With data like the following, a frequency table in data frame form, I'd
like to fit a collection of loglm models
of independence of ~ attitude + memory for each combination of education
and age.
I can use apply() if I first convert the data to a 2 x 2 x 3 x 3 array,
but I can't figure out an
equivalently simple use of an apply() approach with the data frame form.
> library(MASS)
> data("Punishment", package = "vcd")
> str(Punishment)
'data.frame': 36 obs. of 5 variables:
$ Freq : num 1 3 20 2 8 4 2 6 1 26 ...
$ attitude : Factor w/ 2 levels "no","moderate": 1 1 1 1 1 1 1 1 1 1 ...
$ memory : Factor w/ 2 levels "yes","no": 1 1 1 1 1 1 1 1 1 2 ...
$ education: Factor w/ 3 levels "elementary","secondary",..: 1 1 1 2 2
2 3 3 3 1 ...
$ age : Factor w/ 3 levels "15-24","25-39",..: 1 2 3 1 2 3 1 2 3
1 ...
> pun <- xtabs(Freq ~ memory + attitude + age + education, data =
Punishment)
>
> mods.list <- apply(pun, c("age", "education"), function(x)
loglm(~memory + attitude, data=x))
> GSQ <- matrix( sapply(mods.list, function(x)x$lrt), 3, 3)
> dimnames(GSQ) <- dimnames(mods.list)
> GSQ
education
age elementary secondary high
15-24 4.639061 0.08066111 0.09354563
25-39 10.441996 0.96287690 0.48273162
40- 12.680802 6.71016542 3.58752829
> sum(GSQ)
[1] 39.67937
With the data in data frame format, I can do the same using the subset=
argument, and
a series of separate calls (or for loops), but I'd rather us an apply()
(or plyr) approach.
> mod.1 <- loglm(Freq ~ memory + attitude, subset=age=="15-24" &
education=="elementary", data=Punishment)
> mod.2 <- loglm(Freq ~ memory + attitude, subset=age=="25-39" &
education=="elementary", data=Punishment)
> mod.3 <- loglm(Freq ~ memory + attitude, subset=age=="40-" &
education=="elementary", data=Punishment)
> mod.4 <- loglm(Freq ~ memory + attitude, subset=age=="15-24" &
education=="secondary", data=Punishment)
> mod.5 <- loglm(Freq ~ memory + attitude, subset=age=="25-39" &
education=="secondary", data=Punishment)
> mod.6 <- loglm(Freq ~ memory + attitude, subset=age=="40-" &
education=="secondary", data=Punishment)
> mod.7 <- loglm(Freq ~ memory + attitude, subset=age=="15-24" &
education=="high", data=Punishment)
> mod.8 <- loglm(Freq ~ memory + attitude, subset=age=="25-39" &
education=="high", data=Punishment)
> mod.9 <- loglm(Freq ~ memory + attitude, subset=age=="40-" &
education=="high", data=Punishment)
>
> mod.list <- list(mod.1, mod.2,mod.3, mod.4, mod.5, mod.6, mod.7,
mod.8, mod.9)
>
> GSQ <- matrix( sapply(mod.list, function(x)x$lrt), 3, 3)
> dimnames(GSQ) <- list(age = levels(Punishment$age),
+ education = levels(Punishment$education)
+ )
> GSQ
education
age elementary secondary high
15-24 4.639061 0.08066111 0.09354563
25-39 10.441996 0.96287690 0.48273162
40- 12.680802 6.71016542 3.58752829
>
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, Quantitative Methods
York University Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street Web:http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
______________________________________________
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.
