ordAveEffPlot {DAMisc} | R Documentation |
For objects of class polr
the function plots the average effect of a
single variable holding all other variables at their observed values.
ordAveEffPlot(
obj,
varname,
data,
R = 1500,
nvals = 25,
plot = TRUE,
returnInd = FALSE,
returnMprob = FALSE,
...
)
obj |
An object of class |
varname |
A string providing the name of the variable for which you want the plot to be drawn. |
data |
Data used to estimate |
R |
Number of simulations to generate confidence intervals. |
nvals |
Number of evaluation points of the function |
plot |
Logical indicating whether or not the result should be plotted
(if |
returnInd |
Logical indicating whether average individual probabilities should be returned. |
returnMprob |
Logical indicating whether marginal probabilities, averaged over individuals, should be returned. |
... |
Arguments passed down to the call to |
Following the advice of Hanmer and Kalkan (2013) the function calculates the average effect of a variable holding all other variables at observed values and then plots the result.
Either a plot or a list with a data frame containing the variables
mean |
The average effect (i.e., predicted probability) |
lower |
The lower 95% confidence bound |
upper |
The upper 95% confidence bound |
y |
The values of the dependent variable being predicted |
x |
The values of the independent variable being manipulated |
and the elements Ind or Mprob, as requested.
Dave Armstrong
Hanmer, M.J. and K.O. Kalkan. 2013. ‘Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models’. American Journal of Political Science. 57(1): 263-277.
library(MASS)
data(france)
polr.mod <- polr(vote ~ age + male + retnat + lrself, data=france)
## Not run: ordAveEffPlot(polr.mod, "lrself", data=france)