darckeen wrote:
I'm trying to get the following section of code to work, I think the problem
is being caused by the assignment of data to the lm function not evaluating
to "train" in the parent environment but I can't seem to figure out how to
do this.
I'd suggest simplifying this. Packing things into S4 objects probably
doesn't have any relevance, but it makes the code harder to understand.
Similarly,
as.formula("y ~ 1")
could be simplified to just
y ~ 1
and then it would be clear what it does. (It declares that y is a
variable in the current environment. I suspect as.formula does the
same, but I'd have to check.)
And the do.call() calls don't seem necessary for a scoping question
(though they might be necessary in your application, I don't know.)
Duncan Murdoch
fitmodel <- function(trial,data)
{
wrap.lm <- function(formula,data,...) { cat("in wrap
lm",NROW(data),"\n");
lm(formula,data,...) }
wrap.step <- function(object,scope,...) { cat("in wrap
step",NROW(train),"\n"); step(object,scope,...) }
train <- data[1:tr...@n,]
null <- as.formula("y ~ 1")
full <- as.formula("y ~ 1 + x1 + x2")
cat("in fit",NROW(train),"\n")
model <- do.call(tr...@fit$func,tr...@fit$args)
print(model$call)
select <- do.call(tr...@step$func,tr...@step$args)
}
mydf <- data.frame(y <- runif(100),x1 <- runif(100),x2 <- runif(100))
myfit <- list(func="wrap.lm",args=alist(formula=null,data=train))
mystep <- list(func="wrap.step",args=alist(object=model,scope=full,trace=0))
setClass("trial",representation(n="numeric",fit="list",step="list"))
trial1 <- new("trial",n=50,fit=myfit,step=mystep)
trial2 <- new("trial",n=75,fit=myfit,step=mystep)
result <- lapply(list(trial1,trial2),fitmodel,mydf)
print(coef(result[[1]]))
print(coef(result[[2]]))
cat("result #1",NROW(result[[1]]$model),"\n")
cat("result #2",NROW(result[[2]]$model),"\n")
The resulting output is:
in fit 50
in wrap lm 50
lm(formula = formula, data = data)
in wrap step 50
in fit 75
in wrap lm 75
lm(formula = formula, data = data)
in wrap step 75
(Intercept)
0.5276266
(Intercept)
0.5276266
result #1 100
result #2 100
I think what is happening is that step is reassigning the "data" for lm to
the fitmodel environment thus using resulting in using 100 rows. Any ideas
would be appreciated.
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