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a better way to build and plot randomForest partial plots
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library('ggplot2') | |
library('randomForest') | |
set.seed(2014) | |
rf_predict<-function(rf_object, data){ | |
if (rf_object$type=="classification"){ | |
p <-predict(rf_object, data, type="prob") | |
p<-as.vector(p[,2]) | |
} else { | |
p <-predict(rf_object, data) | |
} | |
return (p) | |
} | |
plot_partial<- | |
function(rf, X, dv, iv, conf_int_lb=.25, | |
conf_int_ub=.75, range_low=NULL, | |
range_high=NULL, delta=FALSE, num_sample=NULL) | |
{ | |
iv_name<-substitute(iv) | |
dv_name<-substitute(dv) | |
if (is.factor(X[, iv_name])==TRUE){ | |
factor_var<-unique(iris[, iv_name]) | |
#the test set needs all factor levels. so, we build them and will drop them before we plot | |
factor_names <- attributes(factor_var)$levels | |
fix_factor_df<-data.frame(X[1:length(factor_names),]) | |
fix_factor_df[, iv_name]<-factor_names | |
y_hat_df <- data.frame(matrix(vector(),0, 2)) | |
y_temp <- data.frame(matrix(vector(), nrow(X), 2)) | |
y<-predict(rf, X) | |
for (i in 1:length(factor_names)){ | |
X[, iv_name] <- factor_names[i] | |
X[, iv_name] <- factor(X[, iv_name]) | |
X_temp<-rbind(X, fix_factor_df) | |
p<-rf_predict(rf, X_temp) | |
y_temp[,1]<-p[1:nrow(X)] #drop the fix_factor_df rows | |
if (delta==TRUE){ | |
y_temp[,1]<-y_temp[,1]-y | |
} | |
y_temp[,2]<-factor_names[i] | |
y_hat_df<-rbind(y_hat_df, y_temp) | |
} | |
plot<- qplot(y_hat_df[,2], y_hat_df[,1], | |
data = y_hat_df, | |
geom="boxplot", | |
main = paste("Partial Dependence of", (iv_name), "on", (dv_name))) + | |
ylab(bquote("Predicted values of" ~ .(dv_name))) + | |
xlab(iv_name) | |
return (plot) | |
} else { | |
conf_int <-(conf_int_ub-conf_int_lb)*100 | |
temp<-sort(X[, iv_name]) | |
if (is.null(num_sample)==FALSE){ | |
temp<-sample(temp, num_sample) | |
} | |
if (is.null(range_low)==FALSE & is.null(range_high)==FALSE){ | |
low_value<-quantile(temp, range_low) | |
high_value<-quantile(temp, range_high) | |
temp<-temp[temp<high_value & temp>low_value] | |
} | |
y_hat_mean<-vector() | |
y_hat_lb<-vector() | |
y_hat_ub<-vector() | |
y<-rf_predict(rf, X) | |
for (i in 1:length(temp)){ | |
X[, iv_name] <- temp[i] | |
y_hat<-rf_predict(rf, X) | |
if (delta==TRUE){ | |
y_hat<-y_hat-y | |
} | |
y_hat_mean[i]<-weighted.mean(y_hat) | |
y_hat_lb[i]<-quantile(y_hat, conf_int_lb) | |
y_hat_ub[i]<-quantile(y_hat, conf_int_ub) | |
} | |
df_new<-as.data.frame(cbind(temp, y_hat_mean, y_hat_lb, y_hat_ub)) | |
plot<- ggplot(df_new, aes(temp)) + | |
geom_line(aes(y=y_hat_mean), colour="blue") + | |
geom_ribbon(aes(ymin=y_hat_lb, ymax=y_hat_ub), alpha=0.2) + | |
geom_rug(aes()) + | |
xlab(iv_name) + | |
ylab(bquote("Predicted values of" ~ .(dv_name))) + | |
ggtitle(paste("Partial Dependence of", (iv_name), "on", (dv_name), "\n with", (conf_int), "% Confidence Intervals")) | |
return (plot) | |
} | |
} |
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