indexing in a function doesn't work?

Hi,

Glancing through your code it was not immediately obvious to me why it
does not work, but I can see a lot of things that could be simplified.
 It would really help if you could give us a reproducible example.
Find/upload/create (in R) some data, and examples of how you would use
the function.  Right now, I can only guess what your data etc. are
like and based on your description plus what the code you wrote seems
to expect to be given.  I could try to give code suggestions, but I
have no easy way of testing them so it would be very easy to make
typos, etc.  Then you just get back my edits to your code that still
do not work and maybe it is because of something fundamentally wrong
with what I have done, a simple typo, or something else still wrong in
your code that I did not fix.

Anyway, if you send some data and an example using your function
(i.e., using the data you send, write our form1, form2, type, etc.), I
will take a look at your function and see if I can make it run.

Cheers,

Josh

On Sun, Apr 1, 2012 at 3:13 PM, Benjamin Caldwell
<btcaldwell at berkeley.edu> wrote:

Hello,

I've written a small function that's supposed to save me some time, and
it's ending up killing it- the intention is to iteratively subset a

dataset

fram on framevec, fit a model (either lm or nls depending on type) and
return the r2 or AIC from the model, respectively. Although as far as I

can

tell in my code the plots are dependent on the fit of the model to the

data

and the r2 and AIC reported are also dependent on the re-fitted model,

the

plots show the same linear or non-linear model used for every subset of

the

data.

However, the r2 and AIC values come back different for each subset.

When I do the subsetting and model fitting outside the function, the

model

fit is different, with different slopes, for each subset of the data.

I'm going to end up doing this without the function if I don't solve this
soon. Any help much appreciated.


#a is the number of times to loop the tenth of percent, b is first
dimension of mfrow, frame is the dataframe, framevec is the vector within
the dataframe you're using to subset (should be a percentage), form 1 is
the simple y~x for the plot, form 2 is y~x for regression, type is lm 1

or

2 nls ,form 3 is the formula for the nls, sx and sn are the start values
for nls

plotter<-function(a,b,fram,framvec,form1,form2,form3, type=1, xlm, ylm,
sx=.01,sn=25){
g<-ceiling(a/b)
par(mfrow=c(b,g))
 num<-rep(0,a)
sub.plotter<-function(i,fram,framvec,form1,form2,form3,type,
xlm,ylm,var1,var2){
temp.i<-fram[framvec <=(i*.10),]
 plot(form1, data=temp.i, xlim=c(0,xlm), ylim=c(0,ylm), main=((i-1)*.10))
if(type==1){
 mod<-lm(form2,data=temp.i)
r2<-summary(mod)$adj.r.squared
num<-r2
 legend("bottomright", legend=signif(r2), col="black")
abline(mod)
 num}
else{
if(type==2){
 try(mod<-nls(form3, data=temp.i, start=list(x=sx,n=sn),
na.action="na.omit"), silent=TRUE)
try(x1<-summary(mod)$coefficients[1,1], silent=TRUE)
 try(n1<-summary(mod)$coefficients[2,1], silent=TRUE)
try(lines((1/exp(c(0:70)*x1)*n1)), silent=TRUE)
 try(num[i]<-AIC(mod), silent=TRUE)
try(legend("bottomright", legend=round(num[i],3) , col="black"),
silent=TRUE)
 try((num), silent=TRUE)
 }
}}
for(i in 0:a+1){
 num<-sub.plotter(i,fram,framvec,form1,form2,form3,type,xlm,ylm)
}
plot.cor<-function(x){
temp<-a+1
lengthx<-c(1:temp)
 plot(x~c(1:temp))
m2<-lm(x~c(1:temp))
abline(m2)
 n<-summary(m2)$adj.r.squared
legend("bottomright", legend=signif(n), col="black")
 slope<-(coef(m2)[2])# slope
values<-(num)#values for aic or adj r2
r2ofr2<-(n) #r2 of r2 or AIC
 output<-data.frame(lengthx,slope,values,r2ofr2)
}
plot.cor(num)
write.csv(plot.cor(num)$output,"output.csv") # can't seem to use
paste(substitute(form3),".csv",sep="") to name it at the moment
par(mfrow=c(1,1))
}

Ben

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