# Dependent variable

I have a dependent value in the range of [0,1]. Meaning 0 and 1, and all values in between are included. Therefore this is a proportional value such as for instance the percentage of land a farmer fertilizes.

# Model

The model I am currently focusing on is a logistic model.

• However, as an output, I would like to see how my dependent variable is predicted by the model (to compare the real values with the estimated values).

However, a logistic regression normally gives as an output "the probability". As a result, I am now a little bit confused.

My model =

out <- glm(cbind(fertilized, total_land-fertilized) ~ X-variables,
family=binomial(cloglog), data=Alldata)


To predict the estimated percentage of fertilized land I use

Alldata\$estimated_fertilized<-predict(out,data=newdata,type="response"))


Is this correct? Or does this line give me the probability instead of the predicted percentage? If not correct, what should I do to get what I want?

# UPDATE

Given the fact that there are questions on the correctness of the chosen model, I provide some additional information:

# Distribution of the dependent variables (which is a proportion for 0-1, 0 and 1 included).

• You are not really modelling a probability so an alternative model like beta regression is worth considering. Dec 22, 2016 at 14:23
• You may also be interested in this Q&A stats.stackexchange.com/questions/239422/… which differentiates between counted proportions and continuous proportions. Dec 22, 2016 at 16:29
• Do you have the numerator and denominator of the proportion? Dec 22, 2016 at 17:03
• I think I am following all your reasoning and based on that I would say logistic regression does not apply at all in your case. Not does probability as a thing to be modeled. You want to model a granular outcome, not a yes/no and not the probability of yes or of no. As to what sort of regression is best, I'd say OLS, beta, and censored are candidates, and you'll get the best answers about that choice if you post an image of your dependent variable's distribution. Dec 24, 2016 at 4:25
• So most farmers do not use any fertiliser, some use it everywhere and some have intermediate practices. It looks as though you may need to model this in two stages: first model use versus not use with logistic regression, second, conditional on using any fertiliser model the amount. Dec 24, 2016 at 14:41