library( knitr )
library(ANTsR)
library(visreg)
library(robustbase)
library(groupdata2)
library(ggplot2)
mypath<-'/Users/alex/GitHub/adforesight/' #'/Volumes/CivmUsers/omega/alex/GitHub/adforesight/'
# Load in Behavior and Imaging Data
#setwd( '/Users/alex/GitHub/adforesight/' )
setwd(mypath)
extension<-'CHIsp0p05'
output.path <- paste(mypath,'/mydata/outdata/',extension, '/', sep='') #sd2_projall_noscale/'
#setwd( '/Users/omega/alex/adforesight/' )
source(paste(mypath, '/R/myr2score.R',sep=''))
#output.path <- '/Users/omega/alex/adforesight/mydata/outdata/sd2_projall_noscale/'
if (dir.exists(output.path)){ 1} else {dir.create(output.path, recursive=TRUE)}
#if (dir.exists(paste(output.path,'/',extension, '/', sep='')){ 1} else {dir.create(paste(output.path,'/',extension, '/'), recursive=TRUE)}
# Load in Behavior and Imaging Data
behavior <- read.csv('./mydata/All_Behavior.csv')
labled.set <-read.csv('./mydata/legendsCHASS2symmetric.csv')
labeled.brain.img <- antsImageRead('./mydata/MDT_labels_chass_symmetric.nii.gz')
mask <- antsImageRead('./mydata/MDT_mask_e3.nii')
mask <- thresholdImage( mask, 0.1, Inf )
mang_files <- list.files(path = "./mydata/imdata/", pattern = "T2_to_MDT",full.names = T,recursive = T)
mang_mat <- imagesToMatrix(mang_files,mask)
jac_files <- list.files(path = "./mydata/imdata/", pattern = "jac_to_MDT",full.names = T,recursive = T)
jac_mat <- imagesToMatrix(jac_files,mask)
suscept_files <- list.files(path = "./mydata/imdata/", pattern = "X_to_MDT",full.names = T,recursive = T)
suscept_mat <- imagesToMatrix(suscept_files,mask)
#pick your contrast
mang_files <- suscept_files
mang_mat <- imagesToMatrix(mang_files,mask)
rows.test <- as.integer(c(2,3,4,9,15,17))
rows.train <- as.integer(c(23,8,18,13,14,19,16,12,11,24,20,7,6,1,22,10,5,21))
set.seed(1)
#https://cran.r-project.org/web/packages/groupdata2/vignettes/cross-validation_with_groupdata2.html#creating-folds-for-cross-validation
mygroup <- behavior$genotype[1:24]
myindex <- c(1:24)
mydfb <- data.frame("mysubject_index" = factor(as.integer(myindex)),"mygenotype"=mygroup)
kable(mydfb, align = 'c')
parts <- partition(mydfb, p = c(0.25), id_col = "mysubject_index", cat_col = 'mygenotype')
parts <- partition(mydfb, p = c(0.3), id_col = "mysubject_index", cat_col = 'mygenotype')
test_set <- parts[[1]]
train_set <- parts[[2]]
# %superseeding randomness
test_set$mysubject_index <- rows.test
test_set$mygenotype <- mygroup[rows.test]
train_set$mysubject_index <- rows.train
train_set$mygenotype <- mygroup[rows.train]
# Show test_set
test_set %>% kable()
train_set %>% kable()
train_set <- fold(train_set, k = 4, cat_col = 'mygenotype', id_col = 'mysubject_index')
train_set <- train_set[order(train_set$.folds),]
train_set %>% kable()
# Order by .folds
train_set <- train_set[order(train_set$.folds),]
train_set %>% kable()
set.seed(1)
k<-4
mysp <- 0.05 # 0.2 #0.05 # was 0.005 sparseness
mynvecs <- 50 #put back to 50 alex #tested with 10
myell1 <- 1 # make this smaller 0.5, Brian says it is not what i think just switch between l0 and l1
myits<-5 #put back to 5
mysmooth<-0
performances <- c()
myBICs <- c()
myR2score<-c()
myps<-c()
for (fold in 1:k){
# for (fold in 3){
gc(verbose = TRUE, reset = FALSE, full = TRUE)
print('fold:',fold)
print(fold)
# Create training set for this iteration
# Subset all the datapoints where .folds does not match the current fold
training_set <- train_set[train_set$.folds != fold, ]
## training_set %>% kable()
# Create test set for this iteration
# Subset all the datapoints where .folds matches the current fold
testing_set <- train_set[train_set$.folds == fold, ]
## testing_set %>% kable()
# # Train linear model on training set
# model <- lm(model, training_set)
# # Predict the dependent variable in the testing_set with the trained model
# predicted <- predict(model, testing_set, allow.new.levels=TRUE)
# RMSE <- rmse(predicted, testing_set[[dependent]])
# performances[fold] <- RMSE
rows.train <- as.integer(training_set$mysubject_index)
rows.test <- as.integer(testing_set$mysubject_index)
mang.train <- mang_mat[rows.train, ]
mang.test <- mang_mat[rows.test, ]
start_time <- Sys.time()
#alx adds powerit=1, statdir=output.path, robust=1
#considers scale(mat) and smooth=0.01
#ell1 = myell1
#statdir=paste0(output.path,'/sp5_20vec/')
#eanat_mang <- sparseDecom( inmatrix=mang.train, inmask=mask, nvecs=mynvecs, sparseness=mysp, cthresh=250, its=1, mycoption=0, verbose=1, powerit=0) #smooth = 0.15,
#make it 50 vectors
eanat_mang<-sparseDecom( inmatrix=mang.train, inmask=mask, nvecs=mynvecs, sparseness=mysp, cthresh=250, its=myits, mycoption=0,verbose=1,statdir=output.path, smooth=mysmooth )
end_time <- Sys.time()
t1time<-end_time - start_time
print(t1time)
behav.train <- behavior[training_set$mysubject_index, ]
behav.test <- behavior[testing_set$mysubject_index, ]
gc(verbose = TRUE, reset = FALSE, full = TRUE)
e1l<-list(eanat_mang$eigenanatomyimages)
#alex figure out mask formar
jeanat_mang <- joinEigenanatomy(mang.train, mask=NA, eanat_mang$eigenanatomyimages, graphdensity=0.1, joinMethod='multilevel', verbose=TRUE)
useeig_mang <- jeanat_mang$fusedlist
#alex prompt
avgmat_mang <- jeanat_mang$fusedlist
avgmat_mang <- avgmat_mang/rowSums(abs(avgmat_mang))
imgmat_train_mang <- (mang.train %*% t(avgmat_mang) )
imgmat_test_mang <- (mang.test %*% t(avgmat_mang) )
dist4.train <- behav.train[,'d4']
dist4.test <- behav.test[,'d4']
projs.train <- data.frame(cbind(dist4.train,imgmat_train_mang)) # column combined the behavior wth the projections
colnames(projs.train) <- c('Dist_4', paste0('Proj', c(1:ncol(imgmat_train_mang)))) # insert column names
projs.test <- data.frame(cbind(dist4.test,imgmat_test_mang)) # column combind the behavior wth the projections
colnames(projs.test) <- c('Dist_4', paste0('Proj', c(1:ncol(imgmat_test_mang)))) # insert column names
mylm <- lm('Dist_4 ~ .', data=projs.train) # behavior correlation with the number of projections
# step <- stepAIC(mylm, direction="both")
# step$anova # display results
distpred <- predict.lm(mylm, newdata=projs.test) # based on the linear model predict the distances for the same day
modsum <-summary(lm(distpred~dist4.test))
r2 <- modsum$adj.r.squared
my.p <- modsum$coefficients[2,4]
plot(dist4.test, distpred, xlab = 'Real Swim Distance on Day 4', ylab = 'Predicted Swim Distance on day 4',
main='Predicted vs. Real Swim Distance on Day 4', ylim = c(0,1000),xlim = c(0,1000)) # generate plot
mymodel<-lm(distpred~dist4.test)
mytheme <- theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
panel.background = element_rect(fill = "white"))
RSS <- c(crossprod(mymodel$residuals))
MSE <- RSS / length(mymodel$residuals)
RMSE <- sqrt(MSE)
#mymodelset[[fold]]<-mymodel
performances[fold]<-RMSE
myBICs[fold] <- BIC(mymodel)
myR2score[fold]<-myr2score(distpred,dist4.test)
myps[fold]<-my.p
myplot<- visreg(mymodel, gg=TRUE)
myplot + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid')) +
ggtitle(paste("RMSE=",formatC(RMSE,digits=2, format="f"), "R2score=",formatC(myR2score[fold],digits=2, format="f"), " BIC=", formatC(BIC(mymodel),digits=2, format="f")))
ggsave(paste(output.path,extension,'Mnfold',toString(fold),'.pdf',sep=''), plot = last_plot(), device = 'pdf',
scale = 1, width = 4, height = 4, units = c("in"),dpi = 300)
save(mylm, file=paste(output.path , "model", toString(fold), ".Rdata", sep=''))
#myplot + annotate("text", x = 4, y = 25, label = paste("RMSE=",RMSE))
}
###################################
##### for validation now ####
###################################
myminfold<-which(performances == min(performances), arr.ind = TRUE)
fold<-myminfold
load(file=paste(output.path , "model", toString(fold), ".Rdata", sep=''))
#get model for fold 3 or whatever the minimum is
rows.valid <- as.integer(test_set$mysubject_index)
mang.valid <- mang_mat[rows.valid, ]
imgmat_mang_valid <- (mang.valid %*% t(avgmat_mang) )
dist4.valid <- behavior[rows.valid, 'd4']
projs.valid <- data.frame(cbind(dist4.valid,imgmat_mang_valid))
colnames(projs.valid) <- c('Dist_4', paste0('Proj', c(1:ncol(imgmat_mang_valid )))) # insert column names
distpred <- predict.lm(mylm, newdata=projs.valid)
mymodel<-lm(distpred~dist4.valid)
RSS <- c(crossprod(mymodel$residuals))
MSE <- RSS / length(mymodel$residuals)
RMSE <- sqrt(MSE)
mysummary <-summary(mymodel)
r2pred <- mysummary$adj.r.squared
ppred <- mysummary$coefficients[2,4]
max(behavior$d4[1:24])
#mymodelset[[fold]]<-mymodel
RMSE_valid<-RMSE
BIC_valid <- BIC(mymodel)
R2score_valid<-myr2score(distpred,dist4.valid)
#panel.background = element_rect(fill = "white")
myplot<- visreg(mymodel, gg=TRUE)
myplot + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid')) +
#scale_x_continuous(limits = c(min(behavior$d4[1:24]), max(behavior$d4[1:24])))+
#scale_y_continuous(limits = c(min(behavior$d4[1:24]), max(behavior$d4[1:24])))+
#scale_x_continuous(limits=c(min(dist4.valid,distpred), max(dist4.valid,distpred))) +
#scale_y_continuous(limits=c(min(dist4.valid,distpred), max(dist4.valid,distpred))) +
#xlim(0,1200)+ylim(0,1200)+coord_cartesian(xlim = c(100, 1200),ylim = c(100, 1200)) + coord_equal()+
ggtitle(paste("RMSE=",formatC(RMSE_valid,digits=2, format="f"), "R2score=",formatC(R2score_valid,digits=2, format="f"), " BIC=", formatC(BIC_valid,digits=2, format="f"),
" R2=", formatC(r2pred,digits=2, format="f"), " p= ", formatC(ppred,digits=4, format="f")))
ggsave(paste(output.path,extension,'MnValidationSet',toString(fold),'.pdf',sep=''), plot = last_plot(), device = 'pdf',
scale = 1, width = 4, height = 4, units = c("in"),dpi = 300)
numcols<-dim(imgmat_mang_valid)[2]
rd4 <- t(t(dist4.valid)[rep(1,c(numcols)),])
pcor<-c(numcols)
corval<-c(numcols)
cor(rd4,imgmat_mang_valid)
for (i in 1:numcols) {
mypcor<-cor.test(t(dist4.valid),t(imgmat_mang_valid[,i]))
pcor[i]<-mypcor$p.value
corval[i]<-mypcor$estimate
}
for (i in 1:numcols){
res1eig<-matrixToImages(jeanat_mang$fusedlist,mask = mask)[[i]] #eig1
antsImageWrite(res1eig,paste(output.path ,extension,'JoinEanatMang' ,as.character(i), '.nii.gz',sep="_"))
}
mycorsdf_eig2d4<-data.frame(rbind(pcor,corval),row.names=c("pval","cor"))
write.csv(mycorsdf_eig2d4, file = paste(output.path ,extension,'eig2d4cors.csv'))
#redo fold min or save models
# reportAnatomy <- function( eigenImageList, mask, weight = 0.3 )
# {
# sccanAalLabels <- c()
# for( eigenImage in eigenImageList )
# {
# nonZeroIndices<- abs( eigenImage[mask == 1] ) > 0
# sccanAalLabels <- append( sccanAalLabels, aalImage[mask == 1][nonZeroIndices] )
# }
# }
# reportedAnatomy <- reportAnatomy( SccanImages, mask )
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