| glmlist | R Documentation |
glmlist creates a glmlist object containing a list of fitted
glm objects with their names. loglmlist does the same for
loglm objects.
glmlist(...)
loglmlist(...)
## S3 method for class 'glmlist'
coef(object, result = c("list", "matrix", "data.frame"), ...)
... |
One or more model objects, as appropriate to the function, optionally assigned names as in |
object |
a |
result |
type of the result to be returned |
The intention is to provide object classes to facilitate model comparison,
extraction, summary and plotting of model components, etc., perhaps using
lapply or similar.
There exists a anova.glm method for glmlist
objects. Here, a coef method is also defined, collecting the
coefficients from all models in a single object of type determined by
result.
The arguments to glmlist or loglmlist are of the form
value or name=value.
Any objects which do not inherit the appropriate class glm or
loglm are excluded, with a warning.
In the coef method, coefficients from the different models are
matched by name in the list of unique names across all models.
An object of class glmlist loglmlist, just like a list, except that each model is given a name attribute.
Michael Friendly; coef method by John Fox
The function llist in package Hmisc is
similar, but perplexingly more general.
The function anova.glm also handles glmlist objects
LRstats gives LR statistics and tests for a glmlist
object.
Other glmlist functions:
Kway(),
LRstats(),
mosaic.glmlist()
Other loglinear models:
joint(),
seq_loglm()
data(Mental)
indep <- glm(Freq ~ mental+ses,
family = poisson, data = Mental)
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
coleff <- glm(Freq ~ mental + ses + Rscore:ses,
family = poisson, data = Mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore,
family = poisson, data = Mental)
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore,
family = poisson, data = Mental)
# use object names
mods <- glmlist(indep, coleff, roweff, linlin)
names(mods)
# assign new names
mods <- glmlist(Indep=indep, Col=coleff, Row=roweff, LinxLin=linlin)
names(mods)
LRstats(mods)
coef(mods, result='data.frame')
#extract model components
unlist(lapply(mods, deviance))
res <- lapply(mods, residuals)
boxplot(as.data.frame(res), main="Residuals from various models")
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