On this Picostat.com statistics page, you will find information about the mdvis data set which pertains to mdvis. The mdvis data set is found in the COUNT R package. You can load the mdvis data set in R by issuing the following command at the console data("mdvis"). This will load the data into a variable called mdvis. If R says the mdvis data set is not found, you can try installing the package by issuing this command install.packages("COUNT") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the mdvis R data set. The size of this file is about 93,542 bytes.
Data from a subset of the German Socio-Economic Panel (SOEP). The subset was created
by Rabe-Hesketh and Skrondal (2005). Only working women are included in these data.
Beginning in 1997, German health reform in part entailed a 200
co-payment as well as limits in provider reimbursement. Patients were surveyed for the
one year panel (1996) prior to and the one year panel (1998) after reform to assess
whether the number of physician visits by patients declined - which was the goal of
The response, or variable to be explained by the model, is numvisit, which
indicates the number of patient visits to a physician's office during a three month period.
A data frame with 2,227 observations on the following 13 variables.
visits to MD office 3mo prior
1=interview yr post-reform: 1998;0=pre-reform:1996
1=bad health; 0 = not bad health
educ1= 7-10 years
educ2= 10.5-12 years
educ3= post secondary or high school
age: 1=20-39; 2=40-49; 3=50-60
log(household income in DM)
mdvis is saved as a data frame.
Count models typically use docvis as response variable. 0 counts are included
German Socio-Economic Panel (SOEP), 1995 pre-reform; 1998 post reform. Created
by Rabe-Hesketh and Skrondal (2005).
Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press
Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC
Rabe-Hesketh, S. and A. Skrondal (2005). Multilevel and Longitudinal Modeling Using Stata,
College Station: Stata Press.
glmmdp <- glm(numvisit ~ reform + factor(educ) + factor(agegrp), family=poisson, data=mdvis)
glmmdnb <- glm.nb(numvisit ~ reform + factor(educ) + factor(agegrp), data=mdvis)
Dataset imported from https://www.r-project.org.