R help - Jan 2011 - predict() for bootstrapped model coefficients

I run a multinomial regression on a data set with an outcome that 
has three values. First, I build an initial model, b.mod. Then I 
run a loop to bootstrap the coefficients. For the initial model, 
using "predict()", I can print the wrong/false predictions table. 
But how do I get this table (that is, the predictions) for the 
bootstrapped model? Thanks for hints, *S*

df <- data.frame(
   "y"=factor(sample(LETTERS[1:3], 200, repl=T)),
   "a"=rnorm(200),
   "b"=rnorm(200)
)
library(nnet)
b.mod <- multinom(y ~ ., data=df, trace=F)
i <- 1; k <- 50
c.mat.1 <- matrix(nrow=k, ncol=length(names(df)), 
dimnames=list(NULL, c("cons", names(df)[-1])))
c.mat.2 <- matrix(nrow=k, ncol=length(names(df)), 
dimnames=list(NULL, c("cons", names(df)[-1])))
set.seed(123)
while(i <= k){
   l <- sample(1:length(df[, 1]), replace=T)
   m <- update(b.mod, . ~ ., data=df[l, ], trace=F)
   c.mat.1[i, ] <- coef(m)[1, ]
   c.mat.2[i, ] <- coef(m)[2, ]
   i <- i + 1
}
(coefs1 <- apply(c.mat.1, 2, mean))
(coefs2 <- apply(c.mat.2, 2, mean))
summary(predict(b.mod)==model.frame(b.mod)$y)

-- 
Sascha Vieweg, saschaview at gmail.com

On 11-01-20 17:05, Sascha Vieweg wrote:
> I run a multinomial regression on a data set with an outcome that has three
> values. First, I build an initial model, b.mod. Then I run a loop to 
> bootstrap the coefficients. For the initial model, using
"predict()", I can
> print the wrong/false predictions table. But how do I get this table (that 
> is, the predictions) for the bootstrapped model? Thanks for hints, *S*
>
> df <- data.frame(
>   "y"=factor(sample(LETTERS[1:3], 200, repl=T)),
>   "a"=rnorm(200),
>   "b"=rnorm(200)
> )
> library(nnet)
> b.mod <- multinom(y ~ ., data=df, trace=F)
> i <- 1; k <- 50
> c.mat.1 <- matrix(nrow=k, ncol=length(names(df)), dimnames=list(NULL, 
> c("cons", names(df)[-1])))
> c.mat.2 <- matrix(nrow=k, ncol=length(names(df)), dimnames=list(NULL, 
> c("cons", names(df)[-1])))
> set.seed(123)
> while(i <= k){
>   l <- sample(1:length(df[, 1]), replace=T)
>   m <- update(b.mod, . ~ ., data=df[l, ], trace=F)
>   c.mat.1[i, ] <- coef(m)[1, ]
>   c.mat.2[i, ] <- coef(m)[2, ]
>   i <- i + 1
> }
> (coefs1 <- apply(c.mat.1, 2, mean))
> (coefs2 <- apply(c.mat.2, 2, mean))
> summary(predict(b.mod)==model.frame(b.mod)$y)
I give it a second trial to poll some ideas. I *mean* that predict 
uses information from the model object, here b.mod. My idea was 
now to "fake" an object and fill it with relevant data from my 
bootstrap to run predict on that faked object:

f.b.mod <- b.mod
f.b.mod$n <- 14:16 # senseless, anyway
f.b.mod[1:3]

What I don't know, and don't get from the predict() source code, 
and thus requesting help upon is, which parts of the model object 
does predict use and how can I replace them with my loop data? 
Thanks for hints, *S*

-- 
Sascha Vieweg, saschaview at gmail.com