R/plot.mnps.2.R
In mattcefalu/twang: Toolkit for Weighting and Analysis of Nonequivalent Groups
Defines functions
plot.mnps
Documented in
plot.mnps
##require(twang); data(AOD); AOD$crimjust[198:202] <- NA; mnps.AOD <- mnps(treat ~ illact + crimjust + subprob + subdep + white, data = AOD, estimand = "ATT", stop.method = c("ks.max","es.max"), n.trees = 1000, treatATT = 'community')
plot.mnps <- function(x,plots="optimize", pairwiseMax = TRUE, figureRows = 1, color = TRUE, subset = NULL, ...)
{
# Creates diag.plot plots and sends to current device
# x: ps object
# label: Label added to the plot titles
# extract the propensity scores and weights from the ps object
if(is.null(subset)) subset <- 1:length(x$stopMethods)
ltBl <- ifelse(color, "lightblue","gray80")
rdCol <- ifelse(color, "red","black")
stripBgCol <- ifelse(color, "#ffe5cc", "transparent")
ptSymCol <- ifelse(color, "#0080ff", "black")
ifelse(x$estimand == "ATE", noKS <- TRUE, noKS <- FALSE)
subst <- whichVar <- pVal <- weighted <- NULL
if(length(plots) > 1) stop("The `plots' argument must be of length 1.")
if(!(plots %in% c(1,2,3,4,5)) & !(plots %in% c("boxplot","ks","optimize","es","t")))
stop("Invalid choice of `plots' argument.")
if(plots == 2 | plots == "boxplot"){
boxplot(x, color = color, stop.method = subset, ...)
}
else{
if(!pairwiseMax | plots == "optimize" | plots == 1){
nPlot <- x$nFits
ptHld <- vector(mode = "list", length = nPlot)
for(i in 1:nPlot){
if(x$estimand == "ATT") ptNm <- paste("Balance for", x$levExceptTreatATT[i], "versus unweighted", x$treatATT)
else ptNm <- paste("Balance for", x$treatLev[i], "against others")
ptHld[[i]] <- plot(x$psList[[i]], main = ptNm, plots = plots, noKS = TRUE, color = color, subset=subset, ...)
}
figCol <- ceiling(nPlot/figureRows)
if(dev.cur() == 1) dev.new()
curCol <- curRow <- 1
for(i in 1:(nPlot-1)){
print(ptHld[[i]], split = c(curCol,curRow,nx = figCol,ny = figureRows), more = TRUE)
if(curCol < figCol){
curCol <- curCol + 1
}
else {
curCol <- 1
curRow <- curRow + 1
}
}
print(ptHld[[nPlot]], split = c(curCol,curRow,nx = figCol,ny = figureRows), more = FALSE)
}
else{ ## if pairwiseMax and plots == something other than optimize
n.tp <- length(x$psList[[1]]$desc)
n.psFits <- length(x$psList)
# return(esDat)
if (plots == "es" || plots == 3) { ## es plot
esDat <- makePlotDat(x$psList[[1]], whichPlot = 3, subsetStopMeth = subset)
nVar <- length(makePlotDat(x$psList[[1]], whichPlot = 3, subsetStopMeth = 1, yOnly = TRUE, incUnw = FALSE)$pVal)
effSzList <- effSzListUnw <- pValListUnw <- pValList <- matrix(NA, nrow = nVar, ncol = length(x$psList))
pwc <- bal.table(x, collapse.to = "covariate")
#if(x$estimand == "ATE"){
# pwc <- pairwiseComparison(x, collapse.to = "covariate")
# }
cnt <- 0
for(k in subset){
cnt <- cnt + 1
#if(x$estimand == "ATT"){
#for(j in 1:length(x$psList)){
#x2 <- x$psList[[j]]
#effSzList[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = k, yOnly = TRUE, incUnw = FALSE)$effectSize
#effSzListUnw[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = 1, yOnly = TRUE, incUnw = TRUE)$effectSize[(nVar + 1):(2*nVar)]
#pValListUnw[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = 1, yOnly = TRUE, incUnw = TRUE)$pVal[(nVar + 1):(2*nVar)]
#pValList[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = k, yOnly = TRUE, incUnw = FALSE)$pVal
#}
#effSzList <- abs(effSzList)
#effSzListUnw <- abs(effSzListUnw)
#collapsed <- apply(effSzList, 1, max)
#collapsedUnw <- apply(effSzListUnw, 1, max)
#collapsedP <- apply(pValList < .05, 1, max)
#collapsedUnwP <- apply(pValListUnw < .05, 1, max)
collapsed <- pwc$max.std.eff.sz[pwc$stop.method == x$stopMethods[k]]
collapsedP <- pwc$min.p[pwc$stop.method == x$stopMethods[k]] < .05
# collapsedUnw <- rep(pwc$max.std.eff.sz[pwc$stop.method == "unw"], length(subset))
# collapsedUnwP <- rep(pwc$min.p[pwc$stop.method == "unw"], length(subset)) < .05
collapsedUnw <- pwc$max.std.eff.sz[pwc$stop.method == "unw"]
collapsedUnwP <- pwc$min.p[pwc$stop.method == "unw"] < .05
#}
esBigHold <- collapsed > collapsedUnw
esDat$effectSize[(1:(2*nVar)) + (cnt-1)*2*nVar] <- c(collapsed, collapsedUnw)
esDat$pVal[(1:(2*nVar)) + (cnt-1)*2*nVar] <- c(collapsedP, collapsedUnwP)
esDat$esBig[(1:(2*nVar)) + (cnt-1)*2*nVar] <- rep(esBigHold, 2)
}
yMax <- min(3,max(esDat[,1])) + .05
if(max(esDat[,1], na.rm=TRUE) > 3)
warning("Some effect sizes are larger than 3 and may not have been plotted.\n")
nullPlot <- TRUE
subsetHold <- !esDat$esBig
if(any(subsetHold)){
esDatTmp <- esDat
esDatTmp$effectSize[!subsetHold] <- NA
pt1.1 <- xyplot(effectSize ~ weighted | whichComp, groups = whichVar, data = esDatTmp, scales = list(alternating = 1),
ylim = c(-.05, yMax), type = "l", col = ltBl, as.table = TRUE,
ylab = "Absolute standardized difference \n (maximum pairwise)", xlab = NULL, par.settings = list(strip.background = list(col=stripBgCol)),
panel = function(...){
panel.abline(h=c(.2,.5,.8), col="gray80")
panel.xyplot(...)
})
nullPlot <- FALSE
currPt <- pt1.1
}
subsetHold <- esDat$esBig
if(any(subsetHold)){
esDatTmp <- esDat
esDatTmp$effectSize[!subsetHold] <- NA
pt1.2 <- xyplot(effectSize ~ weighted | whichComp, groups = whichVar,
data = esDatTmp, ylab = "Absolute standard difference", xlab = NULL, as.table = TRUE,
ylim = c(-.05, yMax), type = "l", col = rdCol, par.settings = list(strip.background = list(col=stripBgCol)),
lwd = 2, ...)
if(!nullPlot){
currPt <- currPt + pt1.2
}
else{
currPt <- pt1.2
nullPlot <- FALSE
}
}
if(all(esDat$pVal >= .05)) pchHold <- 19
else if(all(esDat$pVal < .05)) pchHold <- 1
else pchHold <- c(19,1)
pt2 <- xyplot(effectSize ~ weighted | whichComp, groups = (pVal < 0.05), data = esDat,
ylab = "Absolute standard difference", xlab = NULL, as.table = TRUE,
ylim = c(-.05, yMax), type = "p", col = rdCol, pch = pchHold,par.settings = list(strip.background = list(col=stripBgCol)),
...)
if(!nullPlot) currPt <- currPt + pt2
else currPt <- pt2
return(currPt)
}
if (plots == "t" || plots == 4) { ## t p-values plot
esDat <- makePlotDat(x$psList[[1]], whichPlot = 4, subsetStopMeth = subset)
nVar <- length(makePlotDat(x$psList[[1]], whichPlot = 4, subsetStopMeth = 1, yOnly = TRUE, incUnw = FALSE))
effSzList <- effSzListUnw <- matrix(NA, nrow = nVar, ncol = length(x$psList))
pwc <- bal.table(x, collapse.to = "covariate")
cnt <- 0
for(k in subset){
cnt <- cnt + 1
for(j in 1:length(x$psList)){
collapsed <- pwc$min.p[pwc$stop.method == x$stopMethods[k]]
collapsedUnw <- pwc$min.p[pwc$stop.method == 'unw']
collRanks <- rank(collapsed, ties.method = "first")
esBigHold <- collapsed > collapsedUnw
esDat$tPVal[(1:(2*nVar)) + (cnt - 1)*2*nVar] <- c(collapsed, collapsedUnw)
esDat$tRank[(1:(2*nVar)) + (cnt - 1)*2*nVar] <- rep(collRanks, 2)
}
}
# if(is.null(subst)) subst <- 1:length(levels(as.factor(esDat$whichComp)))
n.var2 <- max(esDat$tRank * (!is.na(esDat$tPVal)), na.rm=TRUE)
pt1 <- xyplot(tPVal~tRank|whichComp, groups = weighted, data=esDat, xlab = "Rank of p-value rank for pretreatment variables \n (hollow is weighted, solid is unweighted)", ylab = "t- and chi-squared p-values \n (pairwise minimum)", pch = c(19,1), col = "black", scales = list(alternating = 1), par.settings = list(strip.background = list(col=stripBgCol)),
#subst = (as.factor(esDat$whichComp) %in% levels(as.factor(esDat$whichComp))[subst]) & (esDat$tRank <= n.var2),
ylim = c(-.1, 1.1), ...,
panel = function(...){
panel.xyplot(x=c(1,n.var2), y=c(0,1), col=ltBl, type="l")
# panel.abline(a=0, b=1, col="lightblue")
panel.xyplot(...)
}
)
}
if (plots =="ks" || plots ==5) { ## ks plot
if(x$estimand =="ATE") pwc <- pairwiseComparison(x, collapse.to = "covariate")
esDat <- makePlotDat(x$psList[[1]], whichPlot = 5, subsetStopMeth = subset)
nVar <- length(makePlotDat(x$psList[[1]], whichPlot = 5, subsetStopMeth = 1, yOnly = TRUE, incUnw = FALSE))
effSzList <- effSzListUnw <- matrix(NA, nrow = nVar, ncol = length(x$psList))
cnt <- 0
for(k in subset){
cnt <- cnt + 1
for(j in 1:length(x$psList)){
x2 <- x$psList[[j]]
effSzList[,j] <- makePlotDat(x2, whichPlot = 5, subsetStopMeth = k, yOnly = TRUE, incUnw = FALSE)
effSzListUnw[,j] <- makePlotDat(x2, whichPlot = 5, subsetStopMeth = 1, yOnly = TRUE, incUnw = TRUE)[1:nVar]
collapsed <- apply(effSzList, 1, max)
if(x$estimand == "ATE") collapsed <- pwc$min.ks.pval[pwc$stop.method == x$stopMethods[k]]
collapsedUnw <- apply(effSzListUnw, 1, max)
if(x$estimand == "ATE") collapsedUnw <- pwc$min.ks.pval[pwc$stop.method == "unw"]
collRanks <- rank(collapsed, ties.method = "first")
esBigHold <- collapsed > collapsedUnw
esDat$ksPVal[(1:(2*nVar)) + (cnt-1)*2*nVar] <- c(collapsed, collapsedUnw)
esDat$ksRank[(1:(2*nVar)) + (cnt-1)*2*nVar] <- rep(collRanks, 2)
}
}
if(is.null(subst)) subst <- 1:length(levels(as.factor(esDat$whichComp)))
n.var2 <- max(esDat$ksRank*(!is.na(esDat$ksPVal)), na.rm=TRUE)
pt1 <- xyplot(ksPVal~ksRank|whichComp, groups=weighted, scales = list(alternating = 1), data = esDat,ylim = c(-.1, 1.1), ..., xlab = "Rank of p-value rank for pretreatment variables \n (hollow is weighted, solid is unweighted)", ylab = "KS test p-values", pch = c(19,1), col="black",par.settings = list(strip.background = list(col=stripBgCol)),
subst = (as.factor(esDat$whichComp) %in% levels(as.factor(esDat$whichComp))[subst]) & (esDat$ksRank <= n.var2),
panel = function(...){
panel.xyplot(x=c(1,n.var2), y=c(0,1), col=ltBl, type="l")
panel.xyplot(...)
})
}
return(pt1)
}
}
}
mattcefalu/twang documentation built on Oct. 24, 2021, 6:38 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions plot.mnps
Documented in plot.mnps
##require(twang); data(AOD); AOD$crimjust[198:202] <- NA; mnps.AOD <- mnps(treat ~ illact + crimjust + subprob + subdep + white, data = AOD, estimand = "ATT", stop.method = c("ks.max","es.max"), n.trees = 1000, treatATT = 'community')
plot.mnps <- function(x,plots="optimize", pairwiseMax = TRUE, figureRows = 1, color = TRUE, subset = NULL, ...)
{
# Creates diag.plot plots and sends to current device
# x: ps object
# label: Label added to the plot titles
# extract the propensity scores and weights from the ps object
if(is.null(subset)) subset <- 1:length(x$stopMethods)
ltBl <- ifelse(color, "lightblue","gray80")
rdCol <- ifelse(color, "red","black")
stripBgCol <- ifelse(color, "#ffe5cc", "transparent")
ptSymCol <- ifelse(color, "#0080ff", "black")
ifelse(x$estimand == "ATE", noKS <- TRUE, noKS <- FALSE)
subst <- whichVar <- pVal <- weighted <- NULL
if(length(plots) > 1) stop("The `plots' argument must be of length 1.")
if(!(plots %in% c(1,2,3,4,5)) & !(plots %in% c("boxplot","ks","optimize","es","t")))
stop("Invalid choice of `plots' argument.")
if(plots == 2 | plots == "boxplot"){
boxplot(x, color = color, stop.method = subset, ...)
}
else{
if(!pairwiseMax | plots == "optimize" | plots == 1){
nPlot <- x$nFits
ptHld <- vector(mode = "list", length = nPlot)
for(i in 1:nPlot){
if(x$estimand == "ATT") ptNm <- paste("Balance for", x$levExceptTreatATT[i], "versus unweighted", x$treatATT)
else ptNm <- paste("Balance for", x$treatLev[i], "against others")
ptHld[[i]] <- plot(x$psList[[i]], main = ptNm, plots = plots, noKS = TRUE, color = color, subset=subset, ...)
}
figCol <- ceiling(nPlot/figureRows)
if(dev.cur() == 1) dev.new()
curCol <- curRow <- 1
for(i in 1:(nPlot-1)){
print(ptHld[[i]], split = c(curCol,curRow,nx = figCol,ny = figureRows), more = TRUE)
if(curCol < figCol){
curCol <- curCol + 1
}
else {
curCol <- 1
curRow <- curRow + 1
}
}
print(ptHld[[nPlot]], split = c(curCol,curRow,nx = figCol,ny = figureRows), more = FALSE)
}
else{ ## if pairwiseMax and plots == something other than optimize
n.tp <- length(x$psList[[1]]$desc)
n.psFits <- length(x$psList)
# return(esDat)
if (plots == "es" || plots == 3) { ## es plot
esDat <- makePlotDat(x$psList[[1]], whichPlot = 3, subsetStopMeth = subset)
nVar <- length(makePlotDat(x$psList[[1]], whichPlot = 3, subsetStopMeth = 1, yOnly = TRUE, incUnw = FALSE)$pVal)
effSzList <- effSzListUnw <- pValListUnw <- pValList <- matrix(NA, nrow = nVar, ncol = length(x$psList))
pwc <- bal.table(x, collapse.to = "covariate")
#if(x$estimand == "ATE"){
# pwc <- pairwiseComparison(x, collapse.to = "covariate")
# }
cnt <- 0
for(k in subset){
cnt <- cnt + 1
#if(x$estimand == "ATT"){
#for(j in 1:length(x$psList)){
#x2 <- x$psList[[j]]
#effSzList[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = k, yOnly = TRUE, incUnw = FALSE)$effectSize
#effSzListUnw[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = 1, yOnly = TRUE, incUnw = TRUE)$effectSize[(nVar + 1):(2*nVar)]
#pValListUnw[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = 1, yOnly = TRUE, incUnw = TRUE)$pVal[(nVar + 1):(2*nVar)]
#pValList[,j] <- makePlotDat(x2, whichPlot = 3, subsetStopMeth = k, yOnly = TRUE, incUnw = FALSE)$pVal
#}
#effSzList <- abs(effSzList)
#effSzListUnw <- abs(effSzListUnw)
#collapsed <- apply(effSzList, 1, max)
#collapsedUnw <- apply(effSzListUnw, 1, max)
#collapsedP <- apply(pValList < .05, 1, max)
#collapsedUnwP <- apply(pValListUnw < .05, 1, max)
collapsed <- pwc$max.std.eff.sz[pwc$stop.method == x$stopMethods[k]]
collapsedP <- pwc$min.p[pwc$stop.method == x$stopMethods[k]] < .05
# collapsedUnw <- rep(pwc$max.std.eff.sz[pwc$stop.method == "unw"], length(subset))
# collapsedUnwP <- rep(pwc$min.p[pwc$stop.method == "unw"], length(subset)) < .05
collapsedUnw <- pwc$max.std.eff.sz[pwc$stop.method == "unw"]
collapsedUnwP <- pwc$min.p[pwc$stop.method == "unw"] < .05
#}
esBigHold <- collapsed > collapsedUnw
esDat$effectSize[(1:(2*nVar)) + (cnt-1)*2*nVar] <- c(collapsed, collapsedUnw)
esDat$pVal[(1:(2*nVar)) + (cnt-1)*2*nVar] <- c(collapsedP, collapsedUnwP)
esDat$esBig[(1:(2*nVar)) + (cnt-1)*2*nVar] <- rep(esBigHold, 2)
}
yMax <- min(3,max(esDat[,1])) + .05
if(max(esDat[,1], na.rm=TRUE) > 3)
warning("Some effect sizes are larger than 3 and may not have been plotted.\n")
nullPlot <- TRUE
subsetHold <- !esDat$esBig
if(any(subsetHold)){
esDatTmp <- esDat
esDatTmp$effectSize[!subsetHold] <- NA
pt1.1 <- xyplot(effectSize ~ weighted | whichComp, groups = whichVar, data = esDatTmp, scales = list(alternating = 1),
ylim = c(-.05, yMax), type = "l", col = ltBl, as.table = TRUE,
ylab = "Absolute standardized difference \n (maximum pairwise)", xlab = NULL, par.settings = list(strip.background = list(col=stripBgCol)),
panel = function(...){
panel.abline(h=c(.2,.5,.8), col="gray80")
panel.xyplot(...)
})
nullPlot <- FALSE
currPt <- pt1.1
}
subsetHold <- esDat$esBig
if(any(subsetHold)){
esDatTmp <- esDat
esDatTmp$effectSize[!subsetHold] <- NA
pt1.2 <- xyplot(effectSize ~ weighted | whichComp, groups = whichVar,
data = esDatTmp, ylab = "Absolute standard difference", xlab = NULL, as.table = TRUE,
ylim = c(-.05, yMax), type = "l", col = rdCol, par.settings = list(strip.background = list(col=stripBgCol)),
lwd = 2, ...)
if(!nullPlot){
currPt <- currPt + pt1.2
}
else{
currPt <- pt1.2
nullPlot <- FALSE
}
}
if(all(esDat$pVal >= .05)) pchHold <- 19
else if(all(esDat$pVal < .05)) pchHold <- 1
else pchHold <- c(19,1)
pt2 <- xyplot(effectSize ~ weighted | whichComp, groups = (pVal < 0.05), data = esDat,
ylab = "Absolute standard difference", xlab = NULL, as.table = TRUE,
ylim = c(-.05, yMax), type = "p", col = rdCol, pch = pchHold,par.settings = list(strip.background = list(col=stripBgCol)),
...)
if(!nullPlot) currPt <- currPt + pt2
else currPt <- pt2
return(currPt)
}
if (plots == "t" || plots == 4) { ## t p-values plot
esDat <- makePlotDat(x$psList[[1]], whichPlot = 4, subsetStopMeth = subset)
nVar <- length(makePlotDat(x$psList[[1]], whichPlot = 4, subsetStopMeth = 1, yOnly = TRUE, incUnw = FALSE))
effSzList <- effSzListUnw <- matrix(NA, nrow = nVar, ncol = length(x$psList))
pwc <- bal.table(x, collapse.to = "covariate")
cnt <- 0
for(k in subset){
cnt <- cnt + 1
for(j in 1:length(x$psList)){
collapsed <- pwc$min.p[pwc$stop.method == x$stopMethods[k]]
collapsedUnw <- pwc$min.p[pwc$stop.method == 'unw']
collRanks <- rank(collapsed, ties.method = "first")
esBigHold <- collapsed > collapsedUnw
esDat$tPVal[(1:(2*nVar)) + (cnt - 1)*2*nVar] <- c(collapsed, collapsedUnw)
esDat$tRank[(1:(2*nVar)) + (cnt - 1)*2*nVar] <- rep(collRanks, 2)
}
}
# if(is.null(subst)) subst <- 1:length(levels(as.factor(esDat$whichComp)))
n.var2 <- max(esDat$tRank * (!is.na(esDat$tPVal)), na.rm=TRUE)
pt1 <- xyplot(tPVal~tRank|whichComp, groups = weighted, data=esDat, xlab = "Rank of p-value rank for pretreatment variables \n (hollow is weighted, solid is unweighted)", ylab = "t- and chi-squared p-values \n (pairwise minimum)", pch = c(19,1), col = "black", scales = list(alternating = 1), par.settings = list(strip.background = list(col=stripBgCol)),
#subst = (as.factor(esDat$whichComp) %in% levels(as.factor(esDat$whichComp))[subst]) & (esDat$tRank <= n.var2),
ylim = c(-.1, 1.1), ...,
panel = function(...){
panel.xyplot(x=c(1,n.var2), y=c(0,1), col=ltBl, type="l")
# panel.abline(a=0, b=1, col="lightblue")
panel.xyplot(...)
}
)
}
if (plots =="ks" || plots ==5) { ## ks plot
if(x$estimand =="ATE") pwc <- pairwiseComparison(x, collapse.to = "covariate")
esDat <- makePlotDat(x$psList[[1]], whichPlot = 5, subsetStopMeth = subset)
nVar <- length(makePlotDat(x$psList[[1]], whichPlot = 5, subsetStopMeth = 1, yOnly = TRUE, incUnw = FALSE))
effSzList <- effSzListUnw <- matrix(NA, nrow = nVar, ncol = length(x$psList))
cnt <- 0
for(k in subset){
cnt <- cnt + 1
for(j in 1:length(x$psList)){
x2 <- x$psList[[j]]
effSzList[,j] <- makePlotDat(x2, whichPlot = 5, subsetStopMeth = k, yOnly = TRUE, incUnw = FALSE)
effSzListUnw[,j] <- makePlotDat(x2, whichPlot = 5, subsetStopMeth = 1, yOnly = TRUE, incUnw = TRUE)[1:nVar]
collapsed <- apply(effSzList, 1, max)
if(x$estimand == "ATE") collapsed <- pwc$min.ks.pval[pwc$stop.method == x$stopMethods[k]]
collapsedUnw <- apply(effSzListUnw, 1, max)
if(x$estimand == "ATE") collapsedUnw <- pwc$min.ks.pval[pwc$stop.method == "unw"]
collRanks <- rank(collapsed, ties.method = "first")
esBigHold <- collapsed > collapsedUnw
esDat$ksPVal[(1:(2*nVar)) + (cnt-1)*2*nVar] <- c(collapsed, collapsedUnw)
esDat$ksRank[(1:(2*nVar)) + (cnt-1)*2*nVar] <- rep(collRanks, 2)
}
}
if(is.null(subst)) subst <- 1:length(levels(as.factor(esDat$whichComp)))
n.var2 <- max(esDat$ksRank*(!is.na(esDat$ksPVal)), na.rm=TRUE)
pt1 <- xyplot(ksPVal~ksRank|whichComp, groups=weighted, scales = list(alternating = 1), data = esDat,ylim = c(-.1, 1.1), ..., xlab = "Rank of p-value rank for pretreatment variables \n (hollow is weighted, solid is unweighted)", ylab = "KS test p-values", pch = c(19,1), col="black",par.settings = list(strip.background = list(col=stripBgCol)),
subst = (as.factor(esDat$whichComp) %in% levels(as.factor(esDat$whichComp))[subst]) & (esDat$ksRank <= n.var2),
panel = function(...){
panel.xyplot(x=c(1,n.var2), y=c(0,1), col=ltBl, type="l")
panel.xyplot(...)
})
}
return(pt1)
}
}
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.