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Last data update: 2014.03.03

R: Separation plots for variables with more than two outcome...

sp.categorical

R Documentation

Separation plots for variables with more than two outcome levels

Description

This function generates separation plots for polytomous dependent variables.

Usage

sp.categorical(pred, actual, file = NULL, cex = 1.5, ...)

Arguments

`pred`	A matrix of fitted values. Each row represents one observation, and each column represents the probability of obtaining that outcome. The column names correspond to the outcome categories.
`actual`	A vector containing the actual outcomes corresponding to each observation.
`file`	The name and file path of where the pdf output should be written, if desired. If `file=NULL` the output will be written to the screen.
`cex`	Character expansion factor used for the outcome category labels.
`...`	Additional arguments passed to `separationplot`.

Details

This function is a wrapper for separationplot that generates a series of separation plots for each outcome category for a variable with more than two outcomes.

Please see the paper by Greenhill, Ward and Sacks for more information on the features of the separation plot.

Value

None. This function is used for its side effects only.

Note

This function is still being developed. Please contact Brian Greenhill with any questions or comments.

Author(s)

Brian Greenhill <brian.d.greenhill@dartmouth.edu>

References

Contact Brian Greenhill <brian.d.greenhill@dartmouth.edu> for a copy of the paper by Greenhill, Ward and Sacks that explains the concept of the separation plot.

Examples


## no reason was given for donttest here.
# Example using an ordered probit model from 
# Neumayer (2005) "Do international human rights treaties improve respect
#  for human rights?"
#  Journal of Conflict Resolution, 49 (6), 2005,  pp. 925-953 

library(foreign) 
library(MASS)

neumayer <- 
  read.dta("http://www2.lse.ac.uk/dataFiles/geographyAndEnvironment/Replication/Article%20for%20JCR%20(Human%20Rights).dta")

# create a new dataframe called "data4" that just contains the variables
#  we're interested in (and with simpler names).
data6<-na.omit(data.frame(DV=neumayer$aipts, laggedDV=neumayer$laipts,
   rat=neumayer$iccprmainrat, ingo.pc=neumayer$wiikngointerpc,
   dem=neumayer$politycorr020, extwar=neumayer$uppsalaexternalincountry,
   civwar=neumayer$uppsalainternal, gdp=neumayer$lngdp1995pc,
    pop=neumayer$lnpop, country=neumayer$country, year=neumayer$year))

# run the model (note that this is Model 6 of Table 2 of the published paper):
model6 <-
   polr(as.ordered(DV) ~ laggedDV+rat+rat:ingo.pc+rat:dem+ingo.pc+dem+extwar+civwar+gdp+pop,
        data=data6, Hess=TRUE, method="probit")
summary(model6) 

sp.categorical(pred=model6$fitted.values, actual=as.character(model6$model[,1]),
               cex=2.5)