Petar Milin wrote:
Thank you very much for the help!
Now, I have an additional question regarding transcending from Design to
rms: Before, it was possible to plot interaction, and get lines with
plot(ols2, gender=NA, marital.status=NA, xlab='gender',
ylab='anxiety', conf.int=FALSE)
Now, I am lost how to do that. If model is:
ols2 <- ols(anxiety ~ marital.status * gender)
Then, I need:
p2 <- Predict(ols2, marital.status=., gender=.)
And plot(p2) does not give me lines, but points with conf.int. Also,
axes are transposed as to what old plot(ols2) would give.
You're right, the plot is not optimal for the case of two factor
predictors. Here is a way to take control:
m <- sample(c('a','b'), 2*n, TRUE)
d <- datadist(d, m)
f <- ols(anxiety ~ gender*m)
p <- Predict(f, m=., gender=.)
plot(p) # default plot
Key(.5, .5)
im <- as.integer(p$m)
xYplot(Cbind(yhat,lower,upper) ~ im, groups=gender,
type='l', data=p, ylim=c(5,30), xlab='m',
scales=list(x=list(at=1:2, labels=levels(p$m))),
label.curve=list(offset=unit(.3,"in")))
For the future I'll see if I can incorporate this automatically in
plot.Predict with there are only 2 predictors being varied and they are
both factors.
In addition, if I try old function (plot(ols2), above), error message
returns:
Error in predictDesign(fit, adj, type = "x", non.slopes = non.slopes)
could not find function "Varcov"
That is because we need to update Design to be compatible with the
latest Hmisc.
Frank
Best,
PM
Frank E Harrell Jr wrote:
Petar Milin wrote:
I would like to have a line on this plot, instead of two points:
x1 = rnorm(100, 10, 2.5)
x2 = rnorm(100, 26, 3.2)
x1 = as.data.frame(x1)
x2 = as.data.frame(x2)
colnames(x1) = 'anxiety'
colnames(x2) = 'anxiety'
x1$gender = 'male'
x2$gender = 'female'
dat = rbind(x1, x2)
require(Design)
attach(dat)
d=datadist(gender)
options(datadist="d")
ols1 <- ols(anxiety ~ gender, data=dat, x=T, y=T)
plot(ols1, gender=NA, xlab="gender", ylab="anxiety",
ylim=c(5,30), conf.int=FALSE)
detach(dat)
Your code has many problems and inefficiencies in it. Here are some
suggested fixes and the commands needed using the new rms package:
require(rms)
n <- 100
anxiety <- c(rnorm(n, 10, 2.5), rnorm(n, 26, 3.2))
gender <- c(rep('male', n), rep('female',n))
d <- datadist(gender); options(datadist='d')
f <- ols(anxiety ~ gender)
p <- Predict(f, gender=.)
# For this case could also do p <- Predict(f); plot(p) would give a
# vertical dot chart
p # print estimates
plot(p) # horizontal dot chart; preferred for categorical predictors
# To take control using lines:
with(p, plot(1:2, yhat, type='l', xlab='gender numeric'))
Frank
Thanks!
PM
Frank E Harrell Jr wrote:
Petar Milin wrote:
Hello ALL!
I have a problem to plot factor (lets say gender) as a line, or at
least both line and point, from ols model:
ols1 <- ols(Y ~ gender, data=dat, x=T, y=T)
plot(ols1, gender=NA, xlab="gender", ylab="Y",
ylim=c(5,30), conf.int=FALSE)
If I convert gender into discrete numeric predictor, and use
forceLines=TRUE, plot is not nice and true, since it shows values
between 1 and 2.
Thanks!
PM
Petar,
forceLines seems to be doing what it was intended to do. I'm not
clear on why you need a line, though. If you provide self-contained
code and data that replicate your problem I may be able to help
more, or you can try a new package I'm about to announce.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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