Hello,
I am working on some examples of GLMM for my students but I am afraid
that my way of preparing a dataframe to pass to lmer will make them
think that R is a very difficult and un-natural language. Here is for
example a simple data set about approval ratings on two different
surveys for a random sample of 1600 individuals.
> ## Example: Ratings of prime minister (Agresti, Table 12.1, p.494)
> rating <- matrix(c(794, 86, 150, 570), 2, 2)
> dimnames(rating) <- list(First = c("approve", "disapprove"),
+ Second = c("approve", "disapprove"))
> rating
Second
First approve disapprove
approve 794 150
disapprove 86 570
It seems to me that, in order to fit a model using lmer, I cannot use
the table directly, but I need a dataframe with 1600 x 2 rows and
columns response (Approve/Disapprove), survey (First/Second), and
subject id. So I proceeded to create such a dataframe:
> approval <- factor(c("Approve", "Disapprove"),
+ levels = c("Disapprove", "Approve"))
> survey <- factor(c("First", "Second"))
> tmp <- data.frame(approval = unlist(expand.grid(approval, approval)),
+ survey = rep(survey, each = 4))
> rat.df <- cbind(tmp[rep(1:8, rep(rating, 2)), ],
+ id = factor(rep(1:sum(rating), 2)))
> row.names(rat.df) <- NULL
That does the job, since now I can call lmer:
> m1 <- lmer(approval ~ survey + (1 | id), family = binomial, data = rat.df,
+ method = "Laplace")
The issue I have is that creating the 'rat.df' dataframe above will
likely make all of my students look for a different software. So my
question is the following. Is there a more elegant way to create the
dataframe needed by lmer from the tabular form in which one is more
likely to find these kind of data?
Consider also that the next simplest example is the following, in
which there are three items on a questionnaire and gender is included
in the model:
> ### Example: Support for legalizing abortion (Agresti, Table 10.13, p.441)
> legalize <- matrix(c(342, 440, 26, 25, 6, 14, 21, 18, 11, 14,
+ 32, 47, 19, 22, 356, 457), nr = 2)
> dimnames(legalize) <- list(Gender = c("Male", "Female"),
+ Three = c(111, 112, 211, 212, 121,
+ 122, 221, 222))
> legalize
Three
Gender 111 112 211 212 121 122 221 222
Male 342 26 6 21 11 32 19 356
Female 440 25 14 18 14 47 22 457
(Here '111' means (Yes, Yes, Yes) on the three items, etc.)
How can I tranform elegantly this table into a dataframe that I can
feed to lmer?
Thank you in advance for your replies!
Giovanni Petris
--
Giovanni Petris <[EMAIL PROTECTED]>
Department of Mathematical Sciences
University of Arkansas - Fayetteville, AR 72701
Ph: (479) 575-6324, 575-8630 (fax)
http://definetti.uark.edu/~gpetris/
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