R help - Jun 2009 - predicted values after fitting gamma2 function

Question: after fitting a gamma function to some data, how do I get  
predicted values?  I'm a SAS programmer, I new R, and am having  
problems getting my brain to function with the concept of "object as  
class ...".  The following is specifics of what I am doing:

I'm trying to determine the pdf from data I have created in a  
simulation.
I have generated frequency counts using the following:

   Max.brks <- pretty(range(Max.Spread$Distance), 100)
   Max.f<-hist(x=Max.Spread$Distance,
                breaks=Max.brks,plot=FALSE )
   Max.cnt<-as.data.frame(cbind(sim,Max.f$mids,Max.f$counts))
  
colnames(Max.cnt)<-c("Simulation","MidPoint","Count")

then I fit this to a gamma distribution function:
   modl<- 
vglm 
(Count 
~ 
MidPoint 
,gamma2 
,data 
=subset(Max.cnt,select=(simulation,MidPoint,Count),trace=TRUE,crit="c")
   print(coef(modl2,matrix=TRUE))
   print(summary(modl2))

This produces the output:

VGLM    linear loop  1 :  coefficients   3.231473495,  0.597085743, -0.195591168
...
VGLM    linear loop  20 :  coefficients   3.663316903,  0.897355891,
-0.620449146

                log(mu) log(shape)
(Intercept)  3.6633169  0.8973559
MidPoint    -0.6204491  0.0000000
Call:
vglm(formula = Count ~ MidPoint, family = gamma2, data = modl.subset,
     trace = TRUE, crit = "c")

Pearson Residuals:
                Min        1Q   Median      3Q     Max
log(mu)    -1.4846 -0.715285 -0.15436 0.61641 2.17298
log(shape) -5.8348 -0.099617  0.42094 0.61865 0.70901

Coefficients:
                  Value Std. Error  t value
(Intercept):1  3.66332   0.131643  27.8277
(Intercept):2  0.89736   0.138591   6.4749
MidPoint      -0.62045   0.047505 -13.0607

Number of linear predictors:  2
Names of linear predictors: log(mu), log(shape)
Dispersion Parameter for gamma2 family:   1
Log-likelihood: -276.0009 on 181 degrees of freedom
Number of Iterations: 20


Now - how do I get this information to give me predicted values given  
the same x-values I used in the experimental model (i.e. from Max.brks  
<- pretty(range(Max.Spread$Distance), 100)).


Steven Matthew Anderson

Anderson Research, LLC
Statistical Programming and Analysis
SAS (R) Certified Professional
AdAstra69 at mac.com

Ad Astra per Aspera

???????

On Jun 25, 2009, at 11:30 PM, Steven Matthew Anderson wrote:
> Question: after fitting a gamma function to some data, how do I get  
> predicted values?  I'm a SAS programmer, I new R, and am having  
> problems getting my brain to function with the concept of "object as  
> class ...".  The following is specifics of what I am doing:
>
> I'm trying to determine the pdf from data I have created in a  
> simulation.
> I have generated frequency counts using the following:
>
>  Max.brks <- pretty(range(Max.Spread$Distance), 100)
>  Max.f<-hist(x=Max.Spread$Distance,
>               breaks=Max.brks,plot=FALSE )
>  Max.cnt<-as.data.frame(cbind(sim,Max.f$mids,Max.f$counts))
> 
colnames(Max.cnt)<-c("Simulation","MidPoint","Count")
>
> then I fit this to a gamma distribution function:
Using a non-base function without including the appropriate require()  
or library() call is a bit like asking a SAS programmer to debug code  
but not telling what PROC it's from;
>  modl<- 
> vglm 
> (Count 
> ~ 
> MidPoint 
> ,gamma2 
> ,data 
> = 
>
subset(Max.cnt,select=(simulation,MidPoint,Count),trace=TRUE,crit="c")
>  print(coef(modl2,matrix=TRUE))
>  print(summary(modl2))
>
> This produces the output:
>
snipped output
>
>
> Now - how do I get this information to give me predicted values  
> given the same x-values I used in the experimental model (i.e. from  
> Max.brks <- pretty(range(Max.Spread$Distance), 100)).
Most regression functions in R, and vglm is no exception, have predict  
methods. The default is to give back predictions for the data from  
which the parameters were estimated, but if you want predictions on  
specific new values there is a newdata option.

?predict.vglm


David Winsemius, MD
Heritage Laboratories
West Hartford, CT