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)
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