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modelCreation.R
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testSinglePatient$X <- NULL
library(doParallel)
library(foreach)
library(randomForest)
registerDoParallel(cores=4)
<<<<<<< HEAD
Patient.rf <- foreach(ntree = rep(7, 3), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(Patient[,4:ncol(Patient)], Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
=======
Patient.rf <- foreach(ntree = rep(45, 3), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(Patient[,4:ncol(Patient)], Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
#random forest on cfs
cfsPatient.rf <- foreach(ntree = rep(38, 3), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(cfsPatient[,4:ncol(cfsPatient)], cfsPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#random forest on lasso
<<<<<<< HEAD
lassoPatient.rf <- foreach(ntree = rep(2, 2), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(lassoPatient, SinglePatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
=======
lassoPatient.rf <- foreach(ntree = rep(1, 2), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(lassoPatient, Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
r <- Patient.rf
a <- importance(Patient.rf, scale = TRUE, type = 1)
a<-data.frame(a)
a$Vars<-row.names(a)
a <- a[order(a$X.IncMSE, decreasing = TRUE),][1:40,]
<<<<<<< HEAD
SinglePatientRFdata <- SinglePatient[,row.names(a)]
=======
PatientRFdata <- Patient[,row.names(a)]
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
library(randomForest)
#random forest selection
#nzv correlated features removed dataset
#low
set.seed(4353)
#low15NzvPatient.rf <- randomForest(dmIndicator~., data = data.matrix(low15NzvPatient), ntree = 35, keep.forest = FALSE, importance = TRUE)
<<<<<<< HEAD
low15NzvPatient.rf <- foreach(ntree = rep(2,3), .combine = combine, .packages = 'randomForest') %dopar% randomForest(low15NzvPatient[,4:ncol(low15NzvPatient)], low15NzvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
=======
low15NzvPatient.rf <- foreach(ntree = rep(3,4), .combine = combine, .packages = 'randomForest') %dopar% randomForest(low15NzvPatient[,4:ncol(low15NzvPatient)], low15NzvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
#high
#high95NzvPatient.rf <- randomForest(dmIndicator~., data = data.matrix(high95NzvPatient), ntree = 80, keep.forest = TRUE, importance = TRUE)
high95NzvPatient.rf <- foreach(ntree = rep(5, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(high95NzvPatient[,4:ncol(high95NzvPatient)], high95NzvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#zv correlated features removed dataset
#low
set.seed(9856)
<<<<<<< HEAD
low15ZvPatient.rf <- foreach(ntree = rep(21,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(low15ZvPatient[,4:ncol(low15ZvPatient)], SinglePatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#low15ZvPatient.rf <- randomForest(dmIndicator~., data = data.matrix(low15ZvPatient) , ntree = 350, keep.forest = FALSE, importance = TRUE)
#high
#high95ZvPatient.rf <- randomForest(dmIndicator~., data = data.matrix(high95NzvPatient), ntree = 500, keep.forest = FALSE, importance = TRUE)
high95ZvPatient.rf <- foreach(ntree = rep(30,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(high95ZvPatient[,4:ncol(high95ZvPatient)], high95ZvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#FS with rfimportance
SinglePatientRFdata.rf <- foreach(ntree = rep(3,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(SinglePatientRFdata, SinglePatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
=======
low15ZvPatient.rf <- foreach(ntree = rep(25,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(low15ZvPatient[,4:ncol(low15ZvPatient)], low15ZvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#low15ZvPatient.rf <- randomForest(dmIndicator~., data = data.matrix(low15ZvPatient) , ntree = 350, keep.forest = FALSE, importance = TRUE)
#high
#high95ZvPatient.rf <- randomForest(dmIndicator~., data = data.matrix(high95NzvPatient), ntree = 500, keep.forest = FALSE, importance = TRUE)
high95ZvPatient.rf <- foreach(ntree = rep(31,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(high95ZvPatient[,4:ncol(high95ZvPatient)], high95ZvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#FS with rfimportance
PatientRFdata.rf <- foreach(ntree = rep(3,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(PatientRFdata, Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
#random forest on above data
#model creation for classification
library(doParallel)
library(foreach)
library(randomForest)
registerDoParallel(cores=4)
CPatient.rf <- foreach(ntree = rep(5, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(Patient[,4:ncol(Patient)], Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#random forest on cfs
CcfsPatient.rf <- foreach(ntree = rep(5, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(cfsPatient[,4:ncol(cfsPatient)], cfsPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#random forest on lasso
<<<<<<< HEAD
ClassoPatient.rf <- foreach(ntree = rep(1, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(lassoPatient, SinglePatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
=======
ClassoPatient.rf <- foreach(ntree = rep(1, 3), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(lassoPatient, Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
Clow15NzvPatient.rf <- foreach(ntree = rep(2,3), .combine = combine, .packages = 'randomForest') %dopar% randomForest(low15NzvPatient[,4:ncol(low15NzvPatient)], low15NzvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#high
Chigh95NzvPatient.rf <- foreach(ntree = rep(2, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(high95NzvPatient[,4:ncol(high95NzvPatient)], high95NzvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#zv correlated feat#RFS for classification
library(doParallel)
library(foreach)
library(randomForest)
registerDoParallel(cores=4)
<<<<<<< HEAD
=======
CPatient.rf <- foreach(ntree = rep(5, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(Patient[,4:ncol(Patient)], Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#random forest on cfs
CcfsPatient.rf <- foreach(ntree = rep(5, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(cfsPatient[,4:ncol(cfsPatient)], cfsPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#random forest on lasso
ClassoPatient.rf <- foreach(ntree = rep(1, 3), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(lassoPatient, Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
Clow15NzvPatient.rf <- foreach(ntree = rep(2,3), .combine = combine, .packages = 'randomForest') %dopar% randomForest(low15NzvPatient[,4:ncol(low15NzvPatient)], low15NzvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#high
Chigh95NzvPatient.rf <- foreach(ntree = rep(2, 4), .combine = combine, .multicombine=TRUE, .packages= 'randomForest') %dopar% randomForest(high95NzvPatient[,4:ncol(high95NzvPatient)], high95NzvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#zv correlated features removed dataset
#low
Clow15ZvPatient.rf <- foreach(ntree = rep(6,3), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(low15ZvPatient[,4:ncol(low15ZvPatient)], low15ZvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#high
Chigh95ZvPatient.rf <- foreach(ntree = rep(5,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(high95ZvPatient[,4:ncol(high95ZvPatient)], high95ZvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
Cr <- CPatient.rf
a <- importance(CPatient.rf, scale = TRUE, type = 1)
a<-data.frame(a)
a$Vars<-row.names(a)
a <- a[order(a$X.IncMSE, decreasing = TRUE),][1:40,]
<<<<<<< HEAD
CSinglePatientRFdata <- SinglePatient[,row.names(a)]
CSinglePatientRFdata.rf <- foreach(ntree = rep(2,3), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(SinglePatientRFdata, SinglePatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
=======
CPatientRFdata <- Patient[,row.names(a)]
CPatientRFdata.rf <- foreach(ntree = rep(2,3), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(PatientRFdata, Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#low
Clow15ZvPatient.rf <- foreach(ntree = rep(6,3), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(low15ZvPatient[,4:ncol(low15ZvPatient)], low15ZvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#high
Chigh95ZvPatient.rf <- foreach(ntree = rep(5,4), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(high95ZvPatient[,4:ncol(high95ZvPatient)], high95ZvPatient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
Cr <- CPatient.rf
a <- importance(CPatient.rf, scale = TRUE, type = 1)
a<-data.frame(a)
a$Vars<-row.names(a)
a <- a[order(a$X.IncMSE, decreasing = TRUE),][1:40,]
CPatientRFdata <- Patient[,row.names(a)]
CPatientRFdata.rf <- foreach(ntree = rep(2,3), .combine = combine, .multicombine=TRUE, .packages = 'randomForest') %dopar% randomForest(PatientRFdata, Patient[,1], ntree = ntree, keep.forest = TRUE, importance = TRUE)
#random forest on above data
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
#Prediction
set.seed(4314)
<<<<<<< HEAD
low15NzvPatient.pred <- predict(low15NzvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
low15ZvPatient.pred <- predict(low15ZvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
high95NzvPatient.pred <- predict(high95NzvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
high95ZvPatient.pred <- predict(high95ZvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
cfsPatient.pred <- predict(cfsPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
lassoPatient.pred <- predict(lassoPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
Patient.pred <- predict(Patient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
PatientRFdata.pred <- predict(SinglePatientRFdata.rf, testSinglePatient, type = 'response', predict.all = TRUE)
a <- as.data.frame(PatientRFdata.pred$aggregate)
library(caret)
=======
low15NzvPatient.pred <- predict(Clow15NzvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
low15ZvPatient.pred <- predict(Clow15ZvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
high95NzvPatient.pred <- predict(Chigh95NzvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
high95ZvPatient.pred <- predict(Chigh95ZvPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
cfsPatient.pred <- predict(CcfsPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
lassoPatient.pred <- predict(ClassoPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
Patient.pred <- predict(CPatient.rf, testSinglePatient, type = 'response', predict.all = TRUE)
PatientRFdata.pred <- predict(CPatientRFdata.rf, testSinglePatient, type = 'response', predict.all = TRUE)
a <- as.data.frame(PatientRFdata.pred$aggregate)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
library(ROCR)
#ROC Curve
trainPatientRFdata.pred <- predict(CPatientRFdata.rf, SinglePatient[2:ncol(SinglePatient)], type = 'response', predict.all = TRUE)
cutoff <- 0.47
a <- as.data.frame(trainPatientRFdata.pred$aggregate)
a$`trainPatientRFdata.pred$aggregate`[a$`trainPatientRFdata.pred$aggregate`< cutoff] <- 0
a$`trainPatientRFdata.pred$aggregate`[a$`trainPatientRFdata.pred$aggregate`>= cutoff] <- 1
confusionMatrix( a$`trainPatientRFdata.pred$aggregate`, SinglePatient$dmIndicator)
p <- prediction(a, SinglePatient$dmIndicator)
perf <- performance(p, measure="tpr", x.measure="fpr")
plot(perf)
library(glmnet)
#LOGISTIC
#RIDGE
benchmark$DMIndicator[benchmark$DMIndicator < cutoff] <- 0
benchmark$DMIndicator[benchmark$DMIndicator >= cutoff] <- 1
<<<<<<< HEAD
x <- data.matrix(SinglePatientRFdata)
Y <- data.matrix(SinglePatient[,1])
cutoff <- 0.25
#RIDGE regression
#all 404 variables
ridgeFit <- glmnet(x, Y, family = "gaussian", alpha = 0, lambda = 0.00001)
predictRidge <- predict(ridgeFit, newx = x, type = "link")
=======
cutoff <- 0.38
#RIDGE regression
#all 404 variables
ridgeFit <- glmnet(mPatient, mY, family = "gaussian", alpha = 0, lambda = 0.00001)
predictRidge <- predict(ridgeFit, newx = mPatient, type = "link")
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
<<<<<<< HEAD
confusionMatrix(predictRidge, Y, dnn = c("Prediction", "Reference"))
#PatientRF
ridgeFit <- glmnet(data.matrix(SinglePatientRFdata), Y, family = "gaussian", alpha = 0, lambda = 0.001)
a <- testSinglePatient[, colnames(SinglePatientRFdata)]
=======
confusionMatrix(predictRidge, mY, dnn = c("Prediction", "Reference"))
#PatientRF
x <- data.matrix(PatientRFdata)
ridgeFit <- glmnet(x, mY, family = "gaussian", alpha = 0, lambda = 0.001)
a <- testSinglePatient[, colnames(PatientRFdata)]
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
predictRidge <- predict(ridgeFit, newx = data.matrix(a) , type = "link")
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
confusionMatrix(predictRidge, benchmark[,2], dnn = c("Prediction", "Reference"))
#lasso
<<<<<<< HEAD
ridgeFit <- glmnet(data.matrix(lassoPatient), Y, family = "gaussian", alpha = 0, lambda = 0.001)
=======
ridgeFit <- glmnet(data.matrix(lassoPatient), mY, family = "gaussian", alpha = 0, lambda = 0.001)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
a <- testSinglePatient[, colnames(lassoPatient)]
predictRidge <- predict(ridgeFit, newx = data.matrix(a) , type = "link")
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
confusionMatrix(predictRidge, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
#CFS
<<<<<<< HEAD
ridgeFit <- glmnet(data.matrix(cfsPatient[2:ncol(cfsPatient)]), Y, family = "gaussian", alpha = 0, lambda = 0.001)
=======
ridgeFit <- glmnet(data.matrix(cfsPatient[2:ncol(cfsPatient)]), mY, family = "gaussian", alpha = 0, lambda = 0.001)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
a <- testSinglePatient[, colnames(cfsPatient[2:ncol(cfsPatient)])]
predictRidge <- predict(ridgeFit, newx = data.matrix(a) , type = "link")
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
confusionMatrix(predictRidge, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
#correlations
#low15NzvPatient
<<<<<<< HEAD
ridgeFit <- glmnet(data.matrix(low15NzvPatient[2:ncol(low15NzvPatient)]), Y, family = "gaussian", alpha = 0, lambda = 0.001)
=======
ridgeFit <- glmnet(data.matrix(low15NzvPatient[2:ncol(low15NzvPatient)]), mY, family = "gaussian", alpha = 0, lambda = 0.001)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
a <- testSinglePatient[, colnames(low15NzvPatient[2:ncol(low15NzvPatient)])]
predictRidge <- predict(ridgeFit, newx = data.matrix(a) , type = "link")
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
confusionMatrix(predictRidge, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
#high95NzvPatient
<<<<<<< HEAD
ridgeFit <- glmnet(data.matrix(high95NzvPatient[2:ncol(high95NzvPatient)]), Y, family = "gaussian", alpha = 0, lambda = 0.001)
=======
ridgeFit <- glmnet(data.matrix(high95NzvPatient[2:ncol(high95NzvPatient)]), mY, family = "gaussian", alpha = 0, lambda = 0.001)
>>>>>>> 4d00ac53cdf216cad0acae7a23ba8413d6fd7fb9
a <- testSinglePatient[, colnames(high95NzvPatient[2:ncol(high95NzvPatient)])]
predictRidge <- predict(ridgeFit, newx = data.matrix(a) , type = "link")
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
confusionMatrix(predictRidge, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
#low15ZvPatient
<<<<<<< HEAD
ridgeFit <- glmnet(data.matrix(low15ZvPatient[2:ncol(low15ZvPatient)]), Y, family = "gaussian", alpha = 0, lambda = 0.001)
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ridgeFit <- glmnet(data.matrix(low15ZvPatient[2:ncol(low15ZvPatient)]), mY, family = "gaussian", alpha = 0, lambda = 0.001)
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a <- testSinglePatient[, colnames(low15ZvPatient[2:ncol(low15ZvPatient)])]
predictRidge <- predict(ridgeFit, newx = data.matrix(a) , type = "link")
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
confusionMatrix(predictRidge, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
#high95ZvPatient
<<<<<<< HEAD
ridgeFit <- glmnet(data.matrix(high95ZvPatient[2:ncol(high95ZvPatient)]), Y, family = "gaussian", alpha = 0, lambda = 0.001)
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ridgeFit <- glmnet(data.matrix(high95ZvPatient[2:ncol(high95ZvPatient)]), mY, family = "gaussian", alpha = 0, lambda = 0.001)
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a <- testSinglePatient[, colnames(high95ZvPatient[2:ncol(high95ZvPatient)])]
predictRidge <- predict(ridgeFit, newx = data.matrix(a) , type = "link")
predictRidge[predictRidge < cutoff] <- 0
predictRidge[predictRidge >= cutoff] <- 1
confusionMatrix(predictRidge, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictRidge)^2)
print(rmse)
<<<<<<< HEAD
cutoff <- 0.25
#LASSO regression
#all 404 variables
newx = data.matrix(testSinglePatient)
lassoFit <- glmnet(x, Y, family = "gaussian", alpha = 1, lambda = 0.00001)
predictLasso <- predict(lassoFit, x, type = "link")
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cutoff <- 0.43
#LASSO regression
#all 404 variables
newx = data.matrix(testSinglePatient)
lassoFit <- glmnet(mPatient, mY, family = "gaussian", alpha = 1, lambda = 0.00001)
predictLasso <- predict(lassoFit, mPatient, type = "link")
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rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
<<<<<<< HEAD
confusionMatrix(predictLasso, Y, dnn = c("Prediction", "Reference"))
#PatientRFdata
x <- data.matrix(SinglePatientRFdata)
lassoFit <- glmnet(x, Y, family = "gaussian", alpha = 1, lambda = 0.001)
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confusionMatrix(predictLasso, mY, dnn = c("Prediction", "Reference"))
#PatientRFdata
x <- data.matrix(PatientRFdata)
lassoFit <- glmnet(x, mY, family = "gaussian", alpha = 1, lambda = 0.001)
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a <- testSinglePatient[, colnames(PatientRFdata)]
predictLasso <- predict(lassoFit, newx = data.matrix(a) , type = "link")
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
confusionMatrix(predictLasso, benchmark[,2], dnn = c("Prediction", "Reference"))
#lasso
<<<<<<< HEAD
lassoFit <- glmnet(data.matrix(lassoPatient), Y, family = "gaussian", alpha = 1, lambda = 0.001)
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lassoFit <- glmnet(data.matrix(lassoPatient), mY, family = "gaussian", alpha = 1, lambda = 0.001)
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a <- testSinglePatient[, colnames(lassoPatient)]
predictLasso <- predict(lassoFit, newx = data.matrix(a) , type = "link")
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
confusionMatrix(predictLasso, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
#CFS
<<<<<<< HEAD
lassoFit <- glmnet(data.matrix(cfsPatient[2:ncol(cfsPatient)]), Y, family = "gaussian", alpha = 1, lambda = 0.001)
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lassoFit <- glmnet(data.matrix(cfsPatient[2:ncol(cfsPatient)]), mY, family = "gaussian", alpha = 1, lambda = 0.001)
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a <- testSinglePatient[, colnames(cfsPatient[2:ncol(cfsPatient)])]
predictLasso <- predict(lassoFit, newx = data.matrix(a) , type = "link")
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
confusionMatrix(predictLasso, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
#low15NzvPatient
<<<<<<< HEAD
lassoFit <- glmnet(data.matrix(low15NzvPatient[2:ncol(low15NzvPatient)]), Y, family = "gaussian", alpha = 1, lambda = 0.001)
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lassoFit <- glmnet(data.matrix(low15NzvPatient[2:ncol(low15NzvPatient)]), mY, family = "gaussian", alpha = 1, lambda = 0.001)
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a <- testSinglePatient[, colnames(low15NzvPatient[2:ncol(low15NzvPatient)])]
predictLasso <- predict(lassoFit, newx = data.matrix(a) , type = "link")
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
confusionMatrix(predictLasso, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
#high95NzvPatient
<<<<<<< HEAD
lassoFit <- glmnet(data.matrix(high95NzvPatient[2:ncol(high95NzvPatient)]), Y, family = "gaussian", alpha = 1, lambda = 0.001)
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lassoFit <- glmnet(data.matrix(high95NzvPatient[2:ncol(high95NzvPatient)]), mY, family = "gaussian", alpha = 1, lambda = 0.001)
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a <- testSinglePatient[, colnames(high95NzvPatient[2:ncol(high95NzvPatient)])]
predictLasso <- predict(lassoFit, newx = data.matrix(a) , type = "link")
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
confusionMatrix(predictLasso, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
#low15ZvPatient
<<<<<<< HEAD
lassoFit <- glmnet(data.matrix(low15ZvPatient[2:ncol(low15ZvPatient)]), Y, family = "gaussian", alpha = 1, lambda = 0.001)
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lassoFit <- glmnet(data.matrix(low15ZvPatient[2:ncol(low15ZvPatient)]), mY, family = "gaussian", alpha = 1, lambda = 0.001)
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a <- testSinglePatient[, colnames(low15ZvPatient[2:ncol(low15ZvPatient)])]
predictLasso <- predict(lassoFit, newx = data.matrix(a) , type = "link")
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
confusionMatrix(predictLasso, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
#high95ZvPatient
<<<<<<< HEAD
lassoFit <- glmnet(data.matrix(high95ZvPatient[2:ncol(high95ZvPatient)]), Y, family = "gaussian", alpha = 1, lambda = 0.001)
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lassoFit <- glmnet(data.matrix(high95ZvPatient[2:ncol(high95ZvPatient)]), mY, family = "gaussian", alpha = 1, lambda = 0.001)
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a <- testSinglePatient[, colnames(high95ZvPatient[2:ncol(high95ZvPatient)])]
predictLasso <- predict(lassoFit, newx = data.matrix(a) , type = "link")
predictLasso[predictLasso < cutoff] <- 0
predictLasso[predictLasso >= cutoff] <- 1
confusionMatrix(predictLasso, benchmark[,2], dnn = c("Prediction", "Reference"))
rmse <- mean((benchmark[,2] - predictLasso)^2)
print(rmse)
library(ROCR)
#ROC Curve
<<<<<<< HEAD
p <- prediction(predictRidge, benchmark[,2])
perf <- performance(p, measure="tpr", x.measure="fpr")
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p <- prediction(predictLasso, benchmark[,2])
perf <- performance(p, measure="lift", x.measure="rpp")
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plot(perf)
library(pROC)
<<<<<<< HEAD
r <- roc(as.factor(benchmark[,2]), as.numeric(predictLasso))
r <- smooth(r, n = 512)
plot(r)
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r <- roc(as.factor(benchmark[,2]), as.numeric(predictRidge))
r <- smooth(r, n = 512)
plot(r, add = TRUE)
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#LIFT
<<<<<<< HEAD
#SVM
library(e1071)
svmLasso <- svm(data.matrix(lassoPatient), Y, scale = TRUE)
svmCfs <- svm(data.matrix(cfsPatient), Y, scale = TRUE)
svmSPRF <- svm(data.matrix(SinglePatientRFdata), Y, scale = TRUE)
svmhN <- svm(data.matrix(high95NzvPatient), Y, scale = TRUE)
svmhZ <- svm(data.matrix(high95ZvPatient), Y, scale = TRUE)
svmlN <- svm(data.matrix(low15NzvPatient), Y, scale = TRUE)
svmlZ <- svm(data.matrix(low15ZvPatient), Y, scale = TRUE)
save.image()
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