[R] predicted values after fitting gamma2 function

[Steven Matthew Anderson]

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

שָׁלוֹם