TAREA 5
Dina Monterroza Barrios
2023-12-04
CARGA DE DATOS
library(readr)
df <- read_csv("diabetes.csv")## Rows: 768 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (9): Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, D...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Limpieza de datos
df$Glucose[df$Glucose == 0] <- NA
df$BloodPressure[df$BloodPressure == 0] <- NA
df$SkinThickness[df$SkinThickness == 0] <- NA
df$Insulin[df$Insulin == 0] <- NA
df$BMI[df$BMI == 0] <- NA
# Visualizar
head(df)## # A tibble: 6 × 9
## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 6 148 72 35 NA 33.6
## 2 1 85 66 29 NA 26.6
## 3 8 183 64 NA NA 23.3
## 4 1 89 66 23 94 28.1
## 5 0 137 40 35 168 43.1
## 6 5 116 74 NA NA 25.6
## # ℹ 3 more variables: DiabetesPedigreeFunction <dbl>, Age <dbl>, Outcome <dbl>La limpieza de datos implica reemplazar valores no válidos o incorrectos, como ceros en este caso, con NA para asegurar la precisión y calidad de los análisis posteriores.
Análisis Exploratorio de Datos
str(df)## spc_tbl_ [768 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Pregnancies : num [1:768] 6 1 8 1 0 5 3 10 2 8 ...
## $ Glucose : num [1:768] 148 85 183 89 137 116 78 115 197 125 ...
## $ BloodPressure : num [1:768] 72 66 64 66 40 74 50 NA 70 96 ...
## $ SkinThickness : num [1:768] 35 29 NA 23 35 NA 32 NA 45 NA ...
## $ Insulin : num [1:768] NA NA NA 94 168 NA 88 NA 543 NA ...
## $ BMI : num [1:768] 33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 NA ...
## $ DiabetesPedigreeFunction: num [1:768] 0.627 0.351 0.672 0.167 2.288 ...
## $ Age : num [1:768] 50 31 32 21 33 30 26 29 53 54 ...
## $ Outcome : num [1:768] 1 0 1 0 1 0 1 0 1 1 ...
## - attr(*, "spec")=
## .. cols(
## .. Pregnancies = col_double(),
## .. Glucose = col_double(),
## .. BloodPressure = col_double(),
## .. SkinThickness = col_double(),
## .. Insulin = col_double(),
## .. BMI = col_double(),
## .. DiabetesPedigreeFunction = col_double(),
## .. Age = col_double(),
## .. Outcome = col_double()
## .. )
## - attr(*, "problems")=<externalptr>La estructura del dataframe muestra que contiene 768 observaciones con 9 variables relacionadas con la diabetes, como el número de embarazos, niveles de glucosa, presión arterial, entre otros.
Resumen estadístico
summary(df)## Pregnancies Glucose BloodPressure SkinThickness
## Min. : 0.000 Min. : 44.0 Min. : 24.00 Min. : 7.00
## 1st Qu.: 1.000 1st Qu.: 99.0 1st Qu.: 64.00 1st Qu.:22.00
## Median : 3.000 Median :117.0 Median : 72.00 Median :29.00
## Mean : 3.845 Mean :121.7 Mean : 72.41 Mean :29.15
## 3rd Qu.: 6.000 3rd Qu.:141.0 3rd Qu.: 80.00 3rd Qu.:36.00
## Max. :17.000 Max. :199.0 Max. :122.00 Max. :99.00
## NA's :5 NA's :35 NA's :227
## Insulin BMI DiabetesPedigreeFunction Age
## Min. : 14.00 Min. :18.20 Min. :0.0780 Min. :21.00
## 1st Qu.: 76.25 1st Qu.:27.50 1st Qu.:0.2437 1st Qu.:24.00
## Median :125.00 Median :32.30 Median :0.3725 Median :29.00
## Mean :155.55 Mean :32.46 Mean :0.4719 Mean :33.24
## 3rd Qu.:190.00 3rd Qu.:36.60 3rd Qu.:0.6262 3rd Qu.:41.00
## Max. :846.00 Max. :67.10 Max. :2.4200 Max. :81.00
## NA's :374 NA's :11
## Outcome
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.349
## 3rd Qu.:1.000
## Max. :1.000
## El resumen estadístico del dataframe muestra rangos, medianas, medias y cuartiles para cada variable, destacando la presencia de valores faltantes en algunas variables como Glucosa, Presión Arterial, Grosor de la Piel e Insulina.
valores faltantes por columna
colSums(is.na(df))## Pregnancies Glucose BloodPressure
## 0 5 35
## SkinThickness Insulin BMI
## 227 374 11
## DiabetesPedigreeFunction Age Outcome
## 0 0 0Los valores faltantes varían entre las columnas, siendo más numerosos en Insulina y Grosor de la Piel, y menos en Glucosa y BMI. No hay valores faltantes en algunas columnas.
Histogramas para cada variable numérica
par(mfrow = c(2, 4))
hist(df$Pregnancies, main = "", xlab = "Pregnancies")
hist(df$Glucose, main = "", xlab = "Glucose")
hist(df$BloodPressure, main = "", xlab = "Blood Pressure")
hist(df$SkinThickness, main = "", xlab = "Skin Thickness")
hist(df$Insulin, main = "", xlab = "Insulin")
hist(df$BMI, main = "", xlab = "BMI")
hist(df$DiabetesPedigreeFunction, main = "", xlab = "Pedigree Function")
hist(df$Age, main = "", xlab = "Age")
Los histogramas indican que la mayoría de los individuos tienen un menor número de Pregnancies, con distribuciones de Glucose, BloodPressure y BMI que parecen aproximadamente normales. SkinThickness e Insulin están sesgados hacia valores más bajos, lo que sugiere una variación limitada en estas medidas en la población. DiabetesPedigreeFunction muestra en su mayoría valores bajos, y Age presenta una frecuencia más alta en individuos más jóvenes.
boxplots para cada variable numérica
par(mfrow = c(2, 4))
boxplot(df$Pregnancies, main = "", ylab = "Pregnancies")
boxplot(df$Glucose, main = "", ylab = "Glucose")
boxplot(df$BloodPressure, main = "", ylab = "Blood Pressure")
boxplot(df$SkinThickness, main = "", ylab = "Skin Thickness")
boxplot(df$Insulin, main = "", ylab = "Insulin")
boxplot(df$BMI, main = "", ylab = "BMI")
boxplot(df$DiabetesPedigreeFunction, main = "", ylab = "Pedigree Function")
boxplot(df$Age, main = "", ylab = "Age")
Los box plot permiten visualizar la distribución de los datos en
los rangos intercuartiles y la presencia de valores atípicos en cada
variable. Destaca Insulin y Diabetes Pedrigree Fuction por la presencia
de muchos valores atipicos.
frecuencia de cada categoría en ‘Outcome’
outcome_counts <- table(df$Outcome)
# Crear el gráfico de barras
outcome_counts <- table(df$Outcome)
barplot(outcome_counts,
main = "Distribution of Outcomes",
xlab = "Outcome",
ylab = "Frequency",
col = c("purple", "red"),
ylim = c(0, max(outcome_counts) + 10))
Outcome probablemente representa el resultado de una prueba clínica de diabetes, donde 0 podría indicar un resultado negativo (no diabético) y 1 un resultado positivo (diabético). En tal caso, el gráfico de barras muestra la distribución de frecuencias de estos resultados, con una mayor cantidad de casos negativos que positivos
Visualización Datos Faltantes
library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(VIM)## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleepmd.pattern(df, plot = TRUE, rotate.names = TRUE)
## Pregnancies DiabetesPedigreeFunction Age Outcome Glucose BMI BloodPressure
## 392 1 1 1 1 1 1 1
## 140 1 1 1 1 1 1 1
## 192 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 0
## 26 1 1 1 1 1 1 0
## 1 1 1 1 1 1 0 1
## 1 1 1 1 1 1 0 1
## 2 1 1 1 1 1 0 1
## 7 1 1 1 1 1 0 0
## 1 1 1 1 1 0 1 1
## 4 1 1 1 1 0 1 1
## 0 0 0 0 5 11 35
## SkinThickness Insulin
## 392 1 1 0
## 140 1 0 1
## 192 0 0 2
## 2 1 0 2
## 26 0 0 3
## 1 1 1 1
## 1 1 0 2
## 2 0 0 3
## 7 0 0 4
## 1 1 1 1
## 4 1 0 2
## 227 374 652El análisis de patrón nos muestra que 392 muestras están completas, mientras que a 140 les falta solo el valor de Insulin y a 192 les falta el valor de Insulin y Skin Thickness, y así sucesivamente nos va mostrando los patrones de datos faltantes. Un mejor análisis puede hacerse utilizando el paquete VIM para el siguiente gráfico.
labels_short <- names(df)
labels_short[labels_short == "DiabetesPedigreeFunction"] <- "DPF"
aggr_plot <- aggr(df, col=c('purple','red'), numbers=TRUE, sortVars=TRUE, labels = labels_short,
cex.axis=.7, gap=3, ylab=c("Histogram of missing data","Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## Insulin 0.486979167
## SkinThickness 0.295572917
## BloodPressure 0.045572917
## BMI 0.014322917
## Glucose 0.006510417
## Pregnancies 0.000000000
## DPF 0.000000000
## Age 0.000000000
## Outcome 0.000000000marginplot(df[, c("Glucose", "BloodPressure")])
marginplot(df[, c("SkinThickness", "Insulin")])
marginplot(df[, c("Glucose", "BMI")])
marginplot(df[, c("BloodPressure", "Age")])
El histograma resalta que la mayor incidencia de datos faltantes se localiza en la columna ‘Insulin’, seguida por ‘SkinThickness’. Además, es notorio que las columnas ‘Pregnancies’, ‘DPF’, ‘Age’ y ‘Outcome’ están completas, sin ausencias. El gráfico complementario confirma estos hallazgos, mostrando que aproximadamente la mitad de las muestras no presentan ninguna falta de datos. También se distinguen patrones específicos de ausencia en ‘Insulin’ y ‘SkinThickness’.En los siguientes graficos se puede visualizar datos faltantes entre algunas columnas y su relación.
Imputación Datos Faltantes
imp_pmm <- mice(df, method = 'pmm', m = 5, maxit = 50, seed = 500)##
## iter imp variable
## 1 1 Glucose BloodPressure SkinThickness Insulin BMI
## 1 2 Glucose BloodPressure SkinThickness Insulin BMI
## 1 3 Glucose BloodPressure SkinThickness Insulin BMI
## 1 4 Glucose BloodPressure SkinThickness Insulin BMI
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## 50 5 Glucose BloodPressure SkinThickness Insulin BMIdf_pmm <- complete(imp_pmm)
head(df_pmm)## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35 83 33.6
## 2 1 85 66 29 55 26.6
## 3 8 183 64 20 175 23.3
## 4 1 89 66 23 94 28.1
## 5 0 137 40 35 168 43.1
## 6 5 116 74 24 175 25.6
## DiabetesPedigreeFunction Age Outcome
## 1 0.627 50 1
## 2 0.351 31 0
## 3 0.672 32 1
## 4 0.167 21 0
## 5 2.288 33 1
## 6 0.201 30 0Imputación con norm.predict
imp_norm_predict <- mice(df, method = 'norm.predict', m = 5, maxit = 50, seed = 500)##
## iter imp variable
## 1 1 Glucose BloodPressure SkinThickness Insulin BMI
## 1 2 Glucose BloodPressure SkinThickness Insulin BMI
## 1 3 Glucose BloodPressure SkinThickness Insulin BMI
## 1 4 Glucose BloodPressure SkinThickness Insulin BMI
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## 7 3 Glucose BloodPressure SkinThickness Insulin BMI
## 7 4 Glucose BloodPressure SkinThickness Insulin BMI
## 7 5 Glucose BloodPressure SkinThickness Insulin BMI
## 8 1 Glucose BloodPressure SkinThickness Insulin BMI
## 8 2 Glucose BloodPressure SkinThickness Insulin BMI
## 8 3 Glucose BloodPressure SkinThickness Insulin BMI
## 8 4 Glucose BloodPressure SkinThickness Insulin BMI
## 8 5 Glucose BloodPressure SkinThickness Insulin BMI
## 9 1 Glucose BloodPressure SkinThickness Insulin BMI
## 9 2 Glucose BloodPressure SkinThickness Insulin BMI
## 9 3 Glucose BloodPressure SkinThickness Insulin BMI
## 9 4 Glucose BloodPressure SkinThickness Insulin BMI
## 9 5 Glucose BloodPressure SkinThickness Insulin BMI
## 10 1 Glucose BloodPressure SkinThickness Insulin BMI
## 10 2 Glucose BloodPressure SkinThickness Insulin BMI
## 10 3 Glucose BloodPressure SkinThickness Insulin BMI
## 10 4 Glucose BloodPressure SkinThickness Insulin BMI
## 10 5 Glucose BloodPressure SkinThickness Insulin BMI
## 11 1 Glucose BloodPressure SkinThickness Insulin BMI
## 11 2 Glucose BloodPressure SkinThickness Insulin BMI
## 11 3 Glucose BloodPressure SkinThickness Insulin BMI
## 11 4 Glucose BloodPressure SkinThickness Insulin BMI
## 11 5 Glucose BloodPressure SkinThickness Insulin BMI
## 12 1 Glucose BloodPressure SkinThickness Insulin BMI
## 12 2 Glucose BloodPressure SkinThickness Insulin BMI
## 12 3 Glucose BloodPressure SkinThickness Insulin BMI
## 12 4 Glucose BloodPressure SkinThickness Insulin BMI
## 12 5 Glucose BloodPressure SkinThickness Insulin BMI
## 13 1 Glucose BloodPressure SkinThickness Insulin BMI
## 13 2 Glucose BloodPressure SkinThickness Insulin BMI
## 13 3 Glucose BloodPressure SkinThickness Insulin BMI
## 13 4 Glucose BloodPressure SkinThickness Insulin BMI
## 13 5 Glucose BloodPressure SkinThickness Insulin BMI
## 14 1 Glucose BloodPressure SkinThickness Insulin BMI
## 14 2 Glucose BloodPressure SkinThickness Insulin BMI
## 14 3 Glucose BloodPressure SkinThickness Insulin BMI
## 14 4 Glucose BloodPressure SkinThickness Insulin BMI
## 14 5 Glucose BloodPressure SkinThickness Insulin BMI
## 15 1 Glucose BloodPressure SkinThickness Insulin BMI
## 15 2 Glucose BloodPressure SkinThickness Insulin BMI
## 15 3 Glucose BloodPressure SkinThickness Insulin BMI
## 15 4 Glucose BloodPressure SkinThickness Insulin BMI
## 15 5 Glucose BloodPressure SkinThickness Insulin BMI
## 16 1 Glucose BloodPressure SkinThickness Insulin BMI
## 16 2 Glucose BloodPressure SkinThickness Insulin BMI
## 16 3 Glucose BloodPressure SkinThickness Insulin BMI
## 16 4 Glucose BloodPressure SkinThickness Insulin BMI
## 16 5 Glucose BloodPressure SkinThickness Insulin BMI
## 17 1 Glucose BloodPressure SkinThickness Insulin BMI
## 17 2 Glucose BloodPressure SkinThickness Insulin BMI
## 17 3 Glucose BloodPressure SkinThickness Insulin BMI
## 17 4 Glucose BloodPressure SkinThickness Insulin BMI
## 17 5 Glucose BloodPressure SkinThickness Insulin BMI
## 18 1 Glucose BloodPressure SkinThickness Insulin BMI
## 18 2 Glucose BloodPressure SkinThickness Insulin BMI
## 18 3 Glucose BloodPressure SkinThickness Insulin BMI
## 18 4 Glucose BloodPressure SkinThickness Insulin BMI
## 18 5 Glucose BloodPressure SkinThickness Insulin BMI
## 19 1 Glucose BloodPressure SkinThickness Insulin BMI
## 19 2 Glucose BloodPressure SkinThickness Insulin BMI
## 19 3 Glucose BloodPressure SkinThickness Insulin BMI
## 19 4 Glucose BloodPressure SkinThickness Insulin BMI
## 19 5 Glucose BloodPressure SkinThickness Insulin BMI
## 20 1 Glucose BloodPressure SkinThickness Insulin BMI
## 20 2 Glucose BloodPressure SkinThickness Insulin BMI
## 20 3 Glucose BloodPressure SkinThickness Insulin BMI
## 20 4 Glucose BloodPressure SkinThickness Insulin BMI
## 20 5 Glucose BloodPressure SkinThickness Insulin BMI
## 21 1 Glucose BloodPressure SkinThickness Insulin BMI
## 21 2 Glucose BloodPressure SkinThickness Insulin BMI
## 21 3 Glucose BloodPressure SkinThickness Insulin BMI
## 21 4 Glucose BloodPressure SkinThickness Insulin BMI
## 21 5 Glucose BloodPressure SkinThickness Insulin BMI
## 22 1 Glucose BloodPressure SkinThickness Insulin BMI
## 22 2 Glucose BloodPressure SkinThickness Insulin BMI
## 22 3 Glucose BloodPressure SkinThickness Insulin BMI
## 22 4 Glucose BloodPressure SkinThickness Insulin BMI
## 22 5 Glucose BloodPressure SkinThickness Insulin BMI
## 23 1 Glucose BloodPressure SkinThickness Insulin BMI
## 23 2 Glucose BloodPressure SkinThickness Insulin BMI
## 23 3 Glucose BloodPressure SkinThickness Insulin BMI
## 23 4 Glucose BloodPressure SkinThickness Insulin BMI
## 23 5 Glucose BloodPressure SkinThickness Insulin BMI
## 24 1 Glucose BloodPressure SkinThickness Insulin BMI
## 24 2 Glucose BloodPressure SkinThickness Insulin BMI
## 24 3 Glucose BloodPressure SkinThickness Insulin BMI
## 24 4 Glucose BloodPressure SkinThickness Insulin BMI
## 24 5 Glucose BloodPressure SkinThickness Insulin BMI
## 25 1 Glucose BloodPressure SkinThickness Insulin BMI
## 25 2 Glucose BloodPressure SkinThickness Insulin BMI
## 25 3 Glucose BloodPressure SkinThickness Insulin BMI
## 25 4 Glucose BloodPressure SkinThickness Insulin BMI
## 25 5 Glucose BloodPressure SkinThickness Insulin BMI
## 26 1 Glucose BloodPressure SkinThickness Insulin BMI
## 26 2 Glucose BloodPressure SkinThickness Insulin BMI
## 26 3 Glucose BloodPressure SkinThickness Insulin BMI
## 26 4 Glucose BloodPressure SkinThickness Insulin BMI
## 26 5 Glucose BloodPressure SkinThickness Insulin BMI
## 27 1 Glucose BloodPressure SkinThickness Insulin BMI
## 27 2 Glucose BloodPressure SkinThickness Insulin BMI
## 27 3 Glucose BloodPressure SkinThickness Insulin BMI
## 27 4 Glucose BloodPressure SkinThickness Insulin BMI
## 27 5 Glucose BloodPressure SkinThickness Insulin BMI
## 28 1 Glucose BloodPressure SkinThickness Insulin BMI
## 28 2 Glucose BloodPressure SkinThickness Insulin BMI
## 28 3 Glucose BloodPressure SkinThickness Insulin BMI
## 28 4 Glucose BloodPressure SkinThickness Insulin BMI
## 28 5 Glucose BloodPressure SkinThickness Insulin BMI
## 29 1 Glucose BloodPressure SkinThickness Insulin BMI
## 29 2 Glucose BloodPressure SkinThickness Insulin BMI
## 29 3 Glucose BloodPressure SkinThickness Insulin BMI
## 29 4 Glucose BloodPressure SkinThickness Insulin BMI
## 29 5 Glucose BloodPressure SkinThickness Insulin BMI
## 30 1 Glucose BloodPressure SkinThickness Insulin BMI
## 30 2 Glucose BloodPressure SkinThickness Insulin BMI
## 30 3 Glucose BloodPressure SkinThickness Insulin BMI
## 30 4 Glucose BloodPressure SkinThickness Insulin BMI
## 30 5 Glucose BloodPressure SkinThickness Insulin BMI
## 31 1 Glucose BloodPressure SkinThickness Insulin BMI
## 31 2 Glucose BloodPressure SkinThickness Insulin BMI
## 31 3 Glucose BloodPressure SkinThickness Insulin BMI
## 31 4 Glucose BloodPressure SkinThickness Insulin BMI
## 31 5 Glucose BloodPressure SkinThickness Insulin BMI
## 32 1 Glucose BloodPressure SkinThickness Insulin BMI
## 32 2 Glucose BloodPressure SkinThickness Insulin BMI
## 32 3 Glucose BloodPressure SkinThickness Insulin BMI
## 32 4 Glucose BloodPressure SkinThickness Insulin BMI
## 32 5 Glucose BloodPressure SkinThickness Insulin BMI
## 33 1 Glucose BloodPressure SkinThickness Insulin BMI
## 33 2 Glucose BloodPressure SkinThickness Insulin BMI
## 33 3 Glucose BloodPressure SkinThickness Insulin BMI
## 33 4 Glucose BloodPressure SkinThickness Insulin BMI
## 33 5 Glucose BloodPressure SkinThickness Insulin BMI
## 34 1 Glucose BloodPressure SkinThickness Insulin BMI
## 34 2 Glucose BloodPressure SkinThickness Insulin BMI
## 34 3 Glucose BloodPressure SkinThickness Insulin BMI
## 34 4 Glucose BloodPressure SkinThickness Insulin BMI
## 34 5 Glucose BloodPressure SkinThickness Insulin BMI
## 35 1 Glucose BloodPressure SkinThickness Insulin BMI
## 35 2 Glucose BloodPressure SkinThickness Insulin BMI
## 35 3 Glucose BloodPressure SkinThickness Insulin BMI
## 35 4 Glucose BloodPressure SkinThickness Insulin BMI
## 35 5 Glucose BloodPressure SkinThickness Insulin BMI
## 36 1 Glucose BloodPressure SkinThickness Insulin BMI
## 36 2 Glucose BloodPressure SkinThickness Insulin BMI
## 36 3 Glucose BloodPressure SkinThickness Insulin BMI
## 36 4 Glucose BloodPressure SkinThickness Insulin BMI
## 36 5 Glucose BloodPressure SkinThickness Insulin BMI
## 37 1 Glucose BloodPressure SkinThickness Insulin BMI
## 37 2 Glucose BloodPressure SkinThickness Insulin BMI
## 37 3 Glucose BloodPressure SkinThickness Insulin BMI
## 37 4 Glucose BloodPressure SkinThickness Insulin BMI
## 37 5 Glucose BloodPressure SkinThickness Insulin BMI
## 38 1 Glucose BloodPressure SkinThickness Insulin BMI
## 38 2 Glucose BloodPressure SkinThickness Insulin BMI
## 38 3 Glucose BloodPressure SkinThickness Insulin BMI
## 38 4 Glucose BloodPressure SkinThickness Insulin BMI
## 38 5 Glucose BloodPressure SkinThickness Insulin BMI
## 39 1 Glucose BloodPressure SkinThickness Insulin BMI
## 39 2 Glucose BloodPressure SkinThickness Insulin BMI
## 39 3 Glucose BloodPressure SkinThickness Insulin BMI
## 39 4 Glucose BloodPressure SkinThickness Insulin BMI
## 39 5 Glucose BloodPressure SkinThickness Insulin BMI
## 40 1 Glucose BloodPressure SkinThickness Insulin BMI
## 40 2 Glucose BloodPressure SkinThickness Insulin BMI
## 40 3 Glucose BloodPressure SkinThickness Insulin BMI
## 40 4 Glucose BloodPressure SkinThickness Insulin BMI
## 40 5 Glucose BloodPressure SkinThickness Insulin BMI
## 41 1 Glucose BloodPressure SkinThickness Insulin BMI
## 41 2 Glucose BloodPressure SkinThickness Insulin BMI
## 41 3 Glucose BloodPressure SkinThickness Insulin BMI
## 41 4 Glucose BloodPressure SkinThickness Insulin BMI
## 41 5 Glucose BloodPressure SkinThickness Insulin BMI
## 42 1 Glucose BloodPressure SkinThickness Insulin BMI
## 42 2 Glucose BloodPressure SkinThickness Insulin BMI
## 42 3 Glucose BloodPressure SkinThickness Insulin BMI
## 42 4 Glucose BloodPressure SkinThickness Insulin BMI
## 42 5 Glucose BloodPressure SkinThickness Insulin BMI
## 43 1 Glucose BloodPressure SkinThickness Insulin BMI
## 43 2 Glucose BloodPressure SkinThickness Insulin BMI
## 43 3 Glucose BloodPressure SkinThickness Insulin BMI
## 43 4 Glucose BloodPressure SkinThickness Insulin BMI
## 43 5 Glucose BloodPressure SkinThickness Insulin BMI
## 44 1 Glucose BloodPressure SkinThickness Insulin BMI
## 44 2 Glucose BloodPressure SkinThickness Insulin BMI
## 44 3 Glucose BloodPressure SkinThickness Insulin BMI
## 44 4 Glucose BloodPressure SkinThickness Insulin BMI
## 44 5 Glucose BloodPressure SkinThickness Insulin BMI
## 45 1 Glucose BloodPressure SkinThickness Insulin BMI
## 45 2 Glucose BloodPressure SkinThickness Insulin BMI
## 45 3 Glucose BloodPressure SkinThickness Insulin BMI
## 45 4 Glucose BloodPressure SkinThickness Insulin BMI
## 45 5 Glucose BloodPressure SkinThickness Insulin BMI
## 46 1 Glucose BloodPressure SkinThickness Insulin BMI
## 46 2 Glucose BloodPressure SkinThickness Insulin BMI
## 46 3 Glucose BloodPressure SkinThickness Insulin BMI
## 46 4 Glucose BloodPressure SkinThickness Insulin BMI
## 46 5 Glucose BloodPressure SkinThickness Insulin BMI
## 47 1 Glucose BloodPressure SkinThickness Insulin BMI
## 47 2 Glucose BloodPressure SkinThickness Insulin BMI
## 47 3 Glucose BloodPressure SkinThickness Insulin BMI
## 47 4 Glucose BloodPressure SkinThickness Insulin BMI
## 47 5 Glucose BloodPressure SkinThickness Insulin BMI
## 48 1 Glucose BloodPressure SkinThickness Insulin BMI
## 48 2 Glucose BloodPressure SkinThickness Insulin BMI
## 48 3 Glucose BloodPressure SkinThickness Insulin BMI
## 48 4 Glucose BloodPressure SkinThickness Insulin BMI
## 48 5 Glucose BloodPressure SkinThickness Insulin BMI
## 49 1 Glucose BloodPressure SkinThickness Insulin BMI
## 49 2 Glucose BloodPressure SkinThickness Insulin BMI
## 49 3 Glucose BloodPressure SkinThickness Insulin BMI
## 49 4 Glucose BloodPressure SkinThickness Insulin BMI
## 49 5 Glucose BloodPressure SkinThickness Insulin BMI
## 50 1 Glucose BloodPressure SkinThickness Insulin BMI
## 50 2 Glucose BloodPressure SkinThickness Insulin BMI
## 50 3 Glucose BloodPressure SkinThickness Insulin BMI
## 50 4 Glucose BloodPressure SkinThickness Insulin BMI
## 50 5 Glucose BloodPressure SkinThickness Insulin BMIdf_norm_predict <- complete(imp_norm_predict)
head(df_norm_predict)## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35.00000 220.41825 33.6
## 2 1 85 66 29.00000 69.60061 26.6
## 3 8 183 64 21.44380 256.23203 23.3
## 4 1 89 66 23.00000 94.00000 28.1
## 5 0 137 40 35.00000 168.00000 43.1
## 6 5 116 74 21.87459 117.51189 25.6
## DiabetesPedigreeFunction Age Outcome
## 1 0.627 50 1
## 2 0.351 31 0
## 3 0.672 32 1
## 4 0.167 21 0
## 5 2.288 33 1
## 6 0.201 30 0Imputación con norm.nob
imp_norm_nob <- mice(df, method = 'norm.nob', m = 5, maxit = 50, seed = 500)##
## iter imp variable
## 1 1 Glucose BloodPressure SkinThickness Insulin BMI
## 1 2 Glucose BloodPressure SkinThickness Insulin BMI
## 1 3 Glucose BloodPressure SkinThickness Insulin BMI
## 1 4 Glucose BloodPressure SkinThickness Insulin BMI
## 1 5 Glucose BloodPressure SkinThickness Insulin BMI
## 2 1 Glucose BloodPressure SkinThickness Insulin BMI
## 2 2 Glucose BloodPressure SkinThickness Insulin BMI
## 2 3 Glucose BloodPressure SkinThickness Insulin BMI
## 2 4 Glucose BloodPressure SkinThickness Insulin BMI
## 2 5 Glucose BloodPressure SkinThickness Insulin BMI
## 3 1 Glucose BloodPressure SkinThickness Insulin BMI
## 3 2 Glucose BloodPressure SkinThickness Insulin BMI
## 3 3 Glucose BloodPressure SkinThickness Insulin BMI
## 3 4 Glucose BloodPressure SkinThickness Insulin BMI
## 3 5 Glucose BloodPressure SkinThickness Insulin BMI
## 4 1 Glucose BloodPressure SkinThickness Insulin BMI
## 4 2 Glucose BloodPressure SkinThickness Insulin BMI
## 4 3 Glucose BloodPressure SkinThickness Insulin BMI
## 4 4 Glucose BloodPressure SkinThickness Insulin BMI
## 4 5 Glucose BloodPressure SkinThickness Insulin BMI
## 5 1 Glucose BloodPressure SkinThickness Insulin BMI
## 5 2 Glucose BloodPressure SkinThickness Insulin BMI
## 5 3 Glucose BloodPressure SkinThickness Insulin BMI
## 5 4 Glucose BloodPressure SkinThickness Insulin BMI
## 5 5 Glucose BloodPressure SkinThickness Insulin BMI
## 6 1 Glucose BloodPressure SkinThickness Insulin BMI
## 6 2 Glucose BloodPressure SkinThickness Insulin BMI
## 6 3 Glucose BloodPressure SkinThickness Insulin BMI
## 6 4 Glucose BloodPressure SkinThickness Insulin BMI
## 6 5 Glucose BloodPressure SkinThickness Insulin BMI
## 7 1 Glucose BloodPressure SkinThickness Insulin BMI
## 7 2 Glucose BloodPressure SkinThickness Insulin BMI
## 7 3 Glucose BloodPressure SkinThickness Insulin BMI
## 7 4 Glucose BloodPressure SkinThickness Insulin BMI
## 7 5 Glucose BloodPressure SkinThickness Insulin BMI
## 8 1 Glucose BloodPressure SkinThickness Insulin BMI
## 8 2 Glucose BloodPressure SkinThickness Insulin BMI
## 8 3 Glucose BloodPressure SkinThickness Insulin BMI
## 8 4 Glucose BloodPressure SkinThickness Insulin BMI
## 8 5 Glucose BloodPressure SkinThickness Insulin BMI
## 9 1 Glucose BloodPressure SkinThickness Insulin BMI
## 9 2 Glucose BloodPressure SkinThickness Insulin BMI
## 9 3 Glucose BloodPressure SkinThickness Insulin BMI
## 9 4 Glucose BloodPressure SkinThickness Insulin BMI
## 9 5 Glucose BloodPressure SkinThickness Insulin BMI
## 10 1 Glucose BloodPressure SkinThickness Insulin BMI
## 10 2 Glucose BloodPressure SkinThickness Insulin BMI
## 10 3 Glucose BloodPressure SkinThickness Insulin BMI
## 10 4 Glucose BloodPressure SkinThickness Insulin BMI
## 10 5 Glucose BloodPressure SkinThickness Insulin BMI
## 11 1 Glucose BloodPressure SkinThickness Insulin BMI
## 11 2 Glucose BloodPressure SkinThickness Insulin BMI
## 11 3 Glucose BloodPressure SkinThickness Insulin BMI
## 11 4 Glucose BloodPressure SkinThickness Insulin BMI
## 11 5 Glucose BloodPressure SkinThickness Insulin BMI
## 12 1 Glucose BloodPressure SkinThickness Insulin BMI
## 12 2 Glucose BloodPressure SkinThickness Insulin BMI
## 12 3 Glucose BloodPressure SkinThickness Insulin BMI
## 12 4 Glucose BloodPressure SkinThickness Insulin BMI
## 12 5 Glucose BloodPressure SkinThickness Insulin BMI
## 13 1 Glucose BloodPressure SkinThickness Insulin BMI
## 13 2 Glucose BloodPressure SkinThickness Insulin BMI
## 13 3 Glucose BloodPressure SkinThickness Insulin BMI
## 13 4 Glucose BloodPressure SkinThickness Insulin BMI
## 13 5 Glucose BloodPressure SkinThickness Insulin BMI
## 14 1 Glucose BloodPressure SkinThickness Insulin BMI
## 14 2 Glucose BloodPressure SkinThickness Insulin BMI
## 14 3 Glucose BloodPressure SkinThickness Insulin BMI
## 14 4 Glucose BloodPressure SkinThickness Insulin BMI
## 14 5 Glucose BloodPressure SkinThickness Insulin BMI
## 15 1 Glucose BloodPressure SkinThickness Insulin BMI
## 15 2 Glucose BloodPressure SkinThickness Insulin BMI
## 15 3 Glucose BloodPressure SkinThickness Insulin BMI
## 15 4 Glucose BloodPressure SkinThickness Insulin BMI
## 15 5 Glucose BloodPressure SkinThickness Insulin BMI
## 16 1 Glucose BloodPressure SkinThickness Insulin BMI
## 16 2 Glucose BloodPressure SkinThickness Insulin BMI
## 16 3 Glucose BloodPressure SkinThickness Insulin BMI
## 16 4 Glucose BloodPressure SkinThickness Insulin BMI
## 16 5 Glucose BloodPressure SkinThickness Insulin BMI
## 17 1 Glucose BloodPressure SkinThickness Insulin BMI
## 17 2 Glucose BloodPressure SkinThickness Insulin BMI
## 17 3 Glucose BloodPressure SkinThickness Insulin BMI
## 17 4 Glucose BloodPressure SkinThickness Insulin BMI
## 17 5 Glucose BloodPressure SkinThickness Insulin BMI
## 18 1 Glucose BloodPressure SkinThickness Insulin BMI
## 18 2 Glucose BloodPressure SkinThickness Insulin BMI
## 18 3 Glucose BloodPressure SkinThickness Insulin BMI
## 18 4 Glucose BloodPressure SkinThickness Insulin BMI
## 18 5 Glucose BloodPressure SkinThickness Insulin BMI
## 19 1 Glucose BloodPressure SkinThickness Insulin BMI
## 19 2 Glucose BloodPressure SkinThickness Insulin BMI
## 19 3 Glucose BloodPressure SkinThickness Insulin BMI
## 19 4 Glucose BloodPressure SkinThickness Insulin BMI
## 19 5 Glucose BloodPressure SkinThickness Insulin BMI
## 20 1 Glucose BloodPressure SkinThickness Insulin BMI
## 20 2 Glucose BloodPressure SkinThickness Insulin BMI
## 20 3 Glucose BloodPressure SkinThickness Insulin BMI
## 20 4 Glucose BloodPressure SkinThickness Insulin BMI
## 20 5 Glucose BloodPressure SkinThickness Insulin BMI
## 21 1 Glucose BloodPressure SkinThickness Insulin BMI
## 21 2 Glucose BloodPressure SkinThickness Insulin BMI
## 21 3 Glucose BloodPressure SkinThickness Insulin BMI
## 21 4 Glucose BloodPressure SkinThickness Insulin BMI
## 21 5 Glucose BloodPressure SkinThickness Insulin BMI
## 22 1 Glucose BloodPressure SkinThickness Insulin BMI
## 22 2 Glucose BloodPressure SkinThickness Insulin BMI
## 22 3 Glucose BloodPressure SkinThickness Insulin BMI
## 22 4 Glucose BloodPressure SkinThickness Insulin BMI
## 22 5 Glucose BloodPressure SkinThickness Insulin BMI
## 23 1 Glucose BloodPressure SkinThickness Insulin BMI
## 23 2 Glucose BloodPressure SkinThickness Insulin BMI
## 23 3 Glucose BloodPressure SkinThickness Insulin BMI
## 23 4 Glucose BloodPressure SkinThickness Insulin BMI
## 23 5 Glucose BloodPressure SkinThickness Insulin BMI
## 24 1 Glucose BloodPressure SkinThickness Insulin BMI
## 24 2 Glucose BloodPressure SkinThickness Insulin BMI
## 24 3 Glucose BloodPressure SkinThickness Insulin BMI
## 24 4 Glucose BloodPressure SkinThickness Insulin BMI
## 24 5 Glucose BloodPressure SkinThickness Insulin BMI
## 25 1 Glucose BloodPressure SkinThickness Insulin BMI
## 25 2 Glucose BloodPressure SkinThickness Insulin BMI
## 25 3 Glucose BloodPressure SkinThickness Insulin BMI
## 25 4 Glucose BloodPressure SkinThickness Insulin BMI
## 25 5 Glucose BloodPressure SkinThickness Insulin BMI
## 26 1 Glucose BloodPressure SkinThickness Insulin BMI
## 26 2 Glucose BloodPressure SkinThickness Insulin BMI
## 26 3 Glucose BloodPressure SkinThickness Insulin BMI
## 26 4 Glucose BloodPressure SkinThickness Insulin BMI
## 26 5 Glucose BloodPressure SkinThickness Insulin BMI
## 27 1 Glucose BloodPressure SkinThickness Insulin BMI
## 27 2 Glucose BloodPressure SkinThickness Insulin BMI
## 27 3 Glucose BloodPressure SkinThickness Insulin BMI
## 27 4 Glucose BloodPressure SkinThickness Insulin BMI
## 27 5 Glucose BloodPressure SkinThickness Insulin BMI
## 28 1 Glucose BloodPressure SkinThickness Insulin BMI
## 28 2 Glucose BloodPressure SkinThickness Insulin BMI
## 28 3 Glucose BloodPressure SkinThickness Insulin BMI
## 28 4 Glucose BloodPressure SkinThickness Insulin BMI
## 28 5 Glucose BloodPressure SkinThickness Insulin BMI
## 29 1 Glucose BloodPressure SkinThickness Insulin BMI
## 29 2 Glucose BloodPressure SkinThickness Insulin BMI
## 29 3 Glucose BloodPressure SkinThickness Insulin BMI
## 29 4 Glucose BloodPressure SkinThickness Insulin BMI
## 29 5 Glucose BloodPressure SkinThickness Insulin BMI
## 30 1 Glucose BloodPressure SkinThickness Insulin BMI
## 30 2 Glucose BloodPressure SkinThickness Insulin BMI
## 30 3 Glucose BloodPressure SkinThickness Insulin BMI
## 30 4 Glucose BloodPressure SkinThickness Insulin BMI
## 30 5 Glucose BloodPressure SkinThickness Insulin BMI
## 31 1 Glucose BloodPressure SkinThickness Insulin BMI
## 31 2 Glucose BloodPressure SkinThickness Insulin BMI
## 31 3 Glucose BloodPressure SkinThickness Insulin BMI
## 31 4 Glucose BloodPressure SkinThickness Insulin BMI
## 31 5 Glucose BloodPressure SkinThickness Insulin BMI
## 32 1 Glucose BloodPressure SkinThickness Insulin BMI
## 32 2 Glucose BloodPressure SkinThickness Insulin BMI
## 32 3 Glucose BloodPressure SkinThickness Insulin BMI
## 32 4 Glucose BloodPressure SkinThickness Insulin BMI
## 32 5 Glucose BloodPressure SkinThickness Insulin BMI
## 33 1 Glucose BloodPressure SkinThickness Insulin BMI
## 33 2 Glucose BloodPressure SkinThickness Insulin BMI
## 33 3 Glucose BloodPressure SkinThickness Insulin BMI
## 33 4 Glucose BloodPressure SkinThickness Insulin BMI
## 33 5 Glucose BloodPressure SkinThickness Insulin BMI
## 34 1 Glucose BloodPressure SkinThickness Insulin BMI
## 34 2 Glucose BloodPressure SkinThickness Insulin BMI
## 34 3 Glucose BloodPressure SkinThickness Insulin BMI
## 34 4 Glucose BloodPressure SkinThickness Insulin BMI
## 34 5 Glucose BloodPressure SkinThickness Insulin BMI
## 35 1 Glucose BloodPressure SkinThickness Insulin BMI
## 35 2 Glucose BloodPressure SkinThickness Insulin BMI
## 35 3 Glucose BloodPressure SkinThickness Insulin BMI
## 35 4 Glucose BloodPressure SkinThickness Insulin BMI
## 35 5 Glucose BloodPressure SkinThickness Insulin BMI
## 36 1 Glucose BloodPressure SkinThickness Insulin BMI
## 36 2 Glucose BloodPressure SkinThickness Insulin BMI
## 36 3 Glucose BloodPressure SkinThickness Insulin BMI
## 36 4 Glucose BloodPressure SkinThickness Insulin BMI
## 36 5 Glucose BloodPressure SkinThickness Insulin BMI
## 37 1 Glucose BloodPressure SkinThickness Insulin BMI
## 37 2 Glucose BloodPressure SkinThickness Insulin BMI
## 37 3 Glucose BloodPressure SkinThickness Insulin BMI
## 37 4 Glucose BloodPressure SkinThickness Insulin BMI
## 37 5 Glucose BloodPressure SkinThickness Insulin BMI
## 38 1 Glucose BloodPressure SkinThickness Insulin BMI
## 38 2 Glucose BloodPressure SkinThickness Insulin BMI
## 38 3 Glucose BloodPressure SkinThickness Insulin BMI
## 38 4 Glucose BloodPressure SkinThickness Insulin BMI
## 38 5 Glucose BloodPressure SkinThickness Insulin BMI
## 39 1 Glucose BloodPressure SkinThickness Insulin BMI
## 39 2 Glucose BloodPressure SkinThickness Insulin BMI
## 39 3 Glucose BloodPressure SkinThickness Insulin BMI
## 39 4 Glucose BloodPressure SkinThickness Insulin BMI
## 39 5 Glucose BloodPressure SkinThickness Insulin BMI
## 40 1 Glucose BloodPressure SkinThickness Insulin BMI
## 40 2 Glucose BloodPressure SkinThickness Insulin BMI
## 40 3 Glucose BloodPressure SkinThickness Insulin BMI
## 40 4 Glucose BloodPressure SkinThickness Insulin BMI
## 40 5 Glucose BloodPressure SkinThickness Insulin BMI
## 41 1 Glucose BloodPressure SkinThickness Insulin BMI
## 41 2 Glucose BloodPressure SkinThickness Insulin BMI
## 41 3 Glucose BloodPressure SkinThickness Insulin BMI
## 41 4 Glucose BloodPressure SkinThickness Insulin BMI
## 41 5 Glucose BloodPressure SkinThickness Insulin BMI
## 42 1 Glucose BloodPressure SkinThickness Insulin BMI
## 42 2 Glucose BloodPressure SkinThickness Insulin BMI
## 42 3 Glucose BloodPressure SkinThickness Insulin BMI
## 42 4 Glucose BloodPressure SkinThickness Insulin BMI
## 42 5 Glucose BloodPressure SkinThickness Insulin BMI
## 43 1 Glucose BloodPressure SkinThickness Insulin BMI
## 43 2 Glucose BloodPressure SkinThickness Insulin BMI
## 43 3 Glucose BloodPressure SkinThickness Insulin BMI
## 43 4 Glucose BloodPressure SkinThickness Insulin BMI
## 43 5 Glucose BloodPressure SkinThickness Insulin BMI
## 44 1 Glucose BloodPressure SkinThickness Insulin BMI
## 44 2 Glucose BloodPressure SkinThickness Insulin BMI
## 44 3 Glucose BloodPressure SkinThickness Insulin BMI
## 44 4 Glucose BloodPressure SkinThickness Insulin BMI
## 44 5 Glucose BloodPressure SkinThickness Insulin BMI
## 45 1 Glucose BloodPressure SkinThickness Insulin BMI
## 45 2 Glucose BloodPressure SkinThickness Insulin BMI
## 45 3 Glucose BloodPressure SkinThickness Insulin BMI
## 45 4 Glucose BloodPressure SkinThickness Insulin BMI
## 45 5 Glucose BloodPressure SkinThickness Insulin BMI
## 46 1 Glucose BloodPressure SkinThickness Insulin BMI
## 46 2 Glucose BloodPressure SkinThickness Insulin BMI
## 46 3 Glucose BloodPressure SkinThickness Insulin BMI
## 46 4 Glucose BloodPressure SkinThickness Insulin BMI
## 46 5 Glucose BloodPressure SkinThickness Insulin BMI
## 47 1 Glucose BloodPressure SkinThickness Insulin BMI
## 47 2 Glucose BloodPressure SkinThickness Insulin BMI
## 47 3 Glucose BloodPressure SkinThickness Insulin BMI
## 47 4 Glucose BloodPressure SkinThickness Insulin BMI
## 47 5 Glucose BloodPressure SkinThickness Insulin BMI
## 48 1 Glucose BloodPressure SkinThickness Insulin BMI
## 48 2 Glucose BloodPressure SkinThickness Insulin BMI
## 48 3 Glucose BloodPressure SkinThickness Insulin BMI
## 48 4 Glucose BloodPressure SkinThickness Insulin BMI
## 48 5 Glucose BloodPressure SkinThickness Insulin BMI
## 49 1 Glucose BloodPressure SkinThickness Insulin BMI
## 49 2 Glucose BloodPressure SkinThickness Insulin BMI
## 49 3 Glucose BloodPressure SkinThickness Insulin BMI
## 49 4 Glucose BloodPressure SkinThickness Insulin BMI
## 49 5 Glucose BloodPressure SkinThickness Insulin BMI
## 50 1 Glucose BloodPressure SkinThickness Insulin BMI
## 50 2 Glucose BloodPressure SkinThickness Insulin BMI
## 50 3 Glucose BloodPressure SkinThickness Insulin BMI
## 50 4 Glucose BloodPressure SkinThickness Insulin BMI
## 50 5 Glucose BloodPressure SkinThickness Insulin BMIdf_norm_nob <- complete(imp_norm_nob)
head(df_norm_nob)## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35.00000 282.7451 33.6
## 2 1 85 66 29.00000 130.6682 26.6
## 3 8 183 64 10.33126 253.1314 23.3
## 4 1 89 66 23.00000 94.0000 28.1
## 5 0 137 40 35.00000 168.0000 43.1
## 6 5 116 74 20.24396 -130.7138 25.6
## DiabetesPedigreeFunction Age Outcome
## 1 0.627 50 1
## 2 0.351 31 0
## 3 0.672 32 1
## 4 0.167 21 0
## 5 2.288 33 1
## 6 0.201 30 0Imputación con norm
imp_norm <- mice(df, method = 'norm', m = 5, maxit = 50, seed = 500)##
## iter imp variable
## 1 1 Glucose BloodPressure SkinThickness Insulin BMI
## 1 2 Glucose BloodPressure SkinThickness Insulin BMI
## 1 3 Glucose BloodPressure SkinThickness Insulin BMI
## 1 4 Glucose BloodPressure SkinThickness Insulin BMI
## 1 5 Glucose BloodPressure SkinThickness Insulin BMI
## 2 1 Glucose BloodPressure SkinThickness Insulin BMI
## 2 2 Glucose BloodPressure SkinThickness Insulin BMI
## 2 3 Glucose BloodPressure SkinThickness Insulin BMI
## 2 4 Glucose BloodPressure SkinThickness Insulin BMI
## 2 5 Glucose BloodPressure SkinThickness Insulin BMI
## 3 1 Glucose BloodPressure SkinThickness Insulin BMI
## 3 2 Glucose BloodPressure SkinThickness Insulin BMI
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## 50 1 Glucose BloodPressure SkinThickness Insulin BMI
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## 50 3 Glucose BloodPressure SkinThickness Insulin BMI
## 50 4 Glucose BloodPressure SkinThickness Insulin BMI
## 50 5 Glucose BloodPressure SkinThickness Insulin BMIdf_norm <- complete(imp_norm)
head(df_norm)## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35.00000 456.01723 33.6
## 2 1 85 66 29.00000 60.66381 26.6
## 3 8 183 64 16.75604 186.58545 23.3
## 4 1 89 66 23.00000 94.00000 28.1
## 5 0 137 40 35.00000 168.00000 43.1
## 6 5 116 74 23.76877 299.81798 25.6
## DiabetesPedigreeFunction Age Outcome
## 1 0.627 50 1
## 2 0.351 31 0
## 3 0.672 32 1
## 4 0.167 21 0
## 5 2.288 33 1
## 6 0.201 30 0Visualización de antes y después de imputación con pmm
library(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ stringr 1.5.1
## ✔ forcats 1.0.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks mice::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(hrbrthemes)## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrowlibrary(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combineggp1 <- ggplot(data.frame(value=df$Glucose), aes(x=value)) +
geom_histogram(fill="#049CD8", color="#E52521", alpha=0.9, bins=30) +
ggtitle("Original Glucose Data") +
xlab('Glucose') + ylab('Frequency') +
theme_ipsum() +
theme(plot.title = element_text(size=15))
ggp2 <- ggplot(data.frame(value=df_pmm$Glucose), aes(x=value)) +
geom_histogram(fill="#43B047", color="#FBD000", alpha=0.9, bins=30) +
ggtitle("Glucose Data After PMM Imputation") +
xlab('Glucose') + ylab('Frequency') +
theme_ipsum() +
theme(plot.title = element_text(size=15))
grid.arrange(ggp1, ggp2, ncol = 2)## Warning: Removed 5 rows containing non-finite values (`stat_bin()`).
Se evidencia una distribución similar antes y después del método de imputación pmm. Por lo tanto nos quedaremos con este Dataframe para continuar con la fase de detección y tratamiento de valores atípicos
Detección Valores Atípicos-Percentiles
for(variable in names(df_pmm)) {
if(is.numeric(df_pmm[[variable]])) {
lower_bound <- quantile(df_pmm[[variable]], 0.025, na.rm = TRUE)
upper_bound <- quantile(df_pmm[[variable]], 0.975, na.rm = TRUE)
outlier_indices <- which(df_pmm[[variable]] < lower_bound | df_pmm[[variable]] > upper_bound)
cat("Percentiles para", variable, ":\n")
cat("Percentiles:", lower_bound, upper_bound, "\n")
cat("Número de valores atípicos:", length(outlier_indices), "\n")
cat("Índices de valores atípicos:", outlier_indices, "\n\n")
}
}## Percentiles para Pregnancies :
## Percentiles: 0 12
## Número de valores atípicos: 14
## Índices de valores atípicos: 29 73 87 89 160 275 299 324 358 456 519 636 692 745
##
## Percentiles para Glucose :
## Percentiles: 73.175 189
## Número de valores atípicos: 39
## Índices de valores atípicos: 9 23 48 56 63 77 98 147 183 184 186 207 229 259 261 274 320 353 360 400 404 409 462 490 499 503 521 538 562 580 590 597 618 662 673 676 681 738 760
##
## Percentiles para BloodPressure :
## Percentiles: 50 97.65
## Número de valores atípicos: 37
## Índices de valores atípicos: 5 19 44 76 81 85 98 107 118 126 178 188 208 294 304 347 358 363 370 380 388 406 441 465 550 576 577 598 600 659 663 673 674 692 698 708 742
##
## Percentiles para SkinThickness :
## Percentiles: 11 48
## Número de valores atípicos: 32
## Índices de valores atípicos: 23 45 56 58 87 100 121 124 151 212 255 274 276 280 314 333 340 383 410 435 446 467 513 520 533 540 580 592 597 672 694 729
##
## Percentiles para Insulin :
## Percentiles: 25.7 480
## Número de valores atípicos: 39
## Índices de valores atípicos: 9 12 14 48 53 63 109 112 150 154 183 187 229 238 248 287 353 405 410 446 456 489 503 511 567 582 585 598 614 618 622 630 656 662 676 695 712 754 761
##
## Percentiles para BMI :
## Percentiles: 21 46.665
## Número de valores atípicos: 39
## Índices de valores atípicos: 10 34 51 58 69 85 91 93 98 100 121 126 155 156 178 194 204 240 248 289 304 317 336 379 382 419 439 446 454 461 520 527 591 608 618 640 674 682 747
##
## Percentiles para DiabetesPedigreeFunction :
## Percentiles: 0.123525 1.31345
## Número de valores atípicos: 40
## Índices de valores atípicos: 5 13 40 46 59 82 136 147 148 150 181 188 226 229 230 244 258 260 269 309 326 330 331 371 372 396 431 446 528 561 568 594 599 603 622 623 634 662 705 715
##
## Percentiles para Age :
## Percentiles: 21 63
## Número de valores atípicos: 17
## Índices de valores atípicos: 124 149 222 295 363 364 454 460 490 496 510 538 553 667 675 685 760
##
## Percentiles para Outcome :
## Percentiles: 0 1
## Número de valores atípicos: 0
## Índices de valores atípicos:Por medio de los percentalies se evidencia la cantidad de valores atipicos fuera de estos rangos intercuartiles y sus indices. Esto se realiza en cada una de las variables.
Hampel
library(outliers)
for(variable in names(df_pmm)) {
if(variable == "Outcome") { # Saltar la variable 'Outcome
next
}
if(is.numeric(df_pmm[[variable]])) {
median_val <- median(df_pmm[[variable]], na.rm = TRUE)
mad_val <- mad(df_pmm[[variable]], constant = 1, na.rm = TRUE)
lower_bound <- median_val - 3 * mad_val
upper_bound <- median_val + 3 * mad_val
outlier_indices <- which(df_pmm[[variable]] < lower_bound | df_pmm[[variable]] > upper_bound)
cat("Filtro de Hampel para", variable, ":\n")
cat("Límites:", lower_bound, upper_bound, "\n")
cat("Número de valores atípicos:", length(outlier_indices), "\n")
cat("Valores atípicos:", df_pmm[outlier_indices, variable], "\n\n")
}
}## Filtro de Hampel para Pregnancies :
## Límites: -3 9
## Número de valores atípicos: 58
## Valores atípicos: 10 10 10 11 10 13 10 11 13 13 15 10 17 11 12 10 12 11 10 13 10 14 10 13 10 12 13 12 12 12 14 10 10 10 12 13 10 11 11 10 12 11 11 10 13 11 11 10 10 10 13 10 10 10 11 13 12 10
##
## Filtro de Hampel para Glucose :
## Límites: 57 177
## Número de valores atípicos: 55
## Valores atípicos: 183 197 189 196 180 180 187 44 188 179 194 181 196 184 179 197 181 179 184 193 191 182 194 179 180 178 196 189 193 197 184 181 189 180 194 195 198 180 186 187 189 198 197 188 183 181 183 179 199 195 56 187 187 181 190
##
## Filtro de Hampel para BloodPressure :
## Límites: 48 96
## Número de valores atípicos: 32
## Valores atípicos: 40 30 110 44 108 122 30 110 98 104 98 46 100 108 102 100 100 104 98 110 44 44 24 38 106 106 106 100 114 44 46 44
##
## Filtro de Hampel para SkinThickness :
## Límites: 8 50
## Número de valores atípicos: 15
## Valores atípicos: 60 54 51 56 7 54 7 52 60 63 7 52 99 60 7
##
## Filtro de Hampel para Insulin :
## Límites: -42 282
## Número de valores atípicos: 78
## Valores atípicos: 543 545 846 342 480 300 342 304 415 440 495 325 325 284 485 285 370 392 495 285 318 300 478 744 330 370 680 285 680 402 375 293 480 545 360 330 480 325 293 465 325 285 321 415 540 579 310 474 540 370 285 328 480 375 321 543 326 330 370 600 321 293 321 440 540 846 318 510 321 480 335 387 291 392 480 480 510 318
##
## Filtro de Hampel para BMI :
## Límites: 18.5 46.1
## Número de valores atípicos: 28
## Valores atípicos: 46.8 46.8 48.8 46.7 49.7 53.2 55 47.9 50 67.1 52.3 46.2 18.4 52.3 52.9 47.9 48.3 18.2 18.2 59.4 46.5 18.2 46.2 46.8 57.3 49.6 49.3 46.3
##
## Filtro de Hampel para DiabetesPedigreeFunction :
## Límites: -0.13 0.875
## Número de valores atípicos: 78
## Valores atípicos: 2.288 1.441 0.966 1.39 1.893 0.962 1.781 1.222 0.93 1.114 1.4 1.189 0.956 1.321 0.905 1.224 1.072 2.329 1.318 1.213 0.926 1.353 0.997 0.933 1.101 1.136 0.881 1.224 1.391 1.127 1.476 0.932 0.893 0.962 2.137 1.731 1.021 0.947 1.268 0.949 1.6 0.944 1.191 1.076 1.095 1.138 0.955 2.42 1.001 1.022 1.159 1.144 0.968 0.917 1.251 0.968 1.034 0.892 1.154 0.925 1.699 1.258 0.878 1.282 1.698 1.461 1.162 1.292 1.394 0.88 0.886 0.904 0.905 0.97 1.174 1.096 1.182 1.057
##
## Filtro de Hampel para Age :
## Límites: 8 50
## Número de valores atípicos: 81
## Valores atípicos: 53 54 57 59 51 51 57 60 56 54 58 54 60 61 69 62 55 65 60 55 57 52 60 66 61 51 51 63 52 57 52 51 65 58 59 57 63 65 67 58 58 55 72 62 51 81 59 63 58 67 66 55 64 58 53 51 60 67 56 53 66 58 54 62 62 52 52 54 51 54 70 68 53 69 52 56 52 53 52 66 63Tras aplicar el filtro de Hampel a cada variable en tu conjunto de datos df_pmm, se identificaron varios valores atípicos en todas las variables con limites establecidos y evidenciados en cada variable.
Grubbs
library(outliers)
for(variable in names(df_pmm)) {
if(variable == "Outcome") {
next
}
if(is.numeric(df_pmm[[variable]])) {
data_variable <- na.omit(df_pmm[[variable]])
cat("Grubbs Test for", variable, ":\n")
print(grubbs.test(data_variable))
}
}## Grubbs Test for Pregnancies :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 3.9040, U = 0.9801, p-value = 0.03367
## alternative hypothesis: highest value 17 is an outlier
##
## Grubbs Test for Glucose :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 2.53239, U = 0.99163, p-value = 1
## alternative hypothesis: lowest value 44 is an outlier
##
## Grubbs Test for BloodPressure :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 4.01506, U = 0.97895, p-value = 0.02096
## alternative hypothesis: highest value 122 is an outlier
##
## Grubbs Test for SkinThickness :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 6.76548, U = 0.94025, p-value = 2.505e-09
## alternative hypothesis: highest value 99 is an outlier
##
## Grubbs Test for Insulin :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 6.0952, U = 0.9515, p-value = 2.637e-07
## alternative hypothesis: highest value 846 is an outlier
##
## Grubbs Test for BMI :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 5.01023, U = 0.96723, p-value = 0.0001693
## alternative hypothesis: highest value 67.1 is an outlier
##
## Grubbs Test for DiabetesPedigreeFunction :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 5.87973, U = 0.95487, p-value = 1.056e-06
## alternative hypothesis: highest value 2.42 is an outlier
##
## Grubbs Test for Age :
##
## Grubbs test for one outlier
##
## data: data_variable
## G = 4.06107, U = 0.97847, p-value = 0.01716
## alternative hypothesis: highest value 81 is an outlierLa prueba de Grubbs permite detectar si el valor más alto o más bajo de un conjunto de datos es un valor atípico. Esta prueba detecta un valor atipico a la vez con hipotesis nula y alternavita. En los resultados se evidencia para cada variable el resultado y la hioptesis aceptada.
Dixon test
for(variable in names(df_pmm)) {
if(is.numeric(df_pmm[[variable]])) {
# Tomar las primeras 20 observaciones, excluyendo NA
data_variable <- head(na.omit(df_pmm[[variable]]), 20)
cat("Prueba de Dixon para", variable, ":\n")
# Prueba para el valor más bajo
test_low <- dixon.test(data_variable, opposite = TRUE)
cat("Prueba para el valor más bajo:\n")
print(test_low)
# Prueba para el valor más alto
test_high <- dixon.test(data_variable)
cat("Prueba para el valor más alto:\n")
print(test_high)
} else {
cat("No suficientes datos para realizar la prueba en", variable, "\n")
}
}## Prueba de Dixon para Pregnancies :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.1, p-value = 0.2656
## alternative hypothesis: lowest value 0 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0, p-value < 2.2e-16
## alternative hypothesis: highest value 10 is an outlier
##
## Prueba de Dixon para Glucose :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.10476, p-value = 0.2898
## alternative hypothesis: lowest value 78 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.12963, p-value = 0.4255
## alternative hypothesis: highest value 197 is an outlier
##
## Prueba de Dixon para BloodPressure :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.26087, p-value = 0.7948
## alternative hypothesis: highest value 96 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.37037, p-value = 0.2901
## alternative hypothesis: lowest value 30 is an outlier
##
## Prueba de Dixon para SkinThickness :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.11538, p-value = 0.3464
## alternative hypothesis: lowest value 19 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.08, p-value = 0.1749
## alternative hypothesis: highest value 47 is an outlier
##
## Prueba de Dixon para Insulin :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.068093, p-value = 0.1315
## alternative hypothesis: lowest value 29 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.38747, p-value = 0.2372
## alternative hypothesis: highest value 846 is an outlier
##
## Prueba de Dixon para BMI :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.125, p-value = 0.4
## alternative hypothesis: lowest value 23.3 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.16667, p-value = 0.6399
## alternative hypothesis: highest value 46.8 is an outlier
##
## Prueba de Dixon para DiabetesPedigreeFunction :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.061338, p-value = 0.1112
## alternative hypothesis: lowest value 0.134 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.7619, p-value < 2.2e-16
## alternative hypothesis: highest value 2.288 is an outlier
##
## Prueba de Dixon para Age :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.24242, p-value = 0.9027
## alternative hypothesis: lowest value 21 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0.16667, p-value = 0.6399
## alternative hypothesis: highest value 59 is an outlier
##
## Prueba de Dixon para Outcome :
## Prueba para el valor más bajo:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0, p-value < 2.2e-16
## alternative hypothesis: highest value 1 is an outlier
##
## Prueba para el valor más alto:
##
## Dixon test for outliers
##
## data: data_variable
## Q = 0, p-value < 2.2e-16
## alternative hypothesis: lowest value 0 is an outlierAl igual que la prueba de Grubbs, la prueba de Dixon se utiliza para comprobar si un único valor bajo o alto es un valor atípico. En este caso se imprime el resultado para cada uno de los valores, el mas alto y mas bajo, de cada una de las variables.
Rosner test
library(EnvStats)##
## Attaching package: 'EnvStats'## The following objects are masked from 'package:stats':
##
## predict, predict.lmfor(variable in names(df_pmm)) {
if(variable == "Outcome") {
next
}
if(is.numeric(df_pmm[[variable]])) {
data_variable <- na.omit(df_pmm[[variable]])
if(length(data_variable) >= 20) {
cat("\nRosner Test for", variable, ":\n")
test <- tryCatch({
rosnerTest(data_variable, k = 3)
}, error = function(e) {
cat("Error: ", e$message, "\n")
return(NULL)
})
if(!is.null(test)) {
outliers_detected <- test$all.stats$Outlier
if(any(outliers_detected)) {
cat("Se detectaron valores atipicos \n")
print(test$all.stats[outliers_detected, ])
} else {
cat("No se detectaron valores atipicos \n")
}
}
} else {
cat("No suficientes datos para realizar la prueba en", variable, "\n")
}
}
}##
## Rosner Test for Pregnancies :
## No se detectaron valores atipicos
##
## Rosner Test for Glucose :
## No se detectaron valores atipicos
##
## Rosner Test for BloodPressure :
## Se detectaron valores atipicos
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 72.41536 12.34967 122 107 4.015058 3.974092 TRUE
##
## Rosner Test for SkinThickness :
## Se detectaron valores atipicos
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 28.82161 10.373 99 580 6.765485 3.974092 TRUE
##
## Rosner Test for Insulin :
## Se detectaron valores atipicos
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 148.5651 114.4233 846 14 6.095217 3.974092 TRUE
## 2 1 147.6558 111.6868 846 662 6.252699 3.973762 TRUE
## 3 2 146.7441 108.8666 744 229 5.486128 3.973432 TRUE
##
## Rosner Test for BMI :
## Se detectaron valores atipicos
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 32.46745 6.912365 67.1 178 5.010232 3.974092 TRUE
##
## Rosner Test for DiabetesPedigreeFunction :
## Se detectaron valores atipicos
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 0.4718763 0.3313286 2.420 446 5.879733 3.974092 TRUE
## 2 1 0.4693364 0.3239768 2.329 229 5.740114 3.973762 TRUE
## 3 2 0.4669086 0.3171301 2.288 5 5.742410 3.973432 TRUE
##
## Rosner Test for Age :
## Se detectaron valores atipicos
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 33.24089 11.76023 81 460 4.061069 3.974092 TRUEEsta prueba Ronser se utiliza para detectar varios valores atípicos a la vez. En los resultados se organizó para visualizar cuantos y cuales son los valores atipicos en cada variable.
Datos Atípicos-Eliminación
df_pmm_without_outliers <- df_pmm
rows_to_remove <- rep(FALSE, nrow(df_pmm_without_outliers))
for(variable in names(df_pmm_without_outliers)) {
if(variable != "Outcome" && is.numeric(df_pmm_without_outliers[[variable]])) {
qnt <- quantile(df_pmm_without_outliers[[variable]], probs=c(.05, .95), na.rm = TRUE)
H <- 1.5 * IQR(df_pmm_without_outliers[[variable]], na.rm = TRUE)
outlier_indices <- which(df_pmm_without_outliers[[variable]] < (qnt[1] - H) | df_pmm_without_outliers[[variable]] > (qnt[2] + H))
rows_to_remove[outlier_indices] <- TRUE
}
}
df_pmm_without_outliers <- df_pmm_without_outliers[!rows_to_remove, ]
par(mfrow = c(1, 2))
boxplot(df$SkinThickness, main = "", ylab = "SkinThickness before")
boxplot(df_pmm_without_outliers$SkinThickness, main = "", ylab = "SkinThickness after")
Al evaluar el boxplot actual y compararlo con el previo para la misma variable, notamos una reducción en la cantidad de valores atípicos. No obstante, persisten algunos valores atípicos que están próximos a los límites establecidos. Este fenómeno puede atribuirse a las técnicas de detección de atípicos empleadas, las cuales tienden a descartar únicamente aquellos valores que exceden los umbrales definidos. Los valores atípicos menos extremos, que caen cerca de dichos umbrales, pueden permanecer en el conjunto de datos debido a la naturaleza conservadora de los criterios aplicados para su identificación. Es una buena forma de visualizarlo.
df_capped <- df_pmm
for(variable in names(df_capped)) {
if(variable != "Outcome" && is.numeric(df_capped[[variable]])) {
caps <- quantile(df_capped[[variable]], probs=c(.05, .95), na.rm = TRUE)
df_capped[[variable]][df_capped[[variable]] < caps[1]] <- caps[1]
df_capped[[variable]][df_capped[[variable]] > caps[2]] <- caps[2]
}
}
# Para confirmar que se elminaron los valores atípicos se visualiza un boxplot de la variable Pregnancies y debe estar sin valores atipicos.
boxplot(df_capped$Pregnancies, main = "", ylab = "Pregnancies")
El método de Capping se aplicó para sustituir valores fuera del rango intercuartílico. El éxito de esta técnica se confirma mediante un boxplot, donde ya no se observan valores atípicos en la variable analizada. Las pruebas de detección de valores atípicos pueden realizarse nuevamente para una validación adicional.
Imputación Atípicos por PPM
df_pmm_2 <- df_pmm # Crear una copia para la imputación
for(variable in names(df_pmm_2)) {
if(variable != "Outcome" && is.numeric(df_pmm_2[[variable]])) {
# Identificar valores atípicos por percentiles
qnt <- quantile(df_pmm_2[[variable]], probs=c(.05, .95), na.rm = TRUE)
H <- 1.5 * IQR(df_pmm_2[[variable]], na.rm = TRUE)
outlier_indices <- which(df_pmm_2[[variable]] < (qnt[1] - H) | df_pmm_2[[variable]] > (qnt[2] + H))
# Sustituir valores atípicos por NA
df_pmm_2[[variable]][outlier_indices] <- NA
}
}
# Aplicar imputación
imp <- mice(df_pmm_2, method = 'pmm', m = 5, maxit = 50, seed = 500)##
## iter imp variable
## 1 1 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
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## 48 4 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 48 5 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 49 1 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 49 2 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 49 3 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 49 4 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 49 5 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 50 1 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 50 2 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 50 3 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 50 4 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction
## 50 5 BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunctiondf_imputed <- complete(imp)
# Para confirmar que se elminaron los valores atípicos se visualiza un boxplot de la variable SkinThickness y debe estar sin valores atipicos.
boxplot(df_capped$SkinThickness, main = "", ylab = "Skin Thickness")
El código identifica valores atípicos en el conjunto de datos utilizando percentiles y luego los sustituye por NA. A continuación, se aplica el método de imputación PMM para reemplazar estos NA, resultando en un nuevo conjunto de datos con menos valores atípicos. La eficacia de este proceso se verifica graficando un boxplot de una de las variables.