R Dataset / Package COUNT / badhealth
| Attachment | Size |
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| dataset-90943.csv | 7.78 KB |
| R Dataset Help |
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On this Picostat.com statistics page, you will find information about the badhealth data set which pertains to badhealth. The badhealth data set is found in the COUNT R package. You can load the badhealth data set in R by issuing the following command at the console data("badhealth"). This will load the data into a variable called badhealth. If R says the badhealth 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 badhealth R data set. The size of this file is about 7,967 bytes. |
| Documentation |
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badhealthDescriptionFrom German health survey data for the year 1998 only. Usagedata(badhealth) FormatA data frame with 1,127 observations on the following 3 variables.
Detailsbadhealth is saved as a data frame. Count models use numvisit as the response variable, 0 counts are included. SourceGerman Health Survey, amended in Hilbe and Greene (2008). ReferencesHilbe, Joseph M (2011), Negative Binomial Regression, Cambridge University Press Hilbe, J. and W. Greene (2008). Count Response Regression Models, in ed. C.R. Rao, J.P Miller, and D.C. Rao, Epidemiology and Medical Statistics, Elsevier Handbook of Statistics Series. London, UK: Elsevier. Examplesdata(badhealth) glmbadp <- glm(numvisit ~ badh + age, family=poisson, data=badhealth) summary(glmbadp) exp(coef(glmbadp)) library(MASS) glmbadnb <- glm.nb(numvisit ~ badh + age, data=badhealth) summary(glmbadnb) exp(coef(glmbadnb)) -- Dataset imported from https://www.r-project.org. |
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| All Public Datasets | File Size |
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| R Output | Date |
|---|---|
| Picostat Output - Simple Linear Regression | Nov 27, 2018 |
| Picostat Output - Simple Linear Regression | Nov 27, 2018 |
| Picostat Output - Simple Linear Regression | Nov 27, 2018 |
| Picostat Output - Simple Linear Regression | Nov 27, 2018 |
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