There are many ways to do this. Here is one.
install.packages('rms')
require(rms)
dd <- datadist(x, y); options(datadist='dd')
f <- ols(z ~ x + y)
plot(Predict(f)) # plot all partial effects
plot(Predict(f, x)) # plot only the effect of x
plot(Predict(f, y)) # plot only the effect of y
f <- ols(z ~ pol(x,2)*pol(y,2) # repeat, not assuming linearity
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
On Sat, 7 Aug 2010, Yi wrote:
Hi, folks,
Happy work in weekends >_<
My question is how to plot the dependent variable against one of the
predictors with other predictors as constant. Not for the original data, but
after prediction. It means y is the predicted value of the dependent
variables. The constane value of the other predictors may be the average or
some fixed value.
#######
y=1:10
x=10:1
z=2:11
lin_model=lm(z~x+y)
x_new=11:2
#######
How to plot predicted value of z from the regression model with x takes
x_new and y as a constant (let's say y=1)
I am thinking about using 'predict' command to generate the prediction of z
with the new data.frame but there should be a better way.
Thanks all.
Yi
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