fitting structured conditional (subset) models with loglm
Perhaps: ?tapply and/or various wrappers like ?by . Cheers, Bert Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 "Data is not information. Information is not knowledge. And knowledge is certainly not wisdom." H. Gilbert Welch
On Mon, Feb 10, 2014 at 8:05 AM, Michael Friendly <friendly at yorku.ca> wrote:
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
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