I am using glm() function in R with link= log to fit my model. I read on various websites that fitted() returns the value which we can compare with the original data as compared to the predict(). I am facing some problem while fitting the model.
data<-read.csv("training.csv")
data$X2 <- as.Date(data$X2, format="%m/%d/%Y")
data$X3 <- as.Date(data$X3, format="%m/%d/%Y")
data_subset <- subset(...)
attach(data_subset)
#define variable
Y<-cbind(Y)
X<-cbind(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X14)
# correlation among variables
cor(Y,X)
model <- glm(Y ~ X , data_subset,family=Gamma(link="log"))
summary(model)
detach(data_subset)
validation_data<-read.csv("validation.csv")
validation_data$X2 <- as.Date(validation_data$X2, format="%m/%d/%Y")
validation_data$X3 <- as.Date(validation_data$X3, format="%m/%d/%Y")
attach(validation_data)
predicted_valid<-predict(model, newdata=validation_data)
I am not sure how does predict work with gamma log link. I want to transform the predicted values so that it can be compared with the original data. Can someone please help me.
Add type="response" to your predict call, to get predictions on the response scale. See ?predict.glm.
predict(model, newdata=*, type="response")