# [S] Re: Adjusted variable plots (Function) correction

Mon, 16 Mar 1998 22:14:02 -0600

The version of adjvarplot() I sent earlier today has a flaw that prevented it
from analyzing more than one variable. Below is a corrected version.
I'm not resending the help file text. Sorry for the inconvenience.

Department of Geriatrics
University of Arkansas for Medical Sciences
Little Rock Arkansas USA

#########################################################

adjvarplot_function(lm.obj, do.factors = T, diagnostics = T, smoo = T, ...)
{
## Authored March 16, 1998 by John Thaden, University of Arkansas
# for Medical Sciences, Little Rock, AR, based on an algorithm of
# William Venables contributed to S-News. Thanks also to Jim
# Robison-Cox and Jens Oehlschlaegel.
# lines and, if smoo = T, a smoothed curve. If diagnostics = T,
# then diagnostic plots in the style of plot.lm() and a summary are
# produced for the simple regression.
## Adjusted Variables: If the model of lm.obj is Y~Xi, i=1,2,...m,
# then the adjusted variable plot of the k-th X term is the plot of
# the residuals of lm(Y~X[,-k]) on the y-axis, versus on the x-axis
# the residuals of lm(X[,k]~X[,-k]). The regression line on this
# plot has slope equal to the k-th coefficient of the lm.obj.
## Reference: Chambers, Cleveland, Kleiner & Tukey (1983)
# Graphical Methods for Data Analysis (Boston: Duxbury Press).
#
# Check arguments.
if(!is.logical(diagnostics)) stop("Argument diagnostics= must be T or F.")
if(!is.logical(smoo)) stop("Argument smoo= must be T or F.")
# Note value of "subset=" if it exists, for inclusion under plot.
sst <- ifelse (is.null(sst <- lm.obj\$call\$subset), "",
paste("Subset =", paste(sst[c(2,1,3)], collapse = ""))) #
if(!is.logical(do.factors)) stop("Argument do.factors= must be T or F.")
# Ensure that x model.matrix and y vector exist. Use to make plot axis
labels.
lm.obj2 <- update(lm.obj, ~., x=T, y=T)
Ylabel <- paste("adjusted", Ynam <- lm.obj\$call\$formula[[2]]) #
# Make expanded data.frame with columns for Y, and all coefs but (Intercept).
updata <- data.frame(lm.obj2\$y, cbind(lm.obj2\$x[ , -1]), check.names=T)
names(updata)[1] <- Ynam
M <- length( nams <- names(updata) )
N <- length( rows <- row.names(updata) ) #
# Generate an expanded formula from the data.frame
formla <- formula.data.frame(updata) #
# Note X columns that are not dummy variables. (Factors are now only 0's
and 1's.)
dumfunc <- function(x) return(any(is.na(match(x, c(0,1)))))
nodummy <- unlist(lapply(updata[,-1],dumfunc))
# Add 'weights" column to the data.frame if lm.obj regression was weighted.
if(!is.null(lm.obj2\$weights)) {
updata <- data.frame(updata, lm.obj2\$weights)
w.indx <- -(dim(updata)[[2]])
w <- names(updata)[-w.indx] <- lm.obj\$call\$weights
wcomment <- paste("Weighted by", w)
}
else {
w.indx <- 0
wcomment <- {}
} #
# Use the expanded data.frame and formula to update lm.obj, no subsetting.
lm.obj3 <- update(lm.obj2, formula = formla, data = updata,
y = F, subset = ) #
# 1. If current X is a dummy variable, note which observations are 1.
# 2. Create a formula with two response variables (Y and the current X)
# and all predictors except the current X.
# 3. Get adjusted variables (residuals of update() using the new formula).
# 4. Note names of six observations with most extreme adjusted x values.
# 5. Open and name an S-PLUS 4.0 graphsheet NOTE: THE FOUR LINES STARTING
# WITH 'g' MUST BE MODIFIED FOR USE WITH OTHER S-PLUS VERSIONS.
# 6. Make Adjusted Variable Plot, with weights and subset info in subtitle.
# 7. Add points (closed/open dots). Annotate 6 extrema. Draw smooth curve.
# 9. Do simple regression of adjusted Y on adjusted X and draw abline.
# 10. Use plot.lm to generate diagnostic plots for the simple regression.
# 11. Save and close the multi-paged graphsheet. SEE NOTE ABOVE.
#
if(do.factors) Xselect <- 2:M
else Xselect <- (2:M)[nodummy]
for(i in Xselect) {
if(do.factors && !nodummy[i-1])
dum1 <- as.logical(lm.obj3\$x[, i])
else dum1 <- logical(N)
formla <- structure(
parse(text =
paste(
paste("cbind(", nams[i], ",", nams[1], ")", sep = ""),
paste(nams[c(-1, -i, w.indx)], collapse = "+"),
sep = "~"
)
)[[1]],
class = "formula"
)
adjstdXY <- resid(update(lm.obj3, formula = formla, x=F))
graph <- paste("var", ifelse(i < 11, "0", ""), i-1, sep = "")
graphsheet(pages="every graph", Name = graph)
plot(adjstdXY, type = "n", sub = paste(wcomment, sst, sep = " "))
paste(c(rep("", 3), rep(" ", 3)), rows[adjX3rema],
c(rep(" ", 3), rep("", 3)), sep = ""))
if(nodummy[i-1] && smoo)
formla <- formula.data.frame(YX)
if (w.indx != 0) YX <- data.frame(YX, updata[-w.indx])
lm.obj4 <- update(lm.obj3, formula = formla, data = YX, class = "lm")
abline(lm.obj4)
if(diagnostics == T)
plot.lm(lm.obj4, smooth = nodummy[i-1] && smoo, id.n = 6)
guiSave( "GraphSheet", Name = graph, NewName = graph,
FileName = paste(graph, ".sgr", sep = ""))
guiClose("GraphSheet", graph)
}
}

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