Try this.
# the data
y <- sample(0:1, 50, replace=T) # response
x1 <- sample(0:1, 50, replace=T) # explanatory var
x2 <- sample(0:1, 50, replace=T) # explanatory var
# the number of regressors
nregressors <- 2
# all possible combinations
comb <- as.matrix(expand.grid(rep(list(0:1), times=nregressors)))
dimnames(comb)[[2]] <- paste0("x", 1:nregressors)
# create empty list for nls fits
allModelsList <- vector("list", dim(comb)[1])
# fit the nls models
for(i in seq(allModelsList)) {
# the selected regressors
sel.regressors <- (1:nregressors)[comb[i, ]==1]
if(length(sel.regressors) > 0) {
# create the formula
snippet <- paste0("(a", sel.regressors,
" * x", sel.regressors, ")", collapse=" + ")
my.formula <- paste0("y ~ exp(c + ", snippet,
")/ (1 + exp(c + ", snippet, "))")
# create the list of starting values
my.start <- as.list(c(0.2, rep(-0.2,
length(sel.regressors))))
names(my.start) <- c("c", paste0("a", sel.regressors))
# fit the nls model
allModelsList[[i]] <- nls(my.formula, start=my.start)
} else {
# if no regressors were selected
allModelsList[[i]] <- nls(y ~ exp(c) / (1 + exp(c)),
start=list(c=0.2))
}
}
# look at the results
allModelsList
Jean
mael <henriksson.a...@live.se> wrote on 08/23/2012 10:19:38 AM:
>
> Hi all,
>
> I am trying to make a script that prints all possible models from a
model
> I've created using nls(). It is a logisitc model which in total includes
13
> variables. So its >8000 models I need to create, which I don't want to
do
> manually. I've tried modify scripts made for linear models with no
results.
>
> I've tried these scripts on a two variable model (c,a1 and a2 is what I
want
> to estimate here. Where c is a constant and a1 and a2 is a weight for x1
and
> x2):
>
> x1<-sample(0:1,50,replace=T)#explanatory var
> x2<-sample(0:1,50,replace=T)#explanatory var
> y<-sample(0:1,50,replace=T)#response
> # first script I've tried
> mod <- list()
> for (i in 1:3) {
> mod[[i]] <- nls(y~exp(c+(a1*x1)+(a2*x2)
> )/
> (1+exp(c+(a1*x1)+(a2*x2)))
> ,start=list(a1=-0.2,a2=-0.2,c=0.2))
> print(summary(mod[[i]]))
> }
>
> #second script I've tried
> regressors<-c("x1","x2")
> regMat<-expand.grid(c(T,F),c(T,F))
> regMat
>
> allModelsList <- apply(regMat,1,
> function(x)as.formula(nls(y~exp(c+(a1*x1)+(a2*x2)
> )/
> (1+exp(c+(a1*x1)+(a2*x2)))
> ,start=list(a1=-0.2,a2=-0.2,c=0.2))))
>
> allModelsList
> allModelsResults <- lapply(allModelsList,
> function(x) nls(x,
> start=list(a1=-0.2,a2=-0.2,c=0.2)))
> allModelsResults
>
> They both give me the same models repeatedly, which is not what I
wanted.
> Any suggestions on how to do this? Please help.
> Thanks!
> Anna
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