library(reshape2)
library(ggplot2)
library(dplyr)
library(RColorBrewer)
theme_set(theme_bw())
library(gbm)
plot_line_signallevel = function(Mresults, pm, cr, plot_addr){
ifelse(!dir.exists(file.path(plot_addr, paste0('ABSLOPE_pmiss',pm*100,'_corr',cr*10))),
dir.create(file.path(plot_addr, paste0('ABSLOPE_pmiss',pm*100,'_corr',cr*10))),
FALSE)
Sresults2 <- Mresults %>% filter(method == 'ABSLOPE') %>%
filter(p.miss == pm & corr == cr) %>% dplyr::select(-p.miss, -corr) %>%
group_by(method, signallevel, nspr, crit) %>%
summarise(mean_value = mean(value))
#head(Sresults2)
# Power
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_corr',cr*10,'/a_Power.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'pr') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(signallevel), group=factor(signallevel))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Power") +
scale_colour_discrete(name ="Signal strength")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# FDR
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_corr',cr*10,'/b_FDR.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'fdr') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(signallevel), group=factor(signallevel))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("FDR") +
scale_colour_discrete(name ="Signal strength") +
geom_hline(yintercept=0.1, linetype="dotted")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# bias_beta
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_corr',cr*10,'/c_bias_beta.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'bias_beta') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(signallevel), group=factor(signallevel))) +
geom_line() +
geom_point() +
#ylim(0, 1) +
xlab("Number of relevant features") +
ylab(bquote("Relative MSE of" ~ beta )) +
scale_colour_discrete(name ="Signal strength")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# bias_sigma
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_corr',cr*10,'/d_bias_sigma.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'bias_sigma') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(signallevel), group=factor(signallevel))) +
geom_line() +
geom_point() +
#ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Bias of sigma") +
scale_colour_discrete(name ="Signal strength")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# Prediction error (MSE)
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_corr',cr*10,'/e_pred_error.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'MSE') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(signallevel), group=factor(signallevel))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Prediction error") +
scale_colour_discrete(name ="Signal strength")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
}
plot_line_percentna = function(Mresults, sl, cr, plot_addr){
ifelse(!dir.exists(file.path(plot_addr, paste0('ABSLOPE_signallevel',sl,'_corr',cr*10))),
dir.create(file.path(plot_addr, paste0('ABSLOPE_signallevel',sl,'_corr',cr*10))),
FALSE)
Sresults3 <- Mresults %>% filter(method == 'ABSLOPE') %>%
filter(signallevel == sl & corr == cr) %>% dplyr::select(-signallevel, -corr) %>%
group_by(method, p.miss, nspr, crit) %>%
summarise(mean_value = mean(value))
#head(Sresults3)
# Power
pdf(paste0(plot_addr,'ABSLOPE_signallevel',sl,'_corr',cr*10,'/a_Power.pdf'), width = 6, height = 4, paper='special')
p <- Sresults3 %>% filter(crit == 'pr') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(p.miss), group=factor(p.miss))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Power") +
scale_colour_discrete(name ="Percentage NA")
#ggtitle(paste0('ABSLOPE: signal strength=',sl,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# FDR
pdf(paste0(plot_addr,'ABSLOPE_signallevel',sl,'_corr',cr*10,'/b_FDR.pdf'), width = 6, height = 4, paper='special')
p <- Sresults3 %>% filter(crit == 'fdr') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(p.miss), group=factor(p.miss))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("FDR") +
scale_colour_discrete(name ="Percentage NA") +
geom_hline(yintercept=0.1, linetype="dotted")
#ggtitle(paste0('ABSLOPE: signal strength=',sl,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# bias_beta
pdf(paste0(plot_addr,'ABSLOPE_signallevel',sl,'_corr',cr*10,'/c_bias_beta.pdf'), width = 6, height = 4, paper='special')
p <- Sresults3 %>% filter(crit == 'bias_beta') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(p.miss), group=factor(p.miss))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab(bquote("Relative MSE of" ~ beta )) +
scale_colour_discrete(name ="Percentage NA")
#ggtitle(paste0('ABSLOPE: signal strength=',sl,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# bias_sigma
pdf(paste0(plot_addr,'ABSLOPE_signallevel',sl,'_corr',cr*10,'/d_bias_sigma.pdf'), width = 6, height = 4, paper='special')
p <- Sresults3 %>% filter(crit == 'bias_sigma') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(p.miss), group=factor(p.miss))) +
geom_line() +
geom_point() +
#ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Bias of sigma") +
scale_colour_discrete(name ="Percentage NA")
#ggtitle(paste0('ABSLOPE: signal strength=',sl,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
# Prediction error (MSE)
pdf(paste0(plot_addr,'ABSLOPE_signallevel',sl,'_corr',cr*10,'/e_pred_error.pdf'), width = 6, height = 4, paper='special')
p <- Sresults3 %>% filter(crit == 'MSE') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(p.miss), group=factor(p.miss))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Prediction error") +
scale_colour_discrete(name ="Percentage NA")
#ggtitle(paste0('ABSLOPE: signal strength=',sl,', corr=',cr*10,', nb.simu=40'))
print(p)
dev.off()
}
plot_line_corr = function(Mresults, pm, sl, plot_addr){
ifelse(!dir.exists(file.path(plot_addr, paste0('ABSLOPE_pmiss',pm*100,'_signallevel',sl))),
dir.create(file.path(plot_addr, paste0('ABSLOPE_pmiss',pm*100,'_signallevel',sl))),
FALSE)
Sresults2 <- Mresults %>% filter(method == 'ABSLOPE') %>%
filter(p.miss == pm & signallevel == sl) %>% dplyr::select(-p.miss, -signallevel) %>%
group_by(method, corr, nspr, crit) %>%
summarise(mean_value = mean(value))
#head(Sresults2)
# Power
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_signallevel',sl,'/a_Power.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'pr') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(corr), group=factor(corr))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Power") +
scale_colour_discrete(name ="Correlation")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', signallevel=',sl,', nb.simu=40'))
print(p)
dev.off()
# FDR
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_signallevel',sl,'/b_FDR.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'fdr') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(corr), group=factor(corr))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("FDR") +
scale_colour_discrete(name ="Correlation") +
geom_hline(yintercept=0.1, linetype="dotted")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', signallevel=',sl,', nb.simu=40'))
print(p)
dev.off()
# bias_beta
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_signallevel',sl,'/c_bias_beta.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'bias_beta') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(corr), group=factor(corr))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab(bquote("Relative MSE of" ~ beta ))
scale_colour_discrete(name ="Correlation")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', signallevel=',sl,', nb.simu=40'))
print(p)
dev.off()
# bias_sigma
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_signallevel',sl,'/d_bias_sigma.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'bias_sigma') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(corr), group=factor(corr))) +
geom_line() +
geom_point() +
#ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Bias of sigma") +
scale_colour_discrete(name ="Correlation")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', signallevel=',sl,', nb.simu=40'))
print(p)
dev.off()
# Prediction error (MSE)
pdf(paste0(plot_addr,'ABSLOPE_pmiss',pm*100,'_signallevel',sl,'/e_pred_error.pdf'), width = 6, height = 4, paper='special')
p <- Sresults2 %>% filter(crit == 'MSE') %>% rename(obj_value = mean_value) %>%
ggplot(aes(x=nspr, y=obj_value, colour=factor(corr), group=factor(corr))) +
geom_line() +
geom_point() +
ylim(0, 1) +
xlab("Number of relevant features") +
ylab("Prediction error") +
scale_colour_discrete(name ="Correlation")
#ggtitle(paste0('ABSLOPE: p.miss=',pm*100,', signallevel=',sl,', nb.simu=40'))
print(p)
dev.off()
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.