options(scipen = 999999)
library(lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerlibrary(equatiomatic)
library(mlbench)
data(BostonHousing)
modelo_boston<-lm(formula = medv~.,data=BostonHousing)
extract_eq(modelo_boston,wrap = TRUE)\[ \begin{aligned} \operatorname{medv} &= \alpha + \beta_{1}(\operatorname{crim}) + \beta_{2}(\operatorname{zn}) + \beta_{3}(\operatorname{indus})\ + \\ &\quad \beta_{4}(\operatorname{chas}_{\operatorname{1}}) + \beta_{5}(\operatorname{nox}) + \beta_{6}(\operatorname{rm}) + \beta_{7}(\operatorname{age})\ + \\ &\quad \beta_{8}(\operatorname{dis}) + \beta_{9}(\operatorname{rad}) + \beta_{10}(\operatorname{tax}) + \beta_{11}(\operatorname{ptratio})\ + \\ &\quad \beta_{12}(\operatorname{b}) + \beta_{13}(\operatorname{lstat}) + \epsilon \end{aligned} \]
coeftest(modelo_boston)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.45948839 5.10345881 7.1441 0.0000000000032834 ***
## crim -0.10801136 0.03286499 -3.2865 0.0010868 **
## zn 0.04642046 0.01372746 3.3816 0.0007781 ***
## indus 0.02055863 0.06149569 0.3343 0.7382881
## chas1 2.68673382 0.86157976 3.1184 0.0019250 **
## nox -17.76661123 3.81974371 -4.6513 0.0000042456438076 ***
## rm 3.80986521 0.41792525 9.1161 < 0.00000000000000022 ***
## age 0.00069222 0.01320978 0.0524 0.9582293
## dis -1.47556685 0.19945473 -7.3980 0.0000000000006013 ***
## rad 0.30604948 0.06634644 4.6129 0.0000050705290227 ***
## tax -0.01233459 0.00376054 -3.2800 0.0011116 **
## ptratio -0.95274723 0.13082676 -7.2825 0.0000000000013088 ***
## b 0.00931168 0.00268596 3.4668 0.0005729 ***
## lstat -0.52475838 0.05071528 -10.3471 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(stargazer)
#Data para la predicción X'm
X_m<-data.frame(crim=0.05,zn=15,indus=2,chas="0",nox=0.004,
rm=5,age=85,dis=5.56,rad=2,tax=300,ptratio=17,b=0.00005,lstat=5)
# Intervalos de Confianza del 95% y del 99%
confidense<-c(0.95,0.99)
#Predicción usando predict
predict(object = modelo_boston,
newdata = X_m,
interval = "prediction",
level = confidense,
se.fit =TRUE)->predicciones
rownames(predicciones$fit)<-as.character(confidense*100)
colnames(predicciones$fit)<-c("Ym","Li","Ls")
stargazer(predicciones$fit,
title = "Pronósticos e intervalos de confianza",
type = "html")##
## <table style="text-align:center"><caption><strong>Pronósticos e intervalos de confianza</strong></caption>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>Ym</td><td>Li</td><td>Ls</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">95</td><td>26.116</td><td>15.558</td><td>36.673</td></tr>
## <tr><td style="text-align:left">99</td><td>26.116</td><td>12.221</td><td>40.010</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr></table>library(forecast)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(kableExtra)
#Data para la predicción X'm
X_m<-data.frame(crim=0.05,zn=15,indus=2,chas="0",nox=0.004,
rm=5,age=85,dis=5.56,rad=2,tax=300,ptratio=17,b=0.00005,lstat=5)
#Nivel de confianza para el intervalo de confianza
confidense<-c(0.95,0.99)
#Realizando el pronóstico con forecast
pronosticos<-forecast(object = modelo_boston,
level = confidense,
newdata = X_m,ts = FALSE)
kable(pronosticos,
caption = "Pronóstico e intervalos de confianza:",
digits = 2,format = "html")| Point Forecast | Lo 95 | Hi 95 | Lo 99 | Hi 99 |
|---|---|---|---|---|
| 26.12 | 15.56 | 36.67 | 12.22 | 40.01 |
#Funciones para la desigualdad de Theil.
Um<-function(pronosticado,observado){
library(DescTools)
((mean(pronosticado)-mean(observado))^2)/MSE(pronosticado,observado)
}
#Variance Proportion.
Us<-function(pronosticado,observado){
library(DescTools)
((sd(pronosticado)-sd(observado))^2)/MSE(pronosticado,observado)
}
#Covariance Proportion.
Uc<-function(pronosticado,observado){
library(DescTools)
(2*(1-cor(pronosticado,observado))*sd(pronosticado)*sd(observado))/MSE(pronosticado,observado)}
#Coeficiente U de Theil.
THEIL_U<-function(pronosticado,observado){
library(DescTools)
RMSE(pronosticado,observado)/(sqrt(mean(pronosticado^2))+sqrt(mean(observado^2)))
}Usando como datos de entrenamiento el 80% de los datos originales.
Considere 1000 replicas.
Utilice la semilla aleatoria de 50.
#Script de Simulación versión 1.
options(scipen = 999999)
library(dplyr) ##
## Attaching package: 'dplyr'## The following object is masked from 'package:kableExtra':
##
## group_rows## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(DescTools) ##
## Attaching package: 'DescTools'## The following objects are masked from 'package:caret':
##
## MAE, RMSE## The following object is masked from 'package:forecast':
##
## BoxCoxlibrary(stargazer)
set.seed(50)
numero_de_muestras<-1000
proporcion_entrenamiento<-0.8
# Creación de las muestras, aquí usamos la variable endógena que definimos con anterioridad
muestras<- BostonHousing$medv %>%
createDataPartition(p = proporcion_entrenamiento,
times = numero_de_muestras,
list = TRUE)
#Listas vacias para la simulación
Modelos_Entrenamiento<-vector(mode = "list",
length = numero_de_muestras)
Pronostico_Prueba<-vector(mode = "list",
length = numero_de_muestras)
Resultados_Performance_data_entrenamiento<-vector(mode = "list",
length = numero_de_muestras)
Resultados_Performance<-vector(mode = "list",
length = numero_de_muestras)
# Estimar los modelos de cada muestra y sus medidas de desempeño predictivo
for(j in 1:numero_de_muestras){
Datos_Entrenamiento<- BostonHousing[muestras[[j]], ]
Datos_Prueba<- BostonHousing[-muestras[[j]], ]
Modelos_Entrenamiento[[j]]<-lm(formula = medv~.,data=Datos_Entrenamiento)
Pronostico_Prueba[[j]]<-Modelos_Entrenamiento[[j]]%>% predict(Datos_Prueba)
Resultados_Performance_data_entrenamiento[[j]]<-data.frame(
R2 = R2(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv),
RMSE = RMSE(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv),
MAE = MAE(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv),
MAPE= MAPE(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv)*100,
THEIL=TheilU(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv,type = 1),
Um=Um(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv),
Us=Us(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv),
Uc=Uc(Modelos_Entrenamiento[[j]]$fitted.values,
Datos_Entrenamiento$medv)
)
Resultados_Performance[[j]]<-data.frame(
R2 = R2(Pronostico_Prueba[[j]], Datos_Prueba$medv),
RMSE = RMSE(Pronostico_Prueba[[j]], Datos_Prueba$medv),
MAE = MAE(Pronostico_Prueba[[j]], Datos_Prueba$medv),
MAPE= MAPE(Pronostico_Prueba[[j]], Datos_Prueba$medv)*100,
THEIL=TheilU(Pronostico_Prueba[[j]], Datos_Prueba$medv,
type = 1), # También se puede usar la función que creamos: THEIL_U
Um=Um(Pronostico_Prueba[[j]], Datos_Prueba$medv),
Us=Us(Pronostico_Prueba[[j]], Datos_Prueba$medv),
Uc=Uc(Pronostico_Prueba[[j]], Datos_Prueba$medv)
)
}bind_rows(Resultados_Performance_data_entrenamiento) %>%
stargazer(title = "Medidas de Performance Datos del Modelo",
type = "html",
digits = 3)##
## <table style="text-align:center"><caption><strong>Medidas de Performance Datos del Modelo</strong></caption>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Statistic</td><td>N</td><td>Mean</td><td>St. Dev.</td><td>Min</td><td>Max</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">R2</td><td>1,000</td><td>0.743</td><td>0.013</td><td>0.713</td><td>0.794</td></tr>
## <tr><td style="text-align:left">RMSE</td><td>1,000</td><td>4.653</td><td>0.141</td><td>4.177</td><td>4.948</td></tr>
## <tr><td style="text-align:left">MAE</td><td>1,000</td><td>3.265</td><td>0.095</td><td>2.905</td><td>3.512</td></tr>
## <tr><td style="text-align:left">MAPE</td><td>1,000</td><td>16.387</td><td>0.464</td><td>14.813</td><td>17.691</td></tr>
## <tr><td style="text-align:left">THEIL</td><td>1,000</td><td>0.096</td><td>0.003</td><td>0.087</td><td>0.102</td></tr>
## <tr><td style="text-align:left">Um</td><td>1,000</td><td>0.000</td><td>0.000</td><td>0</td><td>0</td></tr>
## <tr><td style="text-align:left">Us</td><td>1,000</td><td>0.074</td><td>0.004</td><td>0.058</td><td>0.085</td></tr>
## <tr><td style="text-align:left">Uc</td><td>1,000</td><td>0.928</td><td>0.004</td><td>0.918</td><td>0.945</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr></table>bind_rows(Resultados_Performance) %>%
stargazer(title = "Medidas de Performance Simulación",
type = "html",
digits = 3)##
## <table style="text-align:center"><caption><strong>Medidas de Performance Simulación</strong></caption>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Statistic</td><td>N</td><td>Mean</td><td>St. Dev.</td><td>Min</td><td>Max</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">R2</td><td>1,000</td><td>0.723</td><td>0.056</td><td>0.452</td><td>0.840</td></tr>
## <tr><td style="text-align:left">RMSE</td><td>1,000</td><td>4.862</td><td>0.575</td><td>3.465</td><td>6.961</td></tr>
## <tr><td style="text-align:left">MAE</td><td>1,000</td><td>3.411</td><td>0.281</td><td>2.633</td><td>4.492</td></tr>
## <tr><td style="text-align:left">MAPE</td><td>1,000</td><td>17.197</td><td>1.618</td><td>12.875</td><td>23.137</td></tr>
## <tr><td style="text-align:left">THEIL</td><td>1,000</td><td>0.101</td><td>0.012</td><td>0.073</td><td>0.148</td></tr>
## <tr><td style="text-align:left">Um</td><td>1,000</td><td>0.011</td><td>0.016</td><td>0.000</td><td>0.205</td></tr>
## <tr><td style="text-align:left">Us</td><td>1,000</td><td>0.081</td><td>0.066</td><td>0.00000</td><td>0.333</td></tr>
## <tr><td style="text-align:left">Uc</td><td>1,000</td><td>0.918</td><td>0.066</td><td>0.667</td><td>1.010</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr></table>#Cargamos los datos.
load("C:/Users/Usuario/Downloads/DATA+")
#Estimamos el modelo.
options(scipen = 999999)
library(lmtest)
library(stargazer)
library(equatiomatic)
modelo_cigarrillos <-lm(formula = cigs~.,data=data)
extract_eq(modelo_cigarrillos,wrap = TRUE)\[ \begin{aligned} \operatorname{cigs} &= \alpha + \beta_{1}(\operatorname{educ}) + \beta_{2}(\operatorname{cigpric}) + \beta_{3}(\operatorname{white})\ + \\ &\quad \beta_{4}(\operatorname{age}) + \beta_{5}(\operatorname{income}) + \beta_{6}(\operatorname{restaurn}) + \beta_{7}(\operatorname{lincome})\ + \\ &\quad \beta_{8}(\operatorname{agesq}) + \beta_{9}(\operatorname{lcigpric}) + \epsilon \end{aligned} \]
coeftest(modelo_cigarrillos)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 340.804374604 260.015587269 1.3107 0.190334
## educ -0.494780616 0.168180198 -2.9420 0.003356 **
## cigpric 2.002267667 1.492831189 1.3413 0.180220
## white -0.531051635 1.460721806 -0.3636 0.716287
## age 0.778359013 0.160555612 4.8479 0.0000015001 ***
## income -0.000046194 0.000133491 -0.3460 0.729402
## restaurn -2.644241351 1.129998690 -2.3400 0.019528 *
## lincome 1.404061178 1.708165841 0.8220 0.411340
## agesq -0.009150353 0.001749292 -5.2309 0.0000002158 ***
## lcigpric -115.273464445 85.424315195 -1.3494 0.177585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1SIMULACION 2
#Script de Simulación versión 2
options(scipen = 999999)
library(dplyr)
library(caret)
library(DescTools)
library(stargazer)
set.seed(50)
numero_de_muestras2<-500
proporcion_entrenamiento2<-0.75
# Creación de las muestras, aquí usamos la variable endógena que definimos con anterioridad
muestras2<- data$cigs %>%
createDataPartition(p = proporcion_entrenamiento2,
times = numero_de_muestras2,
list = TRUE)
#Listas vacias para la simulación
Modelos_Entrenamiento2<-vector(mode = "list",
length = numero_de_muestras2)
Pronostico_Prueba2<-vector(mode = "list",
length = numero_de_muestras2)
Resultados_Performance_data_entrenamiento2<-vector(mode = "list",
length = numero_de_muestras2)
Resultados_Performance2<-vector(mode = "list",
length = numero_de_muestras2)
# Estimar los modelos de cada muestra y sus medidas de desempeño predictivo
for(j in 1:numero_de_muestras2){
Datos_Entrenamiento2<- data[muestras2[[j]], ]
Datos_Prueba2<- data[-muestras2[[j]], ]
Modelos_Entrenamiento2[[j]]<-lm(formula = cigs~.,data=Datos_Entrenamiento2)
Pronostico_Prueba2[[j]]<-Modelos_Entrenamiento2[[j]]%>% predict(Datos_Prueba2)
Resultados_Performance_data_entrenamiento2[[j]]<-data.frame(
R2 = R2(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento2$cigs),
RMSE = RMSE(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento2$cigs),
MAE = MAE(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento2$cigs),
MAPE= MAPE(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento2$cigs)*100,
THEIL=TheilU(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento2$cigs,type = 1),
Um=Um(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento$cigs),
Us=Us(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento2$cigs),
Uc=Uc(Modelos_Entrenamiento2[[j]]$fitted.values,
Datos_Entrenamiento2$cigs)
)
Resultados_Performance2[[j]]<-data.frame(
R2 = R2(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs),
RMSE = RMSE(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs),
MAE = MAE(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs),
MAPE= MAPE(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs)*100,
THEIL=TheilU(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs,
type = 1), # También se puede usar la función que creamos: THEIL_U
Um=Um(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs),
Us=Us(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs),
Uc=Uc(Pronostico_Prueba2[[j]], Datos_Prueba2$cigs)
)
}## Warning in mean.default(observado): argument is not numeric or logical:
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## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NA
## Warning in mean.default(observado): argument is not numeric or logical:
## returning NAbind_rows(Resultados_Performance_data_entrenamiento2) %>%
stargazer(title = "Medidas de Performance Datos del Modelo",
type = "html",
digits = 3)##
## <table style="text-align:center"><caption><strong>Medidas de Performance Datos del Modelo</strong></caption>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Statistic</td><td>N</td><td>Mean</td><td>St. Dev.</td><td>Min</td><td>Max</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">R2</td><td>500</td><td>0.058</td><td>0.007</td><td>0.040</td><td>0.083</td></tr>
## <tr><td style="text-align:left">RMSE</td><td>500</td><td>13.304</td><td>0.198</td><td>12.629</td><td>13.828</td></tr>
## <tr><td style="text-align:left">MAE</td><td>500</td><td>10.562</td><td>0.133</td><td>10.068</td><td>10.969</td></tr>
## <tr><td style="text-align:left">MAPE</td><td>500</td><td>Inf.000</td><td></td><td>Inf</td><td>Inf</td></tr>
## <tr><td style="text-align:left">THEIL</td><td>500</td><td>0.522</td><td>0.006</td><td>0.505</td><td>0.541</td></tr>
## <tr><td style="text-align:left">Us</td><td>500</td><td>0.613</td><td>0.020</td><td>0.554</td><td>0.669</td></tr>
## <tr><td style="text-align:left">Uc</td><td>500</td><td>0.389</td><td>0.020</td><td>0.332</td><td>0.448</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr></table>bind_rows(Resultados_Performance2) %>%
stargazer(title = "Medidas de Performance Simulación",
type = "html",
digits = 3)##
## <table style="text-align:center"><caption><strong>Medidas de Performance Simulación</strong></caption>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Statistic</td><td>N</td><td>Mean</td><td>St. Dev.</td><td>Min</td><td>Max</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">R2</td><td>500</td><td>0.039</td><td>0.018</td><td>0.002</td><td>0.099</td></tr>
## <tr><td style="text-align:left">RMSE</td><td>500</td><td>13.479</td><td>0.595</td><td>11.734</td><td>15.361</td></tr>
## <tr><td style="text-align:left">MAE</td><td>500</td><td>10.728</td><td>0.290</td><td>9.834</td><td>11.626</td></tr>
## <tr><td style="text-align:left">MAPE</td><td>500</td><td>Inf.000</td><td></td><td>Inf</td><td>Inf</td></tr>
## <tr><td style="text-align:left">THEIL</td><td>500</td><td>0.528</td><td>0.013</td><td>0.491</td><td>0.564</td></tr>
## <tr><td style="text-align:left">Um</td><td>500</td><td>0.002</td><td>0.003</td><td>0.00000</td><td>0.021</td></tr>
## <tr><td style="text-align:left">Us</td><td>500</td><td>0.597</td><td>0.051</td><td>0.476</td><td>0.751</td></tr>
## <tr><td style="text-align:left">Uc</td><td>500</td><td>0.405</td><td>0.051</td><td>0.253</td><td>0.529</td></tr>
## <tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr></table>