The following is *significantly* easier to do than try and add in
dummy variables, although the dummy variable approach is going to give
you exactly the same answer as the factor method, but possibly with a
different baseline.
Basically, you might want to search the lm help and possibly consult a
stats book on information about how the design matrix is constructed
in both cases.
> xF <- factor(1:10)
> N <- 1000
> xFs <- sample(x=xF,N,replace = T)
> yFs <- rnorm(N, mean = as.numeric(xFs))
> lm(yFs ~ xFs)
Call:
lm(formula = yFs ~ xFs)
Coefficients:
(Intercept) xFs2 xFs3 xFs4
xFs5 xFs6 xFs7 xFs8
0.7845 1.1620 2.1474 3.1391 4.2183
5.2621 6.0814 7.4170
xFs9 xFs10
8.2193 9.2987
> lm(yFs ~ diag(10)[,1:9][xFs,])
Call:
lm(formula = yFs ~ diag(10)[, 1:9][xFs, ])
Coefficients:
(Intercept) diag(10)[, 1:9][xFs, ]1 diag(10)[, 1:9]
[xFs, ]2 diag(10)[, 1:9][xFs, ]3
10.083 -9.299
-8.137 -7.151
diag(10)[, 1:9][xFs, ]4 diag(10)[, 1:9][xFs, ]5 diag(10)[, 1:9]
[xFs, ]6 diag(10)[, 1:9][xFs, ]7
-6.160 -5.080
-4.037 -3.217
diag(10)[, 1:9][xFs, ]8 diag(10)[, 1:9][xFs, ]9
-1.882 -1.079
On Oct 21, 9:44 am, David Winsemius <dwinsem...@comcast.net> wrote:
> On Oct 20, 2009, at 4:00 PM, Luciano La Sala wrote:
>
>
>
> > Dear R-people,
>
> > I am analyzing epidemiological data using GLMM using the lmer
> > package. I usually explore the assumption of linearity of continuous
> > variables in the logit of the outcome by creating 4 categories of
> > the variable, performing a bivariate logistic regression, and then
> > plotting the coefficients of each category against their mid points.
> > That gives me a pretty good idea about the linearity assumption and
> > possible departures from it.
>
> > I know of people who create 0,1 dummy variables in order to relax
> > the linearity assumption. However, I've read that dummy variables
> > are never needed (nor are desireble) in R! Instead, one should make
> > use of factors variable. That is much easier to work with than dummy
> > variables and the model itself will create the necessary dummy
> > variables.
>
> > Having said that, if my data violates the linearity assumption, does
> > the use of a factors for the variable in question helps overcome the
> > lack of linearity?
>
> No. If done by dividing into samall numbers of categories after
> looking at the data, it merely creates other (and probably more
> severe) problems. If you are in the unusal (although desirable)
> position of having a large number of events across the range of the
> covariates in your data, you may be able to cut your variable into
> quintiles or deciles and analyze the resulting factor, but the
> preferred approach would be to fit a regression spline of sufficient
> complexity.
>
> > Thanks in advance.
>
> --
>
> David Winsemius, MD
> Heritage Laboratories
> West Hartford, CT
>
> ______________________________________________
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> PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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