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RLTtest.r
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RLTtest.r
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# survival analysis
set.seed(1)
n = 500
p = 10
X = cbind(matrix(rnorm(n*p), n, p), matrix(as.integer(runif(n*p)*10), n, p))
X = as.data.frame(X)
for (j in (1:p + p)) X[,j] = as.factor(X[,j])
censor = rbinom(n, 1, 0.5)
y = 10 + X[, 2] * 3 + rnorm(n)
trainn = n/2
testn = n - trainn
ntrees = 300
ncores = 5
nmin = 5
mtry = ncol(X)
sampleprob = 0.8
rule = "best"
nsplit = ifelse(rule == "best", 0, 3)
importance = TRUE
start_time <- Sys.time()
RLTfit <- RLT(X[1:trainn, ], y[1:trainn], censor[1:trainn], ntrees = ntrees, ncores = ncores, nmin = nmin, mtry = mtry, # obs.w = runif(trainn),
split.gen = rule, nsplit = nsplit, replacement = FALSE, resample.prob = sampleprob, kernel.ready = FALSE, importance = importance)
Sys.time() - start_time
RLTfit$cindex
barplot(t(RLTfit$VarImp))
getOneTree(RLTfit, 1)
library(randomForestSRC)
library(survival)
rsffit <- rfsrc(Surv(Y, Censor) ~ ., data = data.frame("x" = X[1:trainn, ], "Y" = y[1:trainn], "Censor" = censor[1:trainn]),
ntree = ntrees, nodesize = nmin, mtry = mtry, nsplit = nsplit, sampsize = trainn*sampleprob)
RLTpred = predict(RLTfit, X[1:trainn + testn, ], kernel = FALSE, ncores = ncores)
rsfpred = predict(rsffit, data = data.frame("x" = X[1:trainn + testn, ]))
apply(abs(rsfpred$survival - RLTpred$Survival), 2, median)
apply(abs(rsfpred$survival - RLTpred$Survival), 2, mean)
for (i in 1:15)
{
plot(rsfpred$time.interest, rsfpred$survival[i,], type = "s", col = "red", xlim = c(min(y), max(y)), ylim = c(0, 1))
points(RLTfit$timepoints, RLTpred$Survival[i,], type = "s", col = "blue")
abline(h = 0.5)
}
############################
set.seed(1)
n = 500
p = 100
#X <- data.frame(as.factor(sample(10,n,replace = TRUE)))
#for(i in 2:p) X <- cbind(X,as.factor(sample(10,n,replace = TRUE)))
#X <- cbind(X, matrix(rnorm(n*p,c(1:p),5/c(1:p)),ncol=p))
#X = cbind(matrix(rnorm(n*p), n, p), matrix(as.integer(runif(n*p)*10), n, p))
#X = as.data.frame(X)
#for (j in (1:p + p)) X[,j] = as.factor(X[,j])
X = cbind( matrix(as.integer(runif(n*p)*3), n, p), matrix(rnorm(n*p), n, p))
X = as.data.frame(X)
for (j in (1:p)) X[,j] = as.factor(X[,j])
# y = 2 + rowSums(data.matrix(X[, 1:5])) * 2 + rowSums(data.matrix(X[,1:5 + p]))*0.5 + rnorm(n)
censor = sample(c(0,1),n,replace = TRUE,prob = c(0.5,0.5))
y = 10 + rnorm(n)
trainn = n/2
testn = n - trainn
ntrees = 500
ncores = 5
nmin = 10
mtry = ncol(X)
sampleprob = 0.85
rule = "random"
nsplit = ifelse(rule == "best", 0, 3)
start_time <- Sys.time()
RLTfit <- RLT(X[1:trainn, ], y[1:trainn], censor[1:trainn], ntrees = ntrees, ncores = ncores, nmin = nmin, mtry = mtry, # obs.w = runif(trainn),
split.gen = rule, nsplit = nsplit, replacement = FALSE, resample.prob = sampleprob, kernel.ready = TRUE, importance = importance)
Sys.time() - start_time
rsffit <- rfsrc(Surv(Y, Censor) ~ ., data = data.frame("x" = X[1:trainn, ], "Y" = y[1:trainn], "Censor" = censor[1:trainn]),
ntree = ntrees, nodesize = nmin, mtry = mtry, nsplit = nsplit, sampsize = trainn*sampleprob)
RLTpred = predict(RLTfit, X[1:trainn + testn, ], kernel = FALSE, ncores = ncores)
rsfpred = predict(rsffit, data = data.frame("x" = X[1:trainn + testn, ]))
apply(abs(rsfpred$survival - RLTpred$Survival), 2, median)
apply(abs(rsfpred$survival - RLTpred$Survival), 2, mean)
RLTfit$cindex
max(rsffit$err.rate, na.rm = TRUE)
for (i in 101:115)
{
plot(rsfpred$time.interest, rsfpred$survival[i,], type = "s", col = "red", xlim = c(min(y), max(y)), ylim = c(0, 1))
points(RLTfit$timepoints, RLTpred$Survival[i,], type = "s", col = "blue")
abline(h = 0.5)
abline(v = 10)
}
barplot(t(RLTfit$VarImp))
getOneTree(RLTfit, 1)
start_time <- Sys.time()
RLTpred = predict(RLTfit, X[1:trainn + testn, ], kernel = FALSE, ncores = ncores)
Sys.time() - start_time
# plot(RLTpred$Survival[, 60], y[1:trainn + testn])
rsfpred = predict(rsffit, data = data.frame("x" = X[1:trainn + testn, ]))
RLTpred = predict(RLTfit, X[1:trainn + testn, ], kernel = FALSE, ncores = ncores)
rsfpred = predict(rsffit, data = data.frame("x" = X[1:trainn + testn, ]))
apply(abs(rsfpred$survival - RLTpred$Survival), 2, median)
apply(abs(rsfpred$survival - RLTpred$Survival), 2, mean)
rsffit$forest$nativeArray[rsffit$forest$nativeArray$treeID == 1, ]
getOneTree(RLTfit, 1)
par(mfrow=c(5, 3))
par(mar = c(0,0,0,0))
for (i in 101:115)
{
plot(rsfpred$time.interest, rsfpred$survival[i,], type = "s", col = "red", xlim = c(min(y), max(y)), ylim = c(0, 1))
points(RLTfit$timepoints, RLTpred$Survival[i,], type = "s", col = "blue")
abline(h = 0.5)
}
abline(v = 2 + X[trainn + i, 2] * 3 + 10)
plot(Surv( y[1:trainn], censor[1:trainn]))
# compare with other models
library(randomForest)
library(randomForestSRC)
set.seed(1)
n = 2000
p = 100
X = cbind(matrix(rnorm(n*p), n, p), matrix(as.integer(runif(n*p)*3), n, p))
for (j in (1:p + p)) X[,j] = as.factor(X[,j])
X = as.data.frame(X)
#y = 2 + as.numeric(rowSums(X[, 1:10])) * 1 + as.numeric(rowSums(X[,1:10 + p]))*0.25 + rnorm(n, 0, 1)
y = 2 + X[, 1] * 2 + X[, 2] * 2 + rnorm(n)
# y = 2 + as.numeric(rowSums(X[, 1:10])) * 1 + rnorm(n, 0, 1)
trainn = n/2
testn = n - trainn
trainX = X[1:trainn, ]
testX = X[1:trainn + testn, ]
trainY = y[1:trainn]
testY = y[1:trainn + testn]
ntrees = 1000
ncores = 1
nmin = 1
mtry = 2
sampleprob = 1
rule = "best"
nsplit = ifelse(rule == "best", 0, 3)
metric = data.frame(matrix(NA, 3, 4))
rownames(metric) = c("rlt", "rsf", "rf")
colnames(metric) = c("fit.time", "pred.time", "accuracy", "obj.size")
start_time <- Sys.time()
RLTfit <- RLT(trainX, trainY, ntrees = ntrees, ncores = ncores, nmin = nmin, mtry = mtry, # obs.w = runif(trainn),
split.gen = rule, nsplit = nsplit, replacement = FALSE, resample.prob = sampleprob, kernel.ready = FALSE)
metric[1, 1] = difftime(Sys.time(), start_time, units = "secs")
start_time <- Sys.time()
RLTPred <- predict(RLTfit, testX, ncores = ncores)
metric[1, 2] = difftime(Sys.time(), start_time, units = "secs")
metric[1, 3] = mean((RLTPred$Prediction - testY)^2)
metric[1, 4] = object.size(RLTfit)
options(rf.cores = ncores)
start_time <- Sys.time()
rsffit <- rfsrc(y ~ ., data = data.frame(trainX, "y"= trainY), ntree = ntrees, nodesize = nmin, mtry = mtry, nsplit = nsplit, sampsize = trainn*sampleprob)
metric[2, 1] = difftime(Sys.time(), start_time, units = "secs")
start_time <- Sys.time()
rsfpred = predict(rsffit, data.frame(testX))
metric[2, 2] = difftime(Sys.time(), start_time, units = "secs")
metric[2, 3] = mean((rsfpred$predicted - testY)^2)
metric[2, 4] = object.size(rsffit)
start_time <- Sys.time()
rf.fit <- randomForest(trainX, trainY, ntree = ntrees, mtry = mtry, nodesize = nmin, sampsize = trainn*sampleprob)
metric[3, 1] = difftime(Sys.time(), start_time, units = "secs")
start_time <- Sys.time()
rf.pred <- predict(rf.fit, testX)
metric[3, 2] = difftime(Sys.time(), start_time, units = "secs")
metric[3, 3] = mean((rf.pred - testY)^2)
metric[3, 4] = object.size(rf.fit)
metric