[R] glm() scale parameters and predicted Values

[Peter Maclean]

In glm() you can use the summary() function to recover the shape parameter (the reciprocal of the dispersion parameter). How do you recover the scale parameter? Also, in the given example, how I estimate and save the geometric mean of the predicted values? For a simple model you can use fitted() or predicted() functions. I will appreciate any help. 
 
 
 
#Call required R packages
require(plyr)  
require(stats) 
require(fitdistrplus)
require(MASS)
#Grouped vector
n <- c(1:10)
yr <-c(1:10)
ny <- list(yr=yr,n=n)
require(utils)
ny <- expand.grid(ny) 
y = rgamma(100, shape=1.5, rate = 1, scale = 2)
Gdata <- cbind(ny,y)
Gdata2<- Gdata
Gdata$x1 <- cos((3.14*yr)/365.25) 
Gdata$x2 <- sin((3.14*yr)/365.25) 
#Fitting Generalized Linear Models 
Gdata <- split(Gdata,Gdata$n)
FGLM <- lapply(Gdata, function(x){
              m <- as.numeric(x$y)
              x1 <- m <- as.numeric(x$x1)
              x2 <- m <- as.numeric(x$x2)
              summary(glm(m~1+x1+x2, family=Gamma),dispersion=NULL) 
               })

#Save the results of the estimated parameters
str(FGLM,no.list = TRUE)
SFGLMC<- ldply(FGLM, function(x) x$coefficients)
SFGLMD<- ldply(FGLM, function(x) x$dispersion)
GLM <- cbind(SFGLMC,SFGLMD)