Oh dear, that doesn't look right at all. I shall have a think about what I did wrong and maybe follow my own advice and consult the doco myself!
On Oct 21, 2:45 pm, andrew <andrewjohnro...@gmail.com> wrote: > 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 > > > ______________________________________________ > > r-h...@r-project.org mailing > > listhttps://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > r-h...@r-project.org mailing listhttps://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
