R Graphical Manual

Last data update: 2014.03.03

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CranContrib
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lalonde (Package: CovSel) : Real data, Lalonde

In order for the code used to create this data frame to work text files available on Dehejia's webpage http://www.nber.org/~rdehejia/data/nswdata2.html need to be downloaded and stored in the working directory. The data frame consists of 297 treated units from a randomized evaluation of a labor training program, the National Supported Work (NSW) Demonstration, and 314 nonexperimental comparison units drawn from survey datasets.

treated <- read.table(file = "nswre74_treated.txt")
controls <- read.table(file = "cps3_controls.txt")
nsw <- rbind(treated, controls)
ue <- function(x) factor(ifelse(x > 0, 0, 1))
UE74 <- mapply(ue, nsw[, 8])
UE75 <- mapply(ue, nsw[, 9])
nsw[, 4:7] <- lapply(nsw[, 4:7], factor)
lalonde <- cbind(nsw[, 1:9], UE74, UE75, nsw[, 10])
colnames(lalonde) <- c("treat", "age", "educ", "black", "hisp", "married",
"nodegr", "re74", "re75", "u74", "u75", "re78")

● Data Source: CranContrib
● Keywords:
● Alias: lalonde
● 0 images

cov.sel (Package: CovSel) : Model-Free Selection of Covariate Sets

Dimension reduction of the covariate vector under unconfoundedness using model-free backward elimination algorithms, based on either marginal co-ordinate hypothesis testing, (MCH), (continuous covariates only) or kernel-based smoothing, (KS).

● Data Source: CranContrib
● Keywords:
● Alias: cov.sel
● 0 images

datc (Package: CovSel) : Simulated Data, Continuous

This data is simulated. The covariates, X, are all generated from a standard normal distribution and they are all independent except for x7 and x8 (cor(x7,x8)=0.5). The code generating the data is

library(MASS)
set.seed(9327529)
n<-1000
eta<-mvrnorm(n,rep(0,2),diag(1,2,2))
Sigma=diag(1,10,10)
Sigma[7,8]<-Sigma[8,7]<-0.5
X<-mvrnorm(n,rep(0,10),Sigma)
y0<-2+2*X[,1]+2*X[,2]+2*X[,5]+2*X[,6]+2*X[,8]+eta[,1]
y1<-4+2*X[,1]+2*X[,2]+2*X[,5]+2*X[,6]+2*X[,8]+eta[,2]
e<-1/(1+exp(-0.5*X[,1]-0.5*X[,2]-0.5*X[,3]-0.5*X[,4]-0.5*X[,7]))
T<-rbinom(n,1,e)
y<-y1*T+y0*(1-T)
datc<-data.frame(x1=X[,1],x2=X[,2],x3=X[,3],x4=X[,4],x5=X[,5],x6=X[,6],
x7=X[,7],x8=X[,8],x9=X[,9],x10=X[,10],y0,y1,y,T)

● Data Source: CranContrib
● Keywords:
● Alias: datc
● 0 images

datfc (Package: CovSel) : Simulated Data, Mixed

This data is simulated. The covariates, X, and the treatment, T, are all generated by simulating from independent or multivariate normal distributions and then some variables are dichotomized to get binary variables with a certain dependence structure. The code generating the data is

library(bindata)
set.seed(9327529)
n<-500
x1 <- rnorm(n, mean = 0, sd = 1)
x2 <- rbinom(n, 1, prob = 0.5)
x25 <- rmvbin(n, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x2 <- x25[,1]
Sigma <- matrix(c(1,0.5,0.5,1),ncol=2)
x34 <- mvrnorm(n, rep(0, 2), Sigma)
x3 <- x34[,1]
x4 <- x34[,2]
x5 <- x25[,2]
x6 <- rbinom(n, 1, prob = 0.5)
x7<- rnorm(n, mean = 0, sd = 1)
x8 <- rbinom(n, 1, prob = 0.5)
e0<-rnorm(n)
e1<-rnorm(n)
p <- 1/(1 + exp(3 - 1.2 * x1 - 3.7 * x2 - 1.5 * x3 - 0.3 * x4 - 0.3 * x5 - 1.9 * x8))
T <- rbinom(n, 1, prob = p)
y0 <- 4 + 2 * x1 + 3 * x4 + 5 * x5 + 2 * x6 + e0
y1 <- 2 + 2 * x1 + 3 * x4+ 5 * x5 + 2 * x6 + e1
y <- y1 * T + y0 * (1 - T)
datfc <- data.frame(x1, x2, x3, x4, x5, x6, x7, x8, y0, y1, y, T)
datfc[, c(2, 5, 6, 8)] <- lapply(datfc[, c(2, 5, 6, 8)], factor)
datfc[, 12] <- as.numeric(datfc[, 12])

● Data Source: CranContrib
● Keywords:
● Alias: datfc
● 0 images

datf (Package: CovSel) : Simulated Data, Factors

This data is simulated. The covariates, X, and the treatment, T, are all generated by simulating independent bernoulli distributions or from a multivariate normal distribution and then dichotomizing to get binary variables with a certain dependence structure.The code generating the data is

library(bindata)
set.seed(9327529)
n<-500
x1 <- rbinom(n, 1, prob = 0.5)
x25 <- rmvbin(n, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x34 <- rmvbin(n, bincorr=cbind(c(1,0.7),c(0.7,1)), margprob=c(0.5,0.5))
x2 <- x25[,1]
x3 <- x34[,1]
x4 <- x34[,2]
x5 <- x25[,2]
x6 <- rbinom(n, 1, prob = 0.5)
x7<- rbinom(n, 1, prob = 0.5)
x8 <- rbinom(n, 1, prob = 0.5)
e0<-rnorm(n)
e1<-rnorm(n)
p <- 1/(1 + exp(3 - 1.5 * x1 - 1.5 * x2 - 1.5 * x3 - 0.1 * x4 - 0.1 * x5 - 1.3 * x8))
T <- rbinom(n, 1, prob = p)
y0 <- 4 + 2 * x1 + 3 * x4 + 5 * x5 + 2 * x6 + e0
y1 <- 2 + 2 * x1 + 3 * x4+ 5 * x5 + 2 * x6 + e1
y <- y1 * T + y0 * (1 - T)
datf <- data.frame(x1, x2, x3, x4, x5, x6, x7, x8, y0, y1, y, T)
datf[, 1:8] <- lapply(datf[, 1:8], factor)
datf[, 12] <- as.numeric(datf[, 12])

● Data Source: CranContrib
● Keywords:
● Alias: datf
● 0 images

summary.cov.sel (Package: CovSel) : Summary

This function produce a summary of the results of the covariate selection done by invoking cov.sel.

● Data Source: CranContrib
● Keywords:
● Alias: summary.cov.sel
● 0 images

cov.sel.np (Package: CovSel) : cov.sel.np

Function called by cov.sel if type="np". Not meant to be used on its own.

● Data Source: CranContrib
● Keywords:
● Alias: cov.sel.np
● 0 images