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logistic_regression_roc_curve_withCV.R
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logistic_regression_roc_curve_withCV.R
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################################################################################################
# Example code to perform logistic regression
# and plot ROC curve and
# precision recall curve
# and calculate AUC and AUPR
#
# 1. Performs test and train split
# 2. Performs cross-validation and picks best model
# 3. Uses the best model to predict and generate ROC and AUPR curves on test set
#
# Acknowledgements:
# Adapted from
# https://stackoverflow.com/questions/18449013/r-logistic-regression-area-under-curve
# answer by user wush978
# and
# https://stats.stackexchange.com/questions/10501/calculating-aupr-in-r
# answer by user arun
#
# Usage:
# nohup R --no-save < logistic_regression_roc_curve_withCV.R
#
# Installation:
# install.packages("pROC")
# install.packages("precrec")
# install.packages("PRROC")
#
################################################################################################
############################
# Load libraries
############################
library(pROC)
library(precrec)
library(PRROC)
library(boot)
library(ggplot2)
# LIBRARY_PREFIX <- "https://egret.psychol.cam.ac.uk/rlib/"
# source(paste0(LIBRARY_PREFIX, "cris_common.R"))
source("https://raw.githubusercontent.com/neelsoumya/rlib/master/cris_common.R")
###############################
# Load synthetic data
###############################
# df_metagene_score_final is a data frame with two columns:
# flag_yes_no: target (1 or 0)
# metagene_score: continuous variable
#setwd("~/logistic_regression_roc_curve")
df_metagene_score_final = read.csv('metagene_score.csv',
sep = ',', header = TRUE,
stringsAsFactors=FALSE, na.strings="..")
########################
# Test train split
########################
#set.seed(1)
TRAIN = sample(c(TRUE,FALSE),
nrow(df_metagene_score_final),
replace = TRUE)
TEST = (!TRAIN)
df_metagene_score_final_TRAIN = df_metagene_score_final[TRAIN,]
df_metagene_score_final_TEST = df_metagene_score_final[TEST,]
###############################
# Train logistic regression
# on training set
###############################
########################
# Fit glm
########################
# call repeatedly
# for a fixed training set
i_fold_cv = 5 # number of folds for cross-validation
i_num_repeat_cv = 100 # number of times to perform repeated cross-validation
f_cv_error_best = -1 # variable to store best cross-validation error
glm_object_best = NULL # variable to store best glm() object for best cross-validation error
for (i_temp_counter in seq(1,i_num_repeat_cv))
{
########################
# create GLM
# on TRAINING SET
# or VALIDATION SET
########################
mylogit <- glm(flag_yes_no ~ metagene_score,
data = df_metagene_score_final_TRAIN,
family = "binomial")
########################
# AIC score
########################
mylogit$aic
# cost function for logistic
cost <- function(r, pi=0) mean(abs(r-pi)>0.5)
cost_classification <- function(r, pi) mean(abs(r-pi) > 0.5)
cost_negloglikelihood <- function(r, pi) -sum(r*log(pi)+(1-r)*log(1-pi) )
########################
# cross-validate
# on TRAINING set
# or VALIDATION SET
########################
cv_err <- cv.glm(data = df_metagene_score_final_TRAIN,
K = i_fold_cv,
cost = cost,
glmfit = mylogit
)
########################
# CV error
# on VALIDATION SET
# or TRAINING SET
########################
cv_err$delta[1]
# if first time then store cv error and glm object
if (i_temp_counter == 1)
{
f_cv_error_best = cv_err$delta[1]
glm_object_best = mylogit
}
# if new iteration has better error then store that
if (cv_err$delta[1] < f_cv_error_best )
{
f_cv_error_best = cv_err$delta[1]
glm_object_best = mylogit
}
}
# end for loop
########################
# Best estimates
########################
f_cv_error_best
glm_object_best
#######################################
# Check linear model distributions
#######################################
miscstat$check_distribution(model = glm_object_best)
#######################################
# Visualize parameter distributions
# holds for linear mixed effects models lmer()
#######################################
# cris$visualize_fixed_effects_from_lmer(lmer_result = glm_object_best)
# cris$fixed_effects_from_lmer(lmer_result = glm_object_best)
###############################
# predict on TEST set
# using best model
###############################
prob = predict(glm_object_best,
type=c("response"),
newdata = df_metagene_score_final_TEST)
df_metagene_score_final_TEST$prob = prob
###############################
# Generate ROC curves
# on TEST SET
###############################
g <- pROC::roc(flag_yes_no ~ prob, data=df_metagene_score_final_TEST)
plot(g)
cat("AUC is:", g$auc)
############################
# Generate
# Precision recall curve
# on TEST SET
############################
# the precision recall curve
# is better suited for cases in which there are class imbalances.
mmdata_flag = mmdata(df_metagene_score_final_TEST$metagene_score,
df_metagene_score_final_TEST$flag_yes_no)
smcurves <- evalmod(mmdata_flag, raw_curves = TRUE)
plot(smcurves, raw_curves = FALSE)
############################
# even better AUC and AUPR
# curves with areas reporred
# on TEST SET
############################
fg <- prob[df_metagene_score_final_TEST$flag_yes_no == 1]
bg <- prob[df_metagene_score_final_TEST$flag_yes_no == 0]
# ROC Curve
roc <- roc.curve(scores.class0 = fg, scores.class1 = bg, curve = TRUE)
plot(roc)
# PR Curve
pr <- pr.curve(scores.class0 = fg, scores.class1 = bg, curve = TRUE)
plot(pr)
cat("AUPR is:", pr$auc.integral)
##################################
# Better AUPR plot using ggplot
##################################
source("convert_aupr_to_ggplot.R")
i_y_line_threshold_signif_aupr = length(which(df_metagene_score_final_TEST$flag_yes_no == 1))/(length(which(df_metagene_score_final_TEST$flag_yes_no == 1)) + length(which(df_metagene_score_final_TEST$flag_yes_no == 0)))
convert_aupr_to_ggplot(i_y_line_threshold_signif=i_y_line_threshold_signif_aupr,
prroc_object=pr,
str_filename_save="aupr_ggplot_CV.pdf")
##################################
# get feature coefficient plot
# and feature importance plot
##################################
summary(glm_object_best)
as.table((glm_object_best$coefficients))
idx_order = order(glm_object_best$coefficients, decreasing = TRUE)
df_intercept_active = as.data.frame(glm_object_best$coefficients)
colnames(df_intercept_active)[1] <- "coeff"
df_intercept_active$gene_name = rownames(df_intercept_active)
#df_intercept_active = sqldf("select * from df_intercept_active
# where gene_name not in ('(Intercept)') ")
#theme_set(theme_classic())
theme_set(theme_gray())
gp <- ggplot(data=df_intercept_active, aes(x=gene_name, y=coeff))
gp <- gp + geom_bar(stat = "identity")
gp <- gp + xlab("Feature Name")
gp <- gp + ylab("Coefficient")
gp <- gp + coord_flip()
gp
ggsave(filename = "feature_importance_plot.pdf", useDingbats=FALSE)