STATS 402 Video Project
Hengyuan Liu
2023-10-26
# install packages
library(corrplot)## corrplot 0.92 loadedlibrary(ggplot2)
library(reshape2)
library(liver)##
## Attaching package: 'liver'## The following object is masked from 'package:base':
##
## transformlibrary(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithslibrary(leaps)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2#read data
data <- data(house)
# Check our NA
length(which(is.na(house), arr.ind=TRUE))## [1] 0EDA
summary(house)## house.age distance.to.MRT stores.number latitude
## Min. : 0.000 Min. : 23.38 Min. : 0.000 Min. :24.93
## 1st Qu.: 9.025 1st Qu.: 289.32 1st Qu.: 1.000 1st Qu.:24.96
## Median :16.100 Median : 492.23 Median : 4.000 Median :24.97
## Mean :17.713 Mean :1083.89 Mean : 4.094 Mean :24.97
## 3rd Qu.:28.150 3rd Qu.:1454.28 3rd Qu.: 6.000 3rd Qu.:24.98
## Max. :43.800 Max. :6488.02 Max. :10.000 Max. :25.01
## longitude unit.price
## Min. :121.5 Min. : 7.60
## 1st Qu.:121.5 1st Qu.: 27.70
## Median :121.5 Median : 38.45
## Mean :121.5 Mean : 37.98
## 3rd Qu.:121.5 3rd Qu.: 46.60
## Max. :121.6 Max. :117.50# Example data frame structure
library(leaflet)
# Create a leaflet map
map <- leaflet(data = house) %>%
addTiles() # You can choose different tilesets with addProviderTiles() if desired
# Add markers to the map based on latitude and longitude
map <- map %>% addMarkers(
lat = ~latitude,
lng = ~longitude,
label = ~unit.price,
popup = ~paste("Median House Price: $", unit.price),
clusterOptions = markerClusterOptions()
)
# Display the map
map# Boxplots for each varaible
boxplot(house$house.age)
boxplot(house$distance.to.MRT)
boxplot(house$stores.number)
boxplot(house$latitude)
boxplot(house$longitude)
boxplot(house$unit.price, main="Box Plot of Unit Price")
library(ggplot2)
# Create a list of plots
plots <- list()
# Loop through each numeric variable and create a density plot with a line
for (i in 1:ncol(house)) {
p <- ggplot(house, aes(x = house[, i])) +
geom_density(color = "blue") +
labs(title = colnames(house)[i], x = "Value", y = "Density")
plots[[i]] <- p
}
# Print the plots one by one
for (i in 1:ncol(house)) {
print(plots[[i]])
}
#Correlation matrix
correlation_matrix <- cor(house)
corrplot(correlation_matrix)
hist(house$unit.price, main="Distribution of Unit Price", xlab="Unit Price")
library(car)## Loading required package: carDatasymbox(house$unit.price, ylab = "unit price", main = "Boxplots for Each Transformations of Unit Price")
transprice <- (house$unit.price)^0.5
hist(transprice, ylab = "unit price", main="Distribution of Unit Price in Square root Transformation", xlab="Unit Price")
names(house)## [1] "house.age" "distance.to.MRT" "stores.number" "latitude"
## [5] "longitude" "unit.price"EDA: Check Assumption
pairs(house)
ggpairs( house )
Model:
\[ Y_{unit.price} = -580.5 -0.02*X_{house.age} -0.0004*X_{distance.to.MRT} + 0.09*X_{stores.number} + 21.83*X_{latitude}+0.365*X_{longitude}\]
model1 <- lm(unit.price^0.5 ~., data=house)
summary(model1)##
## Call:
## lm(formula = unit.price^0.5 ~ ., data = house)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6426 -0.3926 -0.0687 0.3552 4.4799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.805e+02 4.706e+02 -1.233 0.218
## house.age -2.132e-02 2.955e-03 -7.216 2.63e-12 ***
## distance.to.MRT -3.782e-04 5.481e-05 -6.900 1.99e-11 ***
## stores.number 9.132e-02 1.441e-02 6.336 6.25e-10 ***
## latitude 2.183e+01 3.406e+00 6.409 4.05e-10 ***
## longitude 3.446e-01 3.724e+00 0.093 0.926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6793 on 408 degrees of freedom
## Multiple R-squared: 0.6381, Adjusted R-squared: 0.6337
## F-statistic: 143.9 on 5 and 408 DF, p-value: < 2.2e-16plot(model1)
s = summary(model1)
data.frame(s$coefficients)## Estimate Std..Error t.value Pr...t..
## (Intercept) -5.804553e+02 4.706491e+02 -1.23330794 2.181710e-01
## house.age -2.132262e-02 2.954890e-03 -7.21604710 2.629399e-12
## distance.to.MRT -3.782137e-04 5.481049e-05 -6.90038863 1.989437e-11
## stores.number 9.132277e-02 1.441392e-02 6.33573470 6.252533e-10
## latitude 2.182880e+01 3.405926e+00 6.40906402 4.047266e-10
## longitude 3.446381e-01 3.724248e+00 0.09253897 9.263153e-01s$r.squared## [1] 0.6381435Remove Outliers
#Outliers location
outliers <- rstandard(model1)[rstandard(model1) < -2 | rstandard(model1) > 2] #leverage plot
matrix <- as.matrix(outliers)
rownames <- rownames(matrix)
levoutlier<-as.numeric(rownames)
length(levoutlier)## [1] 20outliersmore <- which(model1$fitted.values < 4.5) #residual plot
mat <- as.matrix(outliersmore)
row <- rownames(mat)
resoutlier<-as.numeric(row)
length(resoutlier)## [1] 36outliers <- union(levoutlier, resoutlier)length(outliers)## [1] 53#New data set without the outliers
data_no_outlier <- house[-outliers,]model2 <- lm(unit.price^0.5 ~., data=data_no_outlier)
summary(model2)##
## Call:
## lm(formula = unit.price^0.5 ~ ., data = data_no_outlier)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.25287 -0.28715 0.00386 0.28657 1.66668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.159e+03 3.240e+02 -3.576 0.000397 ***
## house.age -2.896e-02 2.101e-03 -13.781 < 2e-16 ***
## distance.to.MRT -5.908e-04 4.583e-05 -12.891 < 2e-16 ***
## stores.number 6.768e-02 1.035e-02 6.539 2.16e-10 ***
## latitude 2.966e+01 2.496e+00 11.884 < 2e-16 ***
## longitude 3.496e+00 2.547e+00 1.373 0.170742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.455 on 355 degrees of freedom
## Multiple R-squared: 0.7455, Adjusted R-squared: 0.7419
## F-statistic: 208 on 5 and 355 DF, p-value: < 2.2e-16plot(model2)
# Perform the Durbin-Watson test
library(car)
d = durbinWatsonTest(model2)
d## lag Autocorrelation D-W Statistic p-value
## 1 -0.01887703 2.024025 0.826
## Alternative hypothesis: rho != 0s2 <- summary(model2)
data.frame(s2$coefficients)## Estimate Std..Error t.value Pr...t..
## (Intercept) -1.158639e+03 3.239736e+02 -3.576339 3.967260e-04
## house.age -2.895541e-02 2.101116e-03 -13.780967 6.611972e-35
## distance.to.MRT -5.908531e-04 4.583443e-05 -12.891031 1.873129e-31
## stores.number 6.768070e-02 1.034982e-02 6.539313 2.155294e-10
## latitude 2.966045e+01 2.495818e+00 11.884060 1.196885e-27
## longitude 3.495557e+00 2.546659e+00 1.372605 1.707416e-01\[ Y^{0.5}_{unit.price} = -1158.639 -0.029*X_{house.age} -0.0006*X_{distance.to.MRT} + 0.067*X_{stores.number} + 29.66*X_{latitude}+3.49*X_{longitude}\]
Homoscedasticity:
residuals <- model2$residuals
ggplot(data = data.frame(residuals = residuals), aes(x = fitted(model2), y = residuals)) +
geom_point() +
geom_smooth(method = "loess", se = FALSE, color = "blue") +
labs(title = "Residuals vs. Fitted Values", x = "Fitted Values", y = "Residuals")## `geom_smooth()` using formula = 'y ~ x'
qqnorm(residuals)
# QQ-plot
qqnorm(residuals)
qqline(residuals)
# Shapiro-Wilk test
shapiro.test(residuals)##
## Shapiro-Wilk normality test
##
## data: residuals
## W = 0.99652, p-value = 0.6245The p-value is greater than significance level, so fail to reject the null hypothesis, meaning that there is strong evidence to suggest normality.
Multicollinearity:
vif(model2)## house.age distance.to.MRT stores.number latitude longitude
## 1.012439 1.963795 1.479819 1.131224 1.443359vif(model1)## house.age distance.to.MRT stores.number latitude longitude
## 1.014249 4.282985 1.613339 1.599017 2.923881Model Selection:
p=5
models =regsubsets(unit.price^0.5~., data =data_no_outlier, nvmax =p)
summary(models)## Subset selection object
## Call: regsubsets.formula(unit.price^0.5 ~ ., data = data_no_outlier,
## nvmax = p)
## 5 Variables (and intercept)
## Forced in Forced out
## house.age FALSE FALSE
## distance.to.MRT FALSE FALSE
## stores.number FALSE FALSE
## latitude FALSE FALSE
## longitude FALSE FALSE
## 1 subsets of each size up to 5
## Selection Algorithm: exhaustive
## house.age distance.to.MRT stores.number latitude longitude
## 1 ( 1 ) " " "*" " " " " " "
## 2 ( 1 ) "*" "*" " " " " " "
## 3 ( 1 ) "*" "*" " " "*" " "
## 4 ( 1 ) "*" "*" "*" "*" " "
## 5 ( 1 ) "*" "*" "*" "*" "*"modelss <- summary(models)
data.frame(modelss$outmat)## house.age distance.to.MRT stores.number latitude longitude
## 1 ( 1 ) *
## 2 ( 1 ) * *
## 3 ( 1 ) * * *
## 4 ( 1 ) * * * *
## 5 ( 1 ) * * * * *m1 <- lm(unit.price^0.5~distance.to.MRT, data =data_no_outlier)
m2 <- lm(unit.price^0.5~house.age+distance.to.MRT, data =data_no_outlier)
m3 <- lm(unit.price^0.5~house.age+distance.to.MRT+latitude, data =data_no_outlier)
m4 <- lm(unit.price^0.5~house.age+distance.to.MRT+stores.number+latitude, data=data_no_outlier)
m5 <- lm(unit.price^0.5~house.age+distance.to.MRT+stores.number+latitude+longitude, data =data_no_outlier)# Create a vector of model names
model_names <- c("m1", "m2", "m3", "m4", "m5")
# Create an empty data frame to store AIC values
aic_data <- data.frame(Model = character(length(model_names)), AIC = numeric(length(model_names)))
# Calculate and store AIC values for each model
for (i in 1:length(model_names)) {
model <- get(model_names[i]) # Get the model by name
aic_value <- AIC(model)
aic_data[i, ] <- c(model_names[i], aic_value)
}
# Display the table of AIC values
print(aic_data)## Model AIC
## 1 m1 714.114574310383
## 2 m2 627.925936238496
## 3 m3 502.27481418991
## 4 m4 463.809226197423
## 5 m5 463.898403702297result.sum = summary(models)criteria <- data.frame(Nvar = 1:(p),
R2 = result.sum$rsq,
R2adj = result.sum$adjr2,
CP = result.sum$cp,
BIC = result.sum$bic)
criteria <- cbind(criteria, AIC = as.numeric(aic_data$AIC))
print(criteria)## Nvar R2 R2adj CP BIC AIC
## 1 1 0.4796386 0.4781891 368.889675 -224.0389 714.1146
## 2 2 0.5924215 0.5901445 213.560759 -306.3387 627.9259
## 3 3 0.7138176 0.7114127 46.216537 -428.1009 502.2748
## 4 4 0.7441641 0.7412895 5.884046 -462.6776 463.8092
## 5 5 0.7455146 0.7419303 6.000000 -458.6996 463.8984ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = R2), color = "red") +
labs(title = "R2")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = R2adj), color = "green") +
labs(title = "R2adj")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = CP), color = "purple") +
labs(title = "CP")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = BIC), color = "orange") +
labs(title = "BIC")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = AIC), color = "blue") +
labs(title = "AIC")
##Estimated best subset by each criterion >
which.best.subset = data.frame(R2 = which.max(result.sum$rsq),
R2adj = which.max(result.sum$adjr2),
CP = which.min(result.sum$cp),
BIC = which.min(result.sum$bic),
AIC = which.min(criteria$AIC))
which.best.subset## R2 R2adj CP BIC AIC
## 1 5 5 4 4 4model3 <- lm(unit.price^0.5 ~ house.age+ distance.to.MRT+ stores.number+ latitude, data=data_no_outlier)
summary(model3)##
## Call:
## lm(formula = unit.price^0.5 ~ house.age + distance.to.MRT + stores.number +
## latitude, data = data_no_outlier)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.28420 -0.28787 -0.00941 0.28640 1.67750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.221e+02 6.181e+01 -11.682 < 2e-16 ***
## house.age -2.900e-02 2.103e-03 -13.788 < 2e-16 ***
## distance.to.MRT -6.225e-04 3.967e-05 -15.694 < 2e-16 ***
## stores.number 6.732e-02 1.036e-02 6.498 2.74e-10 ***
## latitude 2.919e+01 2.476e+00 11.793 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4556 on 356 degrees of freedom
## Multiple R-squared: 0.7442, Adjusted R-squared: 0.7413
## F-statistic: 258.9 on 4 and 356 DF, p-value: < 2.2e-16s3 <- summary(model3)
data.frame(s3$coefficients)## Estimate Std..Error t.value Pr...t..
## (Intercept) -7.220997e+02 6.181090e+01 -11.682399 6.525818e-27
## house.age -2.900191e-02 2.103449e-03 -13.787784 5.970874e-35
## distance.to.MRT -6.224931e-04 3.966536e-05 -15.693619 1.482819e-42
## stores.number 6.731745e-02 1.035927e-02 6.498279 2.742849e-10
## latitude 2.919293e+01 2.475535e+00 11.792573 2.551752e-27stepwise selection
#backward stepwise selection
Full = lm(unit.price^0.5~., data =data_no_outlier) #includes all predictors
backward = step(Full, direction='backward', scope=formula(Full), trace=0)
backward$anova## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 NA NA 355 73.49790 -562.5752
## 2 - longitude 1 0.3900659 356 73.88797 -562.6644#Forward stepwise selection
Empty =lm(unit.price^0.5 ~ 1, data=data_no_outlier) # 1 means only intercept
forward =step(Empty, direction='forward', scope=formula(Full), trace=0) #results of forward selection
forward$anova## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 NA NA 360 288.80995 -78.54238
## 2 + distance.to.MRT -1 138.524406 359 150.28554 -312.35905
## 3 + house.age -1 32.572803 358 117.71274 -398.54768
## 4 + latitude -1 35.060409 357 82.65233 -524.19881
## 5 + stores.number -1 8.764364 356 73.88797 -562.66439model3 <- lm(unit.price^0.5 ~ house.age+ distance.to.MRT+ stores.number+ latitude, data=data_no_outlier)
summary(model3)##
## Call:
## lm(formula = unit.price^0.5 ~ house.age + distance.to.MRT + stores.number +
## latitude, data = data_no_outlier)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.28420 -0.28787 -0.00941 0.28640 1.67750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.221e+02 6.181e+01 -11.682 < 2e-16 ***
## house.age -2.900e-02 2.103e-03 -13.788 < 2e-16 ***
## distance.to.MRT -6.225e-04 3.967e-05 -15.694 < 2e-16 ***
## stores.number 6.732e-02 1.036e-02 6.498 2.74e-10 ***
## latitude 2.919e+01 2.476e+00 11.793 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4556 on 356 degrees of freedom
## Multiple R-squared: 0.7442, Adjusted R-squared: 0.7413
## F-statistic: 258.9 on 4 and 356 DF, p-value: < 2.2e-16model3$coefficients## (Intercept) house.age distance.to.MRT stores.number latitude
## -7.220997e+02 -2.900191e-02 -6.224931e-04 6.731745e-02 2.919293e+01Final Model
\[ Y_{unit.price} = -722.1 -0.029*X_{house.age} - 0.0006*X_{distance.to.MRT} + 0.067*X_{stores.number} + 29.19*X_{latitude}\]
library(caret)## Loading required package: latticelibrary(lattice)
train_control<- trainControl(method="cv", number= 5, savePredictions = TRUE)
model<- train(unit.price^0.5 ~ house.age+ distance.to.MRT+ stores.number+ latitude, data=data_no_outlier, trControl=train_control, method="lm")
print(model)## Linear Regression
##
## 361 samples
## 4 predictor
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 289, 289, 289, 288, 289
## Resampling results:
##
## RMSE Rsquared MAE
## 0.4551404 0.7445673 0.3613449
##
## Tuning parameter 'intercept' was held constant at a value of TRUEmodel$pred## pred obs rowIndex intercept Resample
## 1 5.368099 4.701064 9 TRUE Fold1
## 2 5.735804 6.434283 10 TRUE Fold1
## 3 4.776349 4.878524 13 TRUE Fold1
## 4 6.324422 6.115554 17 TRUE Fold1
## 5 7.196188 6.906519 19 TRUE Fold1
## 6 6.992344 7.183314 21 TRUE Fold1
## 7 6.311095 5.796551 27 TRUE Fold1
## 8 5.594921 5.848077 30 TRUE Fold1
## 9 5.231551 5.224940 33 TRUE Fold1
## 10 6.252754 6.473021 49 TRUE Fold1
## 11 6.827481 7.314369 50 TRUE Fold1
## 12 5.523318 5.029911 56 TRUE Fold1
## 13 6.318601 6.655825 57 TRUE Fold1
## 14 5.821804 6.066300 67 TRUE Fold1
## 15 6.763571 6.935416 73 TRUE Fold1
## 16 6.168882 6.928203 78 TRUE Fold1
## 17 5.775836 6.403124 83 TRUE Fold1
## 18 6.733428 6.760178 92 TRUE Fold1
## 19 7.282853 8.426150 94 TRUE Fold1
## 20 6.786781 7.183314 99 TRUE Fold1
## 21 5.216700 4.806246 101 TRUE Fold1
## 22 6.356839 5.594640 106 TRUE Fold1
## 23 7.009920 7.576279 110 TRUE Fold1
## 24 6.507841 6.123724 114 TRUE Fold1
## 25 6.232121 6.123724 116 TRUE Fold1
## 26 5.488893 4.560702 119 TRUE Fold1
## 27 7.005077 6.572671 131 TRUE Fold1
## 28 4.573593 4.277850 137 TRUE Fold1
## 29 6.415284 6.115554 140 TRUE Fold1
## 30 6.927294 7.602631 141 TRUE Fold1
## 31 6.775064 7.449832 143 TRUE Fold1
## 32 5.951887 5.531727 145 TRUE Fold1
## 33 5.863222 6.115554 147 TRUE Fold1
## 34 5.042501 4.847680 148 TRUE Fold1
## 35 6.579389 6.480741 154 TRUE Fold1
## 36 6.993521 7.021396 167 TRUE Fold1
## 37 6.122886 6.252999 171 TRUE Fold1
## 38 5.009605 5.157519 177 TRUE Fold1
## 39 5.362240 4.571652 186 TRUE Fold1
## 40 5.481025 6.300794 188 TRUE Fold1
## 41 6.348051 6.618157 190 TRUE Fold1
## 42 6.164545 6.503845 194 TRUE Fold1
## 43 5.608862 5.449771 207 TRUE Fold1
## 44 5.338120 4.806246 214 TRUE Fold1
## 45 5.485963 4.722288 215 TRUE Fold1
## 46 5.994849 6.410928 232 TRUE Fold1
## 47 6.705151 6.332456 233 TRUE Fold1
## 48 7.196188 7.049823 238 TRUE Fold1
## 49 7.403076 6.693280 244 TRUE Fold1
## 50 6.805542 7.141428 250 TRUE Fold1
## 51 6.937737 6.670832 251 TRUE Fold1
## 52 6.054366 4.949747 254 TRUE Fold1
## 53 6.644397 6.519202 255 TRUE Fold1
## 54 5.489731 6.196773 264 TRUE Fold1
## 55 5.048715 4.969909 271 TRUE Fold1
## 56 6.667154 6.480741 276 TRUE Fold1
## 57 6.285115 6.123724 277 TRUE Fold1
## 58 5.864826 5.558777 292 TRUE Fold1
## 59 6.243185 6.082763 296 TRUE Fold1
## 60 7.123241 7.328028 302 TRUE Fold1
## 61 6.795569 6.855655 303 TRUE Fold1
## 62 6.979168 6.715653 312 TRUE Fold1
## 63 4.797085 4.969909 313 TRUE Fold1
## 64 6.849303 6.928203 316 TRUE Fold1
## 65 7.050806 7.035624 327 TRUE Fold1
## 66 5.351546 5.522681 329 TRUE Fold1
## 67 5.964260 6.115554 331 TRUE Fold1
## 68 7.282853 8.348653 332 TRUE Fold1
## 69 6.226262 5.958188 349 TRUE Fold1
## 70 6.837612 6.300794 352 TRUE Fold1
## 71 6.864505 6.418723 353 TRUE Fold1
## 72 6.793580 6.371813 359 TRUE Fold1
## 73 6.952873 7.402702 4 TRUE Fold2
## 74 6.211498 6.348228 7 TRUE Fold2
## 75 6.771048 6.833740 8 TRUE Fold2
## 76 6.323456 6.268971 12 TRUE Fold2
## 77 5.327892 5.412947 20 TRUE Fold2
## 78 5.746468 6.228965 24 TRUE Fold2
## 79 7.148622 7.422937 32 TRUE Fold2
## 80 5.178704 4.785394 34 TRUE Fold2
## 81 5.153515 5.029911 35 TRUE Fold2
## 82 6.861802 6.797058 37 TRUE Fold2
## 83 6.803167 7.341662 40 TRUE Fold2
## 84 6.546145 6.480741 42 TRUE Fold2
## 85 6.256184 6.648308 43 TRUE Fold2
## 86 5.354494 5.196152 45 TRUE Fold2
## 87 6.806037 7.416198 55 TRUE Fold2
## 88 6.724396 7.536577 59 TRUE Fold2
## 89 5.926659 6.066300 72 TRUE Fold2
## 90 6.563455 6.180615 89 TRUE Fold2
## 91 6.929177 7.375636 91 TRUE Fold2
## 92 6.964523 6.862944 95 TRUE Fold2
## 93 6.850320 7.720104 105 TRUE Fold2
## 94 5.411793 5.540758 115 TRUE Fold2
## 95 6.015894 6.496153 118 TRUE Fold2
## 96 6.860322 6.841053 120 TRUE Fold2
## 97 6.474905 6.884766 121 TRUE Fold2
## 98 6.195315 6.123724 126 TRUE Fold2
## 99 6.306234 6.332456 127 TRUE Fold2
## 100 6.680008 7.224957 130 TRUE Fold2
## 101 7.403076 6.685806 134 TRUE Fold2
## 102 6.854582 6.276942 139 TRUE Fold2
## 103 7.355866 7.476630 157 TRUE Fold2
## 104 5.328827 4.636809 160 TRUE Fold2
## 105 4.911920 5.069517 161 TRUE Fold2
## 106 5.911263 6.534524 166 TRUE Fold2
## 107 6.174983 6.049793 169 TRUE Fold2
## 108 5.678095 5.612486 175 TRUE Fold2
## 109 5.167867 5.118594 180 TRUE Fold2
## 110 7.328980 7.615773 185 TRUE Fold2
## 111 6.272606 6.782330 195 TRUE Fold2
## 112 6.929177 7.000000 196 TRUE Fold2
## 113 4.555292 4.358899 199 TRUE Fold2
## 114 5.411793 5.779273 200 TRUE Fold2
## 115 5.243161 4.888763 202 TRUE Fold2
## 116 7.337591 7.259477 218 TRUE Fold2
## 117 6.047529 6.371813 229 TRUE Fold2
## 118 6.662487 6.633250 241 TRUE Fold2
## 119 5.309478 4.847680 246 TRUE Fold2
## 120 5.832071 5.735852 249 TRUE Fold2
## 121 4.985621 4.669047 257 TRUE Fold2
## 122 6.837321 6.789698 260 TRUE Fold2
## 123 5.466063 5.974948 262 TRUE Fold2
## 124 5.351371 5.422177 265 TRUE Fold2
## 125 6.260705 6.496153 272 TRUE Fold2
## 126 7.487954 7.056912 278 TRUE Fold2
## 127 5.540351 5.186521 279 TRUE Fold2
## 128 6.580677 6.140033 280 TRUE Fold2
## 129 5.380877 5.594640 283 TRUE Fold2
## 130 6.024678 6.172520 284 TRUE Fold2
## 131 5.658920 4.857983 287 TRUE Fold2
## 132 5.580036 6.292853 288 TRUE Fold2
## 133 7.127332 7.791020 309 TRUE Fold2
## 134 6.187838 5.753260 317 TRUE Fold2
## 135 5.003570 5.431390 318 TRUE Fold2
## 136 6.870413 6.488451 323 TRUE Fold2
## 137 6.401437 7.190271 324 TRUE Fold2
## 138 6.680008 7.436397 337 TRUE Fold2
## 139 5.008748 5.059644 338 TRUE Fold2
## 140 4.970825 5.594640 341 TRUE Fold2
## 141 6.579148 6.348228 343 TRUE Fold2
## 142 6.270536 6.519202 344 TRUE Fold2
## 143 5.293881 5.648008 345 TRUE Fold2
## 144 4.760425 4.722288 356 TRUE Fold2
## 145 6.971873 6.877500 3 TRUE Fold3
## 146 7.349384 7.622336 11 TRUE Fold3
## 147 5.134664 4.959839 22 TRUE Fold3
## 148 7.017714 6.920983 23 TRUE Fold3
## 149 5.375884 5.196152 25 TRUE Fold3
## 150 6.453676 5.839521 39 TRUE Fold3
## 151 6.903859 7.190271 47 TRUE Fold3
## 152 7.340645 7.681146 62 TRUE Fold3
## 153 5.723083 5.458938 69 TRUE Fold3
## 154 4.354253 4.207137 74 TRUE Fold3
## 155 7.206875 7.127412 76 TRUE Fold3
## 156 6.075561 5.882176 86 TRUE Fold3
## 157 6.895120 7.141428 87 TRUE Fold3
## 158 7.346471 7.886698 88 TRUE Fold3
## 159 6.576007 5.735852 90 TRUE Fold3
## 160 5.797258 5.839521 97 TRUE Fold3
## 161 6.444464 6.276942 100 TRUE Fold3
## 162 6.262918 6.811755 103 TRUE Fold3
## 163 6.795316 6.745369 109 TRUE Fold3
## 164 6.989782 6.971370 111 TRUE Fold3
## 165 7.168083 7.416198 112 TRUE Fold3
## 166 6.240345 6.595453 122 TRUE Fold3
## 167 6.376483 6.519202 123 TRUE Fold3
## 168 6.004248 5.375872 125 TRUE Fold3
## 169 6.337501 6.292853 142 TRUE Fold3
## 170 6.795316 7.429670 144 TRUE Fold3
## 171 7.340645 7.622336 150 TRUE Fold3
## 172 6.069850 5.924525 151 TRUE Fold3
## 173 5.122182 4.857983 158 TRUE Fold3
## 174 4.380318 4.669047 159 TRUE Fold3
## 175 4.230847 4.690416 162 TRUE Fold3
## 176 6.471494 6.655825 163 TRUE Fold3
## 177 6.257304 6.503845 164 TRUE Fold3
## 178 6.011041 5.882176 168 TRUE Fold3
## 179 6.282044 6.942622 170 TRUE Fold3
## 180 6.977699 6.774954 174 TRUE Fold3
## 181 7.079882 7.224957 182 TRUE Fold3
## 182 6.462974 6.387488 189 TRUE Fold3
## 183 6.685099 6.268971 203 TRUE Fold3
## 184 7.573513 7.867655 204 TRUE Fold3
## 185 5.518251 5.779273 210 TRUE Fold3
## 186 6.425972 6.172520 230 TRUE Fold3
## 187 5.352740 4.868265 231 TRUE Fold3
## 188 6.373570 6.363961 235 TRUE Fold3
## 189 6.561442 5.412947 236 TRUE Fold3
## 190 5.897435 5.830952 239 TRUE Fold3
## 191 6.026021 5.576737 242 TRUE Fold3
## 192 5.419754 5.059644 245 TRUE Fold3
## 193 6.577890 5.865151 247 TRUE Fold3
## 194 5.766019 5.338539 259 TRUE Fold3
## 195 6.879386 6.074537 261 TRUE Fold3
## 196 4.825260 4.816638 263 TRUE Fold3
## 197 6.525996 7.085196 267 TRUE Fold3
## 198 4.187817 4.969909 268 TRUE Fold3
## 199 6.801283 6.542171 273 TRUE Fold3
## 200 6.495798 6.519202 282 TRUE Fold3
## 201 6.094872 6.041523 290 TRUE Fold3
## 202 6.093928 5.966574 291 TRUE Fold3
## 203 7.366862 7.314369 297 TRUE Fold3
## 204 6.424480 6.826419 298 TRUE Fold3
## 205 6.367744 6.503845 304 TRUE Fold3
## 206 5.385476 5.347897 305 TRUE Fold3
## 207 6.633277 6.730527 310 TRUE Fold3
## 208 6.577369 6.324555 315 TRUE Fold3
## 209 5.263089 4.571652 320 TRUE Fold3
## 210 6.352269 6.565059 321 TRUE Fold3
## 211 4.909907 4.774935 322 TRUE Fold3
## 212 7.322430 6.826419 336 TRUE Fold3
## 213 5.336971 4.795832 347 TRUE Fold3
## 214 6.126352 6.099180 354 TRUE Fold3
## 215 7.129354 6.363961 355 TRUE Fold3
## 216 7.369775 7.071068 358 TRUE Fold3
## 217 6.931922 6.156298 1 TRUE Fold4
## 218 7.003826 6.496153 2 TRUE Fold4
## 219 6.611916 6.503845 18 TRUE Fold4
## 220 7.178233 7.496666 26 TRUE Fold4
## 221 6.831611 7.021396 31 TRUE Fold4
## 222 6.046394 6.188699 41 TRUE Fold4
## 223 4.656137 3.701351 48 TRUE Fold4
## 224 5.260718 4.615192 52 TRUE Fold4
## 225 7.000786 7.120393 58 TRUE Fold4
## 226 7.468498 7.375636 65 TRUE Fold4
## 227 4.875400 5.059644 68 TRUE Fold4
## 228 5.003496 5.147815 70 TRUE Fold4
## 229 6.588069 6.348228 71 TRUE Fold4
## 230 6.584964 6.610598 75 TRUE Fold4
## 231 5.420356 5.196152 77 TRUE Fold4
## 232 6.154814 6.572671 80 TRUE Fold4
## 233 4.689466 4.669047 81 TRUE Fold4
## 234 4.646324 4.012481 82 TRUE Fold4
## 235 5.918964 5.522681 93 TRUE Fold4
## 236 5.210742 5.157519 96 TRUE Fold4
## 237 5.218200 5.531727 104 TRUE Fold4
## 238 6.366118 6.928203 107 TRUE Fold4
## 239 7.114867 6.284903 117 TRUE Fold4
## 240 6.900585 7.169379 124 TRUE Fold4
## 241 7.015489 6.745369 129 TRUE Fold4
## 242 6.392013 6.300794 132 TRUE Fold4
## 243 6.399675 6.395311 136 TRUE Fold4
## 244 5.916578 6.041523 153 TRUE Fold4
## 245 6.637112 6.058052 155 TRUE Fold4
## 246 6.409011 6.526868 156 TRUE Fold4
## 247 6.257063 6.148170 165 TRUE Fold4
## 248 6.574957 5.621388 172 TRUE Fold4
## 249 6.917667 6.789698 176 TRUE Fold4
## 250 6.396809 6.395311 181 TRUE Fold4
## 251 5.804758 5.576737 184 TRUE Fold4
## 252 6.662892 6.935416 187 TRUE Fold4
## 253 6.157661 6.204837 192 TRUE Fold4
## 254 6.695110 6.964194 193 TRUE Fold4
## 255 6.235317 6.371813 206 TRUE Fold4
## 256 5.417508 5.366563 208 TRUE Fold4
## 257 6.675628 6.434283 209 TRUE Fold4
## 258 6.685284 6.942622 211 TRUE Fold4
## 259 6.652159 6.387488 212 TRUE Fold4
## 260 6.067001 5.477226 216 TRUE Fold4
## 261 4.654659 3.714835 217 TRUE Fold4
## 262 7.318145 7.197222 220 TRUE Fold4
## 263 5.651374 5.366563 224 TRUE Fold4
## 264 5.710278 5.540758 225 TRUE Fold4
## 265 4.807221 4.939636 226 TRUE Fold4
## 266 5.366326 5.630275 228 TRUE Fold4
## 267 6.385354 6.403124 237 TRUE Fold4
## 268 4.855471 5.263079 240 TRUE Fold4
## 269 6.972668 6.737952 243 TRUE Fold4
## 270 7.363780 7.503333 248 TRUE Fold4
## 271 6.575865 5.753260 281 TRUE Fold4
## 272 6.833785 7.880355 285 TRUE Fold4
## 273 6.639495 6.058052 286 TRUE Fold4
## 274 6.830464 6.196773 289 TRUE Fold4
## 275 6.365433 6.024948 293 TRUE Fold4
## 276 7.301063 7.099296 294 TRUE Fold4
## 277 4.669904 6.418723 299 TRUE Fold4
## 278 5.360632 5.594640 307 TRUE Fold4
## 279 6.167822 6.442049 325 TRUE Fold4
## 280 6.933883 7.536577 330 TRUE Fold4
## 281 6.960372 7.300685 333 TRUE Fold4
## 282 7.519871 6.877500 334 TRUE Fold4
## 283 6.226117 6.348228 335 TRUE Fold4
## 284 6.030991 5.941380 342 TRUE Fold4
## 285 6.618082 5.674504 346 TRUE Fold4
## 286 5.244512 5.263079 350 TRUE Fold4
## 287 4.940756 5.300943 357 TRUE Fold4
## 288 6.821923 7.245688 360 TRUE Fold4
## 289 7.346698 7.993748 361 TRUE Fold4
## 290 7.082049 6.565059 5 TRUE Fold5
## 291 5.244090 5.665686 6 TRUE Fold5
## 292 6.637935 5.856620 14 TRUE Fold5
## 293 5.953412 7.106335 15 TRUE Fold5
## 294 7.298401 8.372574 16 TRUE Fold5
## 295 6.335283 6.855655 28 TRUE Fold5
## 296 6.868358 7.556454 29 TRUE Fold5
## 297 6.858499 6.906519 36 TRUE Fold5
## 298 5.588829 5.890671 38 TRUE Fold5
## 299 4.403150 4.549725 44 TRUE Fold5
## 300 6.344216 6.236986 46 TRUE Fold5
## 301 6.224465 6.511528 51 TRUE Fold5
## 302 7.115614 7.949843 53 TRUE Fold5
## 303 4.944039 5.263079 54 TRUE Fold5
## 304 6.353203 6.016644 60 TRUE Fold5
## 305 6.990897 6.480741 61 TRUE Fold5
## 306 5.779150 6.387488 63 TRUE Fold5
## 307 6.329327 6.024948 64 TRUE Fold5
## 308 5.165342 5.431390 66 TRUE Fold5
## 309 6.918175 6.737952 79 TRUE Fold5
## 310 6.800866 7.197222 84 TRUE Fold5
## 311 7.335745 7.713624 85 TRUE Fold5
## 312 5.352034 5.329165 98 TRUE Fold5
## 313 6.738435 7.300685 102 TRUE Fold5
## 314 6.297733 5.700877 108 TRUE Fold5
## 315 6.373760 6.403124 113 TRUE Fold5
## 316 5.177180 5.329165 128 TRUE Fold5
## 317 6.074118 6.964194 133 TRUE Fold5
## 318 5.174220 5.375872 135 TRUE Fold5
## 319 6.398716 5.966574 138 TRUE Fold5
## 320 6.464435 6.587868 146 TRUE Fold5
## 321 7.172186 7.668116 149 TRUE Fold5
## 322 7.235022 6.723095 152 TRUE Fold5
## 323 5.518743 5.049752 173 TRUE Fold5
## 324 7.015320 6.633250 178 TRUE Fold5
## 325 5.656560 5.848077 179 TRUE Fold5
## 326 6.918175 6.595453 183 TRUE Fold5
## 327 6.509829 6.340347 191 TRUE Fold5
## 328 6.577502 6.340347 197 TRUE Fold5
## 329 5.406950 6.826419 198 TRUE Fold5
## 330 6.366820 5.692100 201 TRUE Fold5
## 331 6.228430 6.244998 205 TRUE Fold5
## 332 6.350969 6.371813 213 TRUE Fold5
## 333 4.825504 5.089204 219 TRUE Fold5
## 334 6.455338 5.147815 221 TRUE Fold5
## 335 6.301761 6.625708 222 TRUE Fold5
## 336 7.298401 7.956130 223 TRUE Fold5
## 337 6.905063 7.280110 227 TRUE Fold5
## 338 5.624373 4.795832 234 TRUE Fold5
## 339 5.520178 6.082763 252 TRUE Fold5
## 340 6.815227 7.375636 253 TRUE Fold5
## 341 6.554147 6.172520 256 TRUE Fold5
## 342 6.677922 5.839521 258 TRUE Fold5
## 343 6.896185 7.416198 266 TRUE Fold5
## 344 6.890266 7.280110 269 TRUE Fold5
## 345 4.535878 4.370355 270 TRUE Fold5
## 346 6.840743 6.449806 274 TRUE Fold5
## 347 5.806006 5.224940 275 TRUE Fold5
## 348 5.803527 6.549809 295 TRUE Fold5
## 349 6.802284 6.156298 300 TRUE Fold5
## 350 5.333891 5.549775 301 TRUE Fold5
## 351 4.535746 5.069517 306 TRUE Fold5
## 352 5.168301 5.486347 308 TRUE Fold5
## 353 7.189629 6.700746 311 TRUE Fold5
## 354 6.876003 6.862944 314 TRUE Fold5
## 355 4.806532 4.979960 319 TRUE Fold5
## 356 6.918175 7.224957 326 TRUE Fold5
## 357 5.243951 4.878524 328 TRUE Fold5
## 358 5.054808 5.224940 339 TRUE Fold5
## 359 6.282926 6.212890 340 TRUE Fold5
## 360 6.577290 6.107373 348 TRUE Fold5
## 361 6.571088 5.338539 351 TRUE Fold5model$pred## pred obs rowIndex intercept Resample
## 1 5.368099 4.701064 9 TRUE Fold1
## 2 5.735804 6.434283 10 TRUE Fold1
## 3 4.776349 4.878524 13 TRUE Fold1
## 4 6.324422 6.115554 17 TRUE Fold1
## 5 7.196188 6.906519 19 TRUE Fold1
## 6 6.992344 7.183314 21 TRUE Fold1
## 7 6.311095 5.796551 27 TRUE Fold1
## 8 5.594921 5.848077 30 TRUE Fold1
## 9 5.231551 5.224940 33 TRUE Fold1
## 10 6.252754 6.473021 49 TRUE Fold1
## 11 6.827481 7.314369 50 TRUE Fold1
## 12 5.523318 5.029911 56 TRUE Fold1
## 13 6.318601 6.655825 57 TRUE Fold1
## 14 5.821804 6.066300 67 TRUE Fold1
## 15 6.763571 6.935416 73 TRUE Fold1
## 16 6.168882 6.928203 78 TRUE Fold1
## 17 5.775836 6.403124 83 TRUE Fold1
## 18 6.733428 6.760178 92 TRUE Fold1
## 19 7.282853 8.426150 94 TRUE Fold1
## 20 6.786781 7.183314 99 TRUE Fold1
## 21 5.216700 4.806246 101 TRUE Fold1
## 22 6.356839 5.594640 106 TRUE Fold1
## 23 7.009920 7.576279 110 TRUE Fold1
## 24 6.507841 6.123724 114 TRUE Fold1
## 25 6.232121 6.123724 116 TRUE Fold1
## 26 5.488893 4.560702 119 TRUE Fold1
## 27 7.005077 6.572671 131 TRUE Fold1
## 28 4.573593 4.277850 137 TRUE Fold1
## 29 6.415284 6.115554 140 TRUE Fold1
## 30 6.927294 7.602631 141 TRUE Fold1
## 31 6.775064 7.449832 143 TRUE Fold1
## 32 5.951887 5.531727 145 TRUE Fold1
## 33 5.863222 6.115554 147 TRUE Fold1
## 34 5.042501 4.847680 148 TRUE Fold1
## 35 6.579389 6.480741 154 TRUE Fold1
## 36 6.993521 7.021396 167 TRUE Fold1
## 37 6.122886 6.252999 171 TRUE Fold1
## 38 5.009605 5.157519 177 TRUE Fold1
## 39 5.362240 4.571652 186 TRUE Fold1
## 40 5.481025 6.300794 188 TRUE Fold1
## 41 6.348051 6.618157 190 TRUE Fold1
## 42 6.164545 6.503845 194 TRUE Fold1
## 43 5.608862 5.449771 207 TRUE Fold1
## 44 5.338120 4.806246 214 TRUE Fold1
## 45 5.485963 4.722288 215 TRUE Fold1
## 46 5.994849 6.410928 232 TRUE Fold1
## 47 6.705151 6.332456 233 TRUE Fold1
## 48 7.196188 7.049823 238 TRUE Fold1
## 49 7.403076 6.693280 244 TRUE Fold1
## 50 6.805542 7.141428 250 TRUE Fold1
## 51 6.937737 6.670832 251 TRUE Fold1
## 52 6.054366 4.949747 254 TRUE Fold1
## 53 6.644397 6.519202 255 TRUE Fold1
## 54 5.489731 6.196773 264 TRUE Fold1
## 55 5.048715 4.969909 271 TRUE Fold1
## 56 6.667154 6.480741 276 TRUE Fold1
## 57 6.285115 6.123724 277 TRUE Fold1
## 58 5.864826 5.558777 292 TRUE Fold1
## 59 6.243185 6.082763 296 TRUE Fold1
## 60 7.123241 7.328028 302 TRUE Fold1
## 61 6.795569 6.855655 303 TRUE Fold1
## 62 6.979168 6.715653 312 TRUE Fold1
## 63 4.797085 4.969909 313 TRUE Fold1
## 64 6.849303 6.928203 316 TRUE Fold1
## 65 7.050806 7.035624 327 TRUE Fold1
## 66 5.351546 5.522681 329 TRUE Fold1
## 67 5.964260 6.115554 331 TRUE Fold1
## 68 7.282853 8.348653 332 TRUE Fold1
## 69 6.226262 5.958188 349 TRUE Fold1
## 70 6.837612 6.300794 352 TRUE Fold1
## 71 6.864505 6.418723 353 TRUE Fold1
## 72 6.793580 6.371813 359 TRUE Fold1
## 73 6.952873 7.402702 4 TRUE Fold2
## 74 6.211498 6.348228 7 TRUE Fold2
## 75 6.771048 6.833740 8 TRUE Fold2
## 76 6.323456 6.268971 12 TRUE Fold2
## 77 5.327892 5.412947 20 TRUE Fold2
## 78 5.746468 6.228965 24 TRUE Fold2
## 79 7.148622 7.422937 32 TRUE Fold2
## 80 5.178704 4.785394 34 TRUE Fold2
## 81 5.153515 5.029911 35 TRUE Fold2
## 82 6.861802 6.797058 37 TRUE Fold2
## 83 6.803167 7.341662 40 TRUE Fold2
## 84 6.546145 6.480741 42 TRUE Fold2
## 85 6.256184 6.648308 43 TRUE Fold2
## 86 5.354494 5.196152 45 TRUE Fold2
## 87 6.806037 7.416198 55 TRUE Fold2
## 88 6.724396 7.536577 59 TRUE Fold2
## 89 5.926659 6.066300 72 TRUE Fold2
## 90 6.563455 6.180615 89 TRUE Fold2
## 91 6.929177 7.375636 91 TRUE Fold2
## 92 6.964523 6.862944 95 TRUE Fold2
## 93 6.850320 7.720104 105 TRUE Fold2
## 94 5.411793 5.540758 115 TRUE Fold2
## 95 6.015894 6.496153 118 TRUE Fold2
## 96 6.860322 6.841053 120 TRUE Fold2
## 97 6.474905 6.884766 121 TRUE Fold2
## 98 6.195315 6.123724 126 TRUE Fold2
## 99 6.306234 6.332456 127 TRUE Fold2
## 100 6.680008 7.224957 130 TRUE Fold2
## 101 7.403076 6.685806 134 TRUE Fold2
## 102 6.854582 6.276942 139 TRUE Fold2
## 103 7.355866 7.476630 157 TRUE Fold2
## 104 5.328827 4.636809 160 TRUE Fold2
## 105 4.911920 5.069517 161 TRUE Fold2
## 106 5.911263 6.534524 166 TRUE Fold2
## 107 6.174983 6.049793 169 TRUE Fold2
## 108 5.678095 5.612486 175 TRUE Fold2
## 109 5.167867 5.118594 180 TRUE Fold2
## 110 7.328980 7.615773 185 TRUE Fold2
## 111 6.272606 6.782330 195 TRUE Fold2
## 112 6.929177 7.000000 196 TRUE Fold2
## 113 4.555292 4.358899 199 TRUE Fold2
## 114 5.411793 5.779273 200 TRUE Fold2
## 115 5.243161 4.888763 202 TRUE Fold2
## 116 7.337591 7.259477 218 TRUE Fold2
## 117 6.047529 6.371813 229 TRUE Fold2
## 118 6.662487 6.633250 241 TRUE Fold2
## 119 5.309478 4.847680 246 TRUE Fold2
## 120 5.832071 5.735852 249 TRUE Fold2
## 121 4.985621 4.669047 257 TRUE Fold2
## 122 6.837321 6.789698 260 TRUE Fold2
## 123 5.466063 5.974948 262 TRUE Fold2
## 124 5.351371 5.422177 265 TRUE Fold2
## 125 6.260705 6.496153 272 TRUE Fold2
## 126 7.487954 7.056912 278 TRUE Fold2
## 127 5.540351 5.186521 279 TRUE Fold2
## 128 6.580677 6.140033 280 TRUE Fold2
## 129 5.380877 5.594640 283 TRUE Fold2
## 130 6.024678 6.172520 284 TRUE Fold2
## 131 5.658920 4.857983 287 TRUE Fold2
## 132 5.580036 6.292853 288 TRUE Fold2
## 133 7.127332 7.791020 309 TRUE Fold2
## 134 6.187838 5.753260 317 TRUE Fold2
## 135 5.003570 5.431390 318 TRUE Fold2
## 136 6.870413 6.488451 323 TRUE Fold2
## 137 6.401437 7.190271 324 TRUE Fold2
## 138 6.680008 7.436397 337 TRUE Fold2
## 139 5.008748 5.059644 338 TRUE Fold2
## 140 4.970825 5.594640 341 TRUE Fold2
## 141 6.579148 6.348228 343 TRUE Fold2
## 142 6.270536 6.519202 344 TRUE Fold2
## 143 5.293881 5.648008 345 TRUE Fold2
## 144 4.760425 4.722288 356 TRUE Fold2
## 145 6.971873 6.877500 3 TRUE Fold3
## 146 7.349384 7.622336 11 TRUE Fold3
## 147 5.134664 4.959839 22 TRUE Fold3
## 148 7.017714 6.920983 23 TRUE Fold3
## 149 5.375884 5.196152 25 TRUE Fold3
## 150 6.453676 5.839521 39 TRUE Fold3
## 151 6.903859 7.190271 47 TRUE Fold3
## 152 7.340645 7.681146 62 TRUE Fold3
## 153 5.723083 5.458938 69 TRUE Fold3
## 154 4.354253 4.207137 74 TRUE Fold3
## 155 7.206875 7.127412 76 TRUE Fold3
## 156 6.075561 5.882176 86 TRUE Fold3
## 157 6.895120 7.141428 87 TRUE Fold3
## 158 7.346471 7.886698 88 TRUE Fold3
## 159 6.576007 5.735852 90 TRUE Fold3
## 160 5.797258 5.839521 97 TRUE Fold3
## 161 6.444464 6.276942 100 TRUE Fold3
## 162 6.262918 6.811755 103 TRUE Fold3
## 163 6.795316 6.745369 109 TRUE Fold3
## 164 6.989782 6.971370 111 TRUE Fold3
## 165 7.168083 7.416198 112 TRUE Fold3
## 166 6.240345 6.595453 122 TRUE Fold3
## 167 6.376483 6.519202 123 TRUE Fold3
## 168 6.004248 5.375872 125 TRUE Fold3
## 169 6.337501 6.292853 142 TRUE Fold3
## 170 6.795316 7.429670 144 TRUE Fold3
## 171 7.340645 7.622336 150 TRUE Fold3
## 172 6.069850 5.924525 151 TRUE Fold3
## 173 5.122182 4.857983 158 TRUE Fold3
## 174 4.380318 4.669047 159 TRUE Fold3
## 175 4.230847 4.690416 162 TRUE Fold3
## 176 6.471494 6.655825 163 TRUE Fold3
## 177 6.257304 6.503845 164 TRUE Fold3
## 178 6.011041 5.882176 168 TRUE Fold3
## 179 6.282044 6.942622 170 TRUE Fold3
## 180 6.977699 6.774954 174 TRUE Fold3
## 181 7.079882 7.224957 182 TRUE Fold3
## 182 6.462974 6.387488 189 TRUE Fold3
## 183 6.685099 6.268971 203 TRUE Fold3
## 184 7.573513 7.867655 204 TRUE Fold3
## 185 5.518251 5.779273 210 TRUE Fold3
## 186 6.425972 6.172520 230 TRUE Fold3
## 187 5.352740 4.868265 231 TRUE Fold3
## 188 6.373570 6.363961 235 TRUE Fold3
## 189 6.561442 5.412947 236 TRUE Fold3
## 190 5.897435 5.830952 239 TRUE Fold3
## 191 6.026021 5.576737 242 TRUE Fold3
## 192 5.419754 5.059644 245 TRUE Fold3
## 193 6.577890 5.865151 247 TRUE Fold3
## 194 5.766019 5.338539 259 TRUE Fold3
## 195 6.879386 6.074537 261 TRUE Fold3
## 196 4.825260 4.816638 263 TRUE Fold3
## 197 6.525996 7.085196 267 TRUE Fold3
## 198 4.187817 4.969909 268 TRUE Fold3
## 199 6.801283 6.542171 273 TRUE Fold3
## 200 6.495798 6.519202 282 TRUE Fold3
## 201 6.094872 6.041523 290 TRUE Fold3
## 202 6.093928 5.966574 291 TRUE Fold3
## 203 7.366862 7.314369 297 TRUE Fold3
## 204 6.424480 6.826419 298 TRUE Fold3
## 205 6.367744 6.503845 304 TRUE Fold3
## 206 5.385476 5.347897 305 TRUE Fold3
## 207 6.633277 6.730527 310 TRUE Fold3
## 208 6.577369 6.324555 315 TRUE Fold3
## 209 5.263089 4.571652 320 TRUE Fold3
## 210 6.352269 6.565059 321 TRUE Fold3
## 211 4.909907 4.774935 322 TRUE Fold3
## 212 7.322430 6.826419 336 TRUE Fold3
## 213 5.336971 4.795832 347 TRUE Fold3
## 214 6.126352 6.099180 354 TRUE Fold3
## 215 7.129354 6.363961 355 TRUE Fold3
## 216 7.369775 7.071068 358 TRUE Fold3
## 217 6.931922 6.156298 1 TRUE Fold4
## 218 7.003826 6.496153 2 TRUE Fold4
## 219 6.611916 6.503845 18 TRUE Fold4
## 220 7.178233 7.496666 26 TRUE Fold4
## 221 6.831611 7.021396 31 TRUE Fold4
## 222 6.046394 6.188699 41 TRUE Fold4
## 223 4.656137 3.701351 48 TRUE Fold4
## 224 5.260718 4.615192 52 TRUE Fold4
## 225 7.000786 7.120393 58 TRUE Fold4
## 226 7.468498 7.375636 65 TRUE Fold4
## 227 4.875400 5.059644 68 TRUE Fold4
## 228 5.003496 5.147815 70 TRUE Fold4
## 229 6.588069 6.348228 71 TRUE Fold4
## 230 6.584964 6.610598 75 TRUE Fold4
## 231 5.420356 5.196152 77 TRUE Fold4
## 232 6.154814 6.572671 80 TRUE Fold4
## 233 4.689466 4.669047 81 TRUE Fold4
## 234 4.646324 4.012481 82 TRUE Fold4
## 235 5.918964 5.522681 93 TRUE Fold4
## 236 5.210742 5.157519 96 TRUE Fold4
## 237 5.218200 5.531727 104 TRUE Fold4
## 238 6.366118 6.928203 107 TRUE Fold4
## 239 7.114867 6.284903 117 TRUE Fold4
## 240 6.900585 7.169379 124 TRUE Fold4
## 241 7.015489 6.745369 129 TRUE Fold4
## 242 6.392013 6.300794 132 TRUE Fold4
## 243 6.399675 6.395311 136 TRUE Fold4
## 244 5.916578 6.041523 153 TRUE Fold4
## 245 6.637112 6.058052 155 TRUE Fold4
## 246 6.409011 6.526868 156 TRUE Fold4
## 247 6.257063 6.148170 165 TRUE Fold4
## 248 6.574957 5.621388 172 TRUE Fold4
## 249 6.917667 6.789698 176 TRUE Fold4
## 250 6.396809 6.395311 181 TRUE Fold4
## 251 5.804758 5.576737 184 TRUE Fold4
## 252 6.662892 6.935416 187 TRUE Fold4
## 253 6.157661 6.204837 192 TRUE Fold4
## 254 6.695110 6.964194 193 TRUE Fold4
## 255 6.235317 6.371813 206 TRUE Fold4
## 256 5.417508 5.366563 208 TRUE Fold4
## 257 6.675628 6.434283 209 TRUE Fold4
## 258 6.685284 6.942622 211 TRUE Fold4
## 259 6.652159 6.387488 212 TRUE Fold4
## 260 6.067001 5.477226 216 TRUE Fold4
## 261 4.654659 3.714835 217 TRUE Fold4
## 262 7.318145 7.197222 220 TRUE Fold4
## 263 5.651374 5.366563 224 TRUE Fold4
## 264 5.710278 5.540758 225 TRUE Fold4
## 265 4.807221 4.939636 226 TRUE Fold4
## 266 5.366326 5.630275 228 TRUE Fold4
## 267 6.385354 6.403124 237 TRUE Fold4
## 268 4.855471 5.263079 240 TRUE Fold4
## 269 6.972668 6.737952 243 TRUE Fold4
## 270 7.363780 7.503333 248 TRUE Fold4
## 271 6.575865 5.753260 281 TRUE Fold4
## 272 6.833785 7.880355 285 TRUE Fold4
## 273 6.639495 6.058052 286 TRUE Fold4
## 274 6.830464 6.196773 289 TRUE Fold4
## 275 6.365433 6.024948 293 TRUE Fold4
## 276 7.301063 7.099296 294 TRUE Fold4
## 277 4.669904 6.418723 299 TRUE Fold4
## 278 5.360632 5.594640 307 TRUE Fold4
## 279 6.167822 6.442049 325 TRUE Fold4
## 280 6.933883 7.536577 330 TRUE Fold4
## 281 6.960372 7.300685 333 TRUE Fold4
## 282 7.519871 6.877500 334 TRUE Fold4
## 283 6.226117 6.348228 335 TRUE Fold4
## 284 6.030991 5.941380 342 TRUE Fold4
## 285 6.618082 5.674504 346 TRUE Fold4
## 286 5.244512 5.263079 350 TRUE Fold4
## 287 4.940756 5.300943 357 TRUE Fold4
## 288 6.821923 7.245688 360 TRUE Fold4
## 289 7.346698 7.993748 361 TRUE Fold4
## 290 7.082049 6.565059 5 TRUE Fold5
## 291 5.244090 5.665686 6 TRUE Fold5
## 292 6.637935 5.856620 14 TRUE Fold5
## 293 5.953412 7.106335 15 TRUE Fold5
## 294 7.298401 8.372574 16 TRUE Fold5
## 295 6.335283 6.855655 28 TRUE Fold5
## 296 6.868358 7.556454 29 TRUE Fold5
## 297 6.858499 6.906519 36 TRUE Fold5
## 298 5.588829 5.890671 38 TRUE Fold5
## 299 4.403150 4.549725 44 TRUE Fold5
## 300 6.344216 6.236986 46 TRUE Fold5
## 301 6.224465 6.511528 51 TRUE Fold5
## 302 7.115614 7.949843 53 TRUE Fold5
## 303 4.944039 5.263079 54 TRUE Fold5
## 304 6.353203 6.016644 60 TRUE Fold5
## 305 6.990897 6.480741 61 TRUE Fold5
## 306 5.779150 6.387488 63 TRUE Fold5
## 307 6.329327 6.024948 64 TRUE Fold5
## 308 5.165342 5.431390 66 TRUE Fold5
## 309 6.918175 6.737952 79 TRUE Fold5
## 310 6.800866 7.197222 84 TRUE Fold5
## 311 7.335745 7.713624 85 TRUE Fold5
## 312 5.352034 5.329165 98 TRUE Fold5
## 313 6.738435 7.300685 102 TRUE Fold5
## 314 6.297733 5.700877 108 TRUE Fold5
## 315 6.373760 6.403124 113 TRUE Fold5
## 316 5.177180 5.329165 128 TRUE Fold5
## 317 6.074118 6.964194 133 TRUE Fold5
## 318 5.174220 5.375872 135 TRUE Fold5
## 319 6.398716 5.966574 138 TRUE Fold5
## 320 6.464435 6.587868 146 TRUE Fold5
## 321 7.172186 7.668116 149 TRUE Fold5
## 322 7.235022 6.723095 152 TRUE Fold5
## 323 5.518743 5.049752 173 TRUE Fold5
## 324 7.015320 6.633250 178 TRUE Fold5
## 325 5.656560 5.848077 179 TRUE Fold5
## 326 6.918175 6.595453 183 TRUE Fold5
## 327 6.509829 6.340347 191 TRUE Fold5
## 328 6.577502 6.340347 197 TRUE Fold5
## 329 5.406950 6.826419 198 TRUE Fold5
## 330 6.366820 5.692100 201 TRUE Fold5
## 331 6.228430 6.244998 205 TRUE Fold5
## 332 6.350969 6.371813 213 TRUE Fold5
## 333 4.825504 5.089204 219 TRUE Fold5
## 334 6.455338 5.147815 221 TRUE Fold5
## 335 6.301761 6.625708 222 TRUE Fold5
## 336 7.298401 7.956130 223 TRUE Fold5
## 337 6.905063 7.280110 227 TRUE Fold5
## 338 5.624373 4.795832 234 TRUE Fold5
## 339 5.520178 6.082763 252 TRUE Fold5
## 340 6.815227 7.375636 253 TRUE Fold5
## 341 6.554147 6.172520 256 TRUE Fold5
## 342 6.677922 5.839521 258 TRUE Fold5
## 343 6.896185 7.416198 266 TRUE Fold5
## 344 6.890266 7.280110 269 TRUE Fold5
## 345 4.535878 4.370355 270 TRUE Fold5
## 346 6.840743 6.449806 274 TRUE Fold5
## 347 5.806006 5.224940 275 TRUE Fold5
## 348 5.803527 6.549809 295 TRUE Fold5
## 349 6.802284 6.156298 300 TRUE Fold5
## 350 5.333891 5.549775 301 TRUE Fold5
## 351 4.535746 5.069517 306 TRUE Fold5
## 352 5.168301 5.486347 308 TRUE Fold5
## 353 7.189629 6.700746 311 TRUE Fold5
## 354 6.876003 6.862944 314 TRUE Fold5
## 355 4.806532 4.979960 319 TRUE Fold5
## 356 6.918175 7.224957 326 TRUE Fold5
## 357 5.243951 4.878524 328 TRUE Fold5
## 358 5.054808 5.224940 339 TRUE Fold5
## 359 6.282926 6.212890 340 TRUE Fold5
## 360 6.577290 6.107373 348 TRUE Fold5
## 361 6.571088 5.338539 351 TRUE Fold5model$resample## RMSE Rsquared MAE Resample
## 1 0.4668688 0.7390410 0.3785614 Fold1
## 2 0.4041783 0.8055002 0.3265182 Fold2
## 3 0.3907139 0.8149901 0.3052933 Fold3
## 4 0.4729552 0.7318698 0.3519278 Fold4
## 5 0.5409860 0.6314354 0.4444235 Fold5