Hi Michael,
How about this?
coef.glmlist <- function(object, result=c("list", "matrix", "data.frame"),
...){
result <- match.arg(result)
coefs <- lapply(object, coef)
if (result == "list") return(coefs)
coef.names <- unique(unlist(lapply(coefs, names)))
n.mods <- length(object)
coef.matrix <- matrix(NA, length(coef.names), n.mods)
rownames(coef.matrix) <- coef.names
colnames(coef.matrix) <- names(object)
for (i in 1:n.mods){
coef <- coef(object[[i]])
coef.matrix[names(coef), i] <- coef
}
if (result == "matrix") return(coef.matrix)
as.data.frame(coef.matrix)
}
> coef(mods, result="data.frame")
donner.mod1 donner.mod2 donner.mod3 donner.mod4
(Intercept) 1.59915455 1.85514867 0.8792031 0.7621901
age -0.03379836 -0.04565225 NA NA
sexMale -1.20678665 -1.62177307 -1.3745016 -1.0995718
age:sexMale NA 0.01957257 NA NA
poly(age, 2)1 NA NA -7.9366059 -26.9688970
poly(age, 2)2 NA NA -6.6929413 -30.5626032
poly(age, 2)1:sexMale NA NA NA 22.7210591
poly(age, 2)2:sexMale NA NA NA 28.8975876
Best,
John
> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Michael Friendly
> Sent: Tuesday, October 28, 2014 11:47 AM
> To: R-help
> Subject: [R] merge coefficients from a glmlist of models
>
> In the vcdExtra package, I have a function glmlist to collect a set of
> glm() models as a "glmlist" object,
> and other functions that generate & fit such a collection of models.
>
> This is my working example, fitting a set of models to the Donner data
>
> # install.packages("vcdExtra", repos="http://R-Forge.R-project.org";)#
> needs devel version
> data("Donner", package="vcdExtra")
> # make survived a factor
> Donner$survived <- factor(Donner$survived, labels=c("no", "yes"))
> Donner$family.size <- ave(as.numeric(Donner$family), Donner$family,
> FUN=length)
> # collapse small families into "Other"
> fam <- Donner$family
> levels(fam)[c(3,4,6,7,9)] <- "Other"
> # reorder, putting Other last
> fam = factor(fam,levels(fam)[c(1, 2, 4:6, 3)])
> Donner$family <- fam
>
> # fit models
> donner.mod0 <- glm(survived ~ age, data=Donner, family=binomial)
> donner.mod1 <- glm(survived ~ age + sex, data=Donner, family=binomial)
> donner.mod2 <- glm(survived ~ age * sex , data=Donner, family=binomial)
> donner.mod3 <- glm(survived ~ poly(age,2) + sex, data=Donner,
> family=binomial)
> donner.mod4 <- glm(survived ~ poly(age,2) * sex, data=Donner,
> family=binomial)
> mods <- glmlist(donner.mod1, donner.mod2, donner.mod3, donner.mod4)
>
> I'd like to write other methods for handling a glmlist, similar to the
> way stats::anova.glmlist works, e.g.,
>
> > library(vcdExtra)
> > mods <- glmlist(donner.mod1, donner.mod2, donner.mod3, donner.mod4)
> >
> > anova(mods, test="Chisq")
> Analysis of Deviance Table
>
> Model 1: survived ~ age + sex
> Model 2: survived ~ age * sex
> Model 3: survived ~ poly(age, 2) + sex
> Model 4: survived ~ poly(age, 2) * sex
> Resid. Df Resid. Dev Df Deviance Pr(>Chi)
> 1 87 111.128
> 2 86 110.727 1 0.4003 0.52692
> 3 86 106.731 0 3.9958
> 4 84 97.799 2 8.9321 0.01149 *
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> # gives same result as anova() with an explicit list of models:
>
> > anova(donner.mod1, donner.mod2, donner.mod3, donner.mod4,
> test="Chisq")
> Analysis of Deviance Table
>
> Model 1: survived ~ age + sex
> Model 2: survived ~ age * sex
> Model 3: survived ~ poly(age, 2) + sex
> Model 4: survived ~ poly(age, 2) * sex
> Resid. Df Resid. Dev Df Deviance Pr(>Chi)
> 1 87 111.128
> 2 86 110.727 1 0.4003 0.52692
> 3 86 106.731 0 3.9958
> 4 84 97.799 2 8.9321 0.01149 *
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Then, using the function vcdExtra::Summarise, I can define a
> Summarise.glmlist method that is essentially
>
> sumry <- lapply(mods, Summarise)
> do.call(rbind, sumry)
>
> > Summarise(mods)# not yet added to the package
> Likelihood summary table:
> AIC BIC LR Chisq Df Pr(>Chisq)
> donner.mod1 117.13 124.63 111.128 87 0.04159 *
> donner.mod2 118.73 128.73 110.727 86 0.03755 *
> donner.mod3 114.73 124.73 106.731 86 0.06439 .
> donner.mod4 109.80 124.80 97.799 84 0.14408
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Similarly, I can define a residuals.glmlist method, using using cbind()
> to collect the residuals from all
> models.
> But I'm stumped on a coef() method, because the coefficients fitted in
> various models
> differ.
>
> > coefs <- lapply(mods, "coef")
> > coefs
> $donner.mod1
> (Intercept) age sexMale
> 1.59915455 -0.03379836 -1.20678665
>
> $donner.mod2
> (Intercept) age sexMale age:sexMale
> 1.85514867 -0.04565225 -1.62177307 0.01957257
>
> $donner.mod3
> (Intercept) poly(age, 2)1 poly(age, 2)2 sexMale
> 0.8792031 -7.9366059 -6.6929413 -1.3745016
>
> $donner.mod4
> (Intercept) poly(age, 2)1 poly(age, 2)2 sexMale
> 0.7621901 -26.9688970 -30.5626032 -1.0995718
> poly(age, 2)1:sexMale poly(age, 2)2:sexMale
> 22.7210591 28.8975876
>
> The result I want is a data.frame with columns corresponding to the
> models, and rows corresponding
> to the unique coefficient names, with NA filled in where a term is
> missing.
>
>
> --
> 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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