CODEWITHSUNDEEP
rregression
Greta Juknaitė
Greta Juknaitė

Reputation: 33

Making zero-inflated or hurdle model with R

I need to make a model which could find probability that a registered user will buy some plan or no plan (i.e., will use just a free plan or won't do anything) and if they do, after what time. I have data with around 13 000 rows and around 12 000 of them are free users ( never paid - 0 value ) and the other 1 000 paid after some time ( from 1 to 690 days) also I have some count and categorical data - country, number of user clients, how many times he used plan, plan (premium, free, premium plus).

The mean of time after they paid or not is around 6.37 and variance is 1801.17, without zeros - 100 and 19012, which suggests to me that I should use a negative binomial model.

But I'm not sure which model fits best; I'm thinking about a zero-inflated negative binomial or hurdle model.

Here is histogram of diff.time with 0 and without 0 data :

histogram of data without 0

I tried these models with the pscl package:

summary(m1 <- zeroinfl(diff.time3 ~ 
    factor(Registration.country) + factor(Plan) + Campaigns.sent + 
         Number.of.subscribers |
    factor(Registration.country) + factor(Plan) + Campaigns.sent + 
         Number.of.subscribers, 
data=df , link="logit",dist= "negbin"))

or the same with hurdle() but they gave me an error :

Error in quantile.default(x$residuals): missing values and NaN's not allowed if 'na.rm' is FALSE In addition: Warning message: glm.fit: algorithm did not converge

with hurdle():

Error in solve.default(as.matrix(fit_count$hessian)) : Lapack routine dgesv: system is exactly singular: U[3,3] = 0

I have never tried these models before so I'm not sure how to fix these errors or if I chose the right models.

Unfortunately, I have no opportunuty to share some part of my data, but I'll try to explain them:

1st column "plan" - most of the data are "free"(around 12 000), also "Earning more", "Premium" or "Premium trial", where "free" and "premium trial" are not paid. 2nd column "Plan used" - around 8 000 rows are 0, 1 000 - 1, 3 000 - from 1 to 10 and another 1 000 from 10 to 510 3th column "Clients" describes how many clients user have - around 2 000 have 0, 4 0000 - 1-10, 3 000 - 10-200, 2 000- 200-1000, 2 000 - 1000- 340 000 4th column "registration country" - 36 different countries, over half of data is united states, other have from 5 to few hundreds rows. 5th column is diff.time which should be my dependent variable, as I said before most of the data are 0 (12 000) and others variuos from 1 day to 690 days)

Upvotes: 1

Views: 2890

Answers (1)

Achim Zeileis
Achim Zeileis

Reputation: 17183

If your actual data is similarly structured to the data you posted then you will have problems estimating a model like the one you specified. Let's first have a look at the data you posted on the Google drive:

load("duom.Rdata")
table(a$diff.time3 > 0)
## FALSE  TRUE 
##   950    50

Thus there is some variation in the response but not a lot. You have only 5% non-zeros, overall 50 observations. From this information alone it might seem more reasonable to estimate a bias-reduced binary model (brglm) to the hurdle part (zero vs. non-zero).

For the zero-truncated count part you can possibly fit a model but you need to be careful which effects you want to include because there are only 50 degrees of freedom. You can estimate the zero-truncated part of the hurdle model using the zerotrunc function in package countreg, available from R-Forge.

Also you should clean up your factors. By re-applying the factor function within the formula, levels with zero occurrences are excluded. But there are also levels with only one occurrence for which you will not get meaningful results.

table(factor(a$Plan))
## Earning much more              Free           Mailing           Premium 
##                 1               950                 1                24 
##     Premium trial 
##                24 
table(factor(a$Registration.country))
##  australia  Australia    Austria Bangladesh    Belgium     brasil     Brasil 
##          1        567          7          5         56          1         53 
##   Bulgaria     Canada 
##         10        300

Also, you need to clean up the country levels with all lower-case letters.

After that I would start out by buidling a binary GLM for zero vs. non-zero - and based on those results continue with the zero-truncated count part.

Upvotes: 2

Related Questions