Hi Frank,

Thanks for your reply! Using p <- Predict(f, x2); plot(p, ~x2, nlines=TRUE)
makes a categorical predictor (x2, with two levels) look like a continuous
one. Is there a way to only use dots (error margins), and no continuous
line between the predictor values (as with p <- Predict(f, x2); plot(p),
except with the axes reversed)?

Best,
--Stephanie

On Tue, Oct 23, 2012 at 11:20 AM, Frank Harrell [via R] <
ml-node+s789695n4647187h22@n4.nabble.com> wrote:

> Stephanie,
>
> I just realized this is already implemented.  For your problem use p <-
> Predict(f,x2); plot(p, ~ x2, nlines=TRUE)
> Frank
>
> Frank Harrell wrote
> Stephanie,
>
> I'm working on an option for the plot method for Predict that will allow
> you to do this.  Note that this approach will not result in very readable
> predictor category labels when they are long character strings.  If you are
> using linux I can get you a new version of rms with this option as soon as
> I've finished it, otherwise I can give you a workaround using source( ).
>  If you don't hear from me in a few days please write back.
>
> Frank
>
> stephsus wrote
> Hello all,
>
> I'm trying to plot the effects of variables estimated by a regression
> model fit individually, and for categorical predictors, the independent
> variable shows up on the y-axis, with the dependent variable on the x-axis.
> Is there a way to prevent this reversal?
>
> Sample code with dummy data:
>
> # make dummy data
> set.seed(1)
> x1 <- runif(200)
> x2 <- sample(c(1,2),200, TRUE)
> x3 <- sample(c(0,1),200,T)
> x4 <- runif(200)
>
> # the dependent variable:
> distance <- (x1/3 + x2 + rnorm(200)^2 - x3 - x4/2)
>
> # factor two vars, and add to datadist:
> x3 <- factor(x3)
> x2 <- factor(x2)
>
> d <- datadist(x1,x2,x3,x4)
> options(datadist="d")
>
> # Make a simple model:
> f <- ols(distance ~ x1 + x2 + x4+ x3, x=T)
>
> # plot variable effect of a categorical variable:
> plot(Predict(f, x2))
>
> ^ above step generates a plot with x2 on the y-axis and distance on the
> x-axis, which is the opposite of what I'm aiming for. The continuous
> variables do not have this problem; nor does the plot(Predict(f)) function
> to plot all of the effects at once.
>
> Thank you so much in advance for your replies! My apologies if this
> question has been answered already; I've tried searching to no avail.
>
> Best,
> Stephanie
>
> (Stanford University, Department of Linguistics)
>
>   Frank Harrell
> Department of Biostatistics, Vanderbilt University
>
>
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-- 
____________________________

Stephanie Sin-yun Shih
Ph.D. candidate
Department of Linguistics
Stanford University

stephsus@stanford.edu
http://stanford.edu/~stephsus

____________________________




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