.Rapp.history

?randomForest
require(ROCR)
?prediction
require(ROCR)
?prediction
.lib.loc
.lib.loc()
.Library
require(e1071)
?svm
remove.packages("SuperLearner")
?mclapply
require(parallel)
?mclapply
?invisible
?mclapply
require(parallel)
sessionInfo()
packageVersion("parallel")
?parLapply
remove.packages("SuperLearner")
q()
remove.packages("SuperLearner")
q()
install.packages("SuperLearner")
require(SuperLearner)
SL.rpart
SL.rpartPrune
require(rpart)
rpart.control
require(ipred)
?ipredbagg
require(parallel)
?mclapply
remove.packages("SuperLearner")
require("quantreg")
install.packages("quantreg")
remove.packages("SuperLearner")
require(SuperLearner)
?SuperLearner
require(SuperLearner)#
options(mc.cores=8)
## examples with multicore#
set.seed(23432)#
## training set#
n <- 500#
p <- 50#
X <- matrix(rnorm(n*p), nrow = n, ncol = p)#
colnames(X) <- paste("X", 1:p, sep="")#
X <- data.frame(X)#
Y <- rbinom(n, 1, plogis(X[, 1] + sqrt(abs(X[, 2] * X[, 3])) + X[, 2] - X[, 3] + rnorm(n)))#
## test set#
m <- 1000#
newX <- matrix(rnorm(m*p), nrow = m, ncol = p)#
colnames(newX) <- paste("X", 1:p, sep="")#
newX <- data.frame(newX)#
# generate Library and run Super Learner#
SL.library <- c("SL.glm", "SL.randomForest", "SL.gam", "SL.polymars", "SL.mean")#
testMC <- mcSuperLearner(Y = Y, X = X, newX = newX, SL.library = SL.library, method = "method.NNLS", family="binomial")
?break
?subset
detectCores()
require(parallel)
detectCores()
require(parallel)
getOption("mc.cores", 2L)
?try
remove.packages('gbm')
q9)
q()
require(ipred)
?ipredbagg
require(ltmle)
?ltmle
require(class)
?knn
require(glmnet)
glmnet
?glmnet
remove.packages("SuperLearner")
require(e1071)
?svm
require(gbm)
?gbm
require(parallel)
?mclapply
## Jeremy's simulation ###
f.X = function(x) rnorm(n)#
f.Y.x = function(x) 31.3 + 26.8*x + 1.6*x^2 - 2.2*x^3
mu.Y.x = function(x) 31.3 + 26.8*x + 1.6*x^2 - 2.2*x^3#
sigma_e = 21.2
f.X = function(x) rnorm(n)#
mu.Y.x = function(x) 31.3 + 26.8*x + 1.6*x^2 - 2.2*x^3#
sigma_e = 21.2#
f.Y.x = function(x) rnorm(length(x), mu.Y.x, sigma_e)
n = 164#
X = f.X(n)
f.Y.x = function(x) rnorm(length(x), mu.Y.x(x), sigma_e)
Y = f.Y.x(X)
head(Y)
plot(Y ~ X)
plot(Y ~ X, xlim=c(-2.5,2.5), ylim=c(-25, 120))
require(multcomp)
data("cholesterol")
names(cholesterol)
head(cholesterol)
head(cholesterol$trt)
length(cholesterol$trt)
require(FNN)
require(SuperLearner)
install.packages("FNN")
SL.gam
SL.stepAIC
SL.bayesglm
SL.polymars
remove.packages("SuperLearner")
require(ipred)
?ipredbagg
require(FNN)
?knn.reg
?gc
require(ltmle)
?ltmle
require(ltmle)
?ltmle
require(SuperLearner)
?SuperLearner
remove.packages("ltmle")
q()
?weekdays
?grep
?grepexpr
?gregexpr
require(xlsx)
?read.xlsx
require(SuperLearner)
SL.randomForest
?mtext
?randomForest
require(randomForest)
?randomForest
?model.frame
?colSums
?lda
?nlminb
?t.test
require(reshape)
?reshape
require(class)
?knn
?data.frame
?post
require(rpart)
?post
?postscript
paperseize
papersize
option(papersize)
getOption(papersize)
getOption('papersize')
?cor
?mtext
require(ShortRead)
?readAligned
?write.table
require(ShortRead)
?readAligned
?substr
?stringstr
require(Iso)
install.packages("Iso")
?pava.sa
require(Iso)
?pava.sa
X = dlnorm(seq(-10, 60, length.out = 500), 2, .6)
length(X)
X = dlnorm(seq(-10, 60, length.out = n), 2, .6)
n = 10#
X = dlnorm(seq(-10, 60, length.out = n), 2, .6)
length(X)
n = 10#
sim.size = 1000#
X.mean = rep(NA, sim.size)#
#
set.seed(1)#
for(i in 1:sim.size) {#
	X = dlnorm(seq(-10, 60, length.out = n), 2, .6)#
	X.mean[i] = mean(X)#
}
hist(X.mean)
head(X.mean)
X
plot(density(X.mean))
n = 10#
sim.size = 1000#
X.mean = rep(NA, sim.size)#
#
set.seed(1)#
for(i in 1:sim.size) {#
	X = dlnorm(seq(-10, 60, length.out = n), 2, .6)#
	X.mean[i] = mean(X)#
}#
#
plot(density(X.mean))
n = 5#
sim.size = 1000#
X.mean = rep(NA, sim.size)#
#
set.seed(1)#
for(i in 1:sim.size) {#
	X = dlnorm(seq(-10, 60, length.out = n), 2, .6)#
	X.mean[i] = mean(X)#
}#
#
plot(density(X.mean))
X
?png
require(glmnet)
?glmnet
?all.vars
?pch
?pdf
?points
?legend
?eps
require(Biobase)
?alignData
words <- function(...) paste(substitute(list(...)))[-1]
load.packages = words(grid, ShortRead, Biostrings, ggplot2, magrittr, ggplot2, RColorBrewer)#
lapply(load.packages, require, character.only=T)
?alignData
?read.table
require(polspline)
?polymars
?polyclass
?rep
rep(c(2,3,4), each=2)
?main
?title
?glm
?rbinom
n = 2000#
glm(rbinom(n, 1, .1) ~ -1 + rep(1,n), family=binomial, weight=rep(1,n))
?glm
?grid
require(ltmle)
?ltmle
require(ltmle)
?ltmle
require(ltmle)
require(splines)
?ns
requiree1071
require(e1071)
?svm
?density
?title
?density
require(polspline)
?polymars
require(SuperLearner)
SL.polymars
?polymars
require(ltmle)
?ltmle
?density
?plot.survfit
?survfit
require(survival)
?survfit
?grep
?cor.test
x = c(1,2,3)
y = c(10,20,10)
lm(y ~x)
y = c(10,20,15)
lm(y ~x)
vignette(all=FALSE)
require(gbm)
vignette(all=FALSE)
require(gbm)
vignettes(all=FALSE)
vignette(all=FALSE)
vignette("adjcurve")
tmp = vignette("adjcurve")
print(vl)
print(tmp)
edit(tmp)
library(SuperLearner, quietly=T)
q()
library(SuperLearner, quietly=TRUE, verbase=FALSE)
library(SuperLearner, quietly=TRUE, verbose=FALSE)
q()
suppressWarnings(library(SuperLearner, quietly=TRUE, verbose=FALSE))
suppressMessages(library(SuperLearner, quietly=TRUE, verbose=FALSE))
Sys.getenv("PATH")
getwd()
setwd("./Dropbox/06-Spring-2015/reproducibility/")
list.files()
require(tools)
Sys.setenv(PATH=paste(Sys.getenv("PATH"), "/usr/texbin", sep=":"))
Sys.getenv("PATH")
texi2dvi("writeup.tex", quiet=FALSE, pdf=TRUE)
tools::texi2dvi("writeup.tex", quiet=FALSE, pdf=TRUE, clean=TRUE)
Sys.getenv("PATH")