1.00845381 FALSE FALSE FALSE -0.87513042
## 207 207 264 -0.88924809 FALSE FALSE FALSE -0.70437326
## 208 208 264 -0.88924809 FALSE FALSE FALSE -0.70437326
## 209 209 264 -0.88924809 FALSE FALSE FALSE -0.70437326
## 210 210 264 -0.88924809 FALSE FALSE FALSE -0.70437326
## 211 211 264 -0.88924809 FALSE FALSE FALSE -0.70437326
## 212 212 264 -0.88924809 FALSE FALSE FALSE -0.70437326
## 213 213 264 -0.88924809 FALSE FALSE FALSE -0.70437326
## 214 214 223 -1.13361981 FALSE FALSE FALSE -1.05442543
## 215 215 223 -1.13361981 FALSE FALSE FALSE -1.05442543
## 216 216 223 -1.13361981 FALSE FALSE FALSE -1.05442543
## 217 217 223 -1.13361981 FALSE FALSE FALSE -1.05442543
## 218 218 254 -0.94885095 FALSE FALSE FALSE -0.78975184
## 219 219 254 -0.94885095 FALSE FALSE FALSE -0.78975184
## 220 220 254 -0.94885095 FALSE FALSE FALSE -0.78975184
## 221 221 216 -1.17534181 FALSE FALSE FALSE -1.11419043
## 222 222 216 -1.17534181 FALSE FALSE FALSE -1.11419043
## 223 223 216 -1.17534181 FALSE FALSE FALSE -1.11419043
## 224 224 198 -1.28262696 FALSE FALSE FALSE -1.26787187
## 225 225 285 -0.76408209 FALSE FALSE FALSE -0.52507825
## 226 226 300 -0.67467780 FALSE FALSE FALSE -0.39701038
## 227 227 241 -1.02633466 FALSE FALSE FALSE -0.90074399
## 228 228 293 -0.71639980 FALSE FALSE FALSE -0.45677539
## 229 229 293 -0.71639980 FALSE FALSE FALSE -0.45677539
## 230 230 293 -0.71639980 FALSE FALSE FALSE -0.45677539
## 231 231 245 -1.00249352 FALSE FALSE FALSE -0.86659256
## 232 232 245 -1.00249352 FALSE FALSE FALSE -0.86659256
## 233 233 245 -1.00249352 FALSE FALSE FALSE -0.86659256
## 234 234 289 -0.74024094 FALSE FALSE FALSE -0.49092682
## 235 235 289 -0.74024094 FALSE FALSE FALSE -0.49092682
## 236 236 289 -0.74024094 FALSE FALSE FALSE -0.49092682
## 237 237 358 -0.32898122 FALSE FALSE FALSE 0.09818536
## 238 238 329 -0.50182951 FALSE FALSE FALSE -0.14941251
## 239 239 329 -0.50182951 FALSE FALSE FALSE -0.14941251
## 240 240 222 -1.13958010 FALSE FALSE FALSE -1.06296329
## 241 241 222 -1.13958010 FALSE FALSE FALSE -1.06296329
## 242 242 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 243 243 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 244 244 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 245 245 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 246 246 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 247 247 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 248 248 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 249 249 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 250 250 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 251 251 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 252 252 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 253 253 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 254 254 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 255 255 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 256 256 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 257 257 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 258 258 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 259 259 287 -0.75216152 FALSE FALSE FALSE -0.50800253
## 260 260 430 0.10015936 FALSE FALSE FALSE 0.71291112
## 261 261 430 0.10015936 FALSE FALSE FALSE 0.71291112
## 262 262 430 0.10015936 FALSE FALSE FALSE 0.71291112
## 263 263 304 -0.65083666 FALSE FALSE FALSE -0.36285895
## 264 264 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 265 265 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 266 266 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 267 267 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 268 268 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 269 269 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 270 270 224 -1.12765952 FALSE FALSE FALSE -1.04588757
## 271 271 284 -0.77004237 FALSE FALSE FALSE -0.53361611
## 272 272 422 0.05247707 FALSE FALSE FALSE 0.64460826
## 273 273 370 -0.25745779 FALSE FALSE FALSE 0.20063966
## 274 274 370 -0.25745779 FALSE FALSE FALSE 0.20063966
## 275 275 352 -0.36474294 FALSE FALSE FALSE 0.04695822
## 276 276 352 -0.36474294 FALSE FALSE FALSE 0.04695822
## 277 277 351 -0.37070322 FALSE FALSE FALSE 0.03842036
## 278 278 280 -0.79388352 FALSE FALSE FALSE -0.56776754
## 279 279 335 -0.46606780 FALSE FALSE FALSE -0.09818536
## 280 280 411 -0.01308607 FALSE FALSE FALSE 0.55069182
## 281 281 187 -1.34819010 FALSE FALSE FALSE -1.36178831
## 282 282 334 -0.47202808 FALSE FALSE FALSE -0.10672322
## 283 283 334 -0.47202808 FALSE FALSE FALSE -0.10672322
## 284 284 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 285 285 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 286 286 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 287 287 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 288 288 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 289 289 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 290 290 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 291 291 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 292 292 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 293 293 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 294 294 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 295 295 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 296 296 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 297 297 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 298 298 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 299 299 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 300 300 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 301 301 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 302 302 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 303 303 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 304 304 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 305 305 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 306 306 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 307 307 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 308 308 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 309 309 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 310 310 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 311 311 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 312 312 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 313 313 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 314 314 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 315 315 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 316 316 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 317 317 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 318 318 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 319 319 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 320 320 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 321 321 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 322 322 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 323 323 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 324 324 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 325 325 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 326 326 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 327 327 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 328 328 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 329 329 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 330 330 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 331 331 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 332 332 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 333 333 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 334 334 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 335 335 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 336 336 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 337 337 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 338 338 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 339 339 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 340 340 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 341 341 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 342 342 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 343 343 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 344 344 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 345 345 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 346 346 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 347 347 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 348 348 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 349 349 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 350 350 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 351 351 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 352 352 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 353 353 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 354 354 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 355 355 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 356 356 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 357 357 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 358 358 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 359 359 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 360 360 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 361 361 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 362 362 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 363 363 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 364 364 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 365 365 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 366 366 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 367 367 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 368 368 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 369 369 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 370 370 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 371 371 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 372 372 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 373 373 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 374 374 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 375 375 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 376 376 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 377 377 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 378 378 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 379 379 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 380 380 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 381 381 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 382 382 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 383 383 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 384 384 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 385 385 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 386 386 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 387 387 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 388 388 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 389 389 666 1.50678681 FALSE FALSE FALSE 2.72784554
## 390 390 711 1.77499968 FALSE FALSE FALSE 3.11204914
## 391 391 711 1.77499968 FALSE FALSE FALSE 3.11204914
## 392 392 711 1.77499968 FALSE FALSE FALSE 3.11204914
## 393 393 711 1.77499968 FALSE FALSE FALSE 3.11204914
## 394 394 711 1.77499968 FALSE FALSE FALSE 3.11204914
## 395 395 391 -0.13229179 FALSE FALSE FALSE 0.37993467
## 396 396 391 -0.13229179 FALSE FALSE FALSE 0.37993467
## 397 397 391 -0.13229179 FALSE FALSE FALSE 0.37993467
## 398 398 391 -0.13229179 FALSE FALSE FALSE 0.37993467
## 399 399 391 -0.13229179 FALSE FALSE FALSE 0.37993467
## 400 400 391 -0.13229179 FALSE FALSE FALSE 0.37993467
## 401 401 273 -0.83560552 FALSE FALSE FALSE -0.62753254
## 402 402 273 -0.83560552 FALSE FALSE FALSE -0.62753254
## 403 403 273 -0.83560552 FALSE FALSE FALSE -0.62753254
## 404 404 273 -0.83560552 FALSE FALSE FALSE -0.62753254
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 FALSE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 FALSE
## 289 FALSE
## 290 FALSE
## 291 FALSE
## 292 FALSE
## 293 FALSE
## 294 FALSE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 FALSE
## 299 FALSE
## 300 FALSE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 FALSE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 FALSE
## 322 FALSE
## 323 FALSE
## 324 FALSE
## 325 FALSE
## 326 FALSE
## 327 FALSE
## 328 FALSE
## 329 FALSE
## 330 FALSE
## 331 FALSE
## 332 FALSE
## 333 FALSE
## 334 FALSE
## 335 FALSE
## 336 FALSE
## 337 FALSE
## 338 FALSE
## 339 FALSE
## 340 FALSE
## 341 FALSE
## 342 FALSE
## 343 FALSE
## 344 FALSE
## 345 FALSE
## 346 FALSE
## 347 FALSE
## 348 FALSE
## 349 FALSE
## 350 FALSE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 FALSE
## 355 FALSE
## 356 FALSE
## 357 FALSE
## 358 FALSE
## 359 FALSE
## 360 FALSE
## 361 FALSE
## 362 FALSE
## 363 FALSE
## 364 FALSE
## 365 FALSE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 FALSE
## 370 FALSE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 FALSE
## 378 FALSE
## 379 FALSE
## 380 FALSE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== AL MENOS DOS MÉTODOS =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== LOS TRES MÉTODOS =====
## [1] ID Valor Z_score Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## tax Z-score 404 0 0
## tax IQR 404 0 0
## tax Tukey extremo 404 0 0
## tax MAD 404 0 0## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## [1] ID Valor Z_score Z_robusto
## [5] metodos_evaluados numero_metodos clasificacion
## <0 rows> (o 0- extensión row.names)
##
## ========================================
## VARIABLE: ptratio
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = 11.93533
## Límite superior = 24.93695
## Número de outliers = 0
## [1] ID Valor Z_score
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO IQR ================
## Q1 = 17
## Q3 = 20.2
## IQR = 3.2
## Límite inferior = 12.2
## Límite superior = 25
## Número de outliers = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = 7.4
## Límite superior = 29.8
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 19
## MAD = 1.2
## MAD ajustada = 1.77912
## Número de outliers = 3
## ID Valor Z_robusto
## 1 157 12.6 -3.597284
## 2 158 12.6 -3.597284
## 3 159 12.6 -3.597284
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 15.3 -1.44726834 FALSE FALSE FALSE -2.07967984
## 2 2 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 3 3 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 4 4 18.7 0.12176701 FALSE FALSE FALSE -0.16862269
## 5 5 18.7 0.12176701 FALSE FALSE FALSE -0.16862269
## 6 6 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 7 7 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 8 8 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 9 9 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 10 10 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 11 11 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 12 12 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 13 13 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 14 14 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 15 15 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 16 16 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 17 17 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 18 18 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 19 19 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 20 20 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 21 21 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 22 22 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 23 23 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 24 24 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 25 25 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 26 26 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 27 27 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 28 28 17.9 -0.24741778 FALSE FALSE FALSE -0.61828320
## 29 29 17.9 -0.24741778 FALSE FALSE FALSE -0.61828320
## 30 30 17.9 -0.24741778 FALSE FALSE FALSE -0.61828320
## 31 31 17.9 -0.24741778 FALSE FALSE FALSE -0.61828320
## 32 32 17.9 -0.24741778 FALSE FALSE FALSE -0.61828320
## 33 33 16.8 -0.75504686 FALSE FALSE FALSE -1.23656639
## 34 34 21.1 1.22932138 FALSE FALSE FALSE 1.18035883
## 35 35 17.9 -0.24741778 FALSE FALSE FALSE -0.61828320
## 36 36 17.3 -0.52430637 FALSE FALSE FALSE -0.95552858
## 37 37 15.1 -1.53956454 FALSE FALSE FALSE -2.19209497
## 38 38 19.7 0.58324800 FALSE FALSE FALSE 0.39345294
## 39 39 19.7 0.58324800 FALSE FALSE FALSE 0.39345294
## 40 40 19.7 0.58324800 FALSE FALSE FALSE 0.39345294
## 41 41 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 42 42 16.1 -1.07808355 FALSE FALSE FALSE -1.63001934
## 43 43 16.1 -1.07808355 FALSE FALSE FALSE -1.63001934
## 44 44 18.9 0.21406321 FALSE FALSE FALSE -0.05620756
## 45 45 18.9 0.21406321 FALSE FALSE FALSE -0.05620756
## 46 46 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 47 47 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 48 48 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 49 49 18.7 0.12176701 FALSE FALSE FALSE -0.16862269
## 50 50 18.7 0.12176701 FALSE FALSE FALSE -0.16862269
## 51 51 18.7 0.12176701 FALSE FALSE FALSE -0.16862269
## 52 52 18.7 0.12176701 FALSE FALSE FALSE -0.16862269
## 53 53 18.7 0.12176701 FALSE FALSE FALSE -0.16862269
## 54 54 19.0 0.26021131 FALSE FALSE FALSE 0.00000000
## 55 55 19.0 0.26021131 FALSE FALSE FALSE 0.00000000
## 56 56 19.0 0.26021131 FALSE FALSE FALSE 0.00000000
## 57 57 18.5 0.02947082 FALSE FALSE FALSE -0.28103782
## 58 58 18.5 0.02947082 FALSE FALSE FALSE -0.28103782
## 59 59 18.5 0.02947082 FALSE FALSE FALSE -0.28103782
## 60 60 18.5 0.02947082 FALSE FALSE FALSE -0.28103782
## 61 61 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 62 62 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 63 63 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 64 64 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 65 65 18.2 -0.10897348 FALSE FALSE FALSE -0.44966051
## 66 66 18.2 -0.10897348 FALSE FALSE FALSE -0.44966051
## 67 67 18.2 -0.10897348 FALSE FALSE FALSE -0.44966051
## 68 68 18.0 -0.20126968 FALSE FALSE FALSE -0.56207563
## 69 69 18.0 -0.20126968 FALSE FALSE FALSE -0.56207563
## 70 70 18.0 -0.20126968 FALSE FALSE FALSE -0.56207563
## 71 71 18.0 -0.20126968 FALSE FALSE FALSE -0.56207563
## 72 72 18.0 -0.20126968 FALSE FALSE FALSE -0.56207563
## 73 73 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 74 74 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 75 75 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 76 76 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 77 77 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 78 78 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 79 79 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 80 80 20.9 1.13702519 FALSE FALSE FALSE 1.06794370
## 81 81 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 82 82 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 83 83 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 84 84 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 85 85 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 86 86 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 87 87 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 88 88 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 89 89 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 90 90 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 91 91 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 92 92 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 93 93 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 94 94 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 95 95 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 96 96 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 97 97 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 98 98 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 99 99 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 100 100 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 101 101 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 102 102 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 103 103 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 104 104 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 105 105 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 106 106 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 107 107 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 108 108 21.2 1.27546948 FALSE FALSE FALSE 1.23656639
## 109 109 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 110 110 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 111 111 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 112 112 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 113 113 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 114 114 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 115 115 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 116 116 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 117 117 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 118 118 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 119 119 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 120 120 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 121 121 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 122 122 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 123 123 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 124 124 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 125 125 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 126 126 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 127 127 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 128 128 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 129 129 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 130 130 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 131 131 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 132 132 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 133 133 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 134 134 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 135 135 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 136 136 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 137 137 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 138 138 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 139 139 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 140 140 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 141 141 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 142 142 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 143 143 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 144 144 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 145 145 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 146 146 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 147 147 17.8 -0.29356588 FALSE FALSE FALSE -0.67449076
## 148 148 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 149 149 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 150 150 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 151 151 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 152 152 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 153 153 15.2 -1.49341644 FALSE FALSE FALSE -2.13588741
## 154 154 15.6 -1.30882405 FALSE FALSE FALSE -1.91105715
## 155 155 15.6 -1.30882405 FALSE FALSE FALSE -1.91105715
## 156 156 14.4 -1.86260123 FALSE FALSE FALSE -2.58554791
## 157 157 12.6 -2.69326701 FALSE FALSE FALSE -3.59728405
## 158 158 12.6 -2.69326701 FALSE FALSE FALSE -3.59728405
## 159 159 12.6 -2.69326701 FALSE FALSE FALSE -3.59728405
## 160 160 17.0 -0.66275067 FALSE FALSE FALSE -1.12415127
## 161 161 17.0 -0.66275067 FALSE FALSE FALSE -1.12415127
## 162 162 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 163 163 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 164 164 14.7 -1.72415694 FALSE FALSE FALSE -2.41692522
## 165 165 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 166 166 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 167 167 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 168 168 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 169 169 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 170 170 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 171 171 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 172 172 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 173 173 16.4 -0.93963926 FALSE FALSE FALSE -1.46139665
## 174 174 16.4 -0.93963926 FALSE FALSE FALSE -1.46139665
## 175 175 16.4 -0.93963926 FALSE FALSE FALSE -1.46139665
## 176 176 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 177 177 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 178 178 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 179 179 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 180 180 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 181 181 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 182 182 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 183 183 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 184 184 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 185 185 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 186 186 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 187 187 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 188 188 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 189 189 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 190 190 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 191 191 17.4 -0.47815827 FALSE FALSE FALSE -0.89932101
## 192 192 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 193 193 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 194 194 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 195 195 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 196 196 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 197 197 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 198 198 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 199 199 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 200 200 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 201 201 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 202 202 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 203 203 19.1 0.30635941 FALSE FALSE FALSE 0.05620756
## 204 204 16.4 -0.93963926 FALSE FALSE FALSE -1.46139665
## 205 205 16.4 -0.93963926 FALSE FALSE FALSE -1.46139665
## 206 206 15.9 -1.17037975 FALSE FALSE FALSE -1.74243446
## 207 207 13.0 -2.50867461 FALSE FALSE FALSE -3.37245380
## 208 208 13.0 -2.50867461 FALSE FALSE FALSE -3.37245380
## 209 209 13.0 -2.50867461 FALSE FALSE FALSE -3.37245380
## 210 210 13.0 -2.50867461 FALSE FALSE FALSE -3.37245380
## 211 211 13.0 -2.50867461 FALSE FALSE FALSE -3.37245380
## 212 212 13.0 -2.50867461 FALSE FALSE FALSE -3.37245380
## 213 213 13.0 -2.50867461 FALSE FALSE FALSE -3.37245380
## 214 214 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 215 215 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 216 216 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 217 217 18.6 0.07561891 FALSE FALSE FALSE -0.22483025
## 218 218 17.6 -0.38586207 FALSE FALSE FALSE -0.78690589
## 219 219 17.6 -0.38586207 FALSE FALSE FALSE -0.78690589
## 220 220 17.6 -0.38586207 FALSE FALSE FALSE -0.78690589
## 221 221 14.9 -1.63186074 FALSE FALSE FALSE -2.30451009
## 222 222 14.9 -1.63186074 FALSE FALSE FALSE -2.30451009
## 223 223 14.9 -1.63186074 FALSE FALSE FALSE -2.30451009
## 224 224 13.6 -2.23178602 FALSE FALSE FALSE -3.03520842
## 225 225 15.3 -1.44726834 FALSE FALSE FALSE -2.07967984
## 226 226 15.3 -1.44726834 FALSE FALSE FALSE -2.07967984
## 227 227 18.2 -0.10897348 FALSE FALSE FALSE -0.44966051
## 228 228 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 229 229 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 230 230 16.6 -0.84734306 FALSE FALSE FALSE -1.34898152
## 231 231 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 232 232 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 233 233 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 234 234 16.0 -1.12423165 FALSE FALSE FALSE -1.68622690
## 235 235 16.0 -1.12423165 FALSE FALSE FALSE -1.68622690
## 236 236 16.0 -1.12423165 FALSE FALSE FALSE -1.68622690
## 237 237 14.8 -1.67800884 FALSE FALSE FALSE -2.36071766
## 238 238 16.1 -1.07808355 FALSE FALSE FALSE -1.63001934
## 239 239 16.1 -1.07808355 FALSE FALSE FALSE -1.63001934
## 240 240 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 241 241 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 242 242 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 243 243 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 244 244 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 245 245 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 246 246 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 247 247 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 248 248 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 249 249 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 250 250 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 251 251 18.4 -0.01667728 FALSE FALSE FALSE -0.33724538
## 252 252 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 253 253 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 254 254 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 255 255 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 256 256 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 257 257 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 258 258 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 259 259 19.6 0.53709990 FALSE FALSE FALSE 0.33724538
## 260 260 16.9 -0.70889876 FALSE FALSE FALSE -1.18035883
## 261 261 16.9 -0.70889876 FALSE FALSE FALSE -1.18035883
## 262 262 16.9 -0.70889876 FALSE FALSE FALSE -1.18035883
## 263 263 16.9 -0.70889876 FALSE FALSE FALSE -1.18035883
## 264 264 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 265 265 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 266 266 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 267 267 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 268 268 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 269 269 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 270 270 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 271 271 15.5 -1.35497215 FALSE FALSE FALSE -1.96726472
## 272 272 15.9 -1.17037975 FALSE FALSE FALSE -1.74243446
## 273 273 17.6 -0.38586207 FALSE FALSE FALSE -0.78690589
## 274 274 17.6 -0.38586207 FALSE FALSE FALSE -0.78690589
## 275 275 18.8 0.16791511 FALSE FALSE FALSE -0.11241513
## 276 276 18.8 0.16791511 FALSE FALSE FALSE -0.11241513
## 277 277 17.9 -0.24741778 FALSE FALSE FALSE -0.61828320
## 278 278 17.0 -0.66275067 FALSE FALSE FALSE -1.12415127
## 279 279 19.7 0.58324800 FALSE FALSE FALSE 0.39345294
## 280 280 18.3 -0.06282538 FALSE FALSE FALSE -0.39345294
## 281 281 17.0 -0.66275067 FALSE FALSE FALSE -1.12415127
## 282 282 22.0 1.64465427 FALSE FALSE FALSE 1.68622690
## 283 283 22.0 1.64465427 FALSE FALSE FALSE 1.68622690
## 284 284 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 285 285 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 286 286 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 287 287 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 288 288 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 289 289 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 290 290 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 291 291 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 292 292 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 293 293 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 294 294 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 295 295 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 296 296 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 297 297 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 298 298 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 299 299 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 300 300 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 301 301 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 302 302 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 303 303 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 304 304 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 305 305 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 306 306 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 307 307 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 308 308 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 309 309 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 310 310 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 311 311 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 312 312 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 313 313 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 314 314 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 315 315 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 316 316 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 317 317 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 318 318 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 319 319 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 320 320 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 321 321 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 322 322 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 323 323 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 324 324 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 325 325 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 326 326 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 327 327 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 328 328 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 329 329 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 330 330 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 331 331 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 332 332 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 333 333 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 334 334 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 335 335 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 336 336 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 337 337 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 338 338 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 339 339 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 340 340 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 341 341 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 342 342 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 343 343 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 344 344 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 345 345 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 346 346 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 347 347 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 348 348 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 349 349 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 350 350 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 351 351 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 352 352 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 353 353 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 354 354 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 355 355 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 356 356 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 357 357 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 358 358 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 359 359 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 360 360 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 361 361 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 362 362 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 363 363 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 364 364 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 365 365 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 366 366 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 367 367 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 368 368 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 369 369 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 370 370 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 371 371 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 372 372 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 373 373 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 374 374 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 375 375 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 376 376 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 377 377 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 378 378 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 379 379 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 380 380 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 381 381 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 382 382 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 383 383 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 384 384 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 385 385 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 386 386 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 387 387 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 388 388 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 389 389 20.2 0.81398849 FALSE FALSE FALSE 0.67449076
## 390 390 20.1 0.76784040 FALSE FALSE FALSE 0.61828320
## 391 391 20.1 0.76784040 FALSE FALSE FALSE 0.61828320
## 392 392 20.1 0.76784040 FALSE FALSE FALSE 0.61828320
## 393 393 20.1 0.76784040 FALSE FALSE FALSE 0.61828320
## 394 394 20.1 0.76784040 FALSE FALSE FALSE 0.61828320
## 395 395 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 396 396 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 397 397 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 398 398 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 399 399 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 400 400 19.2 0.35250751 FALSE FALSE FALSE 0.11241513
## 401 401 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 402 402 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 403 403 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## 404 404 21.0 1.18317328 FALSE FALSE FALSE 1.12415127
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 TRUE
## 158 TRUE
## 159 TRUE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 FALSE
## 289 FALSE
## 290 FALSE
## 291 FALSE
## 292 FALSE
## 293 FALSE
## 294 FALSE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 FALSE
## 299 FALSE
## 300 FALSE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 FALSE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 FALSE
## 322 FALSE
## 323 FALSE
## 324 FALSE
## 325 FALSE
## 326 FALSE
## 327 FALSE
## 328 FALSE
## 329 FALSE
## 330 FALSE
## 331 FALSE
## 332 FALSE
## 333 FALSE
## 334 FALSE
## 335 FALSE
## 336 FALSE
## 337 FALSE
## 338 FALSE
## 339 FALSE
## 340 FALSE
## 341 FALSE
## 342 FALSE
## 343 FALSE
## 344 FALSE
## 345 FALSE
## 346 FALSE
## 347 FALSE
## 348 FALSE
## 349 FALSE
## 350 FALSE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 FALSE
## 355 FALSE
## 356 FALSE
## 357 FALSE
## 358 FALSE
## 359 FALSE
## 360 FALSE
## 361 FALSE
## 362 FALSE
## 363 FALSE
## 364 FALSE
## 365 FALSE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 FALSE
## 370 FALSE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 FALSE
## 378 FALSE
## 379 FALSE
## 380 FALSE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 157 12.6 -2.693267 FALSE FALSE -3.597284 TRUE
## 2 158 12.6 -2.693267 FALSE FALSE -3.597284 TRUE
## 3 159 12.6 -2.693267 FALSE FALSE -3.597284 TRUE
## metodos_evaluados numero_metodos
## 1 3 1
## 2 3 1
## 3 3 1
##
## ===== AL MENOS DOS MÉTODOS =====
## [1] ID Valor Z_score outlier_Z
## [5] outlier_IQR Z_robusto outlier_MAD metodos_evaluados
## [9] numero_metodos
## <0 rows> (o 0- extensión row.names)
##
## ===== LOS TRES MÉTODOS =====
## [1] ID Valor Z_score Z_robusto
## <0 rows> (o 0- extensión row.names)
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## ptratio Z-score 404 0 0.00
## ptratio IQR 404 0 0.00
## ptratio Tukey extremo 404 0 0.00
## ptratio MAD 404 3 0.74## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 157 12.6 -2.693267 -3.597284 3 1
## 2 158 12.6 -2.693267 -3.597284 3 1
## 3 159 12.6 -2.693267 -3.597284 3 1
## clasificacion
## 1 Outlier según 1 método
## 2 Outlier según 1 método
## 3 Outlier según 1 método
##
## ========================================
## VARIABLE: black
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = 60.10197
## Límite superior = 644.833
## Número de outliers = 20
## ID Valor Z_score
## 1 324 2.60 -3.590035
## 2 325 35.05 -3.257061
## 3 326 28.79 -3.321296
## 4 329 27.25 -3.337098
## 5 330 21.57 -3.395381
## 6 332 16.45 -3.447918
## 7 333 48.45 -3.119562
## 8 337 2.52 -3.590856
## 9 338 3.65 -3.579261
## 10 339 7.68 -3.537909
## 11 340 24.65 -3.363777
## 12 341 18.82 -3.423600
## 13 349 27.49 -3.334636
## 14 355 43.06 -3.174870
## 15 359 0.32 -3.613430
## 16 362 6.68 -3.548170
## 17 363 50.92 -3.094217
## 18 364 10.48 -3.509177
## 19 365 3.50 -3.580800
## 20 370 22.01 -3.390867
##
## ================ MÉTODO IQR ================
## Q1 = 373.475
## Q3 = 396.06
## IQR = 22.585
## Límite inferior = 339.5975
## Límite superior = 429.9375
## Número de outliers = 61
## ID Valor
## 1 18 303.42
## 2 19 306.38
## 3 24 248.31
## 4 75 70.80
## 5 102 262.76
## 6 112 172.91
## 7 113 169.27
## 8 119 261.95
## 9 120 321.02
## 10 121 88.01
## 11 122 88.63
## 12 126 338.92
## 13 131 240.16
## 14 133 227.61
## 15 134 297.09
## 16 135 292.29
## 17 293 316.03
## 18 294 131.42
## 19 317 338.16
## 20 321 329.46
## 21 323 314.64
## 22 324 2.60
## 23 325 35.05
## 24 326 28.79
## 25 327 210.97
## 26 328 88.27
## 27 329 27.25
## 28 330 21.57
## 29 331 127.36
## 30 332 16.45
## 31 333 48.45
## 32 334 318.75
## 33 335 319.98
## 34 336 291.55
## 35 337 2.52
## 36 338 3.65
## 37 339 7.68
## 38 340 24.65
## 39 341 18.82
## 40 342 60.72
## 41 343 83.45
## 42 344 81.33
## 43 345 97.95
## 44 346 100.19
## 45 347 100.63
## 46 348 109.85
## 47 349 27.49
## 48 350 68.95
## 49 354 240.52
## 50 355 43.06
## 51 356 318.01
## 52 358 304.21
## 53 359 0.32
## 54 362 6.68
## 55 363 50.92
## 56 364 10.48
## 57 365 3.50
## 58 369 334.40
## 59 370 22.01
## 60 378 302.76
## 61 392 318.43
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = 305.72
## Límite superior = 463.815
## Número de outliers extremos = 49
## ID Valor
## 1 18 303.42
## 2 24 248.31
## 3 75 70.80
## 4 102 262.76
## 5 112 172.91
## 6 113 169.27
## 7 119 261.95
## 8 121 88.01
## 9 122 88.63
## 10 131 240.16
## 11 133 227.61
## 12 134 297.09
## 13 135 292.29
## 14 294 131.42
## 15 324 2.60
## 16 325 35.05
## 17 326 28.79
## 18 327 210.97
## 19 328 88.27
## 20 329 27.25
## 21 330 21.57
## 22 331 127.36
## 23 332 16.45
## 24 333 48.45
## 25 336 291.55
## 26 337 2.52
## 27 338 3.65
## 28 339 7.68
## 29 340 24.65
## 30 341 18.82
## 31 342 60.72
## 32 343 83.45
## 33 344 81.33
## 34 345 97.95
## 35 346 100.19
## 36 347 100.63
## 37 348 109.85
## 38 349 27.49
## 39 350 68.95
## 40 354 240.52
## 41 355 43.06
## 42 358 304.21
## 43 359 0.32
## 44 362 6.68
## 45 363 50.92
## 46 364 10.48
## 47 365 3.50
## 48 370 22.01
## 49 378 302.76
##
## ================ MÉTODO MAD ================
## Mediana = 391.065
## MAD = 5.835
## MAD ajustada = 8.650971
## Número de outliers = 83
## ID Valor Z_robusto
## 1 18 303.42 -10.131233
## 2 19 306.38 -9.789075
## 3 21 360.17 -3.571275
## 4 23 358.77 -3.733107
## 5 24 248.31 -16.501616
## 6 68 357.98 -3.824426
## 7 75 70.80 -37.020700
## 8 85 344.91 -5.335239
## 9 102 262.76 -14.831283
## 10 112 172.91 -25.217400
## 11 113 169.27 -25.638162
## 12 115 351.85 -4.533017
## 13 117 341.60 -5.717855
## 14 118 343.28 -5.523657
## 15 119 261.95 -14.924914
## 16 120 321.02 -8.096779
## 17 121 88.01 -35.031328
## 18 122 88.63 -34.959659
## 19 124 353.89 -4.297205
## 20 126 338.92 -6.027647
## 21 131 240.16 -17.443707
## 22 133 227.61 -18.894411
## 23 134 297.09 -10.862942
## 24 135 292.29 -11.417793
## 25 136 348.13 -4.963027
## 26 158 354.31 -4.248656
## 27 171 348.93 -4.870552
## 28 188 360.20 -3.567808
## 29 227 341.60 -5.717855
## 30 288 350.65 -4.671730
## 31 290 353.04 -4.395460
## 32 291 354.55 -4.220913
## 33 292 354.70 -4.203574
## 34 293 316.03 -8.673593
## 35 294 131.42 -30.013394
## 36 298 347.88 -4.991925
## 37 317 338.16 -6.115498
## 38 321 329.46 -7.121166
## 39 323 314.64 -8.834268
## 40 324 2.60 -44.904208
## 41 325 35.05 -41.153184
## 42 326 28.79 -41.876802
## 43 327 210.97 -20.817894
## 44 328 88.27 -35.001273
## 45 329 27.25 -42.054817
## 46 330 21.57 -42.711390
## 47 331 127.36 -30.482705
## 48 332 16.45 -43.303232
## 49 333 48.45 -39.604225
## 50 334 318.75 -8.359177
## 51 335 319.98 -8.216997
## 52 336 291.55 -11.503333
## 53 337 2.52 -44.913455
## 54 338 3.65 -44.782834
## 55 339 7.68 -44.316991
## 56 340 24.65 -42.355361
## 57 341 18.82 -43.029274
## 58 342 60.72 -38.185887
## 59 343 83.45 -35.558436
## 60 344 81.33 -35.803495
## 61 345 97.95 -33.882324
## 62 346 100.19 -33.623393
## 63 347 100.63 -33.572532
## 64 348 109.85 -32.506756
## 65 349 27.49 -42.027074
## 66 350 68.95 -37.234549
## 67 354 240.52 -17.402093
## 68 355 43.06 -40.227276
## 69 356 318.01 -8.444717
## 70 358 304.21 -10.039913
## 71 359 0.32 -45.167762
## 72 360 355.29 -4.135374
## 73 362 6.68 -44.432585
## 74 363 50.92 -39.318708
## 75 364 10.48 -43.993327
## 76 365 3.50 -44.800173
## 77 369 334.40 -6.550132
## 78 370 22.01 -42.660529
## 79 377 352.58 -4.448634
## 80 378 302.76 -10.207525
## 81 380 349.48 -4.806975
## 82 391 344.05 -5.434650
## 83 392 318.43 -8.396167
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 2 2 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 3 3 392.83 0.414164759 FALSE FALSE FALSE 0.204023340
## 4 4 394.63 0.432634789 FALSE FALSE FALSE 0.412092469
## 5 5 394.12 0.427401614 FALSE FALSE FALSE 0.353139549
## 6 6 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 7 7 386.63 0.350545769 FALSE FALSE FALSE -0.512659215
## 8 8 386.71 0.351366659 FALSE FALSE FALSE -0.503411698
## 9 9 392.52 0.410983810 FALSE FALSE FALSE 0.168189213
## 10 10 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 11 11 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 12 12 380.02 0.282719716 FALSE FALSE FALSE -1.276735294
## 13 13 386.75 0.351777104 FALSE FALSE FALSE -0.498787940
## 14 14 390.95 0.394873840 FALSE FALSE FALSE -0.013293305
## 15 15 392.53 0.411086421 FALSE FALSE FALSE 0.169345152
## 16 16 394.54 0.431711287 FALSE FALSE FALSE 0.401689013
## 17 17 394.33 0.429556451 FALSE FALSE FALSE 0.377414281
## 18 18 303.42 -0.503282652 FALSE TRUE TRUE -10.131232667
## 19 19 306.38 -0.472909714 FALSE TRUE FALSE -9.789074544
## 20 20 380.23 0.284874553 FALSE FALSE FALSE -1.252460562
## 21 21 360.17 0.079036335 FALSE FALSE FALSE -3.571275409
## 22 22 376.73 0.248960606 FALSE FALSE FALSE -1.657039424
## 23 23 358.77 0.064670756 FALSE FALSE FALSE -3.733106954
## 24 24 248.31 -1.068773389 FALSE TRUE TRUE -16.501615830
## 25 25 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 26 26 377.56 0.257477342 FALSE FALSE FALSE -1.561096436
## 27 27 393.43 0.420321436 FALSE FALSE FALSE 0.273379717
## 28 28 385.41 0.338027193 FALSE FALSE FALSE -0.653683847
## 29 29 383.37 0.317094493 FALSE FALSE FALSE -0.889495526
## 30 30 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 31 31 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 32 32 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 33 33 395.56 0.442177637 FALSE FALSE FALSE 0.519594852
## 34 34 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 35 35 395.93 0.445974255 FALSE FALSE FALSE 0.562364618
## 36 36 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 37 37 392.90 0.414883038 FALSE FALSE FALSE 0.212114918
## 38 38 390.68 0.392103335 FALSE FALSE FALSE -0.044503675
## 39 39 378.08 0.262813129 FALSE FALSE FALSE -1.500987577
## 40 40 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 41 41 393.24 0.418371822 FALSE FALSE FALSE 0.251416864
## 42 42 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 43 43 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 44 44 396.21 0.448847370 FALSE FALSE FALSE 0.594730927
## 45 45 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 46 46 383.73 0.320788499 FALSE FALSE FALSE -0.847881700
## 47 47 390.91 0.394463395 FALSE FALSE FALSE -0.017917064
## 48 48 377.17 0.253475502 FALSE FALSE FALSE -1.606178081
## 49 49 394.92 0.435610516 FALSE FALSE FALSE 0.445614718
## 50 50 383.23 0.315657935 FALSE FALSE FALSE -0.905678680
## 51 51 373.66 0.217458945 FALSE FALSE FALSE -2.011912882
## 52 52 386.96 0.353931941 FALSE FALSE FALSE -0.474513208
## 53 53 396.06 0.447308201 FALSE FALSE FALSE 0.577391833
## 54 54 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 55 55 395.63 0.442895916 FALSE FALSE FALSE 0.527686430
## 56 56 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 57 57 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 58 58 392.30 0.408726362 FALSE FALSE FALSE 0.142758541
## 59 59 395.99 0.446589922 FALSE FALSE FALSE 0.569300255
## 60 60 395.15 0.437970575 FALSE FALSE FALSE 0.472201329
## 61 61 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 62 62 396.06 0.447308201 FALSE FALSE FALSE 0.577391833
## 63 63 392.18 0.407495026 FALSE FALSE FALSE 0.128887266
## 64 64 393.55 0.421552771 FALSE FALSE FALSE 0.287250992
## 65 65 395.01 0.436534017 FALSE FALSE FALSE 0.456018174
## 66 66 396.33 0.450078706 FALSE FALSE FALSE 0.608602202
## 67 67 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 68 68 357.98 0.056564465 FALSE FALSE FALSE -3.824426183
## 69 69 391.83 0.403903632 FALSE FALSE FALSE 0.088429380
## 70 70 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 71 71 393.53 0.421347549 FALSE FALSE FALSE 0.284939113
## 72 72 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 73 73 394.76 0.433968735 FALSE FALSE FALSE 0.427119684
## 74 74 395.58 0.442382860 FALSE FALSE FALSE 0.521906732
## 75 75 70.80 -2.890226135 FALSE TRUE TRUE -37.020699757
## 76 76 394.47 0.430993008 FALSE FALSE FALSE 0.393597435
## 77 77 392.69 0.412728202 FALSE FALSE FALSE 0.187840186
## 78 78 394.05 0.426683335 FALSE FALSE FALSE 0.345047972
## 79 79 387.69 0.361422564 FALSE FALSE FALSE -0.390129617
## 80 80 393.49 0.420937104 FALSE FALSE FALSE 0.280315354
## 81 81 395.59 0.442485471 FALSE FALSE FALSE 0.523062671
## 82 82 394.95 0.435918350 FALSE FALSE FALSE 0.449082537
## 83 83 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 84 84 388.74 0.372196748 FALSE FALSE FALSE -0.268755958
## 85 85 344.91 -0.077548471 FALSE FALSE FALSE -5.335239247
## 86 86 393.30 0.418987489 FALSE FALSE FALSE 0.258352502
## 87 87 394.51 0.431403454 FALSE FALSE FALSE 0.398221194
## 88 88 391.50 0.400517460 FALSE FALSE FALSE 0.050283373
## 89 89 389.15 0.376403810 FALSE FALSE FALSE -0.221362434
## 90 90 377.67 0.258606066 FALSE FALSE FALSE -1.548381101
## 91 91 378.09 0.262915740 FALSE FALSE FALSE -1.499831637
## 92 92 370.31 0.183084168 FALSE FALSE FALSE -2.399152650
## 93 93 379.38 0.276152594 FALSE FALSE FALSE -1.350715428
## 94 94 385.02 0.334025353 FALSE FALSE FALSE -0.698765491
## 95 95 392.11 0.406776748 FALSE FALSE FALSE 0.120795689
## 96 96 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 97 97 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 98 98 395.04 0.436841851 FALSE FALSE FALSE 0.459485993
## 99 99 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 100 100 385.76 0.341618588 FALSE FALSE FALSE -0.613225960
## 101 101 388.69 0.371683691 FALSE FALSE FALSE -0.274535656
## 102 102 262.76 -0.920500097 FALSE TRUE TRUE -14.831283101
## 103 103 394.67 0.433045234 FALSE FALSE FALSE 0.416716228
## 104 104 378.25 0.264557520 FALSE FALSE FALSE -1.481336604
## 105 105 394.08 0.426991169 FALSE FALSE FALSE 0.348515791
## 106 106 392.04 0.406058469 FALSE FALSE FALSE 0.112704111
## 107 107 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 108 108 388.08 0.365424404 FALSE FALSE FALSE -0.345047972
## 109 109 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 110 110 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 111 111 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 112 112 172.91 -1.842462404 FALSE TRUE TRUE -25.217400451
## 113 113 169.27 -1.879812908 FALSE TRUE TRUE -25.638162468
## 114 114 391.71 0.402672297 FALSE FALSE FALSE 0.074558105
## 115 115 351.85 -0.006336246 FALSE FALSE FALSE -4.533017161
## 116 116 372.80 0.208634375 FALSE FALSE FALSE -2.111323688
## 117 117 341.60 -0.111512803 FALSE FALSE FALSE -5.717855256
## 118 118 343.28 -0.094274109 FALSE FALSE FALSE -5.523657402
## 119 119 261.95 -0.928811610 FALSE TRUE TRUE -14.924914209
## 120 120 321.02 -0.322686808 FALSE TRUE FALSE -8.096778963
## 121 121 88.01 -2.713632130 FALSE TRUE TRUE -35.031327697
## 122 122 88.63 -2.707270231 FALSE TRUE TRUE -34.959659442
## 123 123 363.43 0.112487610 FALSE FALSE FALSE -3.194439098
## 124 124 353.89 0.014596454 FALSE FALSE FALSE -4.297205481
## 125 125 364.31 0.121517403 FALSE FALSE FALSE -3.092716413
## 126 126 338.92 -0.139012625 FALSE TRUE FALSE -6.027647070
## 127 127 374.43 0.225360013 FALSE FALSE FALSE -1.922905533
## 128 128 389.61 0.381123929 FALSE FALSE FALSE -0.168189213
## 129 129 388.45 0.369221021 FALSE FALSE FALSE -0.302278207
## 130 130 395.11 0.437560130 FALSE FALSE FALSE 0.467577570
## 131 131 240.16 -1.152401578 FALSE TRUE TRUE -17.443706608
## 132 132 369.30 0.172720429 FALSE FALSE FALSE -2.515902550
## 133 133 227.61 -1.281178729 FALSE TRUE TRUE -18.894410812
## 134 134 297.09 -0.568235589 FALSE TRUE TRUE -10.862942437
## 135 135 292.29 -0.617489001 FALSE TRUE TRUE -11.417793448
## 136 136 348.13 -0.044507641 FALSE FALSE FALSE -4.963026694
## 137 137 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 138 138 395.50 0.441561970 FALSE FALSE FALSE 0.512659215
## 139 139 393.23 0.418269210 FALSE FALSE FALSE 0.250260924
## 140 140 390.96 0.394976451 FALSE FALSE FALSE -0.012137366
## 141 141 393.23 0.418269210 FALSE FALSE FALSE 0.250260924
## 142 142 391.27 0.398157400 FALSE FALSE FALSE 0.023696762
## 143 143 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 144 144 395.56 0.442177637 FALSE FALSE FALSE 0.519594852
## 145 145 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 146 146 391.00 0.395386896 FALSE FALSE FALSE -0.007513607
## 147 147 387.11 0.355471110 FALSE FALSE FALSE -0.457174114
## 148 148 393.87 0.424836332 FALSE FALSE FALSE 0.324241059
## 149 149 382.84 0.311656095 FALSE FALSE FALSE -0.950760325
## 150 150 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 151 151 377.68 0.258708677 FALSE FALSE FALSE -1.547225161
## 152 152 389.71 0.382150042 FALSE FALSE FALSE -0.156629816
## 153 153 390.49 0.390153721 FALSE FALSE FALSE -0.066466527
## 154 154 393.37 0.419705768 FALSE FALSE FALSE 0.266444079
## 155 155 376.70 0.248652773 FALSE FALSE FALSE -1.660507242
## 156 156 394.23 0.428530338 FALSE FALSE FALSE 0.365854885
## 157 157 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 158 158 354.31 0.018906127 FALSE FALSE FALSE -4.248656018
## 159 159 392.20 0.407700249 FALSE FALSE FALSE 0.131199145
## 160 160 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 161 161 384.30 0.326637342 FALSE FALSE FALSE -0.781993143
## 162 162 393.77 0.423810219 FALSE FALSE FALSE 0.312681663
## 163 163 395.38 0.440330635 FALSE FALSE FALSE 0.498787940
## 164 164 392.78 0.413651703 FALSE FALSE FALSE 0.198243642
## 165 165 389.43 0.379276926 FALSE FALSE FALSE -0.188996125
## 166 166 381.32 0.296059182 FALSE FALSE FALSE -1.126463145
## 167 167 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 168 168 393.25 0.418474433 FALSE FALSE FALSE 0.252572804
## 169 169 390.94 0.394771228 FALSE FALSE FALSE -0.014449245
## 170 170 385.81 0.342131644 FALSE FALSE FALSE -0.607446262
## 171 171 348.93 -0.036298739 FALSE FALSE FALSE -4.870551525
## 172 172 393.63 0.422373661 FALSE FALSE FALSE 0.296498509
## 173 173 392.80 0.413856926 FALSE FALSE FALSE 0.200555521
## 174 174 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 175 175 393.74 0.423502385 FALSE FALSE FALSE 0.309213844
## 176 176 391.70 0.402569685 FALSE FALSE FALSE 0.073402165
## 177 177 395.24 0.438894077 FALSE FALSE FALSE 0.482604785
## 178 178 390.39 0.389127608 FALSE FALSE FALSE -0.078025923
## 179 179 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 180 180 385.05 0.334333187 FALSE FALSE FALSE -0.695297672
## 181 181 387.38 0.358241614 FALSE FALSE FALSE -0.425963744
## 182 182 372.08 0.201246363 FALSE FALSE FALSE -2.194551340
## 183 183 377.51 0.256964286 FALSE FALSE FALSE -1.566876134
## 184 184 378.35 0.265583633 FALSE FALSE FALSE -1.469777208
## 185 185 376.14 0.242906541 FALSE FALSE FALSE -1.725239860
## 186 186 385.91 0.343157757 FALSE FALSE FALSE -0.595886866
## 187 187 378.95 0.271740309 FALSE FALSE FALSE -1.400420831
## 188 188 360.20 0.079344169 FALSE FALSE FALSE -3.567807591
## 189 189 376.75 0.249165829 FALSE FALSE FALSE -1.654727544
## 190 190 388.45 0.369221021 FALSE FALSE FALSE -0.302278207
## 191 191 390.07 0.385844047 FALSE FALSE FALSE -0.115015991
## 192 192 383.78 0.321301555 FALSE FALSE FALSE -0.842102002
## 193 193 391.25 0.397952178 FALSE FALSE FALSE 0.021384883
## 194 194 394.62 0.432532178 FALSE FALSE FALSE 0.410936530
## 195 195 372.75 0.208121319 FALSE FALSE FALSE -2.117103386
## 196 196 374.71 0.228233129 FALSE FALSE FALSE -1.890539224
## 197 197 372.49 0.205453426 FALSE FALSE FALSE -2.147157816
## 198 198 390.18 0.386972771 FALSE FALSE FALSE -0.102300655
## 199 199 376.14 0.242906541 FALSE FALSE FALSE -1.725239860
## 200 200 374.71 0.228233129 FALSE FALSE FALSE -1.890539224
## 201 201 393.74 0.423502385 FALSE FALSE FALSE 0.309213844
## 202 202 377.07 0.252449390 FALSE FALSE FALSE -1.617737477
## 203 203 386.09 0.345004760 FALSE FALSE FALSE -0.575079953
## 204 204 392.89 0.414780427 FALSE FALSE FALSE 0.210958978
## 205 205 395.18 0.438278409 FALSE FALSE FALSE 0.475669147
## 206 206 386.34 0.347570042 FALSE FALSE FALSE -0.546181463
## 207 207 389.70 0.382047430 FALSE FALSE FALSE -0.157785756
## 208 208 391.93 0.404929745 FALSE FALSE FALSE 0.099988776
## 209 209 392.80 0.413856926 FALSE FALSE FALSE 0.200555521
## 210 210 388.37 0.368400131 FALSE FALSE FALSE -0.311525724
## 211 211 386.86 0.352905828 FALSE FALSE FALSE -0.486072604
## 212 212 384.07 0.324277282 FALSE FALSE FALSE -0.808579754
## 213 213 390.30 0.388204107 FALSE FALSE FALSE -0.088429380
## 214 214 391.34 0.398875679 FALSE FALSE FALSE 0.031788339
## 215 215 388.65 0.371273246 FALSE FALSE FALSE -0.279159415
## 216 216 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 217 217 394.96 0.436020961 FALSE FALSE FALSE 0.450238476
## 218 218 389.25 0.377429923 FALSE FALSE FALSE -0.209803038
## 219 219 393.45 0.420526658 FALSE FALSE FALSE 0.275691596
## 220 220 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 221 221 387.31 0.357523335 FALSE FALSE FALSE -0.434055322
## 222 222 392.23 0.408008083 FALSE FALSE FALSE 0.134666964
## 223 223 377.07 0.252449390 FALSE FALSE FALSE -1.617737477
## 224 224 395.52 0.441767192 FALSE FALSE FALSE 0.514971094
## 225 225 394.72 0.433558290 FALSE FALSE FALSE 0.422495926
## 226 226 394.72 0.433558290 FALSE FALSE FALSE 0.422495926
## 227 227 341.60 -0.111512803 FALSE FALSE FALSE -5.717855256
## 228 228 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 229 229 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 230 230 371.72 0.197552357 FALSE FALSE FALSE -2.236165166
## 231 231 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 232 232 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 233 233 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 234 234 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 235 235 392.85 0.414369982 FALSE FALSE FALSE 0.206335219
## 236 236 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 237 237 368.24 0.161843634 FALSE FALSE FALSE -2.638432148
## 238 238 395.75 0.444127252 FALSE FALSE FALSE 0.541557705
## 239 239 390.43 0.389538053 FALSE FALSE FALSE -0.073402165
## 240 240 393.68 0.422886718 FALSE FALSE FALSE 0.302278207
## 241 241 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 242 242 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 243 243 396.24 0.449155204 FALSE FALSE FALSE 0.598198746
## 244 244 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 245 245 396.30 0.449770872 FALSE FALSE FALSE 0.605134383
## 246 246 393.39 0.419910991 FALSE FALSE FALSE 0.268755958
## 247 247 395.69 0.443511584 FALSE FALSE FALSE 0.534622067
## 248 248 396.42 0.451002207 FALSE FALSE FALSE 0.619005658
## 249 249 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 250 250 395.21 0.438586243 FALSE FALSE FALSE 0.479136966
## 251 251 396.23 0.449052593 FALSE FALSE FALSE 0.597042806
## 252 252 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 253 253 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 254 254 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 255 255 391.13 0.396720843 FALSE FALSE FALSE 0.007513607
## 256 256 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 257 257 393.68 0.422886718 FALSE FALSE FALSE 0.302278207
## 258 258 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 259 259 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 260 260 382.44 0.307551644 FALSE FALSE FALSE -0.996997909
## 261 261 375.21 0.233363693 FALSE FALSE FALSE -1.832742244
## 262 262 368.57 0.165229806 FALSE FALSE FALSE -2.600286141
## 263 263 394.02 0.426375501 FALSE FALSE FALSE 0.341580153
## 264 264 389.71 0.382150042 FALSE FALSE FALSE -0.156629816
## 265 265 389.40 0.378969092 FALSE FALSE FALSE -0.192463944
## 266 266 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 267 267 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 268 268 394.81 0.434481792 FALSE FALSE FALSE 0.432899382
## 269 269 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 270 270 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 271 271 394.74 0.433763513 FALSE FALSE FALSE 0.424807805
## 272 272 389.96 0.384715323 FALSE FALSE FALSE -0.127731326
## 273 273 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 274 274 387.97 0.364295680 FALSE FALSE FALSE -0.357763308
## 275 275 385.64 0.340387253 FALSE FALSE FALSE -0.627097236
## 276 276 364.61 0.124595741 FALSE FALSE FALSE -3.058038225
## 277 277 392.43 0.410060308 FALSE FALSE FALSE 0.157785756
## 278 278 390.94 0.394771228 FALSE FALSE FALSE -0.014449245
## 279 279 389.85 0.383586599 FALSE FALSE FALSE -0.140446662
## 280 280 370.78 0.187906898 FALSE FALSE FALSE -2.344823489
## 281 281 384.46 0.328279122 FALSE FALSE FALSE -0.763498109
## 282 282 382.80 0.311245650 FALSE FALSE FALSE -0.955384083
## 283 283 376.04 0.241880428 FALSE FALSE FALSE -1.736799256
## 284 284 377.73 0.259221734 FALSE FALSE FALSE -1.541445463
## 285 285 391.34 0.398875679 FALSE FALSE FALSE 0.031788339
## 286 286 395.43 0.440843691 FALSE FALSE FALSE 0.504567638
## 287 287 374.56 0.226693960 FALSE FALSE FALSE -1.907878318
## 288 288 350.65 -0.018649599 FALSE FALSE FALSE -4.671729913
## 289 289 380.79 0.290620784 FALSE FALSE FALSE -1.187727944
## 290 290 353.04 0.005874496 FALSE FALSE FALSE -4.395460348
## 291 291 354.55 0.021368798 FALSE FALSE FALSE -4.220913467
## 292 292 354.70 0.022907967 FALSE FALSE FALSE -4.203574373
## 293 293 316.03 -0.373889834 FALSE TRUE FALSE -8.673592826
## 294 294 131.42 -2.268196585 FALSE TRUE TRUE -30.013393872
## 295 295 375.52 0.236544642 FALSE FALSE FALSE -1.796908116
## 296 296 392.05 0.406161080 FALSE FALSE FALSE 0.113860051
## 297 297 366.15 0.140397877 FALSE FALSE FALSE -2.880023526
## 298 298 347.88 -0.047072922 FALSE FALSE FALSE -4.991925184
## 299 299 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 300 300 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 301 301 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 302 302 363.02 0.108280548 FALSE FALSE FALSE -3.241832622
## 303 303 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 304 304 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 305 305 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 306 306 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 307 307 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 308 308 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 309 309 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 310 310 372.92 0.209865711 FALSE FALSE FALSE -2.097452413
## 311 311 378.38 0.265891467 FALSE FALSE FALSE -1.466309389
## 312 312 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 313 313 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 314 314 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 315 315 391.98 0.405442801 FALSE FALSE FALSE 0.105768474
## 316 316 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 317 317 338.16 -0.146811082 FALSE TRUE FALSE -6.115498480
## 318 318 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 319 319 376.11 0.242598707 FALSE FALSE FALSE -1.728707679
## 320 320 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 321 321 329.46 -0.236082891 FALSE TRUE FALSE -7.121165936
## 322 322 384.97 0.333512297 FALSE FALSE FALSE -0.704545189
## 323 323 314.64 -0.388152801 FALSE TRUE FALSE -8.834268431
## 324 324 2.60 -3.590035031 TRUE TRUE TRUE -44.904207863
## 325 325 35.05 -3.257061443 TRUE TRUE TRUE -41.153183845
## 326 326 28.79 -3.321296102 TRUE TRUE TRUE -41.876802038
## 327 327 210.97 -1.451923891 FALSE TRUE TRUE -20.817894315
## 328 328 88.27 -2.710964237 FALSE TRUE TRUE -35.001273267
## 329 329 27.25 -3.337098238 TRUE TRUE TRUE -42.054816737
## 330 330 21.57 -3.395381442 TRUE TRUE TRUE -42.711390432
## 331 331 127.36 -2.309856762 FALSE TRUE TRUE -30.482705352
## 332 332 16.45 -3.447918415 TRUE TRUE TRUE -43.303231510
## 333 333 48.45 -3.119562335 TRUE TRUE TRUE -39.604224774
## 334 334 318.75 -0.345979567 FALSE TRUE FALSE -8.359177253
## 335 335 319.98 -0.333358380 FALSE TRUE FALSE -8.216996682
## 336 336 291.55 -0.625082235 FALSE TRUE TRUE -11.503332978
## 337 337 2.52 -3.590855921 TRUE TRUE TRUE -44.913455380
## 338 338 3.65 -3.579260847 TRUE TRUE TRUE -44.782834204
## 339 339 7.68 -3.537908503 TRUE TRUE TRUE -44.316990544
## 340 340 24.65 -3.363777169 TRUE TRUE TRUE -42.355361034
## 341 341 18.82 -3.423599543 TRUE TRUE TRUE -43.029273824
## 342 342 60.72 -2.993658300 FALSE TRUE TRUE -38.185886879
## 343 343 83.45 -2.760422872 FALSE TRUE TRUE -35.558436157
## 344 344 81.33 -2.782176462 FALSE TRUE TRUE -35.803495353
## 345 345 97.95 -2.611636523 FALSE TRUE TRUE -33.882323730
## 346 346 100.19 -2.588651597 FALSE TRUE TRUE -33.623393258
## 347 347 100.63 -2.584136701 FALSE TRUE TRUE -33.572531916
## 348 348 109.85 -2.489529105 FALSE TRUE TRUE -32.506755600
## 349 349 27.49 -3.334635567 TRUE TRUE TRUE -42.027074186
## 350 350 68.95 -2.909209220 FALSE TRUE TRUE -37.234548584
## 351 351 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 352 352 391.45 0.400004403 FALSE FALSE FALSE 0.044503675
## 353 353 395.69 0.443511584 FALSE FALSE FALSE 0.534622067
## 354 354 240.52 -1.148707573 FALSE TRUE TRUE -17.402092782
## 355 355 43.06 -3.174869812 TRUE TRUE TRUE -40.227276221
## 356 356 318.01 -0.353572801 FALSE TRUE FALSE -8.444716784
## 357 357 388.52 0.369939300 FALSE FALSE FALSE -0.294186629
## 358 358 304.21 -0.495176361 FALSE TRUE TRUE -10.039913439
## 359 359 0.32 -3.613430402 TRUE TRUE TRUE -45.167762093
## 360 360 355.29 0.028962032 FALSE FALSE FALSE -4.135373937
## 361 361 375.87 0.240136037 FALSE FALSE FALSE -1.756450230
## 362 362 6.68 -3.548169631 TRUE TRUE TRUE -44.432584504
## 363 363 50.92 -3.094217350 TRUE TRUE TRUE -39.318707692
## 364 364 10.48 -3.509177346 TRUE TRUE TRUE -43.993327454
## 365 365 3.50 -3.580800016 TRUE TRUE TRUE -44.800173298
## 366 366 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 367 367 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 368 368 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 369 369 334.40 -0.185392921 FALSE TRUE FALSE -6.550131771
## 370 370 22.01 -3.390866546 TRUE TRUE TRUE -42.660529090
## 371 371 368.74 0.166974198 FALSE FALSE FALSE -2.580635168
## 372 372 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 373 373 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 374 374 395.33 0.439817578 FALSE FALSE FALSE 0.493008242
## 375 375 393.37 0.419705768 FALSE FALSE FALSE 0.266444079
## 376 376 374.68 0.227925295 FALSE FALSE FALSE -1.894007043
## 377 377 352.58 0.001154377 FALSE FALSE FALSE -4.448633570
## 378 378 302.76 -0.510054996 FALSE TRUE TRUE -10.207524681
## 379 379 396.21 0.448847370 FALSE FALSE FALSE 0.594730927
## 380 380 349.48 -0.030655118 FALSE FALSE FALSE -4.806974847
## 381 381 379.70 0.279436155 FALSE FALSE FALSE -1.313725361
## 382 382 383.32 0.316581437 FALSE FALSE FALSE -0.895275224
## 383 383 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 384 384 393.07 0.416627430 FALSE FALSE FALSE 0.231765891
## 385 385 395.28 0.439304522 FALSE FALSE FALSE 0.487228543
## 386 386 392.92 0.415088261 FALSE FALSE FALSE 0.214426797
## 387 387 370.73 0.187393841 FALSE FALSE FALSE -2.350603187
## 388 388 388.62 0.370965413 FALSE FALSE FALSE -0.282627233
## 389 389 388.22 0.366860962 FALSE FALSE FALSE -0.328864818
## 390 390 395.09 0.437354908 FALSE FALSE FALSE 0.465265691
## 391 391 344.05 -0.086373041 FALSE FALSE FALSE -5.434650053
## 392 392 318.43 -0.349263128 FALSE TRUE FALSE -8.396167320
## 393 393 390.11 0.386254493 FALSE FALSE FALSE -0.110392232
## 394 394 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 395 395 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 396 396 393.29 0.418884878 FALSE FALSE FALSE 0.257196562
## 397 397 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 398 398 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 399 399 395.77 0.444332474 FALSE FALSE FALSE 0.543869584
## 400 400 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 401 401 391.99 0.405545412 FALSE FALSE FALSE 0.106924413
## 402 402 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## 403 403 393.45 0.420526658 FALSE FALSE FALSE 0.275691596
## 404 404 396.90 0.455927548 FALSE FALSE FALSE 0.674490759
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 TRUE
## 19 TRUE
## 20 FALSE
## 21 TRUE
## 22 FALSE
## 23 TRUE
## 24 TRUE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 TRUE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 TRUE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 TRUE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 TRUE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 TRUE
## 113 TRUE
## 114 FALSE
## 115 TRUE
## 116 FALSE
## 117 TRUE
## 118 TRUE
## 119 TRUE
## 120 TRUE
## 121 TRUE
## 122 TRUE
## 123 FALSE
## 124 TRUE
## 125 FALSE
## 126 TRUE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 TRUE
## 132 FALSE
## 133 TRUE
## 134 TRUE
## 135 TRUE
## 136 TRUE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 TRUE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 TRUE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 TRUE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 TRUE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 TRUE
## 289 FALSE
## 290 TRUE
## 291 TRUE
## 292 TRUE
## 293 TRUE
## 294 TRUE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 TRUE
## 299 FALSE
## 300 FALSE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 TRUE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 TRUE
## 322 FALSE
## 323 TRUE
## 324 TRUE
## 325 TRUE
## 326 TRUE
## 327 TRUE
## 328 TRUE
## 329 TRUE
## 330 TRUE
## 331 TRUE
## 332 TRUE
## 333 TRUE
## 334 TRUE
## 335 TRUE
## 336 TRUE
## 337 TRUE
## 338 TRUE
## 339 TRUE
## 340 TRUE
## 341 TRUE
## 342 TRUE
## 343 TRUE
## 344 TRUE
## 345 TRUE
## 346 TRUE
## 347 TRUE
## 348 TRUE
## 349 TRUE
## 350 TRUE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 TRUE
## 355 TRUE
## 356 TRUE
## 357 FALSE
## 358 TRUE
## 359 TRUE
## 360 TRUE
## 361 FALSE
## 362 TRUE
## 363 TRUE
## 364 TRUE
## 365 TRUE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 TRUE
## 370 TRUE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 TRUE
## 378 TRUE
## 379 FALSE
## 380 TRUE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 TRUE
## 392 TRUE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 18 303.42 -0.503282652 FALSE TRUE -10.131233 TRUE
## 2 19 306.38 -0.472909714 FALSE TRUE -9.789075 TRUE
## 3 21 360.17 0.079036335 FALSE FALSE -3.571275 TRUE
## 4 23 358.77 0.064670756 FALSE FALSE -3.733107 TRUE
## 5 24 248.31 -1.068773389 FALSE TRUE -16.501616 TRUE
## 6 68 357.98 0.056564465 FALSE FALSE -3.824426 TRUE
## 7 75 70.80 -2.890226135 FALSE TRUE -37.020700 TRUE
## 8 85 344.91 -0.077548471 FALSE FALSE -5.335239 TRUE
## 9 102 262.76 -0.920500097 FALSE TRUE -14.831283 TRUE
## 10 112 172.91 -1.842462404 FALSE TRUE -25.217400 TRUE
## 11 113 169.27 -1.879812908 FALSE TRUE -25.638162 TRUE
## 12 115 351.85 -0.006336246 FALSE FALSE -4.533017 TRUE
## 13 117 341.60 -0.111512803 FALSE FALSE -5.717855 TRUE
## 14 118 343.28 -0.094274109 FALSE FALSE -5.523657 TRUE
## 15 119 261.95 -0.928811610 FALSE TRUE -14.924914 TRUE
## 16 120 321.02 -0.322686808 FALSE TRUE -8.096779 TRUE
## 17 121 88.01 -2.713632130 FALSE TRUE -35.031328 TRUE
## 18 122 88.63 -2.707270231 FALSE TRUE -34.959659 TRUE
## 19 124 353.89 0.014596454 FALSE FALSE -4.297205 TRUE
## 20 126 338.92 -0.139012625 FALSE TRUE -6.027647 TRUE
## 21 131 240.16 -1.152401578 FALSE TRUE -17.443707 TRUE
## 22 133 227.61 -1.281178729 FALSE TRUE -18.894411 TRUE
## 23 134 297.09 -0.568235589 FALSE TRUE -10.862942 TRUE
## 24 135 292.29 -0.617489001 FALSE TRUE -11.417793 TRUE
## 25 136 348.13 -0.044507641 FALSE FALSE -4.963027 TRUE
## 26 158 354.31 0.018906127 FALSE FALSE -4.248656 TRUE
## 27 171 348.93 -0.036298739 FALSE FALSE -4.870552 TRUE
## 28 188 360.20 0.079344169 FALSE FALSE -3.567808 TRUE
## 29 227 341.60 -0.111512803 FALSE FALSE -5.717855 TRUE
## 30 288 350.65 -0.018649599 FALSE FALSE -4.671730 TRUE
## 31 290 353.04 0.005874496 FALSE FALSE -4.395460 TRUE
## 32 291 354.55 0.021368798 FALSE FALSE -4.220913 TRUE
## 33 292 354.70 0.022907967 FALSE FALSE -4.203574 TRUE
## 34 293 316.03 -0.373889834 FALSE TRUE -8.673593 TRUE
## 35 294 131.42 -2.268196585 FALSE TRUE -30.013394 TRUE
## 36 298 347.88 -0.047072922 FALSE FALSE -4.991925 TRUE
## 37 317 338.16 -0.146811082 FALSE TRUE -6.115498 TRUE
## 38 321 329.46 -0.236082891 FALSE TRUE -7.121166 TRUE
## 39 323 314.64 -0.388152801 FALSE TRUE -8.834268 TRUE
## 40 324 2.60 -3.590035031 TRUE TRUE -44.904208 TRUE
## 41 325 35.05 -3.257061443 TRUE TRUE -41.153184 TRUE
## 42 326 28.79 -3.321296102 TRUE TRUE -41.876802 TRUE
## 43 327 210.97 -1.451923891 FALSE TRUE -20.817894 TRUE
## 44 328 88.27 -2.710964237 FALSE TRUE -35.001273 TRUE
## 45 329 27.25 -3.337098238 TRUE TRUE -42.054817 TRUE
## 46 330 21.57 -3.395381442 TRUE TRUE -42.711390 TRUE
## 47 331 127.36 -2.309856762 FALSE TRUE -30.482705 TRUE
## 48 332 16.45 -3.447918415 TRUE TRUE -43.303232 TRUE
## 49 333 48.45 -3.119562335 TRUE TRUE -39.604225 TRUE
## 50 334 318.75 -0.345979567 FALSE TRUE -8.359177 TRUE
## 51 335 319.98 -0.333358380 FALSE TRUE -8.216997 TRUE
## 52 336 291.55 -0.625082235 FALSE TRUE -11.503333 TRUE
## 53 337 2.52 -3.590855921 TRUE TRUE -44.913455 TRUE
## 54 338 3.65 -3.579260847 TRUE TRUE -44.782834 TRUE
## 55 339 7.68 -3.537908503 TRUE TRUE -44.316991 TRUE
## 56 340 24.65 -3.363777169 TRUE TRUE -42.355361 TRUE
## 57 341 18.82 -3.423599543 TRUE TRUE -43.029274 TRUE
## 58 342 60.72 -2.993658300 FALSE TRUE -38.185887 TRUE
## 59 343 83.45 -2.760422872 FALSE TRUE -35.558436 TRUE
## 60 344 81.33 -2.782176462 FALSE TRUE -35.803495 TRUE
## 61 345 97.95 -2.611636523 FALSE TRUE -33.882324 TRUE
## 62 346 100.19 -2.588651597 FALSE TRUE -33.623393 TRUE
## 63 347 100.63 -2.584136701 FALSE TRUE -33.572532 TRUE
## 64 348 109.85 -2.489529105 FALSE TRUE -32.506756 TRUE
## 65 349 27.49 -3.334635567 TRUE TRUE -42.027074 TRUE
## 66 350 68.95 -2.909209220 FALSE TRUE -37.234549 TRUE
## 67 354 240.52 -1.148707573 FALSE TRUE -17.402093 TRUE
## 68 355 43.06 -3.174869812 TRUE TRUE -40.227276 TRUE
## 69 356 318.01 -0.353572801 FALSE TRUE -8.444717 TRUE
## 70 358 304.21 -0.495176361 FALSE TRUE -10.039913 TRUE
## 71 359 0.32 -3.613430402 TRUE TRUE -45.167762 TRUE
## 72 360 355.29 0.028962032 FALSE FALSE -4.135374 TRUE
## 73 362 6.68 -3.548169631 TRUE TRUE -44.432585 TRUE
## 74 363 50.92 -3.094217350 TRUE TRUE -39.318708 TRUE
## 75 364 10.48 -3.509177346 TRUE TRUE -43.993327 TRUE
## 76 365 3.50 -3.580800016 TRUE TRUE -44.800173 TRUE
## 77 369 334.40 -0.185392921 FALSE TRUE -6.550132 TRUE
## 78 370 22.01 -3.390866546 TRUE TRUE -42.660529 TRUE
## 79 377 352.58 0.001154377 FALSE FALSE -4.448634 TRUE
## 80 378 302.76 -0.510054996 FALSE TRUE -10.207525 TRUE
## 81 380 349.48 -0.030655118 FALSE FALSE -4.806975 TRUE
## 82 391 344.05 -0.086373041 FALSE FALSE -5.434650 TRUE
## 83 392 318.43 -0.349263128 FALSE TRUE -8.396167 TRUE
## metodos_evaluados numero_metodos
## 1 3 2
## 2 3 2
## 3 3 1
## 4 3 1
## 5 3 2
## 6 3 1
## 7 3 2
## 8 3 1
## 9 3 2
## 10 3 2
## 11 3 2
## 12 3 1
## 13 3 1
## 14 3 1
## 15 3 2
## 16 3 2
## 17 3 2
## 18 3 2
## 19 3 1
## 20 3 2
## 21 3 2
## 22 3 2
## 23 3 2
## 24 3 2
## 25 3 1
## 26 3 1
## 27 3 1
## 28 3 1
## 29 3 1
## 30 3 1
## 31 3 1
## 32 3 1
## 33 3 1
## 34 3 2
## 35 3 2
## 36 3 1
## 37 3 2
## 38 3 2
## 39 3 2
## 40 3 3
## 41 3 3
## 42 3 3
## 43 3 2
## 44 3 2
## 45 3 3
## 46 3 3
## 47 3 2
## 48 3 3
## 49 3 3
## 50 3 2
## 51 3 2
## 52 3 2
## 53 3 3
## 54 3 3
## 55 3 3
## 56 3 3
## 57 3 3
## 58 3 2
## 59 3 2
## 60 3 2
## 61 3 2
## 62 3 2
## 63 3 2
## 64 3 2
## 65 3 3
## 66 3 2
## 67 3 2
## 68 3 3
## 69 3 2
## 70 3 2
## 71 3 3
## 72 3 1
## 73 3 3
## 74 3 3
## 75 3 3
## 76 3 3
## 77 3 2
## 78 3 3
## 79 3 1
## 80 3 2
## 81 3 1
## 82 3 1
## 83 3 2
##
## ===== AL MENOS DOS MÉTODOS =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 18 303.42 -0.5032827 FALSE TRUE -10.131233 TRUE
## 2 19 306.38 -0.4729097 FALSE TRUE -9.789075 TRUE
## 3 24 248.31 -1.0687734 FALSE TRUE -16.501616 TRUE
## 4 75 70.80 -2.8902261 FALSE TRUE -37.020700 TRUE
## 5 102 262.76 -0.9205001 FALSE TRUE -14.831283 TRUE
## 6 112 172.91 -1.8424624 FALSE TRUE -25.217400 TRUE
## 7 113 169.27 -1.8798129 FALSE TRUE -25.638162 TRUE
## 8 119 261.95 -0.9288116 FALSE TRUE -14.924914 TRUE
## 9 120 321.02 -0.3226868 FALSE TRUE -8.096779 TRUE
## 10 121 88.01 -2.7136321 FALSE TRUE -35.031328 TRUE
## 11 122 88.63 -2.7072702 FALSE TRUE -34.959659 TRUE
## 12 126 338.92 -0.1390126 FALSE TRUE -6.027647 TRUE
## 13 131 240.16 -1.1524016 FALSE TRUE -17.443707 TRUE
## 14 133 227.61 -1.2811787 FALSE TRUE -18.894411 TRUE
## 15 134 297.09 -0.5682356 FALSE TRUE -10.862942 TRUE
## 16 135 292.29 -0.6174890 FALSE TRUE -11.417793 TRUE
## 17 293 316.03 -0.3738898 FALSE TRUE -8.673593 TRUE
## 18 294 131.42 -2.2681966 FALSE TRUE -30.013394 TRUE
## 19 317 338.16 -0.1468111 FALSE TRUE -6.115498 TRUE
## 20 321 329.46 -0.2360829 FALSE TRUE -7.121166 TRUE
## 21 323 314.64 -0.3881528 FALSE TRUE -8.834268 TRUE
## 22 324 2.60 -3.5900350 TRUE TRUE -44.904208 TRUE
## 23 325 35.05 -3.2570614 TRUE TRUE -41.153184 TRUE
## 24 326 28.79 -3.3212961 TRUE TRUE -41.876802 TRUE
## 25 327 210.97 -1.4519239 FALSE TRUE -20.817894 TRUE
## 26 328 88.27 -2.7109642 FALSE TRUE -35.001273 TRUE
## 27 329 27.25 -3.3370982 TRUE TRUE -42.054817 TRUE
## 28 330 21.57 -3.3953814 TRUE TRUE -42.711390 TRUE
## 29 331 127.36 -2.3098568 FALSE TRUE -30.482705 TRUE
## 30 332 16.45 -3.4479184 TRUE TRUE -43.303232 TRUE
## 31 333 48.45 -3.1195623 TRUE TRUE -39.604225 TRUE
## 32 334 318.75 -0.3459796 FALSE TRUE -8.359177 TRUE
## 33 335 319.98 -0.3333584 FALSE TRUE -8.216997 TRUE
## 34 336 291.55 -0.6250822 FALSE TRUE -11.503333 TRUE
## 35 337 2.52 -3.5908559 TRUE TRUE -44.913455 TRUE
## 36 338 3.65 -3.5792608 TRUE TRUE -44.782834 TRUE
## 37 339 7.68 -3.5379085 TRUE TRUE -44.316991 TRUE
## 38 340 24.65 -3.3637772 TRUE TRUE -42.355361 TRUE
## 39 341 18.82 -3.4235995 TRUE TRUE -43.029274 TRUE
## 40 342 60.72 -2.9936583 FALSE TRUE -38.185887 TRUE
## 41 343 83.45 -2.7604229 FALSE TRUE -35.558436 TRUE
## 42 344 81.33 -2.7821765 FALSE TRUE -35.803495 TRUE
## 43 345 97.95 -2.6116365 FALSE TRUE -33.882324 TRUE
## 44 346 100.19 -2.5886516 FALSE TRUE -33.623393 TRUE
## 45 347 100.63 -2.5841367 FALSE TRUE -33.572532 TRUE
## 46 348 109.85 -2.4895291 FALSE TRUE -32.506756 TRUE
## 47 349 27.49 -3.3346356 TRUE TRUE -42.027074 TRUE
## 48 350 68.95 -2.9092092 FALSE TRUE -37.234549 TRUE
## 49 354 240.52 -1.1487076 FALSE TRUE -17.402093 TRUE
## 50 355 43.06 -3.1748698 TRUE TRUE -40.227276 TRUE
## 51 356 318.01 -0.3535728 FALSE TRUE -8.444717 TRUE
## 52 358 304.21 -0.4951764 FALSE TRUE -10.039913 TRUE
## 53 359 0.32 -3.6134304 TRUE TRUE -45.167762 TRUE
## 54 362 6.68 -3.5481696 TRUE TRUE -44.432585 TRUE
## 55 363 50.92 -3.0942173 TRUE TRUE -39.318708 TRUE
## 56 364 10.48 -3.5091773 TRUE TRUE -43.993327 TRUE
## 57 365 3.50 -3.5808000 TRUE TRUE -44.800173 TRUE
## 58 369 334.40 -0.1853929 FALSE TRUE -6.550132 TRUE
## 59 370 22.01 -3.3908665 TRUE TRUE -42.660529 TRUE
## 60 378 302.76 -0.5100550 FALSE TRUE -10.207525 TRUE
## 61 392 318.43 -0.3492631 FALSE TRUE -8.396167 TRUE
## metodos_evaluados numero_metodos
## 1 3 2
## 2 3 2
## 3 3 2
## 4 3 2
## 5 3 2
## 6 3 2
## 7 3 2
## 8 3 2
## 9 3 2
## 10 3 2
## 11 3 2
## 12 3 2
## 13 3 2
## 14 3 2
## 15 3 2
## 16 3 2
## 17 3 2
## 18 3 2
## 19 3 2
## 20 3 2
## 21 3 2
## 22 3 3
## 23 3 3
## 24 3 3
## 25 3 2
## 26 3 2
## 27 3 3
## 28 3 3
## 29 3 2
## 30 3 3
## 31 3 3
## 32 3 2
## 33 3 2
## 34 3 2
## 35 3 3
## 36 3 3
## 37 3 3
## 38 3 3
## 39 3 3
## 40 3 2
## 41 3 2
## 42 3 2
## 43 3 2
## 44 3 2
## 45 3 2
## 46 3 2
## 47 3 3
## 48 3 2
## 49 3 2
## 50 3 3
## 51 3 2
## 52 3 2
## 53 3 3
## 54 3 3
## 55 3 3
## 56 3 3
## 57 3 3
## 58 3 2
## 59 3 3
## 60 3 2
## 61 3 2
##
## ===== LOS TRES MÉTODOS =====
## ID Valor Z_score Z_robusto
## 1 324 2.60 -3.590035 -44.90421
## 2 325 35.05 -3.257061 -41.15318
## 3 326 28.79 -3.321296 -41.87680
## 4 329 27.25 -3.337098 -42.05482
## 5 330 21.57 -3.395381 -42.71139
## 6 332 16.45 -3.447918 -43.30323
## 7 333 48.45 -3.119562 -39.60422
## 8 337 2.52 -3.590856 -44.91346
## 9 338 3.65 -3.579261 -44.78283
## 10 339 7.68 -3.537909 -44.31699
## 11 340 24.65 -3.363777 -42.35536
## 12 341 18.82 -3.423600 -43.02927
## 13 349 27.49 -3.334636 -42.02707
## 14 355 43.06 -3.174870 -40.22728
## 15 359 0.32 -3.613430 -45.16776
## 16 362 6.68 -3.548170 -44.43258
## 17 363 50.92 -3.094217 -39.31871
## 18 364 10.48 -3.509177 -43.99333
## 19 365 3.50 -3.580800 -44.80017
## 20 370 22.01 -3.390867 -42.66053
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## black Z-score 404 20 4.95
## black IQR 404 61 15.10
## black Tukey extremo 404 49 12.13
## black MAD 404 83 20.54## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 18 303.42 -0.503282652 -10.131233 3 2
## 2 19 306.38 -0.472909714 -9.789075 3 2
## 3 21 360.17 0.079036335 -3.571275 3 1
## 4 23 358.77 0.064670756 -3.733107 3 1
## 5 24 248.31 -1.068773389 -16.501616 3 2
## 6 68 357.98 0.056564465 -3.824426 3 1
## 7 75 70.80 -2.890226135 -37.020700 3 2
## 8 85 344.91 -0.077548471 -5.335239 3 1
## 9 102 262.76 -0.920500097 -14.831283 3 2
## 10 112 172.91 -1.842462404 -25.217400 3 2
## 11 113 169.27 -1.879812908 -25.638162 3 2
## 12 115 351.85 -0.006336246 -4.533017 3 1
## 13 117 341.60 -0.111512803 -5.717855 3 1
## 14 118 343.28 -0.094274109 -5.523657 3 1
## 15 119 261.95 -0.928811610 -14.924914 3 2
## 16 120 321.02 -0.322686808 -8.096779 3 2
## 17 121 88.01 -2.713632130 -35.031328 3 2
## 18 122 88.63 -2.707270231 -34.959659 3 2
## 19 124 353.89 0.014596454 -4.297205 3 1
## 20 126 338.92 -0.139012625 -6.027647 3 2
## 21 131 240.16 -1.152401578 -17.443707 3 2
## 22 133 227.61 -1.281178729 -18.894411 3 2
## 23 134 297.09 -0.568235589 -10.862942 3 2
## 24 135 292.29 -0.617489001 -11.417793 3 2
## 25 136 348.13 -0.044507641 -4.963027 3 1
## 26 158 354.31 0.018906127 -4.248656 3 1
## 27 171 348.93 -0.036298739 -4.870552 3 1
## 28 188 360.20 0.079344169 -3.567808 3 1
## 29 227 341.60 -0.111512803 -5.717855 3 1
## 30 288 350.65 -0.018649599 -4.671730 3 1
## 31 290 353.04 0.005874496 -4.395460 3 1
## 32 291 354.55 0.021368798 -4.220913 3 1
## 33 292 354.70 0.022907967 -4.203574 3 1
## 34 293 316.03 -0.373889834 -8.673593 3 2
## 35 294 131.42 -2.268196585 -30.013394 3 2
## 36 298 347.88 -0.047072922 -4.991925 3 1
## 37 317 338.16 -0.146811082 -6.115498 3 2
## 38 321 329.46 -0.236082891 -7.121166 3 2
## 39 323 314.64 -0.388152801 -8.834268 3 2
## 40 324 2.60 -3.590035031 -44.904208 3 3
## 41 325 35.05 -3.257061443 -41.153184 3 3
## 42 326 28.79 -3.321296102 -41.876802 3 3
## 43 327 210.97 -1.451923891 -20.817894 3 2
## 44 328 88.27 -2.710964237 -35.001273 3 2
## 45 329 27.25 -3.337098238 -42.054817 3 3
## 46 330 21.57 -3.395381442 -42.711390 3 3
## 47 331 127.36 -2.309856762 -30.482705 3 2
## 48 332 16.45 -3.447918415 -43.303232 3 3
## 49 333 48.45 -3.119562335 -39.604225 3 3
## 50 334 318.75 -0.345979567 -8.359177 3 2
## 51 335 319.98 -0.333358380 -8.216997 3 2
## 52 336 291.55 -0.625082235 -11.503333 3 2
## 53 337 2.52 -3.590855921 -44.913455 3 3
## 54 338 3.65 -3.579260847 -44.782834 3 3
## 55 339 7.68 -3.537908503 -44.316991 3 3
## 56 340 24.65 -3.363777169 -42.355361 3 3
## 57 341 18.82 -3.423599543 -43.029274 3 3
## 58 342 60.72 -2.993658300 -38.185887 3 2
## 59 343 83.45 -2.760422872 -35.558436 3 2
## 60 344 81.33 -2.782176462 -35.803495 3 2
## 61 345 97.95 -2.611636523 -33.882324 3 2
## 62 346 100.19 -2.588651597 -33.623393 3 2
## 63 347 100.63 -2.584136701 -33.572532 3 2
## 64 348 109.85 -2.489529105 -32.506756 3 2
## 65 349 27.49 -3.334635567 -42.027074 3 3
## 66 350 68.95 -2.909209220 -37.234549 3 2
## 67 354 240.52 -1.148707573 -17.402093 3 2
## 68 355 43.06 -3.174869812 -40.227276 3 3
## 69 356 318.01 -0.353572801 -8.444717 3 2
## 70 358 304.21 -0.495176361 -10.039913 3 2
## 71 359 0.32 -3.613430402 -45.167762 3 3
## 72 360 355.29 0.028962032 -4.135374 3 1
## 73 362 6.68 -3.548169631 -44.432585 3 3
## 74 363 50.92 -3.094217350 -39.318708 3 3
## 75 364 10.48 -3.509177346 -43.993327 3 3
## 76 365 3.50 -3.580800016 -44.800173 3 3
## 77 369 334.40 -0.185392921 -6.550132 3 2
## 78 370 22.01 -3.390866546 -42.660529 3 3
## 79 377 352.58 0.001154377 -4.448634 3 1
## 80 378 302.76 -0.510054996 -10.207525 3 2
## 81 380 349.48 -0.030655118 -4.806975 3 1
## 82 391 344.05 -0.086373041 -5.434650 3 1
## 83 392 318.43 -0.349263128 -8.396167 3 2
## clasificacion
## 1 Outlier según 2 métodos
## 2 Outlier según 2 métodos
## 3 Outlier según 1 método
## 4 Outlier según 1 método
## 5 Outlier según 2 métodos
## 6 Outlier según 1 método
## 7 Outlier según 2 métodos
## 8 Outlier según 1 método
## 9 Outlier según 2 métodos
## 10 Outlier según 2 métodos
## 11 Outlier según 2 métodos
## 12 Outlier según 1 método
## 13 Outlier según 1 método
## 14 Outlier según 1 método
## 15 Outlier según 2 métodos
## 16 Outlier según 2 métodos
## 17 Outlier según 2 métodos
## 18 Outlier según 2 métodos
## 19 Outlier según 1 método
## 20 Outlier según 2 métodos
## 21 Outlier según 2 métodos
## 22 Outlier según 2 métodos
## 23 Outlier según 2 métodos
## 24 Outlier según 2 métodos
## 25 Outlier según 1 método
## 26 Outlier según 1 método
## 27 Outlier según 1 método
## 28 Outlier según 1 método
## 29 Outlier según 1 método
## 30 Outlier según 1 método
## 31 Outlier según 1 método
## 32 Outlier según 1 método
## 33 Outlier según 1 método
## 34 Outlier según 2 métodos
## 35 Outlier según 2 métodos
## 36 Outlier según 1 método
## 37 Outlier según 2 métodos
## 38 Outlier según 2 métodos
## 39 Outlier según 2 métodos
## 40 Outlier según 3 métodos
## 41 Outlier según 3 métodos
## 42 Outlier según 3 métodos
## 43 Outlier según 2 métodos
## 44 Outlier según 2 métodos
## 45 Outlier según 3 métodos
## 46 Outlier según 3 métodos
## 47 Outlier según 2 métodos
## 48 Outlier según 3 métodos
## 49 Outlier según 3 métodos
## 50 Outlier según 2 métodos
## 51 Outlier según 2 métodos
## 52 Outlier según 2 métodos
## 53 Outlier según 3 métodos
## 54 Outlier según 3 métodos
## 55 Outlier según 3 métodos
## 56 Outlier según 3 métodos
## 57 Outlier según 3 métodos
## 58 Outlier según 2 métodos
## 59 Outlier según 2 métodos
## 60 Outlier según 2 métodos
## 61 Outlier según 2 métodos
## 62 Outlier según 2 métodos
## 63 Outlier según 2 métodos
## 64 Outlier según 2 métodos
## 65 Outlier según 3 métodos
## 66 Outlier según 2 métodos
## 67 Outlier según 2 métodos
## 68 Outlier según 3 métodos
## 69 Outlier según 2 métodos
## 70 Outlier según 2 métodos
## 71 Outlier según 3 métodos
## 72 Outlier según 1 método
## 73 Outlier según 3 métodos
## 74 Outlier según 3 métodos
## 75 Outlier según 3 métodos
## 76 Outlier según 3 métodos
## 77 Outlier según 2 métodos
## 78 Outlier según 3 métodos
## 79 Outlier según 1 método
## 80 Outlier según 2 métodos
## 81 Outlier según 1 método
## 82 Outlier según 1 método
## 83 Outlier según 2 métodos
##
## ========================================
## VARIABLE: lstat
## ========================================
##
## ================ MÉTODO Z ================
## Límite inferior = -8.569252
## Límite superior = 33.66054
## Número de outliers = 5
## ID Valor Z_score
## 1 299 34.77 3.157632
## 2 300 37.97 3.612287
## 3 326 34.37 3.100800
## 4 328 36.98 3.471628
## 5 350 34.02 3.051072
##
## ================ MÉTODO IQR ================
## Q1 = 6.915
## Q3 = 16.605
## IQR = 9.69
## Límite inferior = -7.62
## Límite superior = 31.14
## Número de outliers = 6
## ID Valor
## 1 299 34.77
## 2 300 37.97
## 3 309 31.99
## 4 326 34.37
## 5 328 36.98
## 6 350 34.02
##
## =========== OUTLIERS EXTREMOS TUKEY ===========
## Límite inferior = -22.155
## Límite superior = 45.675
## Número de outliers extremos = 0
## [1] ID Valor
## <0 rows> (o 0- extensión row.names)
##
## ================ MÉTODO MAD ================
## Mediana = 11.395
## MAD = 4.76
## MAD ajustada = 7.057176
## Número de outliers = 2
## ID Valor Z_robusto
## 1 300 37.97 3.765671
## 2 328 36.98 3.625388
##
## ================ COMPARACIÓN ================
## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 1 4.98 -1.074925092 FALSE FALSE FALSE -0.909003828
## 2 2 9.14 -0.483873142 FALSE FALSE FALSE -0.319532912
## 3 3 4.03 -1.209900898 FALSE FALSE FALSE -1.043618581
## 4 4 2.94 -1.364767875 FALSE FALSE FALSE -1.198071297
## 5 5 5.21 -1.042246739 FALSE FALSE FALSE -0.876412888
## 6 6 19.15 0.938345613 FALSE FALSE FALSE 1.098881479
## 7 7 29.93 2.469965811 FALSE FALSE FALSE 2.626404670
## 8 8 17.10 0.647082032 FALSE FALSE FALSE 0.808397013
## 9 9 20.45 1.123049347 FALSE FALSE FALSE 1.283091140
## 10 10 13.27 0.102916414 FALSE FALSE FALSE 0.265687011
## 11 11 8.26 -0.608903362 FALSE FALSE FALSE -0.444228683
## 12 12 10.26 -0.324743771 FALSE FALSE FALSE -0.160829204
## 13 13 14.67 0.301828128 FALSE FALSE FALSE 0.464066646
## 14 14 11.28 -0.179822379 FALSE FALSE FALSE -0.016295470
## 15 15 13.83 0.182481100 FALSE FALSE FALSE 0.345038865
## 16 16 19.88 1.042063864 FALSE FALSE FALSE 1.202322289
## 17 17 16.30 0.533418195 FALSE FALSE FALSE 0.695037222
## 18 18 16.51 0.563254952 FALSE FALSE FALSE 0.724794167
## 19 19 17.28 0.672656395 FALSE FALSE FALSE 0.833902966
## 20 20 11.98 -0.080366522 FALSE FALSE FALSE 0.082894348
## 21 21 22.60 1.428520908 FALSE FALSE FALSE 1.587745580
## 22 22 13.04 0.070238061 FALSE FALSE FALSE 0.233096071
## 23 23 18.35 0.824681776 FALSE FALSE FALSE 0.985521687
## 24 24 20.34 1.107420570 FALSE FALSE FALSE 1.267504169
## 25 25 9.68 -0.407150052 FALSE FALSE FALSE -0.243015053
## 26 26 11.41 -0.161352006 FALSE FALSE FALSE 0.002125496
## 27 27 10.13 -0.343214144 FALSE FALSE FALSE -0.179250170
## 28 28 4.84 -1.094816263 FALSE FALSE FALSE -0.928841792
## 29 29 5.81 -0.956998861 FALSE FALSE FALSE -0.791393044
## 30 30 10.21 -0.331847760 FALSE FALSE FALSE -0.167914191
## 31 31 14.15 0.227946635 FALSE FALSE FALSE 0.390382782
## 32 32 16.20 0.519210216 FALSE FALSE FALSE 0.680867248
## 33 33 13.45 0.128490778 FALSE FALSE FALSE 0.291192964
## 34 34 14.80 0.320298502 FALSE FALSE FALSE 0.482487613
## 35 35 4.81 -1.099078657 FALSE FALSE FALSE -0.933092784
## 36 36 5.77 -0.962682053 FALSE FALSE FALSE -0.797061034
## 37 37 3.95 -1.221267281 FALSE FALSE FALSE -1.054954560
## 38 38 6.86 -0.807815076 FALSE FALSE FALSE -0.642608318
## 39 39 14.44 0.269149775 FALSE FALSE FALSE 0.431475706
## 40 40 6.73 -0.826285449 FALSE FALSE FALSE -0.661029284
## 41 41 8.05 -0.638740119 FALSE FALSE FALSE -0.473985628
## 42 42 4.67 -1.118969829 FALSE FALSE FALSE -0.952930747
## 43 43 10.24 -0.327585367 FALSE FALSE FALSE -0.163663199
## 44 44 8.10 -0.631636129 FALSE FALSE FALSE -0.466900641
## 45 45 8.79 -0.533601070 FALSE FALSE FALSE -0.369127821
## 46 46 6.72 -0.827706247 FALSE FALSE FALSE -0.662446282
## 47 47 5.52 -0.998202002 FALSE FALSE FALSE -0.832485969
## 48 48 7.54 -0.711200815 FALSE FALSE FALSE -0.546252495
## 49 49 6.78 -0.819181460 FALSE FALSE FALSE -0.653944297
## 50 50 8.94 -0.512289101 FALSE FALSE FALSE -0.347872860
## 51 51 11.97 -0.081787320 FALSE FALSE FALSE 0.081477350
## 52 52 10.27 -0.323322973 FALSE FALSE FALSE -0.159412207
## 53 53 9.10 -0.489556334 FALSE FALSE FALSE -0.325200902
## 54 54 5.29 -1.030880355 FALSE FALSE FALSE -0.865076909
## 55 55 7.22 -0.756666350 FALSE FALSE FALSE -0.591596412
## 56 56 6.72 -0.827706247 FALSE FALSE FALSE -0.662446282
## 57 57 9.62 -0.415674840 FALSE FALSE FALSE -0.251517037
## 58 58 6.53 -0.854701409 FALSE FALSE FALSE -0.689369232
## 59 59 12.86 0.044663698 FALSE FALSE FALSE 0.207590118
## 60 60 8.44 -0.583328999 FALSE FALSE FALSE -0.418722730
## 61 61 5.50 -1.001043598 FALSE FALSE FALSE -0.835319964
## 62 62 5.70 -0.972627639 FALSE FALSE FALSE -0.806980016
## 63 63 8.81 -0.530759474 FALSE FALSE FALSE -0.366293826
## 64 64 8.20 -0.617428150 FALSE FALSE FALSE -0.452730667
## 65 65 8.16 -0.623111342 FALSE FALSE FALSE -0.458398657
## 66 66 6.21 -0.900166943 FALSE FALSE FALSE -0.734713149
## 67 67 10.59 -0.277857438 FALSE FALSE FALSE -0.114068290
## 68 68 6.65 -0.837651833 FALSE FALSE FALSE -0.672365263
## 69 69 11.34 -0.171297591 FALSE FALSE FALSE -0.007793486
## 70 70 4.21 -1.184326535 FALSE FALSE FALSE -1.018112627
## 71 71 3.57 -1.275257604 FALSE FALSE FALSE -1.108800461
## 72 72 6.19 -0.903008539 FALSE FALSE FALSE -0.737547144
## 73 73 9.42 -0.444090799 FALSE FALSE FALSE -0.279856985
## 74 74 7.67 -0.692730441 FALSE FALSE FALSE -0.527831529
## 75 75 10.63 -0.272174246 FALSE FALSE FALSE -0.108400301
## 76 76 13.44 0.127069980 FALSE FALSE FALSE 0.289775967
## 77 77 12.33 -0.030638594 FALSE FALSE FALSE 0.132489256
## 78 78 16.47 0.557571761 FALSE FALSE FALSE 0.719126177
## 79 79 14.09 0.219421847 FALSE FALSE FALSE 0.381880798
## 80 80 13.00 0.064554870 FALSE FALSE FALSE 0.227428082
## 81 81 10.16 -0.338951750 FALSE FALSE FALSE -0.174999178
## 82 82 16.21 0.520631014 FALSE FALSE FALSE 0.682284245
## 83 83 17.09 0.645661234 FALSE FALSE FALSE 0.806980016
## 84 84 10.45 -0.297748609 FALSE FALSE FALSE -0.133906254
## 85 85 15.76 0.456695106 FALSE FALSE FALSE 0.618519362
## 86 86 12.04 -0.071841734 FALSE FALSE FALSE 0.091396332
## 87 87 10.30 -0.319060579 FALSE FALSE FALSE -0.155161215
## 88 88 13.61 0.151223545 FALSE FALSE FALSE 0.313864923
## 89 89 14.37 0.259204190 FALSE FALSE FALSE 0.421556725
## 90 90 14.27 0.244996210 FALSE FALSE FALSE 0.407386751
## 91 91 17.93 0.765008262 FALSE FALSE FALSE 0.926007797
## 92 92 25.41 1.827765134 FALSE FALSE FALSE 1.985921847
## 93 93 17.58 0.715280334 FALSE FALSE FALSE 0.876412888
## 94 94 14.81 0.321719300 FALSE FALSE FALSE 0.483904610
## 95 95 17.19 0.659869213 FALSE FALSE FALSE 0.821149990
## 96 96 15.39 0.404125581 FALSE FALSE FALSE 0.566090459
## 97 97 18.34 0.823260979 FALSE FALSE FALSE 0.984104690
## 98 98 12.60 0.007722951 FALSE FALSE FALSE 0.170748186
## 99 99 12.26 -0.040584179 FALSE FALSE FALSE 0.122570275
## 100 100 11.12 -0.202555146 FALSE FALSE FALSE -0.038967428
## 101 101 15.03 0.352976855 FALSE FALSE FALSE 0.515078553
## 102 102 17.31 0.676918789 FALSE FALSE FALSE 0.838153958
## 103 103 16.96 0.627190860 FALSE FALSE FALSE 0.788559050
## 104 104 16.90 0.618666073 FALSE FALSE FALSE 0.780057065
## 105 105 14.59 0.290461745 FALSE FALSE FALSE 0.452730667
## 106 106 21.32 1.246658770 FALSE FALSE FALSE 1.406369913
## 107 107 18.46 0.840310554 FALSE FALSE FALSE 1.001108659
## 108 108 24.16 1.650165389 FALSE FALSE FALSE 1.808797173
## 109 109 26.82 2.028097646 FALSE FALSE FALSE 2.185718480
## 110 110 26.42 1.971265728 FALSE FALSE FALSE 2.129038584
## 111 111 29.29 2.379034741 FALSE FALSE FALSE 2.535716836
## 112 112 27.80 2.167335846 FALSE FALSE FALSE 2.324584225
## 113 113 16.65 0.583146124 FALSE FALSE FALSE 0.744632130
## 114 114 29.53 2.413133892 FALSE FALSE FALSE 2.569724774
## 115 115 21.45 1.265129143 FALSE FALSE FALSE 1.424790880
## 116 116 14.10 0.220842645 FALSE FALSE FALSE 0.383297795
## 117 117 13.28 0.104337212 FALSE FALSE FALSE 0.267104009
## 118 118 12.12 -0.060475351 FALSE FALSE FALSE 0.102732311
## 119 119 15.79 0.460957500 FALSE FALSE FALSE 0.622770355
## 120 120 15.12 0.365764036 FALSE FALSE FALSE 0.527831529
## 121 121 15.02 0.351556057 FALSE FALSE FALSE 0.513661555
## 122 122 16.14 0.510685428 FALSE FALSE FALSE 0.672365263
## 123 123 4.59 -1.130336212 FALSE FALSE FALSE -0.964266727
## 124 124 6.43 -0.868909388 FALSE FALSE FALSE -0.703539206
## 125 125 7.39 -0.732512784 FALSE FALSE FALSE -0.567507456
## 126 126 5.50 -1.001043598 FALSE FALSE FALSE -0.835319964
## 127 127 1.73 -1.536684428 FALSE FALSE FALSE -1.369527981
## 128 128 1.92 -1.509689267 FALSE FALSE FALSE -1.342605031
## 129 129 3.32 -1.310777553 FALSE FALSE FALSE -1.144225396
## 130 130 11.64 -0.128673653 FALSE FALSE FALSE 0.034716436
## 131 131 9.81 -0.388679679 FALSE FALSE FALSE -0.224594087
## 132 132 3.70 -1.256787230 FALSE FALSE FALSE -1.090379495
## 133 133 12.14 -0.057633755 FALSE FALSE FALSE 0.105566306
## 134 134 11.10 -0.205396742 FALSE FALSE FALSE -0.041801423
## 135 135 14.43 0.267728977 FALSE FALSE FALSE 0.430058709
## 136 136 12.03 -0.073262532 FALSE FALSE FALSE 0.089979335
## 137 137 14.69 0.304669724 FALSE FALSE FALSE 0.466900641
## 138 138 9.04 -0.498081121 FALSE FALSE FALSE -0.333702886
## 139 139 9.64 -0.412833244 FALSE FALSE FALSE -0.248683043
## 140 140 5.33 -1.025197163 FALSE FALSE FALSE -0.859408919
## 141 141 10.11 -0.346055740 FALSE FALSE FALSE -0.182084165
## 142 142 6.92 -0.799290288 FALSE FALSE FALSE -0.634106334
## 143 143 5.04 -1.066400304 FALSE FALSE FALSE -0.900501844
## 144 144 7.56 -0.708359219 FALSE FALSE FALSE -0.543418501
## 145 145 5.68 -0.975469235 FALSE FALSE FALSE -0.809814011
## 146 146 13.98 0.203793069 FALSE FALSE FALSE 0.366293826
## 147 147 13.15 0.085866839 FALSE FALSE FALSE 0.248683043
## 148 148 6.68 -0.833389439 FALSE FALSE FALSE -0.668114271
## 149 149 4.56 -1.134598606 FALSE FALSE FALSE -0.968517719
## 150 150 5.39 -1.016672376 FALSE FALSE FALSE -0.850906935
## 151 151 5.10 -1.057875516 FALSE FALSE FALSE -0.891999859
## 152 152 4.69 -1.116128233 FALSE FALSE FALSE -0.950096753
## 153 153 2.87 -1.374713461 FALSE FALSE FALSE -1.207990278
## 154 154 5.03 -1.067821102 FALSE FALSE FALSE -0.901918841
## 155 155 4.38 -1.160172969 FALSE FALSE FALSE -0.994023672
## 156 156 2.97 -1.360505481 FALSE FALSE FALSE -1.193820304
## 157 157 4.08 -1.202796908 FALSE FALSE FALSE -1.036533594
## 158 158 8.61 -0.559175434 FALSE FALSE FALSE -0.394633774
## 159 159 6.62 -0.841914227 FALSE FALSE FALSE -0.676616256
## 160 160 4.56 -1.134598606 FALSE FALSE FALSE -0.968517719
## 161 161 4.45 -1.150227384 FALSE FALSE FALSE -0.984104690
## 162 162 7.43 -0.726829592 FALSE FALSE FALSE -0.561839467
## 163 163 3.11 -1.340614310 FALSE FALSE FALSE -1.173982341
## 164 164 3.81 -1.241158453 FALSE FALSE FALSE -1.074792523
## 165 165 18.06 0.783478636 FALSE FALSE FALSE 0.944428763
## 166 166 14.66 0.300407330 FALSE FALSE FALSE 0.462649649
## 167 167 23.09 1.498140008 FALSE FALSE FALSE 1.657178452
## 168 168 17.27 0.671235597 FALSE FALSE FALSE 0.832485969
## 169 169 16.03 0.495056650 FALSE FALSE FALSE 0.656778292
## 170 170 9.38 -0.449773991 FALSE FALSE FALSE -0.285524975
## 171 171 29.55 2.415975488 FALSE FALSE FALSE 2.572558769
## 172 172 9.47 -0.436986809 FALSE FALSE FALSE -0.272771998
## 173 173 13.51 0.137015565 FALSE FALSE FALSE 0.299694949
## 174 174 17.92 0.763587464 FALSE FALSE FALSE 0.924590799
## 175 175 10.50 -0.290644620 FALSE FALSE FALSE -0.126821267
## 176 176 9.71 -0.402887658 FALSE FALSE FALSE -0.238764061
## 177 177 21.46 1.266549941 FALSE FALSE FALSE 1.426207877
## 178 178 9.93 -0.371630103 FALSE FALSE FALSE -0.207590118
## 179 179 7.60 -0.702676027 FALSE FALSE FALSE -0.537750511
## 180 180 4.14 -1.194272120 FALSE FALSE FALSE -1.028031609
## 181 181 3.13 -1.337772714 FALSE FALSE FALSE -1.171148346
## 182 182 6.36 -0.878854974 FALSE FALSE FALSE -0.713458188
## 183 183 3.92 -1.225529675 FALSE FALSE FALSE -1.059205552
## 184 184 11.65 -0.127252855 FALSE FALSE FALSE 0.036133434
## 185 185 5.25 -1.036563547 FALSE FALSE FALSE -0.870744899
## 186 186 2.47 -1.431545379 FALSE FALSE FALSE -1.264670174
## 187 187 3.95 -1.221267281 FALSE FALSE FALSE -1.054954560
## 188 188 8.05 -0.638740119 FALSE FALSE FALSE -0.473985628
## 189 189 10.88 -0.236654297 FALSE FALSE FALSE -0.072975366
## 190 190 9.54 -0.427041224 FALSE FALSE FALSE -0.262853017
## 191 191 4.73 -1.110445041 FALSE FALSE FALSE -0.944428763
## 192 192 7.37 -0.735354380 FALSE FALSE FALSE -0.570341451
## 193 193 11.38 -0.165614399 FALSE FALSE FALSE -0.002125496
## 194 194 12.40 -0.020693008 FALSE FALSE FALSE 0.142408238
## 195 195 11.22 -0.188347167 FALSE FALSE FALSE -0.024797454
## 196 196 5.19 -1.045088335 FALSE FALSE FALSE -0.879246883
## 197 197 12.50 -0.006485028 FALSE FALSE FALSE 0.156578212
## 198 198 9.16 -0.481031546 FALSE FALSE FALSE -0.316698918
## 199 199 10.15 -0.340372548 FALSE FALSE FALSE -0.176416176
## 200 200 9.52 -0.429882819 FALSE FALSE FALSE -0.265687011
## 201 201 6.56 -0.850439015 FALSE FALSE FALSE -0.685118240
## 202 202 3.59 -1.272416008 FALSE FALSE FALSE -1.105966466
## 203 203 3.53 -1.280940796 FALSE FALSE FALSE -1.114468450
## 204 204 6.57 -0.849018217 FALSE FALSE FALSE -0.683701243
## 205 205 9.25 -0.468244364 FALSE FALSE FALSE -0.303945941
## 206 206 3.11 -1.340614310 FALSE FALSE FALSE -1.173982341
## 207 207 5.12 -1.055033920 FALSE FALSE FALSE -0.889165865
## 208 208 6.90 -0.802131884 FALSE FALSE FALSE -0.636940329
## 209 209 9.59 -0.419937234 FALSE FALSE FALSE -0.255768030
## 210 210 7.26 -0.750983158 FALSE FALSE FALSE -0.585928422
## 211 211 5.91 -0.942790882 FALSE FALSE FALSE -0.777223071
## 212 212 14.79 0.318877704 FALSE FALSE FALSE 0.481070615
## 213 213 3.16 -1.333510320 FALSE FALSE FALSE -1.166897354
## 214 214 13.65 0.156906737 FALSE FALSE FALSE 0.319532912
## 215 215 13.00 0.064554870 FALSE FALSE FALSE 0.227428082
## 216 216 6.59 -0.846176621 FALSE FALSE FALSE -0.680867248
## 217 217 7.73 -0.684205654 FALSE FALSE FALSE -0.519329545
## 218 218 6.05 -0.922899711 FALSE FALSE FALSE -0.757385107
## 219 219 4.16 -1.191430524 FALSE FALSE FALSE -1.025197614
## 220 220 7.19 -0.760928743 FALSE FALSE FALSE -0.595847404
## 221 221 3.76 -1.248262443 FALSE FALSE FALSE -1.081877510
## 222 222 4.59 -1.130336212 FALSE FALSE FALSE -0.964266727
## 223 223 3.01 -1.354822289 FALSE FALSE FALSE -1.188152315
## 224 224 3.16 -1.333510320 FALSE FALSE FALSE -1.166897354
## 225 225 7.85 -0.667156078 FALSE FALSE FALSE -0.502325576
## 226 226 8.23 -0.613165756 FALSE FALSE FALSE -0.448479675
## 227 227 12.93 0.054609284 FALSE FALSE FALSE 0.217509100
## 228 228 7.14 -0.768032733 FALSE FALSE FALSE -0.602932391
## 229 229 7.60 -0.702676027 FALSE FALSE FALSE -0.537750511
## 230 230 9.51 -0.431303617 FALSE FALSE FALSE -0.267104009
## 231 231 3.33 -1.309356755 FALSE FALSE FALSE -1.142808398
## 232 232 3.56 -1.276678402 FALSE FALSE FALSE -1.110217458
## 233 233 4.70 -1.114707435 FALSE FALSE FALSE -0.948679755
## 234 234 6.27 -0.891642155 FALSE FALSE FALSE -0.726211164
## 235 235 7.39 -0.732512784 FALSE FALSE FALSE -0.567507456
## 236 236 15.84 0.468061489 FALSE FALSE FALSE 0.629855342
## 237 237 4.97 -1.076345890 FALSE FALSE FALSE -0.910420826
## 238 238 9.50 -0.432724415 FALSE FALSE FALSE -0.268521006
## 239 239 4.86 -1.091974667 FALSE FALSE FALSE -0.926007797
## 240 240 6.93 -0.797869490 FALSE FALSE FALSE -0.632689336
## 241 241 7.53 -0.712621613 FALSE FALSE FALSE -0.547669493
## 242 242 4.54 -1.137440202 FALSE FALSE FALSE -0.971351713
## 243 243 9.97 -0.365946911 FALSE FALSE FALSE -0.201922129
## 244 244 5.98 -0.932845296 FALSE FALSE FALSE -0.767304089
## 245 245 11.72 -0.117307269 FALSE FALSE FALSE 0.046052415
## 246 246 7.90 -0.660052088 FALSE FALSE FALSE -0.495240589
## 247 247 9.28 -0.463981970 FALSE FALSE FALSE -0.299694949
## 248 248 11.50 -0.148564824 FALSE FALSE FALSE 0.014878473
## 249 249 15.94 0.482269469 FALSE FALSE FALSE 0.644025316
## 250 250 10.36 -0.310535791 FALSE FALSE FALSE -0.146659230
## 251 251 12.73 0.026193325 FALSE FALSE FALSE 0.189169152
## 252 252 7.20 -0.759507945 FALSE FALSE FALSE -0.594430407
## 253 253 6.87 -0.806394278 FALSE FALSE FALSE -0.641191321
## 254 254 7.70 -0.688468048 FALSE FALSE FALSE -0.523580537
## 255 255 11.74 -0.114465673 FALSE FALSE FALSE 0.048886410
## 256 256 6.12 -0.912954125 FALSE FALSE FALSE -0.747466125
## 257 257 5.08 -1.060717112 FALSE FALSE FALSE -0.894833854
## 258 258 6.15 -0.908691731 FALSE FALSE FALSE -0.743215133
## 259 259 12.79 0.034718112 FALSE FALSE FALSE 0.197671136
## 260 260 9.97 -0.365946911 FALSE FALSE FALSE -0.201922129
## 261 261 7.34 -0.739616774 FALSE FALSE FALSE -0.574592443
## 262 262 9.09 -0.490977132 FALSE FALSE FALSE -0.326617899
## 263 263 12.43 -0.016430614 FALSE FALSE FALSE 0.146659230
## 264 264 5.68 -0.975469235 FALSE FALSE FALSE -0.809814011
## 265 265 6.75 -0.823443854 FALSE FALSE FALSE -0.658195289
## 266 266 8.01 -0.644423311 FALSE FALSE FALSE -0.479653618
## 267 267 9.80 -0.390100477 FALSE FALSE FALSE -0.226011084
## 268 268 10.56 -0.282119832 FALSE FALSE FALSE -0.118319282
## 269 269 9.74 -0.398625264 FALSE FALSE FALSE -0.234513069
## 270 270 9.29 -0.462561172 FALSE FALSE FALSE -0.298277951
## 271 271 5.49 -1.002464396 FALSE FALSE FALSE -0.836736961
## 272 272 8.65 -0.553492242 FALSE FALSE FALSE -0.388965785
## 273 273 7.18 -0.762349541 FALSE FALSE FALSE -0.597264402
## 274 274 4.61 -1.127494616 FALSE FALSE FALSE -0.961432732
## 275 275 10.53 -0.286382226 FALSE FALSE FALSE -0.122570275
## 276 276 12.67 0.017668537 FALSE FALSE FALSE 0.180667168
## 277 277 6.36 -0.878854974 FALSE FALSE FALSE -0.713458188
## 278 278 5.99 -0.931424498 FALSE FALSE FALSE -0.765887091
## 279 279 5.89 -0.945632478 FALSE FALSE FALSE -0.780057065
## 280 280 5.49 -1.002464396 FALSE FALSE FALSE -0.836736961
## 281 281 4.50 -1.143123394 FALSE FALSE FALSE -0.977019703
## 282 282 8.05 -0.638740119 FALSE FALSE FALSE -0.473985628
## 283 283 5.57 -0.991098012 FALSE FALSE FALSE -0.825400982
## 284 284 17.60 0.718121930 FALSE FALSE FALSE 0.879246883
## 285 285 13.27 0.102916414 FALSE FALSE FALSE 0.265687011
## 286 286 11.48 -0.151406420 FALSE FALSE FALSE 0.012044478
## 287 287 7.79 -0.675680866 FALSE FALSE FALSE -0.510827560
## 288 288 14.19 0.233629826 FALSE FALSE FALSE 0.396050772
## 289 289 10.19 -0.334689356 FALSE FALSE FALSE -0.170748186
## 290 290 14.64 0.297565734 FALSE FALSE FALSE 0.459815654
## 291 291 5.29 -1.030880355 FALSE FALSE FALSE -0.865076909
## 292 292 7.12 -0.770874329 FALSE FALSE FALSE -0.605766386
## 293 293 14.00 0.206634665 FALSE FALSE FALSE 0.369127821
## 294 294 13.33 0.111441202 FALSE FALSE FALSE 0.274188996
## 295 295 3.26 -1.319302340 FALSE FALSE FALSE -1.152727380
## 296 296 2.96 -1.361926279 FALSE FALSE FALSE -1.195237302
## 297 297 9.53 -0.428462021 FALSE FALSE FALSE -0.264270014
## 298 298 8.88 -0.520813889 FALSE FALSE FALSE -0.356374845
## 299 299 34.77 3.157632022 TRUE TRUE FALSE 3.312231408
## 300 300 37.97 3.612287368 TRUE TRUE FALSE 3.765670574
## 301 301 13.44 0.127069980 FALSE FALSE FALSE 0.289775967
## 302 302 23.24 1.519451977 FALSE FALSE FALSE 1.678433413
## 303 303 21.24 1.235292386 FALSE FALSE FALSE 1.395033934
## 304 304 23.69 1.583387885 FALSE FALSE FALSE 1.742198296
## 305 305 17.21 0.662710809 FALSE FALSE FALSE 0.823983985
## 306 306 23.60 1.570600704 FALSE FALSE FALSE 1.729445319
## 307 307 24.56 1.706997308 FALSE FALSE FALSE 1.865477069
## 308 308 28.28 2.235534148 FALSE FALSE FALSE 2.392600100
## 309 309 31.99 2.762650190 FALSE TRUE FALSE 2.918306133
## 310 310 30.62 2.568000870 FALSE FALSE FALSE 2.724177490
## 311 311 18.76 0.882934493 FALSE FALSE FALSE 1.043618581
## 312 312 25.68 1.866126679 FALSE FALSE FALSE 2.024180777
## 313 313 15.17 0.372868026 FALSE FALSE FALSE 0.534916516
## 314 314 16.35 0.540522185 FALSE FALSE FALSE 0.702122209
## 315 315 17.12 0.649923628 FALSE FALSE FALSE 0.811231008
## 316 316 19.37 0.969603168 FALSE FALSE FALSE 1.130055422
## 317 317 29.97 2.475649002 FALSE FALSE FALSE 2.632072659
## 318 318 26.77 2.020993656 FALSE FALSE FALSE 2.178633493
## 319 319 20.31 1.103158176 FALSE FALSE FALSE 1.263253177
## 320 320 19.77 1.026435086 FALSE FALSE FALSE 1.186735317
## 321 321 27.38 2.107662332 FALSE FALSE FALSE 2.265070334
## 322 322 22.98 1.482511231 FALSE FALSE FALSE 1.641591481
## 323 323 26.40 1.968424132 FALSE FALSE FALSE 2.126204589
## 324 324 10.11 -0.346055740 FALSE FALSE FALSE -0.182084165
## 325 325 21.22 1.232450790 FALSE FALSE FALSE 1.392199939
## 326 326 34.37 3.100800103 TRUE TRUE FALSE 3.255551512
## 327 327 20.08 1.070479823 FALSE FALSE FALSE 1.230662237
## 328 328 36.98 3.471628370 TRUE TRUE FALSE 3.625387832
## 329 329 29.05 2.344935590 FALSE FALSE FALSE 2.501708899
## 330 330 25.79 1.881755456 FALSE FALSE FALSE 2.039767748
## 331 331 26.64 2.002523283 FALSE FALSE FALSE 2.160212527
## 332 332 20.62 1.147202913 FALSE FALSE FALSE 1.307180096
## 333 333 22.74 1.448412080 FALSE FALSE FALSE 1.607583543
## 334 334 15.02 0.351556057 FALSE FALSE FALSE 0.513661555
## 335 335 15.70 0.448170318 FALSE FALSE FALSE 0.610017378
## 336 336 14.10 0.220842645 FALSE FALSE FALSE 0.383297795
## 337 337 23.29 1.526555967 FALSE FALSE FALSE 1.685518400
## 338 338 17.16 0.655606820 FALSE FALSE FALSE 0.816898998
## 339 339 24.39 1.682843742 FALSE FALSE FALSE 1.841388113
## 340 340 15.69 0.446749520 FALSE FALSE FALSE 0.608600381
## 341 341 14.52 0.280516159 FALSE FALSE FALSE 0.442811686
## 342 342 24.08 1.638799006 FALSE FALSE FALSE 1.797461194
## 343 343 17.64 0.723805122 FALSE FALSE FALSE 0.884914872
## 344 344 19.69 1.015068703 FALSE FALSE FALSE 1.175399338
## 345 345 12.03 -0.073262532 FALSE FALSE FALSE 0.089979335
## 346 346 16.22 0.522051812 FALSE FALSE FALSE 0.683701243
## 347 347 15.17 0.372868026 FALSE FALSE FALSE 0.534916516
## 348 348 23.27 1.523714371 FALSE FALSE FALSE 1.682684405
## 349 349 18.05 0.782057838 FALSE FALSE FALSE 0.943011766
## 350 350 34.02 3.051072175 TRUE TRUE FALSE 3.205956604
## 351 351 22.88 1.468303251 FALSE FALSE FALSE 1.627421507
## 352 352 22.11 1.358901808 FALSE FALSE FALSE 1.518312708
## 353 353 16.59 0.574621336 FALSE FALSE FALSE 0.736130146
## 354 354 23.79 1.597595865 FALSE FALSE FALSE 1.756368270
## 355 355 23.98 1.624591026 FALSE FALSE FALSE 1.783291220
## 356 356 17.79 0.745117091 FALSE FALSE FALSE 0.906169833
## 357 357 16.44 0.553309367 FALSE FALSE FALSE 0.714875185
## 358 358 19.31 0.961078380 FALSE FALSE FALSE 1.121553437
## 359 359 17.44 0.695389162 FALSE FALSE FALSE 0.856574925
## 360 360 17.73 0.736592303 FALSE FALSE FALSE 0.897667849
## 361 361 16.74 0.595933305 FALSE FALSE FALSE 0.757385107
## 362 362 18.71 0.875830503 FALSE FALSE FALSE 1.036533594
## 363 363 18.13 0.793424221 FALSE FALSE FALSE 0.954347745
## 364 364 19.01 0.918454442 FALSE FALSE FALSE 1.079043515
## 365 365 16.94 0.624349265 FALSE FALSE FALSE 0.785725055
## 366 366 14.70 0.306090522 FALSE FALSE FALSE 0.468317639
## 367 367 13.99 0.205213867 FALSE FALSE FALSE 0.367710824
## 368 368 13.22 0.095812425 FALSE FALSE FALSE 0.258602024
## 369 369 14.13 0.225105039 FALSE FALSE FALSE 0.387548787
## 370 370 17.15 0.654186022 FALSE FALSE FALSE 0.815482000
## 371 371 18.13 0.793424221 FALSE FALSE FALSE 0.954347745
## 372 372 14.76 0.314615310 FALSE FALSE FALSE 0.476819623
## 373 373 16.29 0.531997397 FALSE FALSE FALSE 0.693620224
## 374 374 12.87 0.046084496 FALSE FALSE FALSE 0.209007116
## 375 375 14.36 0.257783392 FALSE FALSE FALSE 0.420139727
## 376 376 11.66 -0.125832057 FALSE FALSE FALSE 0.037550431
## 377 377 18.14 0.794845019 FALSE FALSE FALSE 0.955764742
## 378 378 24.10 1.641640602 FALSE FALSE FALSE 1.800295189
## 379 379 18.68 0.871568109 FALSE FALSE FALSE 1.032282601
## 380 380 24.91 1.756725236 FALSE FALSE FALSE 1.915071978
## 381 381 18.03 0.779216242 FALSE FALSE FALSE 0.940177771
## 382 382 13.11 0.080183647 FALSE FALSE FALSE 0.243015053
## 383 383 10.74 -0.256545469 FALSE FALSE FALSE -0.092813329
## 384 384 7.74 -0.682784856 FALSE FALSE FALSE -0.517912547
## 385 385 7.01 -0.786503107 FALSE FALSE FALSE -0.621353357
## 386 386 10.42 -0.302011003 FALSE FALSE FALSE -0.138157246
## 387 387 13.34 0.112862000 FALSE FALSE FALSE 0.275605993
## 388 388 10.58 -0.279278236 FALSE FALSE FALSE -0.115485288
## 389 389 11.45 -0.155668814 FALSE FALSE FALSE 0.007793486
## 390 390 18.06 0.783478636 FALSE FALSE FALSE 0.944428763
## 391 391 23.97 1.623170228 FALSE FALSE FALSE 1.781874223
## 392 392 29.68 2.434445862 FALSE FALSE FALSE 2.590979735
## 393 393 18.07 0.784899434 FALSE FALSE FALSE 0.945845760
## 394 394 13.35 0.114282798 FALSE FALSE FALSE 0.277022990
## 395 395 13.59 0.148381949 FALSE FALSE FALSE 0.311030928
## 396 396 17.60 0.718121930 FALSE FALSE FALSE 0.879246883
## 397 397 21.14 1.221084406 FALSE FALSE FALSE 1.380863960
## 398 398 12.92 0.053188486 FALSE FALSE FALSE 0.216092103
## 399 399 15.10 0.362922440 FALSE FALSE FALSE 0.524997534
## 400 400 14.33 0.253520998 FALSE FALSE FALSE 0.415888735
## 401 401 9.67 -0.408570850 FALSE FALSE FALSE -0.244432050
## 402 402 9.08 -0.492397930 FALSE FALSE FALSE -0.328034897
## 403 403 6.48 -0.861805398 FALSE FALSE FALSE -0.696454219
## 404 404 7.88 -0.662893684 FALSE FALSE FALSE -0.498074584
## outlier_MAD
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 FALSE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSE
## 68 FALSE
## 69 FALSE
## 70 FALSE
## 71 FALSE
## 72 FALSE
## 73 FALSE
## 74 FALSE
## 75 FALSE
## 76 FALSE
## 77 FALSE
## 78 FALSE
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## 85 FALSE
## 86 FALSE
## 87 FALSE
## 88 FALSE
## 89 FALSE
## 90 FALSE
## 91 FALSE
## 92 FALSE
## 93 FALSE
## 94 FALSE
## 95 FALSE
## 96 FALSE
## 97 FALSE
## 98 FALSE
## 99 FALSE
## 100 FALSE
## 101 FALSE
## 102 FALSE
## 103 FALSE
## 104 FALSE
## 105 FALSE
## 106 FALSE
## 107 FALSE
## 108 FALSE
## 109 FALSE
## 110 FALSE
## 111 FALSE
## 112 FALSE
## 113 FALSE
## 114 FALSE
## 115 FALSE
## 116 FALSE
## 117 FALSE
## 118 FALSE
## 119 FALSE
## 120 FALSE
## 121 FALSE
## 122 FALSE
## 123 FALSE
## 124 FALSE
## 125 FALSE
## 126 FALSE
## 127 FALSE
## 128 FALSE
## 129 FALSE
## 130 FALSE
## 131 FALSE
## 132 FALSE
## 133 FALSE
## 134 FALSE
## 135 FALSE
## 136 FALSE
## 137 FALSE
## 138 FALSE
## 139 FALSE
## 140 FALSE
## 141 FALSE
## 142 FALSE
## 143 FALSE
## 144 FALSE
## 145 FALSE
## 146 FALSE
## 147 FALSE
## 148 FALSE
## 149 FALSE
## 150 FALSE
## 151 FALSE
## 152 FALSE
## 153 FALSE
## 154 FALSE
## 155 FALSE
## 156 FALSE
## 157 FALSE
## 158 FALSE
## 159 FALSE
## 160 FALSE
## 161 FALSE
## 162 FALSE
## 163 FALSE
## 164 FALSE
## 165 FALSE
## 166 FALSE
## 167 FALSE
## 168 FALSE
## 169 FALSE
## 170 FALSE
## 171 FALSE
## 172 FALSE
## 173 FALSE
## 174 FALSE
## 175 FALSE
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## 184 FALSE
## 185 FALSE
## 186 FALSE
## 187 FALSE
## 188 FALSE
## 189 FALSE
## 190 FALSE
## 191 FALSE
## 192 FALSE
## 193 FALSE
## 194 FALSE
## 195 FALSE
## 196 FALSE
## 197 FALSE
## 198 FALSE
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## 205 FALSE
## 206 FALSE
## 207 FALSE
## 208 FALSE
## 209 FALSE
## 210 FALSE
## 211 FALSE
## 212 FALSE
## 213 FALSE
## 214 FALSE
## 215 FALSE
## 216 FALSE
## 217 FALSE
## 218 FALSE
## 219 FALSE
## 220 FALSE
## 221 FALSE
## 222 FALSE
## 223 FALSE
## 224 FALSE
## 225 FALSE
## 226 FALSE
## 227 FALSE
## 228 FALSE
## 229 FALSE
## 230 FALSE
## 231 FALSE
## 232 FALSE
## 233 FALSE
## 234 FALSE
## 235 FALSE
## 236 FALSE
## 237 FALSE
## 238 FALSE
## 239 FALSE
## 240 FALSE
## 241 FALSE
## 242 FALSE
## 243 FALSE
## 244 FALSE
## 245 FALSE
## 246 FALSE
## 247 FALSE
## 248 FALSE
## 249 FALSE
## 250 FALSE
## 251 FALSE
## 252 FALSE
## 253 FALSE
## 254 FALSE
## 255 FALSE
## 256 FALSE
## 257 FALSE
## 258 FALSE
## 259 FALSE
## 260 FALSE
## 261 FALSE
## 262 FALSE
## 263 FALSE
## 264 FALSE
## 265 FALSE
## 266 FALSE
## 267 FALSE
## 268 FALSE
## 269 FALSE
## 270 FALSE
## 271 FALSE
## 272 FALSE
## 273 FALSE
## 274 FALSE
## 275 FALSE
## 276 FALSE
## 277 FALSE
## 278 FALSE
## 279 FALSE
## 280 FALSE
## 281 FALSE
## 282 FALSE
## 283 FALSE
## 284 FALSE
## 285 FALSE
## 286 FALSE
## 287 FALSE
## 288 FALSE
## 289 FALSE
## 290 FALSE
## 291 FALSE
## 292 FALSE
## 293 FALSE
## 294 FALSE
## 295 FALSE
## 296 FALSE
## 297 FALSE
## 298 FALSE
## 299 FALSE
## 300 TRUE
## 301 FALSE
## 302 FALSE
## 303 FALSE
## 304 FALSE
## 305 FALSE
## 306 FALSE
## 307 FALSE
## 308 FALSE
## 309 FALSE
## 310 FALSE
## 311 FALSE
## 312 FALSE
## 313 FALSE
## 314 FALSE
## 315 FALSE
## 316 FALSE
## 317 FALSE
## 318 FALSE
## 319 FALSE
## 320 FALSE
## 321 FALSE
## 322 FALSE
## 323 FALSE
## 324 FALSE
## 325 FALSE
## 326 FALSE
## 327 FALSE
## 328 TRUE
## 329 FALSE
## 330 FALSE
## 331 FALSE
## 332 FALSE
## 333 FALSE
## 334 FALSE
## 335 FALSE
## 336 FALSE
## 337 FALSE
## 338 FALSE
## 339 FALSE
## 340 FALSE
## 341 FALSE
## 342 FALSE
## 343 FALSE
## 344 FALSE
## 345 FALSE
## 346 FALSE
## 347 FALSE
## 348 FALSE
## 349 FALSE
## 350 FALSE
## 351 FALSE
## 352 FALSE
## 353 FALSE
## 354 FALSE
## 355 FALSE
## 356 FALSE
## 357 FALSE
## 358 FALSE
## 359 FALSE
## 360 FALSE
## 361 FALSE
## 362 FALSE
## 363 FALSE
## 364 FALSE
## 365 FALSE
## 366 FALSE
## 367 FALSE
## 368 FALSE
## 369 FALSE
## 370 FALSE
## 371 FALSE
## 372 FALSE
## 373 FALSE
## 374 FALSE
## 375 FALSE
## 376 FALSE
## 377 FALSE
## 378 FALSE
## 379 FALSE
## 380 FALSE
## 381 FALSE
## 382 FALSE
## 383 FALSE
## 384 FALSE
## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
## 393 FALSE
## 394 FALSE
## 395 FALSE
## 396 FALSE
## 397 FALSE
## 398 FALSE
## 399 FALSE
## 400 FALSE
## 401 FALSE
## 402 FALSE
## 403 FALSE
## 404 FALSE
##
## ===== AL MENOS UN MÉTODO =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 299 34.77 3.157632 TRUE TRUE 3.312231 FALSE
## 2 300 37.97 3.612287 TRUE TRUE 3.765671 TRUE
## 3 309 31.99 2.762650 FALSE TRUE 2.918306 FALSE
## 4 326 34.37 3.100800 TRUE TRUE 3.255552 FALSE
## 5 328 36.98 3.471628 TRUE TRUE 3.625388 TRUE
## 6 350 34.02 3.051072 TRUE TRUE 3.205957 FALSE
## metodos_evaluados numero_metodos
## 1 3 2
## 2 3 3
## 3 3 1
## 4 3 2
## 5 3 3
## 6 3 2
##
## ===== AL MENOS DOS MÉTODOS =====
## ID Valor Z_score outlier_Z outlier_IQR Z_robusto outlier_MAD
## 1 299 34.77 3.157632 TRUE TRUE 3.312231 FALSE
## 2 300 37.97 3.612287 TRUE TRUE 3.765671 TRUE
## 3 326 34.37 3.100800 TRUE TRUE 3.255552 FALSE
## 4 328 36.98 3.471628 TRUE TRUE 3.625388 TRUE
## 5 350 34.02 3.051072 TRUE TRUE 3.205957 FALSE
## metodos_evaluados numero_metodos
## 1 3 2
## 2 3 3
## 3 3 2
## 4 3 3
## 5 3 2
##
## ===== LOS TRES MÉTODOS =====
## ID Valor Z_score Z_robusto
## 1 300 37.97 3.612287 3.765671
## 2 328 36.98 3.471628 3.625388
##
## ================ RESUMEN ================
## Variable Metodo N_evaluados N_outliers Porcentaje
## lstat Z-score 404 5 1.24
## lstat IQR 404 6 1.49
## lstat Tukey extremo 404 0 0.00
## lstat MAD 404 2 0.50## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
##
## ================ CLASIFICACIÓN FINAL ================
## ID Valor Z_score Z_robusto metodos_evaluados numero_metodos
## 1 299 34.77 3.157632 3.312231 3 2
## 2 300 37.97 3.612287 3.765671 3 3
## 3 309 31.99 2.762650 2.918306 3 1
## 4 326 34.37 3.100800 3.255552 3 2
## 5 328 36.98 3.471628 3.625388 3 3
## 6 350 34.02 3.051072 3.205957 3 2
## clasificacion
## 1 Outlier según 2 métodos
## 2 Outlier según 3 métodos
## 3 Outlier según 1 método
## 4 Outlier según 2 métodos
## 5 Outlier según 3 métodos
## 6 Outlier según 2 métodosInterpretación: Realizando la comparación de los métodos se observa que varias variable spresenta valores atipico los cuales tiene que ser imputados.
resumen_general <- bind_rows(resumenes)
comparacion_general <- bind_rows(resultados)
limites_entrenamiento <- bind_rows(parametros_deteccion)
atipicos_general <- comparacion_general %>%
filter(numero_metodos >= 1)
atipicos_dos_metodos <- comparacion_general %>%
filter(numero_metodos >= 2)
cat("\n================ RESUMEN GENERAL ================\n")##
## ================ RESUMEN GENERAL ================print(resumen_general)## Variable Metodo N_evaluados N_outliers Porcentaje
## outlier_Z...1 crim Z-score 404 7 1.73
## outlier_IQR...2 crim IQR 404 53 13.12
## outlier_extremo...3 crim Tukey extremo 404 23 5.69
## outlier_MAD...4 crim MAD 404 130 32.18
## outlier_Z...5 zn Z-score 404 11 2.72
## outlier_IQR...6 zn IQR 0 0 NA
## outlier_extremo...7 zn Tukey extremo 0 0 NA
## outlier_MAD...8 zn MAD 0 0 NA
## outlier_Z...9 indus Z-score 404 0 0.00
## outlier_IQR...10 indus IQR 404 0 0.00
## outlier_extremo...11 indus Tukey extremo 404 0 0.00
## outlier_MAD...12 indus MAD 404 0 0.00
## outlier_Z...13 nox Z-score 404 0 0.00
## outlier_IQR...14 nox IQR 404 0 0.00
## outlier_extremo...15 nox Tukey extremo 404 0 0.00
## outlier_MAD...16 nox MAD 404 0 0.00
## outlier_Z...17 rm Z-score 404 7 1.73
## outlier_IQR...18 rm IQR 404 23 5.69
## outlier_extremo...19 rm Tukey extremo 404 1 0.25
## outlier_MAD...20 rm MAD 404 12 2.97
## outlier_Z...21 age Z-score 404 0 0.00
## outlier_IQR...22 age IQR 404 0 0.00
## outlier_extremo...23 age Tukey extremo 404 0 0.00
## outlier_MAD...24 age MAD 404 0 0.00
## outlier_Z...25 dis Z-score 404 4 0.99
## outlier_IQR...26 dis IQR 404 4 0.99
## outlier_extremo...27 dis Tukey extremo 404 0 0.00
## outlier_MAD...28 dis MAD 404 4 0.99
## outlier_Z...29 rad Z-score 404 0 0.00
## outlier_IQR...30 rad IQR 404 0 0.00
## outlier_extremo...31 rad Tukey extremo 404 0 0.00
## outlier_MAD...32 rad MAD 404 106 26.24
## outlier_Z...33 tax Z-score 404 0 0.00
## outlier_IQR...34 tax IQR 404 0 0.00
## outlier_extremo...35 tax Tukey extremo 404 0 0.00
## outlier_MAD...36 tax MAD 404 0 0.00
## outlier_Z...37 ptratio Z-score 404 0 0.00
## outlier_IQR...38 ptratio IQR 404 0 0.00
## outlier_extremo...39 ptratio Tukey extremo 404 0 0.00
## outlier_MAD...40 ptratio MAD 404 3 0.74
## outlier_Z...41 black Z-score 404 20 4.95
## outlier_IQR...42 black IQR 404 61 15.10
## outlier_extremo...43 black Tukey extremo 404 49 12.13
## outlier_MAD...44 black MAD 404 83 20.54
## outlier_Z...45 lstat Z-score 404 5 1.24
## outlier_IQR...46 lstat IQR 404 6 1.49
## outlier_extremo...47 lstat Tukey extremo 404 0 0.00
## outlier_MAD...48 lstat MAD 404 2 0.50cat("\n================ AL MENOS DOS MÉTODOS ================\n")##
## ================ AL MENOS DOS MÉTODOS ================print(atipicos_dos_metodos)## ID Valor Z_score outlier_Z outlier_IQR outlier_extremo Z_robusto
## 1 294 13.52220 1.0721124 FALSE TRUE FALSE 40.305817
## 2 297 9.23230 0.6022720 FALSE TRUE FALSE 27.271749
## 3 299 11.10810 0.8077142 FALSE TRUE FALSE 32.971021
## 4 300 18.49820 1.6170961 FALSE TRUE TRUE 55.424473
## 5 301 19.60910 1.7387645 FALSE TRUE TRUE 58.799737
## 6 302 15.28800 1.2655071 FALSE TRUE TRUE 45.670874
## 7 303 9.82349 0.6670206 FALSE TRUE FALSE 29.067971
## 8 304 23.64820 2.1811367 FALSE TRUE TRUE 71.071796
## 9 305 88.97620 9.3360193 TRUE TRUE TRUE 269.558844
## 10 306 9.18702 0.5973128 FALSE TRUE FALSE 27.134174
## 11 308 24.39380 2.2627966 FALSE TRUE TRUE 73.337163
## 12 309 22.59710 2.0660176 FALSE TRUE TRUE 67.878223
## 13 310 14.33370 1.1609898 FALSE TRUE FALSE 42.771410
## 14 312 11.57790 0.8591679 FALSE TRUE FALSE 34.398421
## 15 314 13.35980 1.0543259 FALSE TRUE FALSE 39.812395
## 16 317 9.91655 0.6772127 FALSE TRUE FALSE 29.350716
## 17 318 25.04610 2.3342381 FALSE TRUE TRUE 75.319056
## 18 319 9.59571 0.6420735 FALSE TRUE FALSE 28.375903
## 19 320 24.80170 2.3074708 FALSE TRUE TRUE 74.576492
## 20 321 41.52920 4.1395076 TRUE TRUE TRUE 125.399908
## 21 322 67.92080 7.0299803 TRUE TRUE TRUE 205.585905
## 22 324 51.13580 5.1916460 TRUE TRUE TRUE 154.587786
## 23 325 14.05070 1.1299950 FALSE TRUE FALSE 41.911567
## 24 326 18.81100 1.6513547 FALSE TRUE TRUE 56.374858
## 25 327 28.65580 2.7295813 FALSE TRUE TRUE 86.286462
## 26 328 45.74610 4.6013528 TRUE TRUE TRUE 138.212179
## 27 329 18.08460 1.5717976 FALSE TRUE TRUE 54.167826
## 28 330 10.83420 0.7777160 FALSE TRUE FALSE 32.138826
## 29 331 25.94060 2.4322059 FALSE TRUE TRUE 78.036829
## 30 332 73.53410 7.6447626 TRUE TRUE TRUE 222.640879
## 31 333 11.81230 0.8848399 FALSE TRUE FALSE 35.110602
## 32 334 11.08740 0.8054471 FALSE TRUE FALSE 32.908128
## 33 336 12.04820 0.9106763 FALSE TRUE FALSE 35.827341
## 34 339 15.86030 1.3281868 FALSE TRUE TRUE 47.409702
## 35 340 12.24720 0.9324712 FALSE TRUE FALSE 36.431965
## 36 341 37.66190 3.7159514 TRUE TRUE TRUE 113.649832
## 37 342 9.33889 0.6139460 FALSE TRUE FALSE 27.595603
## 38 344 10.06230 0.6931756 FALSE TRUE FALSE 29.793551
## 39 347 13.91340 1.1149575 FALSE TRUE FALSE 41.494406
## 40 348 11.16040 0.8134422 FALSE TRUE FALSE 33.129925
## 41 349 14.42080 1.1705292 FALSE TRUE FALSE 43.036047
## 42 350 13.67810 1.0891869 FALSE TRUE FALSE 40.779490
## 43 351 9.39063 0.6196127 FALSE TRUE FALSE 27.752806
## 44 352 22.05110 2.0062184 FALSE TRUE TRUE 66.219303
## 45 354 12.80230 0.9932671 FALSE TRUE FALSE 38.118534
## 46 355 10.67180 0.7599296 FALSE TRUE FALSE 31.645404
## 47 357 9.92485 0.6781218 FALSE TRUE FALSE 29.375934
## 48 362 9.51363 0.6330839 FALSE TRUE FALSE 28.126518
## 49 371 15.57570 1.2970167 FALSE TRUE TRUE 46.544997
## 50 372 13.07510 1.0231449 FALSE TRUE FALSE 38.947386
## 51 380 15.02340 1.2365275 FALSE TRUE TRUE 44.866936
## 52 381 10.23300 0.7118711 FALSE TRUE FALSE 30.312191
## 53 382 14.33370 1.1609898 FALSE TRUE FALSE 42.771410
## 54 70 8.06900 2.5734843 FALSE TRUE FALSE 3.671679
## 55 129 8.37500 3.0158522 TRUE TRUE FALSE 4.276054
## 56 180 8.26600 2.8582767 FALSE TRUE FALSE 4.060770
## 57 181 8.04000 2.5315605 FALSE TRUE FALSE 3.614401
## 58 186 8.33700 2.9609177 FALSE TRUE FALSE 4.201001
## 59 187 8.24700 2.8308094 FALSE TRUE FALSE 4.023244
## 60 207 8.70400 3.4914701 TRUE TRUE FALSE 4.925856
## 61 211 8.39800 3.0491021 TRUE TRUE FALSE 4.321481
## 62 291 8.78000 3.6013393 TRUE TRUE FALSE 5.075963
## 63 292 3.56100 -3.9434924 TRUE TRUE TRUE -5.231994
## 64 294 3.86300 -3.5069070 TRUE TRUE FALSE -4.635519
## 65 300 4.13800 -3.1093541 TRUE TRUE FALSE -4.092371
## 66 280 10.71030 3.2981067 TRUE TRUE FALSE 4.126456
## 67 281 12.12650 3.9702120 TRUE TRUE FALSE 4.895085
## 68 282 10.58570 3.2389736 TRUE TRUE FALSE 4.058831
## 69 283 10.58570 3.2389736 TRUE TRUE FALSE 4.058831
## 70 18 303.42000 -0.5032827 FALSE TRUE TRUE -10.131233
## 71 19 306.38000 -0.4729097 FALSE TRUE FALSE -9.789075
## 72 24 248.31000 -1.0687734 FALSE TRUE TRUE -16.501616
## 73 75 70.80000 -2.8902261 FALSE TRUE TRUE -37.020700
## 74 102 262.76000 -0.9205001 FALSE TRUE TRUE -14.831283
## 75 112 172.91000 -1.8424624 FALSE TRUE TRUE -25.217400
## 76 113 169.27000 -1.8798129 FALSE TRUE TRUE -25.638162
## 77 119 261.95000 -0.9288116 FALSE TRUE TRUE -14.924914
## 78 120 321.02000 -0.3226868 FALSE TRUE FALSE -8.096779
## 79 121 88.01000 -2.7136321 FALSE TRUE TRUE -35.031328
## 80 122 88.63000 -2.7072702 FALSE TRUE TRUE -34.959659
## 81 126 338.92000 -0.1390126 FALSE TRUE FALSE -6.027647
## 82 131 240.16000 -1.1524016 FALSE TRUE TRUE -17.443707
## 83 133 227.61000 -1.2811787 FALSE TRUE TRUE -18.894411
## 84 134 297.09000 -0.5682356 FALSE TRUE TRUE -10.862942
## 85 135 292.29000 -0.6174890 FALSE TRUE TRUE -11.417793
## 86 293 316.03000 -0.3738898 FALSE TRUE FALSE -8.673593
## 87 294 131.42000 -2.2681966 FALSE TRUE TRUE -30.013394
## 88 317 338.16000 -0.1468111 FALSE TRUE FALSE -6.115498
## 89 321 329.46000 -0.2360829 FALSE TRUE FALSE -7.121166
## 90 323 314.64000 -0.3881528 FALSE TRUE FALSE -8.834268
## 91 324 2.60000 -3.5900350 TRUE TRUE TRUE -44.904208
## 92 325 35.05000 -3.2570614 TRUE TRUE TRUE -41.153184
## 93 326 28.79000 -3.3212961 TRUE TRUE TRUE -41.876802
## 94 327 210.97000 -1.4519239 FALSE TRUE TRUE -20.817894
## 95 328 88.27000 -2.7109642 FALSE TRUE TRUE -35.001273
## 96 329 27.25000 -3.3370982 TRUE TRUE TRUE -42.054817
## 97 330 21.57000 -3.3953814 TRUE TRUE TRUE -42.711390
## 98 331 127.36000 -2.3098568 FALSE TRUE TRUE -30.482705
## 99 332 16.45000 -3.4479184 TRUE TRUE TRUE -43.303232
## 100 333 48.45000 -3.1195623 TRUE TRUE TRUE -39.604225
## 101 334 318.75000 -0.3459796 FALSE TRUE FALSE -8.359177
## 102 335 319.98000 -0.3333584 FALSE TRUE FALSE -8.216997
## 103 336 291.55000 -0.6250822 FALSE TRUE TRUE -11.503333
## 104 337 2.52000 -3.5908559 TRUE TRUE TRUE -44.913455
## 105 338 3.65000 -3.5792608 TRUE TRUE TRUE -44.782834
## 106 339 7.68000 -3.5379085 TRUE TRUE TRUE -44.316991
## 107 340 24.65000 -3.3637772 TRUE TRUE TRUE -42.355361
## 108 341 18.82000 -3.4235995 TRUE TRUE TRUE -43.029274
## 109 342 60.72000 -2.9936583 FALSE TRUE TRUE -38.185887
## 110 343 83.45000 -2.7604229 FALSE TRUE TRUE -35.558436
## 111 344 81.33000 -2.7821765 FALSE TRUE TRUE -35.803495
## 112 345 97.95000 -2.6116365 FALSE TRUE TRUE -33.882324
## 113 346 100.19000 -2.5886516 FALSE TRUE TRUE -33.623393
## 114 347 100.63000 -2.5841367 FALSE TRUE TRUE -33.572532
## 115 348 109.85000 -2.4895291 FALSE TRUE TRUE -32.506756
## 116 349 27.49000 -3.3346356 TRUE TRUE TRUE -42.027074
## 117 350 68.95000 -2.9092092 FALSE TRUE TRUE -37.234549
## 118 354 240.52000 -1.1487076 FALSE TRUE TRUE -17.402093
## 119 355 43.06000 -3.1748698 TRUE TRUE TRUE -40.227276
## 120 356 318.01000 -0.3535728 FALSE TRUE FALSE -8.444717
## 121 358 304.21000 -0.4951764 FALSE TRUE TRUE -10.039913
## 122 359 0.32000 -3.6134304 TRUE TRUE TRUE -45.167762
## 123 362 6.68000 -3.5481696 TRUE TRUE TRUE -44.432585
## 124 363 50.92000 -3.0942173 TRUE TRUE TRUE -39.318708
## 125 364 10.48000 -3.5091773 TRUE TRUE TRUE -43.993327
## 126 365 3.50000 -3.5808000 TRUE TRUE TRUE -44.800173
## 127 369 334.40000 -0.1853929 FALSE TRUE FALSE -6.550132
## 128 370 22.01000 -3.3908665 TRUE TRUE TRUE -42.660529
## 129 378 302.76000 -0.5100550 FALSE TRUE TRUE -10.207525
## 130 392 318.43000 -0.3492631 FALSE TRUE FALSE -8.396167
## 131 299 34.77000 3.1576320 TRUE TRUE FALSE 3.312231
## 132 300 37.97000 3.6122874 TRUE TRUE FALSE 3.765671
## 133 326 34.37000 3.1008001 TRUE TRUE FALSE 3.255552
## 134 328 36.98000 3.4716284 TRUE TRUE FALSE 3.625388
## 135 350 34.02000 3.0510722 TRUE TRUE FALSE 3.205957
## outlier_MAD metodos_evaluados numero_metodos clasificacion
## 1 TRUE 3 2 Outlier según 2 métodos
## 2 TRUE 3 2 Outlier según 2 métodos
## 3 TRUE 3 2 Outlier según 2 métodos
## 4 TRUE 3 2 Outlier según 2 métodos
## 5 TRUE 3 2 Outlier según 2 métodos
## 6 TRUE 3 2 Outlier según 2 métodos
## 7 TRUE 3 2 Outlier según 2 métodos
## 8 TRUE 3 2 Outlier según 2 métodos
## 9 TRUE 3 3 Outlier según 3 métodos
## 10 TRUE 3 2 Outlier según 2 métodos
## 11 TRUE 3 2 Outlier según 2 métodos
## 12 TRUE 3 2 Outlier según 2 métodos
## 13 TRUE 3 2 Outlier según 2 métodos
## 14 TRUE 3 2 Outlier según 2 métodos
## 15 TRUE 3 2 Outlier según 2 métodos
## 16 TRUE 3 2 Outlier según 2 métodos
## 17 TRUE 3 2 Outlier según 2 métodos
## 18 TRUE 3 2 Outlier según 2 métodos
## 19 TRUE 3 2 Outlier según 2 métodos
## 20 TRUE 3 3 Outlier según 3 métodos
## 21 TRUE 3 3 Outlier según 3 métodos
## 22 TRUE 3 3 Outlier según 3 métodos
## 23 TRUE 3 2 Outlier según 2 métodos
## 24 TRUE 3 2 Outlier según 2 métodos
## 25 TRUE 3 2 Outlier según 2 métodos
## 26 TRUE 3 3 Outlier según 3 métodos
## 27 TRUE 3 2 Outlier según 2 métodos
## 28 TRUE 3 2 Outlier según 2 métodos
## 29 TRUE 3 2 Outlier según 2 métodos
## 30 TRUE 3 3 Outlier según 3 métodos
## 31 TRUE 3 2 Outlier según 2 métodos
## 32 TRUE 3 2 Outlier según 2 métodos
## 33 TRUE 3 2 Outlier según 2 métodos
## 34 TRUE 3 2 Outlier según 2 métodos
## 35 TRUE 3 2 Outlier según 2 métodos
## 36 TRUE 3 3 Outlier según 3 métodos
## 37 TRUE 3 2 Outlier según 2 métodos
## 38 TRUE 3 2 Outlier según 2 métodos
## 39 TRUE 3 2 Outlier según 2 métodos
## 40 TRUE 3 2 Outlier según 2 métodos
## 41 TRUE 3 2 Outlier según 2 métodos
## 42 TRUE 3 2 Outlier según 2 métodos
## 43 TRUE 3 2 Outlier según 2 métodos
## 44 TRUE 3 2 Outlier según 2 métodos
## 45 TRUE 3 2 Outlier según 2 métodos
## 46 TRUE 3 2 Outlier según 2 métodos
## 47 TRUE 3 2 Outlier según 2 métodos
## 48 TRUE 3 2 Outlier según 2 métodos
## 49 TRUE 3 2 Outlier según 2 métodos
## 50 TRUE 3 2 Outlier según 2 métodos
## 51 TRUE 3 2 Outlier según 2 métodos
## 52 TRUE 3 2 Outlier según 2 métodos
## 53 TRUE 3 2 Outlier según 2 métodos
## 54 TRUE 3 2 Outlier según 2 métodos
## 55 TRUE 3 3 Outlier según 3 métodos
## 56 TRUE 3 2 Outlier según 2 métodos
## 57 TRUE 3 2 Outlier según 2 métodos
## 58 TRUE 3 2 Outlier según 2 métodos
## 59 TRUE 3 2 Outlier según 2 métodos
## 60 TRUE 3 3 Outlier según 3 métodos
## 61 TRUE 3 3 Outlier según 3 métodos
## 62 TRUE 3 3 Outlier según 3 métodos
## 63 TRUE 3 3 Outlier según 3 métodos
## 64 TRUE 3 3 Outlier según 3 métodos
## 65 TRUE 3 3 Outlier según 3 métodos
## 66 TRUE 3 3 Outlier según 3 métodos
## 67 TRUE 3 3 Outlier según 3 métodos
## 68 TRUE 3 3 Outlier según 3 métodos
## 69 TRUE 3 3 Outlier según 3 métodos
## 70 TRUE 3 2 Outlier según 2 métodos
## 71 TRUE 3 2 Outlier según 2 métodos
## 72 TRUE 3 2 Outlier según 2 métodos
## 73 TRUE 3 2 Outlier según 2 métodos
## 74 TRUE 3 2 Outlier según 2 métodos
## 75 TRUE 3 2 Outlier según 2 métodos
## 76 TRUE 3 2 Outlier según 2 métodos
## 77 TRUE 3 2 Outlier según 2 métodos
## 78 TRUE 3 2 Outlier según 2 métodos
## 79 TRUE 3 2 Outlier según 2 métodos
## 80 TRUE 3 2 Outlier según 2 métodos
## 81 TRUE 3 2 Outlier según 2 métodos
## 82 TRUE 3 2 Outlier según 2 métodos
## 83 TRUE 3 2 Outlier según 2 métodos
## 84 TRUE 3 2 Outlier según 2 métodos
## 85 TRUE 3 2 Outlier según 2 métodos
## 86 TRUE 3 2 Outlier según 2 métodos
## 87 TRUE 3 2 Outlier según 2 métodos
## 88 TRUE 3 2 Outlier según 2 métodos
## 89 TRUE 3 2 Outlier según 2 métodos
## 90 TRUE 3 2 Outlier según 2 métodos
## 91 TRUE 3 3 Outlier según 3 métodos
## 92 TRUE 3 3 Outlier según 3 métodos
## 93 TRUE 3 3 Outlier según 3 métodos
## 94 TRUE 3 2 Outlier según 2 métodos
## 95 TRUE 3 2 Outlier según 2 métodos
## 96 TRUE 3 3 Outlier según 3 métodos
## 97 TRUE 3 3 Outlier según 3 métodos
## 98 TRUE 3 2 Outlier según 2 métodos
## 99 TRUE 3 3 Outlier según 3 métodos
## 100 TRUE 3 3 Outlier según 3 métodos
## 101 TRUE 3 2 Outlier según 2 métodos
## 102 TRUE 3 2 Outlier según 2 métodos
## 103 TRUE 3 2 Outlier según 2 métodos
## 104 TRUE 3 3 Outlier según 3 métodos
## 105 TRUE 3 3 Outlier según 3 métodos
## 106 TRUE 3 3 Outlier según 3 métodos
## 107 TRUE 3 3 Outlier según 3 métodos
## 108 TRUE 3 3 Outlier según 3 métodos
## 109 TRUE 3 2 Outlier según 2 métodos
## 110 TRUE 3 2 Outlier según 2 métodos
## 111 TRUE 3 2 Outlier según 2 métodos
## 112 TRUE 3 2 Outlier según 2 métodos
## 113 TRUE 3 2 Outlier según 2 métodos
## 114 TRUE 3 2 Outlier según 2 métodos
## 115 TRUE 3 2 Outlier según 2 métodos
## 116 TRUE 3 3 Outlier según 3 métodos
## 117 TRUE 3 2 Outlier según 2 métodos
## 118 TRUE 3 2 Outlier según 2 métodos
## 119 TRUE 3 3 Outlier según 3 métodos
## 120 TRUE 3 2 Outlier según 2 métodos
## 121 TRUE 3 2 Outlier según 2 métodos
## 122 TRUE 3 3 Outlier según 3 métodos
## 123 TRUE 3 3 Outlier según 3 métodos
## 124 TRUE 3 3 Outlier según 3 métodos
## 125 TRUE 3 3 Outlier según 3 métodos
## 126 TRUE 3 3 Outlier según 3 métodos
## 127 TRUE 3 2 Outlier según 2 métodos
## 128 TRUE 3 3 Outlier según 3 métodos
## 129 TRUE 3 2 Outlier según 2 métodos
## 130 TRUE 3 2 Outlier según 2 métodos
## 131 FALSE 3 2 Outlier según 2 métodos
## 132 TRUE 3 3 Outlier según 3 métodos
## 133 FALSE 3 2 Outlier según 2 métodos
## 134 TRUE 3 3 Outlier según 3 métodos
## 135 FALSE 3 2 Outlier según 2 métodos
## Variable
## 1 crim
## 2 crim
## 3 crim
## 4 crim
## 5 crim
## 6 crim
## 7 crim
## 8 crim
## 9 crim
## 10 crim
## 11 crim
## 12 crim
## 13 crim
## 14 crim
## 15 crim
## 16 crim
## 17 crim
## 18 crim
## 19 crim
## 20 crim
## 21 crim
## 22 crim
## 23 crim
## 24 crim
## 25 crim
## 26 crim
## 27 crim
## 28 crim
## 29 crim
## 30 crim
## 31 crim
## 32 crim
## 33 crim
## 34 crim
## 35 crim
## 36 crim
## 37 crim
## 38 crim
## 39 crim
## 40 crim
## 41 crim
## 42 crim
## 43 crim
## 44 crim
## 45 crim
## 46 crim
## 47 crim
## 48 crim
## 49 crim
## 50 crim
## 51 crim
## 52 crim
## 53 crim
## 54 rm
## 55 rm
## 56 rm
## 57 rm
## 58 rm
## 59 rm
## 60 rm
## 61 rm
## 62 rm
## 63 rm
## 64 rm
## 65 rm
## 66 dis
## 67 dis
## 68 dis
## 69 dis
## 70 black
## 71 black
## 72 black
## 73 black
## 74 black
## 75 black
## 76 black
## 77 black
## 78 black
## 79 black
## 80 black
## 81 black
## 82 black
## 83 black
## 84 black
## 85 black
## 86 black
## 87 black
## 88 black
## 89 black
## 90 black
## 91 black
## 92 black
## 93 black
## 94 black
## 95 black
## 96 black
## 97 black
## 98 black
## 99 black
## 100 black
## 101 black
## 102 black
## 103 black
## 104 black
## 105 black
## 106 black
## 107 black
## 108 black
## 109 black
## 110 black
## 111 black
## 112 black
## 113 black
## 114 black
## 115 black
## 116 black
## 117 black
## 118 black
## 119 black
## 120 black
## 121 black
## 122 black
## 123 black
## 124 black
## 125 black
## 126 black
## 127 black
## 128 black
## 129 black
## 130 black
## 131 lstat
## 132 lstat
## 133 lstat
## 134 lstat
## 135 lstat10 7.2 Candidatos a imputar
candidatos_imputacion <- comparacion_general %>%
filter(numero_metodos >= 2) %>%
dplyr::select(
Variable, ID, Valor,
outlier_Z, outlier_IQR, outlier_MAD,
metodos_evaluados, numero_metodos
) %>%
left_join(
referencia_ID_entrenamiento,
by = "ID"
)
print(candidatos_imputacion, 6)## Variable ID Valor outlier_Z outlier_IQR outlier_MAD metodos_evaluados
## 1 crim 294 13.52220 FALSE TRUE TRUE 3
## 2 crim 297 9.23230 FALSE TRUE TRUE 3
## 3 crim 299 11.10810 FALSE TRUE TRUE 3
## 4 crim 300 18.49820 FALSE TRUE TRUE 3
## 5 crim 301 19.60910 FALSE TRUE TRUE 3
## 6 crim 302 15.28800 FALSE TRUE TRUE 3
## 7 crim 303 9.82349 FALSE TRUE TRUE 3
## 8 crim 304 23.64820 FALSE TRUE TRUE 3
## 9 crim 305 88.97620 TRUE TRUE TRUE 3
## 10 crim 306 9.18702 FALSE TRUE TRUE 3
## 11 crim 308 24.39380 FALSE TRUE TRUE 3
## 12 crim 309 22.59710 FALSE TRUE TRUE 3
## 13 crim 310 14.33370 FALSE TRUE TRUE 3
## 14 crim 312 11.57790 FALSE TRUE TRUE 3
## 15 crim 314 13.35980 FALSE TRUE TRUE 3
## 16 crim 317 9.91655 FALSE TRUE TRUE 3
## 17 crim 318 25.04610 FALSE TRUE TRUE 3
## 18 crim 319 9.59571 FALSE TRUE TRUE 3
## 19 crim 320 24.80170 FALSE TRUE TRUE 3
## 20 crim 321 41.52920 TRUE TRUE TRUE 3
## 21 crim 322 67.92080 TRUE TRUE TRUE 3
## 22 crim 324 51.13580 TRUE TRUE TRUE 3
## 23 crim 325 14.05070 FALSE TRUE TRUE 3
## 24 crim 326 18.81100 FALSE TRUE TRUE 3
## 25 crim 327 28.65580 FALSE TRUE TRUE 3
## 26 crim 328 45.74610 TRUE TRUE TRUE 3
## 27 crim 329 18.08460 FALSE TRUE TRUE 3
## 28 crim 330 10.83420 FALSE TRUE TRUE 3
## 29 crim 331 25.94060 FALSE TRUE TRUE 3
## 30 crim 332 73.53410 TRUE TRUE TRUE 3
## 31 crim 333 11.81230 FALSE TRUE TRUE 3
## 32 crim 334 11.08740 FALSE TRUE TRUE 3
## 33 crim 336 12.04820 FALSE TRUE TRUE 3
## 34 crim 339 15.86030 FALSE TRUE TRUE 3
## 35 crim 340 12.24720 FALSE TRUE TRUE 3
## 36 crim 341 37.66190 TRUE TRUE TRUE 3
## 37 crim 342 9.33889 FALSE TRUE TRUE 3
## 38 crim 344 10.06230 FALSE TRUE TRUE 3
## 39 crim 347 13.91340 FALSE TRUE TRUE 3
## 40 crim 348 11.16040 FALSE TRUE TRUE 3
## 41 crim 349 14.42080 FALSE TRUE TRUE 3
## 42 crim 350 13.67810 FALSE TRUE TRUE 3
## 43 crim 351 9.39063 FALSE TRUE TRUE 3
## 44 crim 352 22.05110 FALSE TRUE TRUE 3
## 45 crim 354 12.80230 FALSE TRUE TRUE 3
## 46 crim 355 10.67180 FALSE TRUE TRUE 3
## 47 crim 357 9.92485 FALSE TRUE TRUE 3
## 48 crim 362 9.51363 FALSE TRUE TRUE 3
## 49 crim 371 15.57570 FALSE TRUE TRUE 3
## 50 crim 372 13.07510 FALSE TRUE TRUE 3
## 51 crim 380 15.02340 FALSE TRUE TRUE 3
## 52 crim 381 10.23300 FALSE TRUE TRUE 3
## 53 crim 382 14.33370 FALSE TRUE TRUE 3
## 54 rm 70 8.06900 FALSE TRUE TRUE 3
## 55 rm 129 8.37500 TRUE TRUE TRUE 3
## 56 rm 180 8.26600 FALSE TRUE TRUE 3
## 57 rm 181 8.04000 FALSE TRUE TRUE 3
## 58 rm 186 8.33700 FALSE TRUE TRUE 3
## 59 rm 187 8.24700 FALSE TRUE TRUE 3
## 60 rm 207 8.70400 TRUE TRUE TRUE 3
## 61 rm 211 8.39800 TRUE TRUE TRUE 3
## 62 rm 291 8.78000 TRUE TRUE TRUE 3
## 63 rm 292 3.56100 TRUE TRUE TRUE 3
## 64 rm 294 3.86300 TRUE TRUE TRUE 3
## 65 rm 300 4.13800 TRUE TRUE TRUE 3
## 66 dis 280 10.71030 TRUE TRUE TRUE 3
## 67 dis 281 12.12650 TRUE TRUE TRUE 3
## 68 dis 282 10.58570 TRUE TRUE TRUE 3
## 69 dis 283 10.58570 TRUE TRUE TRUE 3
## 70 black 18 303.42000 FALSE TRUE TRUE 3
## 71 black 19 306.38000 FALSE TRUE TRUE 3
## 72 black 24 248.31000 FALSE TRUE TRUE 3
## 73 black 75 70.80000 FALSE TRUE TRUE 3
## 74 black 102 262.76000 FALSE TRUE TRUE 3
## 75 black 112 172.91000 FALSE TRUE TRUE 3
## 76 black 113 169.27000 FALSE TRUE TRUE 3
## 77 black 119 261.95000 FALSE TRUE TRUE 3
## 78 black 120 321.02000 FALSE TRUE TRUE 3
## 79 black 121 88.01000 FALSE TRUE TRUE 3
## 80 black 122 88.63000 FALSE TRUE TRUE 3
## 81 black 126 338.92000 FALSE TRUE TRUE 3
## 82 black 131 240.16000 FALSE TRUE TRUE 3
## 83 black 133 227.61000 FALSE TRUE TRUE 3
## 84 black 134 297.09000 FALSE TRUE TRUE 3
## 85 black 135 292.29000 FALSE TRUE TRUE 3
## 86 black 293 316.03000 FALSE TRUE TRUE 3
## 87 black 294 131.42000 FALSE TRUE TRUE 3
## 88 black 317 338.16000 FALSE TRUE TRUE 3
## 89 black 321 329.46000 FALSE TRUE TRUE 3
## 90 black 323 314.64000 FALSE TRUE TRUE 3
## 91 black 324 2.60000 TRUE TRUE TRUE 3
## 92 black 325 35.05000 TRUE TRUE TRUE 3
## 93 black 326 28.79000 TRUE TRUE TRUE 3
## 94 black 327 210.97000 FALSE TRUE TRUE 3
## 95 black 328 88.27000 FALSE TRUE TRUE 3
## 96 black 329 27.25000 TRUE TRUE TRUE 3
## 97 black 330 21.57000 TRUE TRUE TRUE 3
## 98 black 331 127.36000 FALSE TRUE TRUE 3
## 99 black 332 16.45000 TRUE TRUE TRUE 3
## 100 black 333 48.45000 TRUE TRUE TRUE 3
## 101 black 334 318.75000 FALSE TRUE TRUE 3
## 102 black 335 319.98000 FALSE TRUE TRUE 3
## 103 black 336 291.55000 FALSE TRUE TRUE 3
## 104 black 337 2.52000 TRUE TRUE TRUE 3
## 105 black 338 3.65000 TRUE TRUE TRUE 3
## 106 black 339 7.68000 TRUE TRUE TRUE 3
## 107 black 340 24.65000 TRUE TRUE TRUE 3
## 108 black 341 18.82000 TRUE TRUE TRUE 3
## 109 black 342 60.72000 FALSE TRUE TRUE 3
## 110 black 343 83.45000 FALSE TRUE TRUE 3
## 111 black 344 81.33000 FALSE TRUE TRUE 3
## 112 black 345 97.95000 FALSE TRUE TRUE 3
## 113 black 346 100.19000 FALSE TRUE TRUE 3
## 114 black 347 100.63000 FALSE TRUE TRUE 3
## 115 black 348 109.85000 FALSE TRUE TRUE 3
## 116 black 349 27.49000 TRUE TRUE TRUE 3
## 117 black 350 68.95000 FALSE TRUE TRUE 3
## 118 black 354 240.52000 FALSE TRUE TRUE 3
## 119 black 355 43.06000 TRUE TRUE TRUE 3
## 120 black 356 318.01000 FALSE TRUE TRUE 3
## 121 black 358 304.21000 FALSE TRUE TRUE 3
## 122 black 359 0.32000 TRUE TRUE TRUE 3
## 123 black 362 6.68000 TRUE TRUE TRUE 3
## 124 black 363 50.92000 TRUE TRUE TRUE 3
## 125 black 364 10.48000 TRUE TRUE TRUE 3
## 126 black 365 3.50000 TRUE TRUE TRUE 3
## 127 black 369 334.40000 FALSE TRUE TRUE 3
## 128 black 370 22.01000 TRUE TRUE TRUE 3
## 129 black 378 302.76000 FALSE TRUE TRUE 3
## 130 black 392 318.43000 FALSE TRUE TRUE 3
## 131 lstat 299 34.77000 TRUE TRUE FALSE 3
## 132 lstat 300 37.97000 TRUE TRUE TRUE 3
## 133 lstat 326 34.37000 TRUE TRUE FALSE 3
## 134 lstat 328 36.98000 TRUE TRUE TRUE 3
## 135 lstat 350 34.02000 TRUE TRUE FALSE 3
## numero_metodos Fila_Boston
## 1 2 368
## 2 2 372
## 3 2 374
## 4 2 375
## 5 2 376
## 6 2 377
## 7 2 378
## 8 2 379
## 9 3 381
## 10 2 383
## 11 2 387
## 12 2 388
## 13 2 389
## 14 2 393
## 15 2 395
## 16 2 400
## 17 2 401
## 18 2 403
## 19 2 404
## 20 3 405
## 21 3 406
## 22 3 411
## 23 2 412
## 24 2 413
## 25 2 414
## 26 3 415
## 27 2 416
## 28 2 417
## 29 2 418
## 30 3 419
## 31 2 420
## 32 2 421
## 33 2 423
## 34 2 426
## 35 2 427
## 36 3 428
## 37 2 430
## 38 2 432
## 39 2 435
## 40 2 436
## 41 2 437
## 42 2 439
## 43 2 440
## 44 2 441
## 45 2 445
## 46 2 446
## 47 2 448
## 48 2 455
## 49 2 469
## 50 2 470
## 51 2 478
## 52 2 479
## 53 2 480
## 54 2 98
## 55 3 164
## 56 2 225
## 57 2 227
## 58 2 233
## 59 2 234
## 60 3 258
## 61 3 263
## 62 3 365
## 63 3 366
## 64 3 368
## 65 3 375
## 66 3 352
## 67 3 354
## 68 3 355
## 69 3 356
## 70 2 26
## 71 2 28
## 72 2 35
## 73 2 103
## 74 2 135
## 75 2 146
## 76 2 147
## 77 2 154
## 78 2 155
## 79 2 156
## 80 2 157
## 81 2 161
## 82 2 166
## 83 2 168
## 84 2 169
## 85 2 171
## 86 2 367
## 87 2 368
## 88 2 400
## 89 2 405
## 90 2 409
## 91 3 411
## 92 3 412
## 93 3 413
## 94 2 414
## 95 2 415
## 96 3 416
## 97 3 417
## 98 2 418
## 99 3 419
## 100 3 420
## 101 2 421
## 102 2 422
## 103 2 423
## 104 3 424
## 105 3 425
## 106 3 426
## 107 3 427
## 108 3 428
## 109 2 430
## 110 2 431
## 111 2 432
## 112 2 433
## 113 2 434
## 114 2 435
## 115 2 436
## 116 3 437
## 117 2 439
## 118 2 445
## 119 3 446
## 120 2 447
## 121 2 450
## 122 3 451
## 123 3 455
## 124 3 456
## 125 3 457
## 126 3 458
## 127 2 466
## 128 3 467
## 129 2 476
## 130 2 491
## 131 2 374
## 132 3 375
## 133 2 413
## 134 3 415
## 135 2 439Interpretación: Para realizar la imputación se tomaron encuenta que que tenia que cumplir 2 métodos para realizar la imputacion de esos datos atipicos.
11 7.3 Reemplzar por NA
datos_NA <- datos_originales
stopifnot(
all(candidatos_imputacion$Variable %in% variables),
!anyNA(candidatos_imputacion$ID),
all(candidatos_imputacion$ID >= 1),
all(candidatos_imputacion$ID <= nrow(datos_NA)),
all(
candidatos_imputacion$ID ==
floor(candidatos_imputacion$ID)
)
)
#Máscara de las celdas que se modificarán
mascara_reemplazo <- matrix(
FALSE,
nrow = nrow(datos_NA),
ncol = ncol(datos_NA),
dimnames = list(NULL, names(datos_NA))
)
for (variable in unique(candidatos_imputacion$Variable)) {
ids <- unique(
candidatos_imputacion$ID[
candidatos_imputacion$Variable == variable
]
)
datos_NA[ids, variable] <- NA
mascara_reemplazo[ids, variable] <- TRUE
}Interpretación: Los datos atipicos son reemplazados por NA, para realizar la respectiva imputacion mediante los métodos que veremos mas adelante.
resumen_NA <- data.frame(
Variable = names(datos_NA),
NA_originales = colSums(is.na(datos_originales)),
NA_despues = colSums(is.na(datos_NA))
)
resumen_NA$Nuevos_NA <-
resumen_NA$NA_despues - resumen_NA$NA_originales
print(resumen_NA)## Variable NA_originales NA_despues Nuevos_NA
## crim crim 0 53 53
## zn zn 0 0 0
## indus indus 0 0 0
## chas chas 0 0 0
## nox nox 0 0 0
## rm rm 0 12 12
## age age 0 0 0
## dis dis 0 4 4
## rad rad 0 0 0
## tax tax 0 0 0
## ptratio ptratio 0 0 0
## black black 0 61 61
## lstat lstat 0 5 5
## medv medv 0 0 0cat(
"\nTotal de celdas reemplazadas:",
sum(resumen_NA$Nuevos_NA), "\n"
)##
## Total de celdas reemplazadas: 135#Visualizar faltantes
print(visdat::vis_miss(datos_NA))
sum(is.na(datos_NA))## [1] 135sapply(datos_NA, function(x) sum(is.na(x)))## crim zn indus chas nox rm age dis rad tax
## 53 0 0 0 0 12 0 4 0 0
## ptratio black lstat medv
## 0 61 5 0Interpretación: Se puede observar en la grafica que hay presencia de valores Na en las variables: crim, rm, age, black y lstat. Las cuales tendremos qeu imputar.
12 8. Imputacion de los valores NA
library(mice)##
## Adjuntando el paquete: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(missForest)
library(ggplot2)
library(dplyr)
library(patchwork)stopifnot(
exists("datos_NA"),
exists("datos_originales"),
exists("data.trn"),
exists("data.tst"),
identical(datos_originales, data.trn),
identical(datos_NA$medv, datos_originales$medv),
identical(datos_NA$chas, datos_originales$chas)
)
prueba_sin_cambios <- data.tst
variables_imputacion <- setdiff(
names(datos_NA),
c("medv", "chas")
)
#medv no participa en los modelos de imputación.
#chas se conserva como predictor categórico.
base_imputacion <- datos_NA[
, setdiff(names(datos_NA), "medv"),
drop = FALSE
]
base_imputacion$chas <- factor(
base_imputacion$chas,
levels = c(0, 1)
)
stopifnot(!anyNA(base_imputacion$chas))
for (v in variables_imputacion) {
observados <- base_imputacion[[v]][
!is.na(base_imputacion[[v]])
]
if (
length(observados) < 2 ||
any(!is.finite(observados))
) {
stop(paste("Datos observados insuficientes o inválidos en", v))
}
}reconstruir_base <- function(base) {
stopifnot(
nrow(base) == nrow(datos_NA),
all(variables_imputacion %in% names(base))
)
salida <- datos_NA
for (v in variables_imputacion) {
pendientes <- is.na(datos_NA[[v]])
observados <- !pendientes
reemplazos <- base[[v]][pendientes]
if (
!is.numeric(reemplazos) ||
any(!is.finite(reemplazos))
) {
stop(paste("Reemplazos inválidos en", v))
}
#Copiar únicamente los valores de las celdas faltantes
salida[[v]][pendientes] <- reemplazos
#Comparar valores, permitiendo conversión entero/decimal
if (!isTRUE(all.equal(
as.numeric(salida[[v]][observados]),
as.numeric(datos_NA[[v]][observados]),
tolerance = 0,
check.attributes = FALSE
))) {
stop(paste("Se modificaron valores observados en", v))
}
}
stopifnot(
identical(salida$medv, datos_NA$medv),
identical(salida$chas, datos_NA$chas),
nrow(salida) == nrow(datos_NA),
all(is.finite(as.matrix(salida)))
)
salida
}13 8.1 Regresión Lineal
reg_predictoras <- base_imputacion
mascara_NA <- is.na(
as.matrix(base_imputacion[variables_imputacion])
)
#Inicializar faltantes por mediana
for (v in variables_imputacion) {
reg_predictoras[[v]][mascara_NA[, v]] <-
median(base_imputacion[[v]], na.rm = TRUE)
}
modelos_regresion <- list()
registro_regresion <- list()
escalas <- vapply(
base_imputacion[variables_imputacion],
sd,
numeric(1),
na.rm = TRUE
)
escalas[!is.finite(escalas) | escalas == 0] <- 1
convergio <- FALSE
for (iteracion in seq_len(100)) {
anterior <- as.matrix(
reg_predictoras[variables_imputacion]
)
for (v in variables_imputacion) {
pendientes <- mascara_NA[, v]
if (!any(pendientes)) next
observados <- !pendientes
predictores <- setdiff(names(reg_predictoras), v)
#Excluir predictores constantes
predictores <- predictores[
vapply(
reg_predictoras[observados, predictores, drop = FALSE],
function(x) length(unique(x)) > 1,
logical(1)
)
]
if (!length(predictores)) {
stop(paste("Sin predictores disponibles para", v))
}
#Ajustar solamente sobre respuestas observadas
modelo <- lm(
reformulate(predictores, response = v),
data = reg_predictoras[observados, , drop = FALSE]
)
pred <- predict(
modelo,
newdata = reg_predictoras[pendientes, , drop = FALSE]
)
if (any(!is.finite(pred))) {
stop(paste("Predicciones inválidas en", v))
}
reg_predictoras[[v]][pendientes] <- as.numeric(pred)
modelos_regresion[[v]] <- modelo
}
cambio <- max(abs(
sweep(
as.matrix(reg_predictoras[variables_imputacion]) - anterior,
2,
escalas,
"/"
)
))
registro_regresion[[iteracion]] <- data.frame(
Iteracion = iteracion,
Cambio_estandarizado = cambio
)
if (cambio < 1e-6) {
convergio <- TRUE
break
}
}
if (!convergio) {
stop("La regresión no convergió en 100 iteraciones.")
}
reg <- reconstruir_base(reg_predictoras)
cat("\n=== NA DESPUÉS DE REGRESIÓN ===\n")##
## === NA DESPUÉS DE REGRESIÓN ===print(colSums(is.na(reg)))## crim zn indus chas nox rm age dis rad tax
## 0 0 0 0 0 0 0 0 0 0
## ptratio black lstat medv
## 0 0 0 0print(bind_rows(registro_regresion))## Iteracion Cambio_estandarizado
## 1 1 2.892267e+00
## 2 2 3.233375e-01
## 3 3 1.190805e-01
## 4 4 3.585483e-02
## 5 5 1.060387e-02
## 6 6 3.127719e-03
## 7 7 9.221043e-04
## 8 8 2.718225e-04
## 9 9 8.012711e-05
## 10 10 2.361950e-05
## 11 11 6.962433e-06
## 12 12 2.052349e-06
## 13 13 6.049805e-07MICE
columnas_NA <- variables_imputacion[
colSums(is.na(base_imputacion[variables_imputacion])) > 0
]
crear_metodo <- function(nombre) {
metodo <- setNames(
rep("", ncol(base_imputacion)),
names(base_imputacion)
)
metodo[columnas_NA] <- nombre
metodo
}
predictores_mice <- mice::make.predictorMatrix(
base_imputacion
)
diag(predictores_mice) <- 0
#chas se utiliza como predictor, pero no se imputa
predictores_mice["chas", ] <- 014 8.2 Mice norm
imp_norm <- mice::mice(
base_imputacion,
method = crear_metodo("norm"),
predictorMatrix = predictores_mice,
m = 5,
maxit = 10,
seed = 123,
printFlag = FALSE
)
norm <- reconstruir_base(
mice::complete(imp_norm, 1)
)
print(colSums(is.na(norm)))## crim zn indus chas nox rm age dis rad tax
## 0 0 0 0 0 0 0 0 0 0
## ptratio black lstat medv
## 0 0 0 015 8.3 Mice pmm
imp_pmm <- mice::mice(
base_imputacion,
method = crear_metodo("pmm"),
predictorMatrix = predictores_mice,
m = 5,
maxit = 10,
seed = 123,
printFlag = FALSE
)
pmm <- reconstruir_base(
mice::complete(imp_pmm, 1)
)
print(colSums(is.na(pmm)))## crim zn indus chas nox rm age dis rad tax
## 0 0 0 0 0 0 0 0 0 0
## ptratio black lstat medv
## 0 0 0 016 8.3 Mice cart
imp_cart <- mice::mice(
base_imputacion,
method = crear_metodo("cart"),
predictorMatrix = predictores_mice,
m = 5,
maxit = 10,
seed = 123,
printFlag = FALSE
)
cart <- reconstruir_base(
mice::complete(imp_cart, 1)
)
print(colSums(is.na(cart)))## crim zn indus chas nox rm age dis rad tax
## 0 0 0 0 0 0 0 0 0 0
## ptratio black lstat medv
## 0 0 0 017 8.4 Mice random forest
imp_rf <- mice::mice(
base_imputacion,
method = crear_metodo("rf"),
predictorMatrix = predictores_mice,
m = 5,
maxit = 10,
seed = 123,
printFlag = FALSE
)
rf <- reconstruir_base(
mice::complete(imp_rf, 1)
)
print(colSums(is.na(rf)))## crim zn indus chas nox rm age dis rad tax
## 0 0 0 0 0 0 0 0 0 0
## ptratio black lstat medv
## 0 0 0 0#Revisar eventos registrados por MICE
objetos_mice <- list(
NORM = imp_norm,
PMM = imp_pmm,
CART = imp_cart,
RF = imp_rf
)
for (nombre in names(objetos_mice)) {
eventos <- objetos_mice[[nombre]]$loggedEvents
if (!is.null(eventos)) {
cat("\nEventos MICE:", nombre, "\n")
print(eventos)
}
}18 8.5 Mice forest
set.seed(123)
if (anyNA(base_imputacion)) {
ajuste_forest <- missForest::missForest(
base_imputacion,
maxiter = 10,
ntree = 100,
verbose = FALSE
)
forest <- reconstruir_base(
as.data.frame(ajuste_forest$ximp)
)
} else {
forest <- reconstruir_base(base_imputacion)
}
print(colSums(is.na(forest)))## crim zn indus chas nox rm age dis rad tax
## 0 0 0 0 0 0 0 0 0 0
## ptratio black lstat medv
## 0 0 0 0metodos <- list(
Regresion = reg,
NORM = norm,
PMM = pmm,
CART = cart,
RF = rf,
missForest = forest
)19 8.6 Comparación de los métodos
#Estas medidas describen la magnitud del reemplazo.
#NO son errores frente a un valor verdadero conocido.
evaluar <- function(base, nombre) {
bind_rows(lapply(variables_imputacion, function(v) {
ids <- which(is.na(datos_NA[[v]]))
diferencia <- base[[v]][ids] -
datos_originales[[v]][ids]
data.frame(
Metodo = nombre,
Variable = v,
N_reemplazos = length(ids),
RMSE_cambio = if (length(ids) > 0) {
sqrt(mean(diferencia^2))
} else {
NA_real_
},
MAE_cambio = if (length(ids) > 0) {
mean(abs(diferencia))
} else {
NA_real_
}
)
}))
}
comparacion_imputacion <- bind_rows(
lapply(names(metodos), function(nombre) {
evaluar(metodos[[nombre]], nombre)
})
)
print(comparacion_imputacion)## Metodo Variable N_reemplazos RMSE_cambio MAE_cambio
## 1 Regresion crim 53 22.234205 14.614125
## 2 Regresion zn 0 NA NA
## 3 Regresion indus 0 NA NA
## 4 Regresion nox 0 NA NA
## 5 Regresion rm 12 1.822594 1.734435
## 6 Regresion age 0 NA NA
## 7 Regresion dis 4 3.413406 3.295178
## 8 Regresion rad 0 NA NA
## 9 Regresion tax 0 NA NA
## 10 Regresion ptratio 0 NA NA
## 11 Regresion black 61 262.597394 230.737609
## 12 Regresion lstat 5 13.304295 12.690558
## 13 NORM crim 53 22.388318 14.750201
## 14 NORM zn 0 NA NA
## 15 NORM indus 0 NA NA
## 16 NORM nox 0 NA NA
## 17 NORM rm 12 1.897866 1.800804
## 18 NORM age 0 NA NA
## 19 NORM dis 4 3.511111 3.427218
## 20 NORM rad 0 NA NA
## 21 NORM tax 0 NA NA
## 22 NORM ptratio 0 NA NA
## 23 NORM black 61 262.402817 230.946219
## 24 NORM lstat 5 16.475687 13.725797
## 25 PMM crim 53 22.042027 14.332018
## 26 PMM zn 0 NA NA
## 27 PMM indus 0 NA NA
## 28 PMM nox 0 NA NA
## 29 PMM rm 12 1.683265 1.591333
## 30 PMM age 0 NA NA
## 31 PMM dis 4 3.828083 3.318675
## 32 PMM rad 0 NA NA
## 33 PMM tax 0 NA NA
## 34 PMM ptratio 0 NA NA
## 35 PMM black 61 268.522668 235.878852
## 36 PMM lstat 5 14.765803 12.414000
## 37 CART crim 53 21.570972 13.582094
## 38 CART zn 0 NA NA
## 39 CART indus 0 NA NA
## 40 CART nox 0 NA NA
## 41 CART rm 12 1.802376 1.492583
## 42 CART age 0 NA NA
## 43 CART dis 4 4.168416 3.808650
## 44 CART rad 0 NA NA
## 45 CART tax 0 NA NA
## 46 CART ptratio 0 NA NA
## 47 CART black 61 261.943981 228.094426
## 48 CART lstat 5 15.620019 13.420000
## 49 RF crim 53 21.733961 14.104171
## 50 RF zn 0 NA NA
## 51 RF indus 0 NA NA
## 52 RF nox 0 NA NA
## 53 RF rm 12 1.809979 1.547917
## 54 RF age 0 NA NA
## 55 RF dis 4 3.392966 3.313725
## 56 RF rad 0 NA NA
## 57 RF tax 0 NA NA
## 58 RF ptratio 0 NA NA
## 59 RF black 61 263.991817 231.465738
## 60 RF lstat 5 14.342764 13.536000
## 61 missForest crim 53 21.884830 14.191288
## 62 missForest zn 0 NA NA
## 63 missForest indus 0 NA NA
## 64 missForest nox 0 NA NA
## 65 missForest rm 12 1.648060 1.570153
## 66 missForest age 0 NA NA
## 67 missForest dis 4 3.941290 3.864620
## 68 missForest rad 0 NA NA
## 69 missForest tax 0 NA NA
## 70 missForest ptratio 0 NA NA
## 71 missForest black 61 261.294243 228.779154
## 72 missForest lstat 5 14.808829 14.598034# ============================================================
# R² DE LA REGRESIÓN SOBRE medv
# ============================================================
#Ajuste dentro del entrenamiento; no evaluación en prueba.
comparacion_R2 <- bind_rows(
lapply(names(metodos), function(nombre) {
modelo <- lm(
medv ~ .,
data = metodos[[nombre]]
)
data.frame(
Metodo = nombre,
R2_entrenamiento = summary(modelo)$r.squared,
R2_ajustado = summary(modelo)$adj.r.squared
)
})
)
print(comparacion_R2)## Metodo R2_entrenamiento R2_ajustado
## 1 Regresion 0.7296629 0.7206517
## 2 NORM 0.7152224 0.7057299
## 3 PMM 0.7247811 0.7156072
## 4 CART 0.7263245 0.7172019
## 5 RF 0.7362946 0.7275045
## 6 missForest 0.7346034 0.725756820 9. Graficamos la imputación
lista_comparar_imputacion <- c(
list(Original = datos_originales),
metodos
)
datos_total <- bind_rows(
lapply(names(lista_comparar_imputacion), function(nombre) {
base <- lista_comparar_imputacion[[nombre]]
base$Metodo <- nombre
base
})
)
datos_total$Metodo <- factor(
datos_total$Metodo,
levels = names(lista_comparar_imputacion)
)graficos_imputacion <- list()
for (v in variables_imputacion) {
#Densidad: ancho de banda común
ancho <- bw.nrd0(datos_originales[[v]])
g_densidad <- ggplot(
datos_total,
aes(x = .data[[v]], colour = Metodo)
) +
geom_density(bw = ancho) +
labs(
title = paste("Densidad:", v),
x = v,
y = "Densidad"
) +
theme_minimal()
#Histograma: cortes comunes
rango <- range(datos_total[[v]])
if (diff(rango) == 0) {
rango <- rango + c(-0.5, 0.5)
}
cortes <- seq(
rango[1], rango[2],
length.out = 31
)
g_histograma <- ggplot(
datos_total,
aes(x = .data[[v]], fill = Metodo)
) +
geom_histogram(
breaks = cortes,
alpha = 0.30,
position = "identity"
) +
labs(
title = paste("Histograma:", v),
x = v,
y = "Frecuencia"
) +
theme_minimal()
#Boxplot
g_boxplot <- ggplot(
datos_total,
aes(x = Metodo, y = .data[[v]], fill = Metodo)
) +
geom_boxplot(show.legend = FALSE) +
labs(
title = paste("Boxplot:", v),
x = NULL,
y = v
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1)
)
#Violin plot
g_violin <- ggplot(
datos_total,
aes(x = Metodo, y = .data[[v]], fill = Metodo)
) +
geom_violin(trim = TRUE, show.legend = FALSE) +
geom_boxplot(
width = 0.10,
fill = "white",
outlier.shape = NA,
show.legend = FALSE
) +
labs(
title = paste("Violin plot:", v),
x = NULL,
y = v
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1)
)
panel_imputacion <- patchwork::wrap_plots(
g_densidad,
g_histograma,
g_boxplot,
g_violin,
ncol = 2,
nrow = 2
) +
patchwork::plot_annotation(
title = paste("Original vs imputaciones:", v)
)
graficos_imputacion[[v]] <- panel_imputacion
print(panel_imputacion)
}
Interpretación: Realizando la imputación con respecto a
los métodos, se observo que: - La variable crim, el mejor método de
imputación es Random Forest. - La variable lstat, el mejor método de
imputación es Random Forest. - La variable indus, el mejor método de
imputación es Random Forest.
21 9.1 Tabla de resumen
resumenes_imputacion <- list()
for (v in variables_imputacion) {
res <- datos_total %>%
group_by(Metodo) %>%
summarise(
N = n(),
Media = mean(.data[[v]]),
Mediana = median(.data[[v]]),
SD = sd(.data[[v]]),
Min = min(.data[[v]]),
Max = max(.data[[v]]),
.groups = "drop"
) %>%
mutate(Variable = v, .before = 1)
resumenes_imputacion[[v]] <- res
cat("\nVariable:", v, "\n")
print(res)
}##
## Variable: crim
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 crim Original 404 3.73 0.256 9.13 0.00632 89.0
## 2 crim Regresion 404 1.82 0.256 2.54 0.00632 8.98
## 3 crim NORM 404 1.80 0.256 2.54 0.00632 8.98
## 4 crim PMM 404 1.85 0.256 2.67 0.00632 8.98
## 5 crim CART 404 1.95 0.256 2.83 0.00632 8.98
## 6 crim RF 404 1.88 0.256 2.73 0.00632 8.98
## 7 crim missForest 404 1.87 0.256 2.64 0.00632 8.98
##
## Variable: zn
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 zn Original 404 11.4 0 24.1 0 100
## 2 zn Regresion 404 11.4 0 24.1 0 100
## 3 zn NORM 404 11.4 0 24.1 0 100
## 4 zn PMM 404 11.4 0 24.1 0 100
## 5 zn CART 404 11.4 0 24.1 0 100
## 6 zn RF 404 11.4 0 24.1 0 100
## 7 zn missForest 404 11.4 0 24.1 0 100
##
## Variable: indus
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 indus Original 404 11.4 9.9 7.01 0.46 27.7
## 2 indus Regresion 404 11.4 9.9 7.01 0.46 27.7
## 3 indus NORM 404 11.4 9.9 7.01 0.46 27.7
## 4 indus PMM 404 11.4 9.9 7.01 0.46 27.7
## 5 indus CART 404 11.4 9.9 7.01 0.46 27.7
## 6 indus RF 404 11.4 9.9 7.01 0.46 27.7
## 7 indus missForest 404 11.4 9.9 7.01 0.46 27.7
##
## Variable: nox
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 nox Original 404 0.557 0.538 0.118 0.385 0.871
## 2 nox Regresion 404 0.557 0.538 0.118 0.385 0.871
## 3 nox NORM 404 0.557 0.538 0.118 0.385 0.871
## 4 nox PMM 404 0.557 0.538 0.118 0.385 0.871
## 5 nox CART 404 0.557 0.538 0.118 0.385 0.871
## 6 nox RF 404 0.557 0.538 0.118 0.385 0.871
## 7 nox missForest 404 0.557 0.538 0.118 0.385 0.871
##
## Variable: rm
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 rm Original 404 6.29 6.21 0.692 3.56 8.78
## 2 rm Regresion 404 6.27 6.21 0.588 4.52 7.93
## 3 rm NORM 404 6.27 6.21 0.589 4.52 7.93
## 4 rm PMM 404 6.28 6.21 0.591 4.52 7.93
## 5 rm CART 404 6.29 6.22 0.607 4.52 7.93
## 6 rm RF 404 6.29 6.21 0.606 4.52 7.93
## 7 rm missForest 404 6.27 6.21 0.591 4.52 7.93
##
## Variable: age
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 age Original 404 69.2 77.8 27.7 2.9 100
## 2 age Regresion 404 69.2 77.8 27.7 2.9 100
## 3 age NORM 404 69.2 77.8 27.7 2.9 100
## 4 age PMM 404 69.2 77.8 27.7 2.9 100
## 5 age CART 404 69.2 77.8 27.7 2.9 100
## 6 age RF 404 69.2 77.8 27.7 2.9 100
## 7 age missForest 404 69.2 77.8 27.7 2.9 100
##
## Variable: dis
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 dis Original 404 3.76 3.11 2.11 1.13 12.1
## 2 dis Regresion 404 3.73 3.11 2.02 1.13 9.22
## 3 dis NORM 404 3.73 3.11 2.02 1.13 9.22
## 4 dis PMM 404 3.73 3.11 2.02 1.13 9.22
## 5 dis CART 404 3.72 3.11 2.01 1.13 9.22
## 6 dis RF 404 3.73 3.11 2.02 1.13 9.22
## 7 dis missForest 404 3.72 3.11 2.01 1.13 9.22
##
## Variable: rad
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 rad Original 404 9.55 5 8.74 1 24
## 2 rad Regresion 404 9.55 5 8.74 1 24
## 3 rad NORM 404 9.55 5 8.74 1 24
## 4 rad PMM 404 9.55 5 8.74 1 24
## 5 rad CART 404 9.55 5 8.74 1 24
## 6 rad RF 404 9.55 5 8.74 1 24
## 7 rad missForest 404 9.55 5 8.74 1 24
##
## Variable: tax
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 tax Original 404 413. 346. 168. 187 711
## 2 tax Regresion 404 413. 346. 168. 187 711
## 3 tax NORM 404 413. 346. 168. 187 711
## 4 tax PMM 404 413. 346. 168. 187 711
## 5 tax CART 404 413. 346. 168. 187 711
## 6 tax RF 404 413. 346. 168. 187 711
## 7 tax missForest 404 413. 346. 168. 187 711
##
## Variable: ptratio
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ptratio Original 404 18.4 19 2.17 12.6 22
## 2 ptratio Regresion 404 18.4 19 2.17 12.6 22
## 3 ptratio NORM 404 18.4 19 2.17 12.6 22
## 4 ptratio PMM 404 18.4 19 2.17 12.6 22
## 5 ptratio CART 404 18.4 19 2.17 12.6 22
## 6 ptratio RF 404 18.4 19 2.17 12.6 22
## 7 ptratio missForest 404 18.4 19 2.17 12.6 22
##
## Variable: black
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 black Original 404 352. 391. 97.5 0.32 397.
## 2 black Regresion 404 387. 391. 11.7 342. 397.
## 3 black NORM 404 387. 392. 12.5 342. 414.
## 4 black PMM 404 388. 393. 12.3 342. 397.
## 5 black CART 404 387. 393. 13.4 342. 397.
## 6 black RF 404 387. 393. 13.0 342. 397.
## 7 black missForest 404 387. 391. 11.8 342. 397.
##
## Variable: lstat
## # A tibble: 7 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 lstat Original 404 12.5 11.4 7.04 1.73 38.0
## 2 lstat Regresion 404 12.4 11.4 6.66 1.73 32.0
## 3 lstat NORM 404 12.4 11.4 6.70 1.73 32.0
## 4 lstat PMM 404 12.4 11.4 6.71 1.73 32.0
## 5 lstat CART 404 12.4 11.4 6.69 1.73 32.0
## 6 lstat RF 404 12.4 11.4 6.66 1.73 32.0
## 7 lstat missForest 404 12.4 11.4 6.62 1.73 32.0tabla_descriptivos <- bind_rows(resumenes_imputacion)
tabla_descriptivos## # A tibble: 84 × 8
## Variable Metodo N Media Mediana SD Min Max
## <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 crim Original 404 3.73 0.256 9.13 0.00632 89.0
## 2 crim Regresion 404 1.82 0.256 2.54 0.00632 8.98
## 3 crim NORM 404 1.80 0.256 2.54 0.00632 8.98
## 4 crim PMM 404 1.85 0.256 2.67 0.00632 8.98
## 5 crim CART 404 1.95 0.256 2.83 0.00632 8.98
## 6 crim RF 404 1.88 0.256 2.73 0.00632 8.98
## 7 crim missForest 404 1.87 0.256 2.64 0.00632 8.98
## 8 zn Original 404 11.4 0 24.1 0 100
## 9 zn Regresion 404 11.4 0 24.1 0 100
## 10 zn NORM 404 11.4 0 24.1 0 100
## # ℹ 74 more rows22 9.2 Normalidad de los datos
# ============================================================
# KOLMOGOROV–SMIRNOV: DISTANCIA DESCRIPTIVA D
# ============================================================
#No se reporta p.value:
#las bases comparten observaciones y no son independientes.
distancia_KS <- function(x, y) {
puntos <- sort(unique(c(x, y)))
max(abs(
ecdf(x)(puntos) - ecdf(y)(puntos)
))
}
resultadoKS <- bind_rows(
lapply(variables_imputacion, function(v) {
bind_rows(lapply(names(metodos), function(nombre) {
data.frame(
Variable = v,
Metodo = nombre,
D = distancia_KS(
datos_originales[[v]],
metodos[[nombre]][[v]]
)
)
}))
})
)
print(resultadoKS)## Variable Metodo D
## 1 crim Regresion 0.13118812
## 2 crim NORM 0.13118812
## 3 crim PMM 0.13118812
## 4 crim CART 0.13118812
## 5 crim RF 0.13118812
## 6 crim missForest 0.13118812
## 7 zn Regresion 0.00000000
## 8 zn NORM 0.00000000
## 9 zn PMM 0.00000000
## 10 zn CART 0.00000000
## 11 zn RF 0.00000000
## 12 zn missForest 0.00000000
## 13 indus Regresion 0.00000000
## 14 indus NORM 0.00000000
## 15 indus PMM 0.00000000
## 16 indus CART 0.00000000
## 17 indus RF 0.00000000
## 18 indus missForest 0.00000000
## 19 nox Regresion 0.00000000
## 20 nox NORM 0.00000000
## 21 nox PMM 0.00000000
## 22 nox CART 0.00000000
## 23 nox RF 0.00000000
## 24 nox missForest 0.00000000
## 25 rm Regresion 0.02227723
## 26 rm NORM 0.02227723
## 27 rm PMM 0.02227723
## 28 rm CART 0.02227723
## 29 rm RF 0.02227723
## 30 rm missForest 0.02227723
## 31 age Regresion 0.00000000
## 32 age NORM 0.00000000
## 33 age PMM 0.00000000
## 34 age CART 0.00000000
## 35 age RF 0.00000000
## 36 age missForest 0.00000000
## 37 dis Regresion 0.00990099
## 38 dis NORM 0.00990099
## 39 dis PMM 0.00990099
## 40 dis CART 0.00990099
## 41 dis RF 0.00990099
## 42 dis missForest 0.00990099
## 43 rad Regresion 0.00000000
## 44 rad NORM 0.00000000
## 45 rad PMM 0.00000000
## 46 rad CART 0.00000000
## 47 rad RF 0.00000000
## 48 rad missForest 0.00000000
## 49 tax Regresion 0.00000000
## 50 tax NORM 0.00000000
## 51 tax PMM 0.00000000
## 52 tax CART 0.00000000
## 53 tax RF 0.00000000
## 54 tax missForest 0.00000000
## 55 ptratio Regresion 0.00000000
## 56 ptratio NORM 0.00000000
## 57 ptratio PMM 0.00000000
## 58 ptratio CART 0.00000000
## 59 ptratio RF 0.00000000
## 60 ptratio missForest 0.00000000
## 61 black Regresion 0.15099010
## 62 black NORM 0.15099010
## 63 black PMM 0.15099010
## 64 black CART 0.15099010
## 65 black RF 0.15099010
## 66 black missForest 0.15099010
## 67 lstat Regresion 0.01237624
## 68 lstat NORM 0.01237624
## 69 lstat PMM 0.01237624
## 70 lstat CART 0.01237624
## 71 lstat RF 0.01237624
## 72 lstat missForest 0.0123762423 10. Correlación de los datos
resultadoCor <- bind_rows(
lapply(variables_imputacion, function(v) {
bind_rows(lapply(names(metodos), function(nombre) {
x <- datos_originales[[v]]
y <- metodos[[nombre]][[v]]
correlacion <- if (sd(x) > 0 && sd(y) > 0) {
cor(x, y)
} else {
NA_real_
}
data.frame(
Variable = v,
Metodo = nombre,
Correlacion = correlacion
)
}))
})
)
print(resultadoCor)## Variable Metodo Correlacion
## 1 crim Regresion 0.6135673
## 2 crim NORM 0.5960956
## 3 crim PMM 0.6180865
## 4 crim CART 0.6450546
## 5 crim RF 0.6443294
## 6 crim missForest 0.6395918
## 7 zn Regresion 1.0000000
## 8 zn NORM 1.0000000
## 9 zn PMM 1.0000000
## 10 zn CART 1.0000000
## 11 zn RF 1.0000000
## 12 zn missForest 1.0000000
## 13 indus Regresion 1.0000000
## 14 indus NORM 1.0000000
## 15 indus PMM 1.0000000
## 16 indus CART 1.0000000
## 17 indus RF 1.0000000
## 18 indus missForest 1.0000000
## 19 nox Regresion 1.0000000
## 20 nox NORM 1.0000000
## 21 nox PMM 1.0000000
## 22 nox CART 1.0000000
## 23 nox RF 1.0000000
## 24 nox missForest 1.0000000
## 25 rm Regresion 0.8918824
## 26 rm NORM 0.8815685
## 27 rm PMM 0.9094062
## 28 rm CART 0.8933577
## 29 rm RF 0.8924348
## 30 rm missForest 0.9137469
## 31 age Regresion 1.0000000
## 32 age NORM 1.0000000
## 33 age PMM 1.0000000
## 34 age CART 1.0000000
## 35 age RF 1.0000000
## 36 age missForest 1.0000000
## 37 dis Regresion 0.9874663
## 38 dis NORM 0.9866970
## 39 dis PMM 0.9838905
## 40 dis CART 0.9809068
## 41 dis RF 0.9876410
## 42 dis missForest 0.9831304
## 43 rad Regresion 1.0000000
## 44 rad NORM 1.0000000
## 45 rad PMM 1.0000000
## 46 rad CART 1.0000000
## 47 rad RF 1.0000000
## 48 rad missForest 1.0000000
## 49 tax Regresion 1.0000000
## 50 tax NORM 1.0000000
## 51 tax PMM 1.0000000
## 52 tax CART 1.0000000
## 53 tax RF 1.0000000
## 54 tax missForest 1.0000000
## 55 ptratio Regresion 1.0000000
## 56 ptratio NORM 1.0000000
## 57 ptratio PMM 1.0000000
## 58 ptratio CART 1.0000000
## 59 ptratio RF 1.0000000
## 60 ptratio missForest 1.0000000
## 61 black Regresion 0.1815106
## 62 black NORM 0.1851088
## 63 black PMM 0.0023149
## 64 black CART 0.1840128
## 65 black RF 0.1350152
## 66 black missForest 0.2168663
## 67 lstat Regresion 0.9783629
## 68 lstat NORM 0.9658181
## 69 lstat PMM 0.9727482
## 70 lstat CART 0.9694323
## 71 lstat RF 0.9745832
## 72 lstat missForest 0.9730380# ============================================================
# COMPARACIÓN DE MEDIA Y DESVIACIÓN ESTÁNDAR
# ============================================================
comparacion_medias_SD <- bind_rows(
lapply(variables_imputacion, function(v) {
bind_rows(lapply(names(metodos), function(nombre) {
data.frame(
Variable = v,
Metodo = nombre,
Media_Original = mean(datos_originales[[v]]),
Media_Imputada = mean(metodos[[nombre]][[v]]),
SD_Original = sd(datos_originales[[v]]),
SD_Imputada = sd(metodos[[nombre]][[v]])
)
}))
})
)
print(comparacion_medias_SD)## Variable Metodo Media_Original Media_Imputada SD_Original SD_Imputada
## 1 crim Regresion 3.7332265 1.8160270 9.1305482 2.5447405
## 2 crim NORM 3.7332265 1.7981754 9.1305482 2.5406779
## 3 crim PMM 3.7332265 1.8530361 9.1305482 2.6712696
## 4 crim CART 3.7332265 1.9514171 9.1305482 2.8346802
## 5 crim RF 3.7332265 1.8829269 9.1305482 2.7312436
## 6 crim missForest 3.7332265 1.8714981 9.1305482 2.6382760
## 7 zn Regresion 11.4195545 11.4195545 24.1411860 24.1411860
## 8 zn NORM 11.4195545 11.4195545 24.1411860 24.1411860
## 9 zn PMM 11.4195545 11.4195545 24.1411860 24.1411860
## 10 zn CART 11.4195545 11.4195545 24.1411860 24.1411860
## 11 zn RF 11.4195545 11.4195545 24.1411860 24.1411860
## 12 zn missForest 11.4195545 11.4195545 24.1411860 24.1411860
## 13 indus Regresion 11.4036881 11.4036881 7.0081388 7.0081388
## 14 indus NORM 11.4036881 11.4036881 7.0081388 7.0081388
## 15 indus PMM 11.4036881 11.4036881 7.0081388 7.0081388
## 16 indus CART 11.4036881 11.4036881 7.0081388 7.0081388
## 17 indus RF 11.4036881 11.4036881 7.0081388 7.0081388
## 18 indus missForest 11.4036881 11.4036881 7.0081388 7.0081388
## 19 nox Regresion 0.5567173 0.5567173 0.1175030 0.1175030
## 20 nox NORM 0.5567173 0.5567173 0.1175030 0.1175030
## 21 nox PMM 0.5567173 0.5567173 0.1175030 0.1175030
## 22 nox CART 0.5567173 0.5567173 0.1175030 0.1175030
## 23 nox RF 0.5567173 0.5567173 0.1175030 0.1175030
## 24 nox missForest 0.5567173 0.5567173 0.1175030 0.1175030
## 25 rm Regresion 6.2888391 6.2748059 0.6917318 0.5879186
## 26 rm NORM 6.2888391 6.2745122 0.6917318 0.5892961
## 27 rm PMM 6.2888391 6.2752550 0.6917318 0.5914639
## 28 rm CART 6.2888391 6.2873465 0.6917318 0.6072137
## 29 rm RF 6.2888391 6.2852079 0.6917318 0.6057598
## 30 rm missForest 6.2888391 6.2738472 0.6917318 0.5912274
## 31 age Regresion 69.2252475 69.2252475 27.6575165 27.6575165
## 32 age NORM 69.2252475 69.2252475 27.6575165 27.6575165
## 33 age PMM 69.2252475 69.2252475 27.6575165 27.6575165
## 34 age CART 69.2252475 69.2252475 27.6575165 27.6575165
## 35 age RF 69.2252475 69.2252475 27.6575165 27.6575165
## 36 age missForest 69.2252475 69.2252475 27.6575165 27.6575165
## 37 dis Regresion 3.7608257 3.7282002 2.1071102 2.0176189
## 38 dis NORM 3.7608257 3.7268929 2.1071102 2.0169130
## 39 dis PMM 3.7608257 3.7279676 2.1071102 2.0234511
## 40 dis CART 3.7608257 3.7231163 2.1071102 2.0110888
## 41 dis RF 3.7608257 3.7280166 2.1071102 2.0168910
## 42 dis missForest 3.7608257 3.7225622 2.1071102 2.0069259
## 43 rad Regresion 9.5544554 9.5544554 8.7413626 8.7413626
## 44 rad NORM 9.5544554 9.5544554 8.7413626 8.7413626
## 45 rad PMM 9.5544554 9.5544554 8.7413626 8.7413626
## 46 rad CART 9.5544554 9.5544554 8.7413626 8.7413626
## 47 rad RF 9.5544554 9.5544554 8.7413626 8.7413626
## 48 rad missForest 9.5544554 9.5544554 8.7413626 8.7413626
## 49 tax Regresion 413.1955446 413.1955446 167.7771885 167.7771885
## 50 tax NORM 413.1955446 413.1955446 167.7771885 167.7771885
## 51 tax PMM 413.1955446 413.1955446 167.7771885 167.7771885
## 52 tax CART 413.1955446 413.1955446 167.7771885 167.7771885
## 53 tax RF 413.1955446 413.1955446 167.7771885 167.7771885
## 54 tax missForest 413.1955446 413.1955446 167.7771885 167.7771885
## 55 ptratio Regresion 18.4361386 18.4361386 2.1669365 2.1669365
## 56 ptratio NORM 18.4361386 18.4361386 2.1669365 2.1669365
## 57 ptratio PMM 18.4361386 18.4361386 2.1669365 2.1669365
## 58 ptratio CART 18.4361386 18.4361386 2.1669365 2.1669365
## 59 ptratio RF 18.4361386 18.4361386 2.1669365 2.1669365
## 60 ptratio missForest 18.4361386 18.4361386 2.1669365 2.1669365
## 61 black Regresion 352.4675000 387.3065945 97.4551772 11.6609235
## 62 black NORM 352.4675000 387.3380925 97.4551772 12.4536995
## 63 black PMM 352.4675000 388.0828713 97.4551772 12.2674509
## 64 black CART 352.4675000 386.9075000 97.4551772 13.3652413
## 65 black RF 352.4675000 387.4165347 97.4551772 13.0227516
## 66 black missForest 352.4675000 387.0108871 97.4551772 11.7891327
## 67 lstat Regresion 12.5456436 12.3885822 7.0382984 6.6605212
## 68 lstat NORM 12.5456436 12.3757698 7.0382984 6.6966478
## 69 lstat PMM 12.5456436 12.3920050 7.0382984 6.7115179
## 70 lstat CART 12.5456436 12.3795545 7.0382984 6.6918493
## 71 lstat RF 12.5456436 12.3781188 7.0382984 6.6645000
## 72 lstat missForest 12.5456436 12.3649748 7.0382984 6.6185795auditoria_imputacion <- bind_rows(
lapply(variables_imputacion, function(v) {
ids <- which(is.na(datos_NA[[v]]))
tabla <- data.frame(
ID = ids,
Variable = rep(v, length(ids)),
Original = datos_originales[[v]][ids]
)
for (nombre in names(metodos)) {
tabla[[nombre]] <- metodos[[nombre]][[v]][ids]
}
tabla
})
)
print(auditoria_imputacion)## ID Variable Original Regresion NORM PMM CART RF
## 1 294 crim 13.52220 5.565717 4.602799 4.64689 3.67822 3.67367
## 2 297 crim 9.23230 5.308294 5.682693 8.79212 5.70818 2.37857
## 3 299 crim 11.10810 5.855279 5.847288 6.28807 7.05042 6.53876
## 4 300 crim 18.49820 5.863227 7.033130 3.16360 8.71675 8.64476
## 5 301 crim 19.60910 5.903519 6.351376 3.77498 4.64689 5.70818
## 6 302 crim 15.28800 6.003955 4.771054 5.58107 8.49213 3.47428
## 7 303 crim 9.82349 6.092021 4.968275 7.02259 8.98296 7.99248
## 8 304 crim 23.64820 6.098250 5.268515 5.66637 8.79212 8.71675
## 9 305 crim 88.97620 5.966660 5.096688 6.80117 8.98296 8.71675
## 10 306 crim 9.18702 6.030237 4.938887 7.99248 7.40389 8.05579
## 11 308 crim 24.39380 6.031517 6.334768 7.52601 8.79212 7.40389
## 12 309 crim 22.59710 6.219734 6.738193 6.28807 5.66637 6.28807
## 13 310 crim 14.33370 6.073340 6.472024 6.28807 8.64476 8.71675
## 14 312 crim 11.57790 6.005705 6.529422 7.52601 8.49213 8.79212
## 15 314 crim 13.35980 5.800599 5.431095 6.65492 7.02259 4.66883
## 16 317 crim 9.91655 6.238546 8.242269 8.98296 5.87205 8.79212
## 17 318 crim 25.04610 6.212061 6.161355 6.28807 8.79212 8.79212
## 18 319 crim 9.59571 5.977757 6.478654 4.75237 8.49213 8.24809
## 19 320 crim 24.80170 5.824432 5.442162 3.84970 8.64476 4.66883
## 20 321 crim 41.52920 6.086407 5.877191 7.99248 5.66637 4.66883
## 21 322 crim 67.92080 5.969178 5.784806 7.99248 7.02259 8.98296
## 22 324 crim 51.13580 5.201203 3.968573 4.03841 4.89822 3.16360
## 23 325 crim 14.05070 5.802714 5.898663 3.77498 5.87205 7.05042
## 24 326 crim 18.81100 5.555291 6.299349 4.55587 8.98296 6.53876
## 25 327 crim 28.65580 5.456856 4.292742 3.77498 5.66637 7.99248
## 26 328 crim 45.74610 5.878811 6.544820 5.44114 8.71675 7.02259
## 27 329 crim 18.08460 6.308147 6.576461 7.52601 6.28807 7.05042
## 28 330 crim 10.83420 6.241328 7.189765 3.83684 8.79212 6.39312
## 29 331 crim 25.94060 5.974325 4.966841 5.87205 8.64476 4.66883
## 30 332 crim 73.53410 5.892923 6.831622 7.02259 5.87205 6.28807
## 31 333 crim 11.81230 6.234820 8.358495 6.71772 6.28807 5.58107
## 32 334 crim 11.08740 5.893272 5.116551 8.20058 7.02259 5.66637
## 33 336 crim 12.04820 5.362600 4.577685 4.34879 3.67822 6.80117
## 34 339 crim 15.86030 6.016221 5.222149 6.71772 5.87205 7.99248
## 35 340 crim 12.24720 5.326505 4.562217 4.83567 7.05042 8.20058
## 36 341 crim 37.66190 5.679726 4.867193 7.83932 3.67367 8.64476
## 37 342 crim 9.33889 6.101924 6.011323 7.99248 6.28807 5.87205
## 38 344 crim 10.06230 5.728702 4.734612 3.77498 7.99248 7.99248
## 39 347 crim 13.91340 5.833473 4.900576 7.40389 7.52601 8.05579
## 40 348 crim 11.16040 6.308982 4.629324 8.98296 5.66637 3.67822
## 41 349 crim 14.42080 6.076099 3.654294 6.71772 7.99248 5.44114
## 42 350 crim 13.67810 5.999167 4.803880 5.66637 4.55587 4.55587
## 43 351 crim 9.39063 6.140181 6.306109 8.98296 8.71675 6.39312
## 44 352 crim 22.05110 6.126370 5.530080 6.28807 8.79212 6.28807
## 45 354 crim 12.80230 6.176687 6.319219 6.28807 7.40389 5.44114
## 46 355 crim 10.67180 6.302605 6.255903 7.52601 7.02259 5.44114
## 47 357 crim 9.92485 5.997013 7.109939 5.87205 5.44114 6.80117
## 48 362 crim 9.51363 6.069630 6.124225 5.66637 6.71772 4.75237
## 49 371 crim 15.57570 5.372994 6.630893 4.34879 4.34879 2.81838
## 50 372 crim 13.07510 5.289801 7.011043 4.03841 5.29305 4.03841
## 51 380 crim 15.02340 5.579422 4.386064 6.65492 8.98296 7.05042
## 52 381 crim 10.23300 5.607288 4.879751 3.67822 5.44114 4.87141
## 53 382 crim 14.33370 5.434344 6.270796 8.79212 5.73116 6.65492
## 54 70 rm 8.06900 6.860486 6.863023 7.17800 7.41600 7.92900
## 55 129 rm 8.37500 6.942388 6.331370 7.01600 7.87500 5.88000
## 56 180 rm 8.26600 6.864236 7.120331 6.56300 7.41200 7.14800
## 57 181 rm 8.04000 6.946256 6.922035 6.86100 7.92900 6.94300
## 58 186 rm 8.33700 6.900312 6.434219 7.27400 7.47000 7.16300
## 59 187 rm 8.24700 6.780624 7.320708 7.24900 7.82000 7.16300
## 60 207 rm 8.70400 7.104611 7.100342 7.01600 6.56300 7.52000
## 61 211 rm 8.39800 7.034569 6.350029 6.81600 6.62500 7.52000
## 62 291 rm 8.78000 6.541198 7.075093 6.95100 6.84900 7.92900
## 63 292 rm 3.56100 6.522513 6.179023 6.45600 6.79400 7.06100
## 64 294 rm 3.86300 6.438487 6.415683 6.15300 6.45400 6.65700
## 65 300 rm 4.13800 6.172900 6.878088 5.75700 6.96800 6.39800
## 66 280 dis 10.71030 7.033164 6.628852 5.11670 6.27000 7.31720
## 67 281 dis 12.12650 7.549661 8.682620 7.30730 5.88500 7.65340
## 68 282 dis 10.58570 8.112556 8.409064 9.22030 8.79210 7.95490
## 69 283 dis 10.58570 8.132106 6.578791 9.08920 7.82650 7.82780
## 70 18 black 303.42000 391.019416 386.878328 396.90000 396.90000 396.42000
## 71 19 black 306.38000 392.103625 378.206394 392.04000 396.90000 396.90000
## 72 24 black 248.31000 394.353353 399.745804 391.34000 364.31000 396.90000
## 73 75 black 70.80000 392.572715 395.666403 389.25000 396.30000 396.42000
## 74 102 black 262.76000 386.660280 398.450396 396.90000 385.76000 395.04000
## 75 112 black 172.91000 380.247902 368.322432 396.90000 388.45000 391.71000
## 76 113 black 169.27000 376.226265 374.990459 396.90000 353.89000 392.04000
## 77 119 black 261.95000 376.030097 376.038965 374.56000 341.60000 379.38000
## 78 120 black 321.02000 373.275898 376.982520 372.80000 372.80000 343.28000
## 79 121 black 88.01000 375.105055 375.286883 391.71000 396.90000 396.24000
## 80 122 black 88.63000 375.399945 366.848697 396.90000 360.17000 395.11000
## 81 126 black 338.92000 377.894486 384.622085 354.70000 369.30000 396.90000
## 82 131 black 240.16000 381.840840 384.564689 395.33000 354.70000 372.80000
## 83 133 black 227.61000 381.180096 379.046856 396.90000 351.85000 341.60000
## 84 134 black 297.09000 382.343815 375.784080 391.93000 372.80000 353.89000
## 85 135 black 292.29000 381.027098 388.891229 372.92000 343.28000 385.02000
## 86 293 black 316.03000 379.614345 401.636914 344.05000 396.90000 353.04000
## 87 294 black 131.42000 386.135138 402.793582 395.33000 396.90000 383.32000
## 88 317 black 338.16000 388.399704 383.907184 377.56000 396.90000 396.90000
## 89 321 black 329.46000 386.733081 385.386858 396.90000 384.97000 396.90000
## 90 323 black 314.64000 390.119478 383.196168 394.33000 396.90000 396.90000
## 91 324 black 2.60000 384.789072 386.500142 396.90000 396.90000 363.02000
## 92 325 black 35.05000 389.521239 384.716672 396.90000 396.90000 396.90000
## 93 326 black 28.79000 386.432899 405.759623 370.31000 396.90000 349.48000
## 94 327 black 210.97000 385.996318 381.030411 396.90000 396.90000 375.52000
## 95 328 black 88.27000 384.129536 379.305495 394.02000 396.90000 393.37000
## 96 329 black 27.25000 388.800124 386.402095 396.90000 396.90000 396.90000
## 97 330 black 21.57000 388.807899 386.377863 387.97000 396.90000 396.90000
## 98 331 black 127.36000 386.265117 393.789514 396.90000 363.02000 391.98000
## 99 332 black 16.45000 385.552714 391.825814 395.18000 396.90000 366.15000
## 100 333 black 48.45000 387.421867 370.155305 395.56000 384.97000 395.33000
## 101 334 black 318.75000 383.959144 375.897347 395.69000 396.90000 366.15000
## 102 335 black 319.98000 384.238336 379.081635 388.22000 396.90000 391.98000
## 103 336 black 291.55000 384.635364 395.727213 386.09000 377.73000 375.87000
## 104 337 black 2.52000 388.951325 387.955018 390.96000 377.73000 352.58000
## 105 338 black 3.65000 388.662110 394.152086 395.50000 375.87000 395.69000
## 106 339 black 7.68000 386.370217 384.630812 396.90000 350.65000 384.97000
## 107 340 black 24.65000 386.564094 382.311692 391.34000 379.70000 396.90000
## 108 341 black 18.82000 384.656176 392.966999 396.90000 396.90000 384.97000
## 109 342 black 60.72000 387.196717 413.903719 374.71000 377.73000 396.90000
## 110 343 black 83.45000 389.808212 388.731852 396.30000 377.73000 396.90000
## 111 344 black 81.33000 389.007656 347.767375 388.74000 395.69000 395.69000
## 112 345 black 97.95000 387.042622 376.026242 395.56000 395.69000 396.90000
## 113 346 black 100.19000 383.716148 381.419147 395.69000 383.32000 375.52000
## 114 347 black 100.63000 383.221908 364.686777 396.90000 350.65000 391.45000
## 115 348 black 109.85000 385.833568 388.446313 396.90000 396.21000 395.33000
## 116 349 black 27.49000 384.265928 376.507949 386.71000 380.79000 395.69000
## 117 350 black 68.95000 383.803949 391.104031 396.90000 396.90000 383.32000
## 118 354 black 240.52000 384.685593 395.632261 396.90000 352.58000 379.70000
## 119 355 black 43.06000 385.881777 364.206270 376.11000 380.79000 391.45000
## 120 356 black 318.01000 383.965511 390.522576 396.90000 395.69000 396.90000
## 121 358 black 304.21000 385.888488 396.480728 392.85000 383.32000 376.11000
## 122 359 black 0.32000 385.361848 377.025001 392.80000 395.69000 396.90000
## 123 362 black 6.68000 384.864168 373.673028 396.90000 396.90000 396.90000
## 124 363 black 50.92000 383.586631 361.369681 392.85000 380.79000 391.34000
## 125 364 black 10.48000 382.436183 394.252880 396.90000 388.62000 355.29000
## 126 365 black 3.50000 384.286691 408.521626 388.22000 395.43000 393.37000
## 127 369 black 334.40000 381.168629 382.327082 389.70000 391.34000 393.37000
## 128 370 black 22.01000 382.382024 395.991037 392.85000 374.68000 396.90000
## 129 378 black 302.76000 389.052103 402.098764 395.24000 379.70000 396.90000
## 130 392 black 318.43000 382.961623 390.652362 366.15000 348.93000 394.67000
## 131 299 lstat 34.77000 24.949524 25.542337 30.62000 26.40000 20.08000
## 132 300 lstat 37.97000 18.512521 8.405752 19.01000 13.99000 28.28000
## 133 326 lstat 34.37000 25.474483 29.099206 30.62000 28.28000 12.03000
## 134 328 lstat 36.98000 26.720877 30.549825 25.68000 30.62000 26.77000
## 135 350 lstat 34.02000 18.999806 15.883893 10.11000 11.72000 23.27000
## missForest
## 1 5.814797
## 2 5.577181
## 3 6.535806
## 4 6.784130
## 5 6.309569
## 6 6.090851
## 7 6.659782
## 8 6.500726
## 9 6.647559
## 10 6.781285
## 11 6.671929
## 12 6.341615
## 13 6.341058
## 14 6.588154
## 15 6.775317
## 16 6.406235
## 17 6.792998
## 18 6.570899
## 19 6.554591
## 20 6.423729
## 21 6.726555
## 22 5.565977
## 23 6.541799
## 24 6.542773
## 25 6.639014
## 26 6.711441
## 27 6.787130
## 28 6.447046
## 29 6.172672
## 30 6.733761
## 31 6.149143
## 32 6.373814
## 33 5.579769
## 34 6.581926
## 35 6.072030
## 36 5.981086
## 37 6.599302
## 38 6.330080
## 39 6.349163
## 40 6.279952
## 41 6.305337
## 42 6.307528
## 43 6.357594
## 44 6.315858
## 45 6.504894
## 46 6.368359
## 47 6.359120
## 48 5.858233
## 49 5.203763
## 50 4.760009
## 51 6.042044
## 52 6.098087
## 53 5.692728
## 54 6.886398
## 55 7.067230
## 56 7.025238
## 57 7.112463
## 58 7.123855
## 59 7.065186
## 60 7.052156
## 61 7.101820
## 62 6.332380
## 63 6.045029
## 64 5.754094
## 65 6.155431
## 66 6.714157
## 67 7.049697
## 68 7.440081
## 69 7.345785
## 70 386.101254
## 71 386.276487
## 72 378.383793
## 73 392.519771
## 74 387.142346
## 75 379.793406
## 76 374.466825
## 77 371.574797
## 78 372.281511
## 79 376.413642
## 80 371.434856
## 81 373.194673
## 82 377.045406
## 83 379.095197
## 84 376.249754
## 85 374.883754
## 86 374.370635
## 87 383.466512
## 88 386.796840
## 89 387.541222
## 90 387.710666
## 91 378.363578
## 92 387.433464
## 93 384.163345
## 94 382.813780
## 95 386.198392
## 96 389.104366
## 97 388.131787
## 98 379.189314
## 99 389.175713
## 100 382.301344
## 101 385.013121
## 102 387.344454
## 103 379.974389
## 104 386.576513
## 105 383.501091
## 106 388.798525
## 107 383.309612
## 108 385.855585
## 109 389.366967
## 110 384.874029
## 111 384.151224
## 112 386.489730
## 113 380.560261
## 114 385.415503
## 115 384.244935
## 116 384.519999
## 117 385.258467
## 118 387.847965
## 119 386.997709
## 120 383.918360
## 121 385.372197
## 122 385.169064
## 123 381.798480
## 124 380.253477
## 125 380.261364
## 126 387.067343
## 127 384.252550
## 128 385.792816
## 129 382.316917
## 130 375.067329
## 131 21.144085
## 132 18.626767
## 133 21.951959
## 134 24.047673
## 135 19.349347#Revisar reemplazos negativos
for (nombre in names(metodos)) {
for (v in variables_imputacion) {
ids <- which(
is.na(datos_NA[[v]]) &
metodos[[nombre]][[v]] < 0
)
if (length(ids)) {
warning(
paste(
nombre, "produjo valores negativos en", v,
"; revisar su plausibilidad."
),
call. = FALSE
)
print(data.frame(
Metodo = nombre,
Variable = v,
ID = ids,
Imputado = metodos[[nombre]][[v]][ids]
))
}
}
}24 11. Verificamos los datos imputados
auditoria_imputacion <- bind_rows(
lapply(variables_imputacion, function(v) {
ids <- which(is.na(datos_NA[[v]]))
tabla <- data.frame(
ID = ids,
Variable = rep(v, length(ids)),
Original = datos_originales[[v]][ids]
)
for (nombre in names(metodos)) {
tabla[[nombre]] <- metodos[[nombre]][[v]][ids]
}
tabla
})
)
print(auditoria_imputacion)## ID Variable Original Regresion NORM PMM CART RF
## 1 294 crim 13.52220 5.565717 4.602799 4.64689 3.67822 3.67367
## 2 297 crim 9.23230 5.308294 5.682693 8.79212 5.70818 2.37857
## 3 299 crim 11.10810 5.855279 5.847288 6.28807 7.05042 6.53876
## 4 300 crim 18.49820 5.863227 7.033130 3.16360 8.71675 8.64476
## 5 301 crim 19.60910 5.903519 6.351376 3.77498 4.64689 5.70818
## 6 302 crim 15.28800 6.003955 4.771054 5.58107 8.49213 3.47428
## 7 303 crim 9.82349 6.092021 4.968275 7.02259 8.98296 7.99248
## 8 304 crim 23.64820 6.098250 5.268515 5.66637 8.79212 8.71675
## 9 305 crim 88.97620 5.966660 5.096688 6.80117 8.98296 8.71675
## 10 306 crim 9.18702 6.030237 4.938887 7.99248 7.40389 8.05579
## 11 308 crim 24.39380 6.031517 6.334768 7.52601 8.79212 7.40389
## 12 309 crim 22.59710 6.219734 6.738193 6.28807 5.66637 6.28807
## 13 310 crim 14.33370 6.073340 6.472024 6.28807 8.64476 8.71675
## 14 312 crim 11.57790 6.005705 6.529422 7.52601 8.49213 8.79212
## 15 314 crim 13.35980 5.800599 5.431095 6.65492 7.02259 4.66883
## 16 317 crim 9.91655 6.238546 8.242269 8.98296 5.87205 8.79212
## 17 318 crim 25.04610 6.212061 6.161355 6.28807 8.79212 8.79212
## 18 319 crim 9.59571 5.977757 6.478654 4.75237 8.49213 8.24809
## 19 320 crim 24.80170 5.824432 5.442162 3.84970 8.64476 4.66883
## 20 321 crim 41.52920 6.086407 5.877191 7.99248 5.66637 4.66883
## 21 322 crim 67.92080 5.969178 5.784806 7.99248 7.02259 8.98296
## 22 324 crim 51.13580 5.201203 3.968573 4.03841 4.89822 3.16360
## 23 325 crim 14.05070 5.802714 5.898663 3.77498 5.87205 7.05042
## 24 326 crim 18.81100 5.555291 6.299349 4.55587 8.98296 6.53876
## 25 327 crim 28.65580 5.456856 4.292742 3.77498 5.66637 7.99248
## 26 328 crim 45.74610 5.878811 6.544820 5.44114 8.71675 7.02259
## 27 329 crim 18.08460 6.308147 6.576461 7.52601 6.28807 7.05042
## 28 330 crim 10.83420 6.241328 7.189765 3.83684 8.79212 6.39312
## 29 331 crim 25.94060 5.974325 4.966841 5.87205 8.64476 4.66883
## 30 332 crim 73.53410 5.892923 6.831622 7.02259 5.87205 6.28807
## 31 333 crim 11.81230 6.234820 8.358495 6.71772 6.28807 5.58107
## 32 334 crim 11.08740 5.893272 5.116551 8.20058 7.02259 5.66637
## 33 336 crim 12.04820 5.362600 4.577685 4.34879 3.67822 6.80117
## 34 339 crim 15.86030 6.016221 5.222149 6.71772 5.87205 7.99248
## 35 340 crim 12.24720 5.326505 4.562217 4.83567 7.05042 8.20058
## 36 341 crim 37.66190 5.679726 4.867193 7.83932 3.67367 8.64476
## 37 342 crim 9.33889 6.101924 6.011323 7.99248 6.28807 5.87205
## 38 344 crim 10.06230 5.728702 4.734612 3.77498 7.99248 7.99248
## 39 347 crim 13.91340 5.833473 4.900576 7.40389 7.52601 8.05579
## 40 348 crim 11.16040 6.308982 4.629324 8.98296 5.66637 3.67822
## 41 349 crim 14.42080 6.076099 3.654294 6.71772 7.99248 5.44114
## 42 350 crim 13.67810 5.999167 4.803880 5.66637 4.55587 4.55587
## 43 351 crim 9.39063 6.140181 6.306109 8.98296 8.71675 6.39312
## 44 352 crim 22.05110 6.126370 5.530080 6.28807 8.79212 6.28807
## 45 354 crim 12.80230 6.176687 6.319219 6.28807 7.40389 5.44114
## 46 355 crim 10.67180 6.302605 6.255903 7.52601 7.02259 5.44114
## 47 357 crim 9.92485 5.997013 7.109939 5.87205 5.44114 6.80117
## 48 362 crim 9.51363 6.069630 6.124225 5.66637 6.71772 4.75237
## 49 371 crim 15.57570 5.372994 6.630893 4.34879 4.34879 2.81838
## 50 372 crim 13.07510 5.289801 7.011043 4.03841 5.29305 4.03841
## 51 380 crim 15.02340 5.579422 4.386064 6.65492 8.98296 7.05042
## 52 381 crim 10.23300 5.607288 4.879751 3.67822 5.44114 4.87141
## 53 382 crim 14.33370 5.434344 6.270796 8.79212 5.73116 6.65492
## 54 70 rm 8.06900 6.860486 6.863023 7.17800 7.41600 7.92900
## 55 129 rm 8.37500 6.942388 6.331370 7.01600 7.87500 5.88000
## 56 180 rm 8.26600 6.864236 7.120331 6.56300 7.41200 7.14800
## 57 181 rm 8.04000 6.946256 6.922035 6.86100 7.92900 6.94300
## 58 186 rm 8.33700 6.900312 6.434219 7.27400 7.47000 7.16300
## 59 187 rm 8.24700 6.780624 7.320708 7.24900 7.82000 7.16300
## 60 207 rm 8.70400 7.104611 7.100342 7.01600 6.56300 7.52000
## 61 211 rm 8.39800 7.034569 6.350029 6.81600 6.62500 7.52000
## 62 291 rm 8.78000 6.541198 7.075093 6.95100 6.84900 7.92900
## 63 292 rm 3.56100 6.522513 6.179023 6.45600 6.79400 7.06100
## 64 294 rm 3.86300 6.438487 6.415683 6.15300 6.45400 6.65700
## 65 300 rm 4.13800 6.172900 6.878088 5.75700 6.96800 6.39800
## 66 280 dis 10.71030 7.033164 6.628852 5.11670 6.27000 7.31720
## 67 281 dis 12.12650 7.549661 8.682620 7.30730 5.88500 7.65340
## 68 282 dis 10.58570 8.112556 8.409064 9.22030 8.79210 7.95490
## 69 283 dis 10.58570 8.132106 6.578791 9.08920 7.82650 7.82780
## 70 18 black 303.42000 391.019416 386.878328 396.90000 396.90000 396.42000
## 71 19 black 306.38000 392.103625 378.206394 392.04000 396.90000 396.90000
## 72 24 black 248.31000 394.353353 399.745804 391.34000 364.31000 396.90000
## 73 75 black 70.80000 392.572715 395.666403 389.25000 396.30000 396.42000
## 74 102 black 262.76000 386.660280 398.450396 396.90000 385.76000 395.04000
## 75 112 black 172.91000 380.247902 368.322432 396.90000 388.45000 391.71000
## 76 113 black 169.27000 376.226265 374.990459 396.90000 353.89000 392.04000
## 77 119 black 261.95000 376.030097 376.038965 374.56000 341.60000 379.38000
## 78 120 black 321.02000 373.275898 376.982520 372.80000 372.80000 343.28000
## 79 121 black 88.01000 375.105055 375.286883 391.71000 396.90000 396.24000
## 80 122 black 88.63000 375.399945 366.848697 396.90000 360.17000 395.11000
## 81 126 black 338.92000 377.894486 384.622085 354.70000 369.30000 396.90000
## 82 131 black 240.16000 381.840840 384.564689 395.33000 354.70000 372.80000
## 83 133 black 227.61000 381.180096 379.046856 396.90000 351.85000 341.60000
## 84 134 black 297.09000 382.343815 375.784080 391.93000 372.80000 353.89000
## 85 135 black 292.29000 381.027098 388.891229 372.92000 343.28000 385.02000
## 86 293 black 316.03000 379.614345 401.636914 344.05000 396.90000 353.04000
## 87 294 black 131.42000 386.135138 402.793582 395.33000 396.90000 383.32000
## 88 317 black 338.16000 388.399704 383.907184 377.56000 396.90000 396.90000
## 89 321 black 329.46000 386.733081 385.386858 396.90000 384.97000 396.90000
## 90 323 black 314.64000 390.119478 383.196168 394.33000 396.90000 396.90000
## 91 324 black 2.60000 384.789072 386.500142 396.90000 396.90000 363.02000
## 92 325 black 35.05000 389.521239 384.716672 396.90000 396.90000 396.90000
## 93 326 black 28.79000 386.432899 405.759623 370.31000 396.90000 349.48000
## 94 327 black 210.97000 385.996318 381.030411 396.90000 396.90000 375.52000
## 95 328 black 88.27000 384.129536 379.305495 394.02000 396.90000 393.37000
## 96 329 black 27.25000 388.800124 386.402095 396.90000 396.90000 396.90000
## 97 330 black 21.57000 388.807899 386.377863 387.97000 396.90000 396.90000
## 98 331 black 127.36000 386.265117 393.789514 396.90000 363.02000 391.98000
## 99 332 black 16.45000 385.552714 391.825814 395.18000 396.90000 366.15000
## 100 333 black 48.45000 387.421867 370.155305 395.56000 384.97000 395.33000
## 101 334 black 318.75000 383.959144 375.897347 395.69000 396.90000 366.15000
## 102 335 black 319.98000 384.238336 379.081635 388.22000 396.90000 391.98000
## 103 336 black 291.55000 384.635364 395.727213 386.09000 377.73000 375.87000
## 104 337 black 2.52000 388.951325 387.955018 390.96000 377.73000 352.58000
## 105 338 black 3.65000 388.662110 394.152086 395.50000 375.87000 395.69000
## 106 339 black 7.68000 386.370217 384.630812 396.90000 350.65000 384.97000
## 107 340 black 24.65000 386.564094 382.311692 391.34000 379.70000 396.90000
## 108 341 black 18.82000 384.656176 392.966999 396.90000 396.90000 384.97000
## 109 342 black 60.72000 387.196717 413.903719 374.71000 377.73000 396.90000
## 110 343 black 83.45000 389.808212 388.731852 396.30000 377.73000 396.90000
## 111 344 black 81.33000 389.007656 347.767375 388.74000 395.69000 395.69000
## 112 345 black 97.95000 387.042622 376.026242 395.56000 395.69000 396.90000
## 113 346 black 100.19000 383.716148 381.419147 395.69000 383.32000 375.52000
## 114 347 black 100.63000 383.221908 364.686777 396.90000 350.65000 391.45000
## 115 348 black 109.85000 385.833568 388.446313 396.90000 396.21000 395.33000
## 116 349 black 27.49000 384.265928 376.507949 386.71000 380.79000 395.69000
## 117 350 black 68.95000 383.803949 391.104031 396.90000 396.90000 383.32000
## 118 354 black 240.52000 384.685593 395.632261 396.90000 352.58000 379.70000
## 119 355 black 43.06000 385.881777 364.206270 376.11000 380.79000 391.45000
## 120 356 black 318.01000 383.965511 390.522576 396.90000 395.69000 396.90000
## 121 358 black 304.21000 385.888488 396.480728 392.85000 383.32000 376.11000
## 122 359 black 0.32000 385.361848 377.025001 392.80000 395.69000 396.90000
## 123 362 black 6.68000 384.864168 373.673028 396.90000 396.90000 396.90000
## 124 363 black 50.92000 383.586631 361.369681 392.85000 380.79000 391.34000
## 125 364 black 10.48000 382.436183 394.252880 396.90000 388.62000 355.29000
## 126 365 black 3.50000 384.286691 408.521626 388.22000 395.43000 393.37000
## 127 369 black 334.40000 381.168629 382.327082 389.70000 391.34000 393.37000
## 128 370 black 22.01000 382.382024 395.991037 392.85000 374.68000 396.90000
## 129 378 black 302.76000 389.052103 402.098764 395.24000 379.70000 396.90000
## 130 392 black 318.43000 382.961623 390.652362 366.15000 348.93000 394.67000
## 131 299 lstat 34.77000 24.949524 25.542337 30.62000 26.40000 20.08000
## 132 300 lstat 37.97000 18.512521 8.405752 19.01000 13.99000 28.28000
## 133 326 lstat 34.37000 25.474483 29.099206 30.62000 28.28000 12.03000
## 134 328 lstat 36.98000 26.720877 30.549825 25.68000 30.62000 26.77000
## 135 350 lstat 34.02000 18.999806 15.883893 10.11000 11.72000 23.27000
## missForest
## 1 5.814797
## 2 5.577181
## 3 6.535806
## 4 6.784130
## 5 6.309569
## 6 6.090851
## 7 6.659782
## 8 6.500726
## 9 6.647559
## 10 6.781285
## 11 6.671929
## 12 6.341615
## 13 6.341058
## 14 6.588154
## 15 6.775317
## 16 6.406235
## 17 6.792998
## 18 6.570899
## 19 6.554591
## 20 6.423729
## 21 6.726555
## 22 5.565977
## 23 6.541799
## 24 6.542773
## 25 6.639014
## 26 6.711441
## 27 6.787130
## 28 6.447046
## 29 6.172672
## 30 6.733761
## 31 6.149143
## 32 6.373814
## 33 5.579769
## 34 6.581926
## 35 6.072030
## 36 5.981086
## 37 6.599302
## 38 6.330080
## 39 6.349163
## 40 6.279952
## 41 6.305337
## 42 6.307528
## 43 6.357594
## 44 6.315858
## 45 6.504894
## 46 6.368359
## 47 6.359120
## 48 5.858233
## 49 5.203763
## 50 4.760009
## 51 6.042044
## 52 6.098087
## 53 5.692728
## 54 6.886398
## 55 7.067230
## 56 7.025238
## 57 7.112463
## 58 7.123855
## 59 7.065186
## 60 7.052156
## 61 7.101820
## 62 6.332380
## 63 6.045029
## 64 5.754094
## 65 6.155431
## 66 6.714157
## 67 7.049697
## 68 7.440081
## 69 7.345785
## 70 386.101254
## 71 386.276487
## 72 378.383793
## 73 392.519771
## 74 387.142346
## 75 379.793406
## 76 374.466825
## 77 371.574797
## 78 372.281511
## 79 376.413642
## 80 371.434856
## 81 373.194673
## 82 377.045406
## 83 379.095197
## 84 376.249754
## 85 374.883754
## 86 374.370635
## 87 383.466512
## 88 386.796840
## 89 387.541222
## 90 387.710666
## 91 378.363578
## 92 387.433464
## 93 384.163345
## 94 382.813780
## 95 386.198392
## 96 389.104366
## 97 388.131787
## 98 379.189314
## 99 389.175713
## 100 382.301344
## 101 385.013121
## 102 387.344454
## 103 379.974389
## 104 386.576513
## 105 383.501091
## 106 388.798525
## 107 383.309612
## 108 385.855585
## 109 389.366967
## 110 384.874029
## 111 384.151224
## 112 386.489730
## 113 380.560261
## 114 385.415503
## 115 384.244935
## 116 384.519999
## 117 385.258467
## 118 387.847965
## 119 386.997709
## 120 383.918360
## 121 385.372197
## 122 385.169064
## 123 381.798480
## 124 380.253477
## 125 380.261364
## 126 387.067343
## 127 384.252550
## 128 385.792816
## 129 382.316917
## 130 375.067329
## 131 21.144085
## 132 18.626767
## 133 21.951959
## 134 24.047673
## 135 19.349347#Revisar reemplazos negativos
for (nombre in names(metodos)) {
for (v in variables_imputacion) {
ids <- which(
is.na(datos_NA[[v]]) &
metodos[[nombre]][[v]] < 0
)
if (length(ids)) {
warning(
paste(
nombre, "produjo valores negativos en", v,
"; revisar su plausibilidad."
),
call. = FALSE
)
print(data.frame(
Metodo = nombre,
Variable = v,
ID = ids,
Imputado = metodos[[nombre]][[v]][ids]
))
}
}
}stopifnot(
identical(data.tst, prueba_sin_cambios)
)
for (nombre in names(metodos)) {
stopifnot(
identical(metodos[[nombre]]$medv, datos_originales$medv),
identical(metodos[[nombre]]$chas, datos_originales$chas),
!anyNA(metodos[[nombre]])
)
}
#Resumen de faltantes por método
resumen_faltantes <- data.frame(
Metodo = names(metodos),
Total_NA = vapply(
metodos,
function(base) as.integer(sum(is.na(base))),
integer(1)
)
)
print(resumen_faltantes)## Metodo Total_NA
## Regresion Regresion 0
## NORM NORM 0
## PMM PMM 0
## CART CART 0
## RF RF 0
## missForest missForest 0Interpretación: Se observa que se realizo la imputacion de todos los datos NA.
25 12. Boxplot de los datos
# BOXPLOTS: ORIGINAL VS DATOS IMPUTADOS
library(ggplot2)
library(dplyr)
library(tidyr)
#Base imputada que deseas visualizar
datos_imputados <- pmm
#Puedes sustituir pmm por: reg, norm, cart, rf o forest
#Unir original e imputada
datos_boxplot <- bind_rows(
Original = datos_originales,
Imputada = datos_imputados,
.id = "Base"
) %>%
pivot_longer(
cols = all_of(variables_imputacion),
names_to = "Variable",
values_to = "Valor"
)
#Boxplots original vs imputada
grafico <- ggplot(
datos_boxplot,
aes(x = Base, y = Valor, fill = Base)
) +
geom_boxplot(
outlier.colour = "red",
outlier.size = 1,
show.legend = FALSE
) +
facet_wrap(~ Variable, scales = "free_y", ncol = 2) +
scale_fill_manual(
values = c(
Original = "lightblue",
Imputada = "lightgreen"
)
) +
labs(
title = "Boston: datos originales vs imputados",
subtitle = "Conjunto de entrenamiento",
x = NULL,
y = "Valor"
) +
theme_minimal()
print(grafico)
Interpretación: Se observa en el grafico que se utilizo un metodo que imputa bien a las variables, y en otras que aun aparece datos atipicos pero siguen el comportamiento inicial de las variables. Se recomienda trabajar con las la data porque si realizmos otra imputacion estariamos cometiendo un sobre ajuste en nuestros datos.
26 13. Comparación de métodos de multivariantes
library(MASS)
library(dplyr)
library(tidyr)
library(skimr)
library(ggplot2)
library(tree)
library(caret)## Cargando paquete requerido: lattice##
## Adjuntando el paquete: 'caret'## The following object is masked from 'package:purrr':
##
## liftlibrary(kernlab)##
## Adjuntando el paquete: 'kernlab'## The following object is masked from 'package:mice':
##
## convergence## The following object is masked from 'package:purrr':
##
## cross## The following object is masked from 'package:ggplot2':
##
## alphalibrary(e1071)##
## Adjuntando el paquete: 'e1071'## The following objects are masked from 'package:PerformanceAnalytics':
##
## kurtosis, skewness## The following object is masked from 'package:Hmisc':
##
## impute## The following object is masked from 'package:ggplot2':
##
## elementlibrary(neuralnet)
library(randomForest)## randomForest 4.7-1.2## Type rfNews() to see new features/changes/bug fixes.##
## Adjuntando el paquete: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## marginlibrary(rpart)
library(rpart.plot)
library(ipred)
library(corrplot)
library(gbm)## Loaded gbm 2.3.1## This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3library(splines)#Usar la partición existente y el entrenamiento imputado
datos_entrenamiento <- datos_imputados
datos_prueba <- data.tst
stopifnot(
nrow(datos_entrenamiento) == nrow(data.trn),
identical(datos_entrenamiento$medv, data.trn$medv),
identical(names(datos_entrenamiento), names(datos_prueba)),
all(vapply(datos_entrenamiento, is.numeric, logical(1))),
all(is.finite(as.matrix(datos_entrenamiento))),
all(is.finite(as.matrix(datos_prueba)))
)
head(datos_entrenamiento)## crim zn indus chas nox rm age dis rad tax ptratio black lstat
## 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## 6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
## 8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 15.2 396.90 19.15
## medv
## 1 24.0
## 2 21.6
## 3 34.7
## 4 33.4
## 6 28.7
## 8 27.1names(datos_entrenamiento)## [1] "crim" "zn" "indus" "chas" "nox" "rm" "age"
## [8] "dis" "rad" "tax" "ptratio" "black" "lstat" "medv"skimr::skim(datos_entrenamiento)| Name | datos_entrenamiento |
| Number of rows | 404 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| crim | 0 | 1 | 1.85 | 2.67 | 0.01 | 0.08 | 0.26 | 3.59 | 8.98 | ▇▁▁▁▁ |
| zn | 0 | 1 | 11.42 | 24.14 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | ▇▁▁▁▁ |
| indus | 0 | 1 | 11.40 | 7.01 | 0.46 | 5.18 | 9.90 | 18.10 | 27.74 | ▆▆▁▇▁ |
| chas | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| nox | 0 | 1 | 0.56 | 0.12 | 0.38 | 0.45 | 0.54 | 0.62 | 0.87 | ▇▇▆▅▁ |
| rm | 0 | 1 | 6.28 | 0.59 | 4.52 | 5.90 | 6.21 | 6.63 | 7.93 | ▁▃▇▃▁ |
| age | 0 | 1 | 69.23 | 27.66 | 2.90 | 45.80 | 77.75 | 94.03 | 100.00 | ▁▂▂▃▇ |
| dis | 0 | 1 | 3.73 | 2.02 | 1.13 | 2.09 | 3.11 | 5.10 | 9.22 | ▇▅▃▂▁ |
| rad | 0 | 1 | 9.55 | 8.74 | 1.00 | 4.00 | 5.00 | 24.00 | 24.00 | ▇▂▁▁▃ |
| tax | 0 | 1 | 413.20 | 167.78 | 187.00 | 284.00 | 346.50 | 666.00 | 711.00 | ▇▇▅▁▇ |
| ptratio | 0 | 1 | 18.44 | 2.17 | 12.60 | 17.00 | 19.00 | 20.20 | 22.00 | ▁▃▅▅▇ |
| black | 0 | 1 | 388.08 | 12.27 | 341.60 | 385.58 | 393.15 | 396.90 | 396.90 | ▁▁▁▁▇ |
| lstat | 0 | 1 | 12.39 | 6.71 | 1.73 | 6.91 | 11.36 | 16.53 | 31.99 | ▇▇▆▂▁ |
| medv | 0 | 1 | 22.46 | 9.07 | 5.00 | 17.08 | 21.20 | 25.00 | 50.00 | ▃▇▅▁▁ |
summary(datos_entrenamiento)## crim zn indus chas
## Min. :0.00632 Min. : 0.00 Min. : 0.460 Min. :0.00000
## 1st Qu.:0.08135 1st Qu.: 0.00 1st Qu.: 5.175 1st Qu.:0.00000
## Median :0.25635 Median : 0.00 Median : 9.900 Median :0.00000
## Mean :1.85304 Mean : 11.42 Mean :11.404 Mean :0.07673
## 3rd Qu.:3.59493 3rd Qu.: 0.00 3rd Qu.:18.100 3rd Qu.:0.00000
## Max. :8.98296 Max. :100.00 Max. :27.740 Max. :1.00000
## nox rm age dis
## Min. :0.3850 Min. :4.519 Min. : 2.90 Min. :1.130
## 1st Qu.:0.4530 1st Qu.:5.896 1st Qu.: 45.80 1st Qu.:2.088
## Median :0.5380 Median :6.212 Median : 77.75 Median :3.107
## Mean :0.5567 Mean :6.275 Mean : 69.23 Mean :3.728
## 3rd Qu.:0.6240 3rd Qu.:6.626 3rd Qu.: 94.03 3rd Qu.:5.104
## Max. :0.8710 Max. :7.929 Max. :100.00 Max. :9.223
## rad tax ptratio black
## Min. : 1.000 Min. :187.0 Min. :12.60 Min. :341.6
## 1st Qu.: 4.000 1st Qu.:284.0 1st Qu.:17.00 1st Qu.:385.6
## Median : 5.000 Median :346.5 Median :19.00 Median :393.1
## Mean : 9.554 Mean :413.2 Mean :18.44 Mean :388.1
## 3rd Qu.:24.000 3rd Qu.:666.0 3rd Qu.:20.20 3rd Qu.:396.9
## Max. :24.000 Max. :711.0 Max. :22.00 Max. :396.9
## lstat medv
## Min. : 1.730 Min. : 5.00
## 1st Qu.: 6.915 1st Qu.:17.07
## Median :11.360 Median :21.20
## Mean :12.392 Mean :22.46
## 3rd Qu.:16.530 3rd Qu.:25.00
## Max. :31.990 Max. :50.00dim(datos_entrenamiento)## [1] 404 14dim(datos_prueba)## [1] 102 14#Funciones para evitar ambigüedades entre paquetes
metricas <- function(real, predicho) {
predicho <- as.numeric(predicho)
stopifnot(
length(real) == length(predicho),
all(is.finite(real)),
all(is.finite(predicho))
)
error <- real - predicho
SST <- sum((real - mean(real))^2)
data.frame(
MSE = mean(error^2),
MAE = mean(abs(error)),
RMSE = sqrt(mean(error^2)),
R2 = if (SST > 0) {
1 - sum(error^2) / SST
} else {
NA_real_
}
)
}
predicciones_modelos <- list()27 13.1 Regresión multiple clásica
modelo_multiple <- lm(
formula = medv ~ .,
data = datos_entrenamiento
)
summary(modelo_multiple)##
## Call:
## lm(formula = medv ~ ., data = datos_entrenamiento)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.9253 -2.8996 -0.5041 2.3353 25.5623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.631294 9.937964 7.007 1.08e-11 ***
## crim -0.003944 0.252576 -0.016 0.98755
## zn 0.038429 0.015323 2.508 0.01255 *
## indus 0.021266 0.068810 0.309 0.75744
## chas 2.516731 0.945653 2.661 0.00810 **
## nox -19.103007 4.387933 -4.354 1.71e-05 ***
## rm 3.754678 0.577901 6.497 2.50e-10 ***
## age 0.003277 0.015322 0.214 0.83073
## dis -1.547157 0.247677 -6.247 1.10e-09 ***
## rad 0.220477 0.091901 2.399 0.01690 *
## tax -0.012543 0.004125 -3.041 0.00252 **
## ptratio -1.005710 0.150981 -6.661 9.27e-11 ***
## black -0.066425 0.020551 -3.232 0.00133 **
## lstat -0.647512 0.061304 -10.562 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.838 on 390 degrees of freedom
## Multiple R-squared: 0.7248, Adjusted R-squared: 0.7156
## F-statistic: 79 on 13 and 390 DF, p-value: < 2.2e-16confint(modelo_multiple)## 2.5 % 97.5 %
## (Intercept) 50.092607209 89.169980576
## crim -0.500525718 0.492637024
## zn 0.008302818 0.068555340
## indus -0.114018303 0.156550604
## chas 0.657515239 4.375946716
## nox -27.729969960 -10.476043437
## rm 2.618486916 4.890868564
## age -0.026846007 0.033400769
## dis -2.034106303 -1.060207117
## rad 0.039795014 0.401159826
## tax -0.020651662 -0.004433532
## ptratio -1.302548910 -0.708870269
## black -0.106830449 -0.026019727
## lstat -0.768039742 -0.526983701par(mfrow = c(2, 2))
plot(modelo_multiple)
par(mfrow = c(1, 1))
corrplot::corrplot.mixed(
corr = cor(
datos_entrenamiento[, c(
"crim", "zn", "nox", "rm", "dis", "rad",
"tax", "ptratio", "black", "lstat", "medv"
)],
method = "pearson"
)
)
predictrm <- predict(
modelo_multiple,
newdata = datos_prueba
)
comparac <- data.frame(
Real = datos_prueba$medv,
Prediccion = predictrm
)
print(comparac)## Real Prediccion
## 5 36.2 27.8161987
## 7 22.9 22.0456477
## 13 21.7 19.8778429
## 16 19.9 18.8023444
## 17 23.1 20.8970848
## 19 20.2 23.4013429
## 21 13.6 12.1682042
## 23 15.2 14.1568947
## 27 16.6 15.6899293
## 29 18.4 19.1821639
## 33 13.2 18.4758364
## 38 21.0 22.4899591
## 40 30.8 30.8964168
## 41 34.9 34.0033274
## 44 24.7 24.4272232
## 45 21.2 22.9745213
## 48 16.6 16.8135173
## 49 14.4 6.1439763
## 52 20.5 23.4573853
## 53 25.0 27.2994232
## 54 23.4 23.3346098
## 60 19.6 19.7858913
## 61 18.7 16.2698540
## 64 25.0 21.1920668
## 69 17.4 16.2046858
## 72 21.7 22.5408246
## 79 21.2 21.0201662
## 84 22.9 24.9476239
## 107 19.5 15.5735367
## 109 19.8 21.8090824
## 110 19.4 18.8382501
## 119 20.4 23.4053211
## 127 15.7 15.1029100
## 142 14.4 0.5503539
## 149 17.8 10.0255837
## 170 22.3 31.3407367
## 178 24.6 29.1377174
## 182 36.2 27.4702816
## 183 37.9 34.3882428
## 187 50.0 36.3671766
## 205 50.0 43.2227759
## 206 22.6 21.9011763
## 207 24.4 23.0696012
## 212 19.3 14.8609352
## 218 28.7 28.1264057
## 226 50.0 40.7281782
## 230 31.5 32.2975953
## 239 23.7 29.0753381
## 246 18.5 11.7289376
## 251 24.4 23.3747292
## 254 42.8 29.1627779
## 259 36.0 37.2643151
## 264 31.0 34.0248605
## 265 36.5 36.1991804
## 266 22.8 28.0152685
## 268 50.0 41.4796694
## 274 35.2 35.4319597
## 275 32.4 35.8012568
## 276 32.0 33.7399005
## 280 35.1 35.2506752
## 294 23.9 25.3766317
## 295 21.7 23.8334404
## 300 29.0 33.0623658
## 301 24.8 30.4048235
## 303 26.4 28.8754899
## 306 28.4 30.0890008
## 307 33.4 34.9296138
## 311 16.1 21.4380722
## 317 17.8 16.4826513
## 333 19.4 25.4649931
## 339 20.6 21.4836502
## 351 22.9 19.8802137
## 353 18.6 15.9217456
## 360 22.6 17.0870010
## 370 50.0 33.0435228
## 380 10.2 15.0419405
## 382 10.9 16.2932838
## 385 8.8 8.8402701
## 386 7.2 4.8151416
## 390 11.5 11.2581758
## 391 15.1 14.7482098
## 398 8.5 13.7353260
## 399 5.0 5.5751599
## 402 7.2 15.5311540
## 407 11.9 8.3054424
## 408 27.9 23.4850858
## 410 27.5 34.2431676
## 429 11.0 33.7932213
## 438 8.7 35.1620583
## 442 17.1 15.3635236
## 444 15.4 16.1851878
## 449 14.1 14.9253299
## 453 16.1 16.5163732
## 459 14.9 24.0239058
## 461 16.4 26.8500442
## 462 17.7 17.7749763
## 464 20.2 20.5874764
## 468 19.1 18.5732507
## 487 19.1 17.4275056
## 494 21.8 19.5498335
## 498 18.3 17.7866234
## 504 23.9 27.7365116print(metricas(datos_prueba$medv, predictrm))## MSE MAE RMSE R2
## 1 35.09588 3.789056 5.924178 0.624757plot(
datos_prueba$medv,
predictrm,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
#Análisis de residuos
qqnorm(residuals(modelo_multiple))
qqline(residuals(modelo_multiple), col = "red")
shapiro.test(residuals(modelo_multiple))##
## Shapiro-Wilk normality test
##
## data: residuals(modelo_multiple)
## W = 0.93032, p-value = 8.735e-13predicciones_modelos$Lineal <- predictrmInterpretación: El modelo presenta asociación global significativa y explica el 72.48 % de la variabilidad en entrenamiento. En prueba obtiene MAE de 3.789 y RMSE de 5.924, con errores elevados en algunas observaciones. La falta de normalidad de los residuos y la ausencia de otros diagnósticos impiden considerarlo plenamente validado.
28 13.2 Regresión mediante arboles: RPART
set.seed(123)
mod1 <- rpart::rpart(
medv ~ .,
data = datos_entrenamiento,
method = "anova",
cp = 0.03
)
print(mod1)## n= 404
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 404 33167.0100 22.46436
## 2) lstat>=9.63 240 5540.0220 17.45875
## 4) lstat>=14.915 126 2247.8950 14.65079 *
## 5) lstat< 14.915 114 1200.6280 20.56228 *
## 3) lstat< 9.63 164 12813.3300 29.78963
## 6) rm< 6.808 102 3410.7530 25.17843
## 12) age< 89.45 95 1432.3890 24.32737 *
## 13) age>=89.45 7 975.7143 36.72857 *
## 7) rm>=6.808 62 3665.6340 37.37581
## 14) rm< 7.435 47 1960.4550 34.55745 *
## 15) rm>=7.435 15 162.0893 46.20667 *rpart.plot(mod1)
plotcp(mod1)
#El error relativo no está necesariamente limitado entre 0 y 1.
predictr <- predict(
mod1,
newdata = datos_prueba
)
datacom <- data.frame(
Prediccion = predictr,
Real = datos_prueba$medv
)
print(datacom)## Prediccion Real
## 5 34.55745 36.2
## 7 20.56228 22.9
## 13 14.65079 21.7
## 16 24.32737 19.9
## 17 24.32737 23.1
## 19 20.56228 20.2
## 21 14.65079 13.6
## 23 14.65079 15.2
## 27 20.56228 16.6
## 29 20.56228 18.4
## 33 14.65079 13.2
## 38 24.32737 21.0
## 40 24.32737 30.8
## 41 34.55745 34.9
## 44 24.32737 24.7
## 45 24.32737 21.2
## 48 14.65079 16.6
## 49 14.65079 14.4
## 52 24.32737 20.5
## 53 24.32737 25.0
## 54 24.32737 23.4
## 60 24.32737 19.6
## 61 20.56228 18.7
## 64 24.32737 25.0
## 69 20.56228 17.4
## 72 20.56228 21.7
## 79 20.56228 21.2
## 84 24.32737 22.9
## 107 14.65079 19.5
## 109 20.56228 19.8
## 110 14.65079 19.4
## 119 14.65079 20.4
## 127 14.65079 15.7
## 142 14.65079 14.4
## 149 14.65079 17.8
## 170 20.56228 22.3
## 178 24.32737 24.6
## 182 24.32737 36.2
## 183 34.55745 37.9
## 187 46.20667 50.0
## 205 46.20667 50.0
## 206 20.56228 22.6
## 207 20.56228 24.4
## 212 14.65079 19.3
## 218 20.56228 28.7
## 226 46.20667 50.0
## 230 24.32737 31.5
## 239 24.32737 23.7
## 246 14.65079 18.5
## 251 24.32737 24.4
## 254 46.20667 42.8
## 259 34.55745 36.0
## 264 20.56228 31.0
## 265 34.55745 36.5
## 266 20.56228 22.8
## 268 46.20667 50.0
## 274 46.20667 35.2
## 275 24.32737 32.4
## 276 34.55745 32.0
## 280 34.55745 35.1
## 294 24.32737 23.9
## 295 20.56228 21.7
## 300 34.55745 29.0
## 301 34.55745 24.8
## 303 24.32737 26.4
## 306 24.32737 28.4
## 307 34.55745 33.4
## 311 20.56228 16.1
## 317 14.65079 17.8
## 333 24.32737 19.4
## 339 24.32737 20.6
## 351 24.32737 22.9
## 353 24.32737 18.6
## 360 20.56228 22.6
## 370 36.72857 50.0
## 380 14.65079 10.2
## 382 14.65079 10.9
## 385 14.65079 8.8
## 386 14.65079 7.2
## 390 14.65079 11.5
## 391 14.65079 15.1
## 398 14.65079 8.5
## 399 14.65079 5.0
## 402 14.65079 7.2
## 407 14.65079 11.9
## 408 20.56228 27.9
## 410 14.65079 27.5
## 429 14.65079 11.0
## 438 14.65079 8.7
## 442 14.65079 17.1
## 444 14.65079 15.4
## 449 14.65079 14.1
## 453 14.65079 16.1
## 459 14.65079 14.9
## 461 14.65079 16.4
## 462 20.56228 17.7
## 464 20.56228 20.2
## 468 14.65079 19.1
## 487 14.65079 19.1
## 494 20.56228 21.8
## 498 20.56228 18.3
## 504 34.55745 23.9print(metricas(datos_prueba$medv, predictr))## MSE MAE RMSE R2
## 1 21.92118 3.562674 4.682007 0.7656201plot(
datos_prueba$medv,
predictr,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
#Importancia del árbol
impor <- mod1$variable.importance
print(impor)## lstat rm indus nox age crim ptratio
## 19722.4583 14596.5348 10194.1889 8854.5990 8590.0519 6571.9531 1962.3775
## tax dis zn
## 1651.1679 1633.5022 647.7197if (length(impor)) {
barplot(
sort(impor, decreasing = TRUE),
las = 2,
main = "Importancia: RPART"
)
}
predicciones_modelos$RPART <- predictrEl árbol CART identificó seis grupos mediante las variables lstat, rm y age. En el conjunto de prueba obtuvo un RMSE de 4.682 y un MAE de 3.563, inferiores a los de la regresión lineal múltiple. Estos resultados respaldan un mejor desempeño predictivo observado en esta partición, aunque la estabilidad del árbol y la selección de su complejidad requieren validación cruzada.
29 13.3 Regresión mediante arboles: TRESS
set.seed(123)
arbol_regresion <- tree::tree(
medv ~ .,
data = datos_entrenamiento,
split = "deviance",
control = tree::tree.control(
nobs = nrow(datos_entrenamiento),
mincut = 20,
minsize = 50
)
)
summary(arbol_regresion)##
## Regression tree:
## tree::tree(formula = medv ~ ., data = datos_entrenamiento, control = tree::tree.control(nobs = nrow(datos_entrenamiento),
## mincut = 20, minsize = 50), split = "deviance")
## Variables actually used in tree construction:
## [1] "lstat" "rm" "crim" "tax"
## Number of terminal nodes: 8
## Residual mean deviance: 19.54 = 7736 / 396
## Distribution of residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -14.61000 -2.25300 -0.06087 0.00000 1.95200 20.57000if (!inherits(arbol_regresion, "singlenode")) {
plot(arbol_regresion, type = "proportional")
text(
arbol_regresion,
splits = TRUE,
pretty = 0,
cex = 0.8,
col = "firebrick"
)
}
predicciones <- predict(
arbol_regresion,
newdata = datos_prueba
)
print(metricas(datos_prueba$medv, predicciones))## MSE MAE RMSE R2
## 1 30.68945 3.899743 5.539806 0.6718703plot(
datos_prueba$medv,
predicciones,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
x <- seq_len(nrow(datos_prueba))
plot(
x,
datos_prueba$medv,
pch = 18,
col = "red",
xlab = "Caso de prueba",
ylab = "medv"
)
lines(x, predicciones, lwd = 1, col = "blue")
predicciones_modelos$TREE <- prediccionesEl árbol de regresión construido con tree utilizó cuatro predictoras y generó ocho nodos terminales. En prueba obtuvo un RMSE de 5.540 y un MAE de 3.900. Aunque redujo el error cuadrático respecto a la regresión lineal, fue superado por el CART anterior, que presentó menores errores en todas las métricas reportadas.
30 13.4 SVM LINEAL CON CARET
set.seed(123)
modelo_svmlineal <- caret::train(
medv ~ .,
method = "svmLinear",
data = datos_entrenamiento
)
print(modelo_svmlineal)## Support Vector Machines with Linear Kernel
##
## 404 samples
## 13 predictor
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 404, 404, 404, 404, 404, 404, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 5.29874 0.6623252 3.402129
##
## Tuning parameter 'C' was held constant at a value of 1predlineal <- predict(
modelo_svmlineal,
newdata = datos_prueba
)
print(metricas(datos_prueba$medv, predlineal))## MSE MAE RMSE R2
## 1 35.22718 3.6286 5.935249 0.6233532plot(
datos_prueba$medv,
predlineal,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
impor0 <- caret::varImp(modelo_svmlineal)
print(impor0)## loess r-squared variable importance
##
## Overall
## lstat 100.000
## rm 76.152
## ptratio 35.193
## indus 29.444
## tax 28.005
## nox 24.189
## crim 22.940
## age 20.045
## rad 17.434
## dis 15.362
## zn 14.963
## black 4.545
## chas 0.000plot(impor0, top = 7)
predicciones_modelos$SVM_lineal <- predlinealLa SVM lineal obtuvo un RMSE de prueba de 5.935 y un MAE de 3.629. Su desempeño fue cercano al de la regresión lineal y menos favorable que el del CART anterior. Estos resultados corresponden a una configuración con C = 1, por lo que no permiten establecer el rendimiento de una SVM cuya regularización haya sido seleccionada mediante validación.
31 13.5 SVM RADIAL CON CARET
set.seed(123)
modelo_svmradial <- caret::train(
medv ~ .,
method = "svmRadial",
data = datos_entrenamiento
)
print(modelo_svmradial)## Support Vector Machines with Radial Basis Function Kernel
##
## 404 samples
## 13 predictor
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 404, 404, 404, 404, 404, 404, ...
## Resampling results across tuning parameters:
##
## C RMSE Rsquared MAE
## 0.25 5.092426 0.7043298 3.018846
## 0.50 4.763462 0.7309076 2.878858
## 1.00 4.543583 0.7468264 2.797269
##
## Tuning parameter 'sigma' was held constant at a value of 0.09019658
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were sigma = 0.09019658 and C = 1.predlradial <- predict(
modelo_svmradial,
newdata = datos_prueba
)
print(metricas(datos_prueba$medv, predlradial))## MSE MAE RMSE R2
## 1 29.90923 3.312934 5.468933 0.6802123plot(
datos_prueba$medv,
predlradial,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
impor1 <- caret::varImp(modelo_svmradial)
print(impor1)## loess r-squared variable importance
##
## Overall
## lstat 100.000
## rm 76.152
## ptratio 35.193
## indus 29.444
## tax 28.005
## nox 24.189
## crim 22.940
## age 20.045
## rad 17.434
## dis 15.362
## zn 14.963
## black 4.545
## chas 0.000plot(impor1, top = 7)
predicciones_modelos$SVM_radial <- predlradialLa SVM radial obtuvo un RMSE de prueba de 5.469 y un MAE de 3.313, mejorando ambos indicadores respecto a la SVM lineal. Entre los modelos evaluados presentó el menor MAE, mientras que CART obtuvo el menor RMSE. En consecuencia, su superioridad depende del criterio de error priorizado y requiere confirmación mediante un procedimiento común de validación.
32 13.6 SVM CON E1071
model_reg <- e1071::svm(
medv ~ .,
data = datos_entrenamiento,
cost = 1,
gamma = 0.07692308,
epsilon = 0.1
)
summary(model_reg)##
## Call:
## svm(formula = medv ~ ., data = datos_entrenamiento, cost = 1, gamma = 0.07692308,
## epsilon = 0.1)
##
##
## Parameters:
## SVM-Type: eps-regression
## SVM-Kernel: radial
## cost: 1
## gamma: 0.07692308
## epsilon: 0.1
##
##
## Number of Support Vectors: 283print(model_reg)##
## Call:
## svm(formula = medv ~ ., data = datos_entrenamiento, cost = 1, gamma = 0.07692308,
## epsilon = 0.1)
##
##
## Parameters:
## SVM-Type: eps-regression
## SVM-Kernel: radial
## cost: 1
## gamma: 0.07692308
## epsilon: 0.1
##
##
## Number of Support Vectors: 283pred <- predict(
model_reg,
newdata = datos_prueba
)
print(metricas(datos_prueba$medv, pred))## MSE MAE RMSE R2
## 1 29.58155 3.273663 5.438892 0.6837159plot(
datos_prueba$medv,
pred,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
predicciones_modelos$SVM_e1071 <- pred
#Comparación visual de TREE y SVM
par(mfrow = c(2, 1))
plot(
datos_prueba$medv,
predicciones,
main = "TREE",
xlab = "Observado",
ylab = "Predicho"
)
abline(0, 1, col = "red")
plot(
datos_prueba$medv,
pred,
main = "SVM",
xlab = "Observado",
ylab = "Predicho"
)
abline(0, 1, col = "red")
par(mfrow = c(1, 1))La regresión de soporte vectorial radial utilizó 283 vectores de soporte y obtuvo un MAE de prueba de 3.274 y un RMSE de 5.439. Presentó el menor MAE entre los modelos mostrados, mientras que CART mantuvo el menor RMSE. La mejora respecto a la SVM radial anterior fue pequeña y requiere validación para establecer su estabilidad.
33 13.7 RED NEURONAL
#El bloque original con modered <- lm(...) era otra regresión
#lineal, no una red neuronal. La red real se ajusta a continuación.
#Normalización con parámetros exclusivos del entrenamiento
maxs <- apply(datos_entrenamiento, 2, max)
mins <- apply(datos_entrenamiento, 2, min)
rangos <- maxs - mins
if (any(rangos <= 0)) {
stop("Hay variables constantes: revisar normalización.")
}
train_nrm <- as.data.frame(
scale(
datos_entrenamiento,
center = mins,
scale = rangos
)
)
test_nrm <- as.data.frame(
scale(
datos_prueba,
center = mins,
scale = rangos
)
)
#Prueba puede tener valores fuera de [0,1].
#No se recalculan sus parámetros ni se recortan esos valores.
nms <- names(train_nrm)
predictores_nn <- setdiff(nms, "medv")
frml <- reformulate(
predictores_nn,
response = "medv"
)
set.seed(123)
modelo.nn <- neuralnet::neuralnet(
frml,
data = train_nrm,
hidden = c(7, 5, 3),
threshold = 0.005,
algorithm = "rprop+",
linear.output = TRUE,
stepmax = 1000000
)
#threshold es un criterio de gradiente, no un 5% de error.
if (
is.null(modelo.nn$weights) ||
length(modelo.nn$weights) == 0
) {
stop("La red neuronal no convergió.")
}
pr.nn <- neuralnet::compute(
modelo.nn,
covariate = test_nrm[
, predictores_nn,
drop = FALSE
]
)$net.result
#Volver a unidades originales usando parámetros de entrenamiento
medv.predict <- as.numeric(pr.nn) *
rangos["medv"] + mins["medv"]
medv.real <- datos_prueba$medv
print(metricas(medv.real, medv.predict))## MSE MAE RMSE R2
## 1 47.56817 4.321949 6.896968 0.4914041se.nn <- mean((medv.real - medv.predict)^2)
g_nn <- ggplot(
data.frame(
Real = medv.real,
Prediccion = medv.predict
),
aes(x = Real, y = Prediccion)
) +
geom_point(color = "steelblue") +
geom_abline(
intercept = 0,
slope = 1,
color = "red"
) +
labs(
title = paste(
"Red neuronal: MSE =",
round(se.nn, 2)
),
x = "medv observado",
y = "medv predicho"
) +
theme_minimal()
print(g_nn)
plot(modelo.nn)
predicciones_modelos$Red_neuronal <- medv.predictLa red neuronal obtuvo un RMSE de prueba de 6.897 y un MAE de 4.322, los mayores errores entre los modelos evaluados. En consecuencia, esta configuración no resulta competitiva para la predicción de medv en la partición analizada.
34 13.8 KNN PARA REGRESIÓN
set.seed(123)
modelo_knn <- caret::train(
medv ~ .,
data = datos_entrenamiento,
method = "knn"
)
predic.knn <- predict(
modelo_knn,
newdata = datos_prueba
)
print(modelo_knn)## k-Nearest Neighbors
##
## 404 samples
## 13 predictor
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 404, 404, 404, 404, 404, 404, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 6.954760 0.4163218 4.780243
## 7 7.029336 0.3971349 4.818830
## 9 7.053850 0.3863383 4.847622
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.print(metricas(datos_prueba$medv, predic.knn))## MSE MAE RMSE R2
## 1 39.07344 4.567647 6.250875 0.5822292plot(
datos_prueba$medv,
predic.knn,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
predicciones_modelos$KNN <- predic.knnKNN seleccionó cinco vecinos y obtuvo un RMSE de prueba de 6.251 y un MAE de 4.568. Su desempeño fue inferior al de CART y las SVM evaluadas. Antes de descartar el método, debe verificarse el escalamiento de las predictoras y ampliarse la búsqueda del número de vecinos.
35 13.9 RANDOM FOREST CON CARET
ctrl <- caret::trainControl(
method = "cv",
number = 10
)
set.seed(123)
modelo_rf <- caret::train(
medv ~ .,
data = datos_entrenamiento,
method = "rf",
trControl = ctrl,
ntree = 50,
tuneLength = 5
)
predic.rf <- predict(
modelo_rf,
newdata = datos_prueba
)
print(modelo_rf)## Random Forest
##
## 404 samples
## 13 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 363, 364, 364, 363, 364, 363, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 2 3.960120 0.8314147 2.663905
## 4 3.754074 0.8420238 2.487804
## 7 3.514262 0.8588893 2.424408
## 10 3.535906 0.8566223 2.386582
## 13 3.562479 0.8534943 2.404967
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 7.print(metricas(datos_prueba$medv, predic.rf))## MSE MAE RMSE R2
## 1 12.33245 2.347558 3.511759 0.8681422plot(
datos_prueba$medv,
predic.rf,
xlab = "medv observado",
ylab = "medv predicho"
)
abline(0, 1, col = "red")
predicciones_modelos$RF_caret <- predic.rfRandom Forest, con mtry = 7, obtuvo un RMSE de prueba de 3.512 y un MAE de 2.348, los menores entre los modelos evaluados. Estos resultados lo sitúan como el candidato con mejor desempeño predictivo observado en esta partición, pendiente de confirmar mediante un procedimiento uniforme de validación.
36 13.10 RANDOM FOREST NATIVO
set.seed(123)
modelo.rf <- randomForest::randomForest(
medv ~ .,
data = datos_entrenamiento,
mtry = 3,
importance = TRUE
)
print(modelo.rf)##
## Call:
## randomForest(formula = medv ~ ., data = datos_entrenamiento, mtry = 3, importance = TRUE)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 3
##
## Mean of squared residuals: 14.12679
## % Var explained: 82.79plot(modelo.rf)
#Diagnóstico OOB del bosque
which.min(modelo.rf$mse)## [1] 463sqrt(min(modelo.rf$mse))## [1] 3.745529#Esto no convierte al mínimo puntual en un bosque final óptimo.
pred_randomForest <- predict(
modelo.rf,
newdata = datos_prueba
)
print(metricas(datos_prueba$medv, pred_randomForest))## MSE MAE RMSE R2
## 1 13.08086 2.498751 3.616748 0.8601402head(pred_randomForest)## 5 7 13 16 17 19
## 33.42292 20.53166 20.69208 19.78475 20.97609 19.40928predicciones_modelos$RF_nativo <- pred_randomForestEl Random Forest de 500 árboles y mtry = 3 obtuvo un RMSE de prueba de 3.617 y un MAE de 2.499. Mostró un desempeño competitivo, aunque ligeramente inferior al bosque previamente ajustado con mtry = 7, que conserva los mejores resultados observados.
37 13.11 BOOSTING CON CARET
set.seed(123)
caret.gbm1 <- caret::train(
medv ~ .,
method = "gbm",
data = datos_entrenamiento,
tuneGrid = data.frame(
n.trees = 100,
interaction.depth = 2,
shrinkage = c(0.3, 0.1, 0.05, 0.01, 0.005),
n.minobsinnode = 10
),
trControl = caret::trainControl(
method = "cv",
number = 5
),
verbose = FALSE
)
print(caret.gbm1)## Stochastic Gradient Boosting
##
## 404 samples
## 13 predictor
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 324, 325, 323, 323, 321
## Resampling results across tuning parameters:
##
## shrinkage RMSE Rsquared MAE
## 0.005 7.022276 0.7229389 4.932496
## 0.010 5.849212 0.7385757 4.041115
## 0.050 4.160123 0.7890028 2.771716
## 0.100 3.949786 0.8086931 2.630274
## 0.300 3.912170 0.8141495 2.796424
##
## Tuning parameter 'n.trees' was held constant at a value of 100
## Tuning
## parameter 'interaction.depth' was held constant at a value of 2
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 100, interaction.depth =
## 2, shrinkage = 0.3 and n.minobsinnode = 10.print(caret::varImp(caret.gbm1))## gbm variable importance
##
## Overall
## lstat 100.000
## rm 56.711
## dis 21.693
## ptratio 16.238
## nox 10.780
## age 10.405
## crim 9.418
## black 7.228
## tax 3.787
## chas 2.097
## rad 1.522
## indus 1.292
## zn 0.000pred1 <- predict(
caret.gbm1,
newdata = datos_prueba
)
print(metricas(datos_prueba$medv, pred1))## MSE MAE RMSE R2
## 1 14.81276 2.822391 3.848735 0.8416229predicciones_modelos$Boosting_caret <- pred138 13.12 BOOSTING NATIVO
set.seed(123)
gbm.fit <- gbm::gbm(
medv ~ .,
data = datos_entrenamiento,
distribution = "gaussian",
verbose = FALSE
)
print(gbm.fit)## gbm::gbm(formula = medv ~ ., distribution = "gaussian", data = datos_entrenamiento,
## verbose = FALSE)
## A gradient boosted model with gaussian loss function.
## 100 iterations were performed.
## There were 13 predictors of which 11 had non-zero influence.summary(gbm.fit)
## var rel.inf
## lstat lstat 56.3618198
## rm rm 27.1278384
## ptratio ptratio 4.6250258
## nox nox 4.1113404
## dis dis 2.3897223
## black black 1.7361318
## crim crim 1.1788200
## chas chas 1.1464074
## age age 0.6098516
## tax tax 0.6029932
## rad rad 0.1100491
## zn zn 0.0000000
## indus indus 0.0000000pred <- predict(
gbm.fit,
newdata = datos_prueba,
n.trees = gbm.fit$n.trees
)
print(metricas(datos_prueba$medv, pred))## MSE MAE RMSE R2
## 1 15.36724 2.845336 3.920108 0.8356944predicciones_modelos$Boosting_nativo <- as.numeric(pred)39 13.13 BAGGING CON CARET
#Eliminar posibles resultados anteriores de Bagging
if (exists("bagged_cv")) rm(bagged_cv)
if (exists("pred_bagging")) rm(pred_bagging)
#Comprobar datos
stopifnot(
is.numeric(datos_entrenamiento$medv),
!anyNA(datos_entrenamiento),
!anyNA(datos_prueba)
)
ctrl <- caret::trainControl(
method = "cv",
number = 10,
allowParallel = FALSE
)
set.seed(123)
bagged_cv <- caret::train(
medv ~ .,
data = datos_entrenamiento,
method = "treebag",
trControl = ctrl,
metric = "RMSE",
nbagg = 100
)
#Ejecutar estas líneas solo si el entrenamiento terminó sin error
print(bagged_cv)## Bagged CART
##
## 404 samples
## 13 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 363, 364, 364, 363, 364, 363, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 4.169714 0.8005339 2.837237pred_bagging <- as.numeric(
predict(
bagged_cv,
newdata = datos_prueba
)
)
print(metricas(datos_prueba$medv, pred_bagging))## MSE MAE RMSE R2
## 1 16.78556 2.860285 4.097019 0.8205298predicciones_modelos$Bagging_caret <- pred_bagging40 13.14 BAGGING NATIVO
set.seed(123)
bagged_m1 <- ipred::bagging(
medv ~ .,
data = datos_entrenamiento,
coob = TRUE
)
print(bagged_m1)##
## Bagging regression trees with 25 bootstrap replications
##
## Call: bagging.data.frame(formula = medv ~ ., data = datos_entrenamiento,
## coob = TRUE)
##
## Out-of-bag estimate of root mean squared error: 4.4754#Evaluar entre 10 y 50 árboles mediante OOB
ntree <- 10:50
rmse <- numeric(length(ntree))
modelos_bagging <- vector("list", length(ntree))
for (i in seq_along(ntree)) {
set.seed(123)
model <- ipred::bagging(
medv ~ .,
data = datos_entrenamiento,
coob = TRUE,
nbagg = ntree[i]
)
rmse[i] <- as.numeric(model$err)
modelos_bagging[[i]] <- model
}
if (any(!is.finite(rmse))) {
stop("Hay errores OOB inválidos en bagging.")
}
indice_bag <- which.min(rmse)
#El original predecía con el último modelo del bucle.
#Ahora se utiliza el seleccionado por OOB.
bagged_m1 <- modelos_bagging[[indice_bag]]
plot(
ntree,
rmse,
type = "l",
lwd = 2,
xlab = "Número de árboles",
ylab = "RMSE OOB"
)
abline(
v = ntree[indice_bag],
col = "red",
lty = "dashed"
)
cat(
"\nÁrboles seleccionados por OOB:",
ntree[indice_bag],
"\n"
)##
## Árboles seleccionados por OOB: 34pred1 <- predict(
bagged_m1,
newdata = datos_prueba
)
print(metricas(datos_prueba$medv, pred1))## MSE MAE RMSE R2
## 1 17.69792 2.936425 4.20689 0.8107749predicciones_modelos$Bagging_nativo <- pred141 13.16 Tabla comparativa
tabla_prueba <- bind_rows(
lapply(names(predicciones_modelos), function(nombre) {
cbind(
Modelo = nombre,
metricas(
datos_prueba$medv,
predicciones_modelos[[nombre]]
)
)
})
) %>%
arrange(RMSE)
cat("\n========== COMPARACIÓN EN PRUEBA ==========\n")##
## ========== COMPARACIÓN EN PRUEBA ==========print(tabla_prueba)## Modelo MSE MAE RMSE R2
## 1 RF_caret 12.33245 2.347558 3.511759 0.8681422
## 2 RF_nativo 13.08086 2.498751 3.616748 0.8601402
## 3 Boosting_caret 14.81276 2.822391 3.848735 0.8416229
## 4 Boosting_nativo 15.36724 2.845336 3.920108 0.8356944
## 5 Bagging_caret 16.78556 2.860285 4.097019 0.8205298
## 6 Bagging_nativo 17.69792 2.936425 4.206890 0.8107749
## 7 RPART 21.92118 3.562674 4.682007 0.7656201
## 8 SVM_e1071 29.58155 3.273663 5.438892 0.6837159
## 9 SVM_radial 29.90923 3.312934 5.468933 0.6802123
## 10 TREE 30.68945 3.899743 5.539806 0.6718703
## 11 Lineal 35.09588 3.789056 5.924178 0.6247570
## 12 SVM_lineal 35.22718 3.628600 5.935249 0.6233532
## 13 KNN 39.07344 4.567647 6.250875 0.5822292
## 14 Red_neuronal 47.56817 4.321949 6.896968 0.4914041#------------------------------------------------------------------#
# GRÁFICO COMPARATIVO DEL RMSE
#------------------------------------------------------------------#
g_rmse <- ggplot(
tabla_prueba,
aes(x = reorder(Modelo, RMSE), y = RMSE)
) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(
title = "Comparación de modelos sobre Boston imputado",
subtitle = "Evaluación en prueba: menor RMSE es mejor",
x = NULL,
y = "RMSE"
) +
theme_minimal()
print(g_rmse)
#------------------------------------------------------------------#
# OBSERVADO VS PREDICHO
#------------------------------------------------------------------#
tabla_predicciones <- bind_rows(
lapply(names(predicciones_modelos), function(nombre) {
data.frame(
Modelo = nombre,
Real = datos_prueba$medv,
Prediccion = as.numeric(
predicciones_modelos[[nombre]]
)
)
})
)
g_predicciones <- ggplot(
tabla_predicciones,
aes(x = Real, y = Prediccion)
) +
geom_point(color = "steelblue", alpha = 0.6) +
geom_abline(
intercept = 0,
slope = 1,
color = "red"
) +
facet_wrap(~ Modelo, ncol = 3) +
coord_equal() +
labs(
title = "Valores observados vs predichos",
x = "medv observado",
y = "medv predicho"
) +
theme_minimal()
print(g_predicciones)
42 14. Conclusiones
Random Forest presentó el mejor desempeño predictivo entre los métodos evaluados. La configuración con mtry = 7 obtuvo el menor MSE (12.33245), MAE (2.34756) y RMSE (3.51176), junto con el mayor \(R^2\) reportado (0.86814) en el conjunto de prueba. Por tanto, se considera el método más adecuado para predecir medv en la partición analizada, siempre que todos los modelos se hayan evaluado sobre las mismas observaciones y sin filtración de información.