A string providing the name of the variable for which you
want the plot to be drawn.
data
Data used to estimate obj.
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 TRUE) or returned to the console (if FALSE).
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 xyplot
Value
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.
Details
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.
References
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.
# NOT RUN {library(MASS)
data(france)
polr.mod <- polr(vote ~ age + male + retnat + lrself, data=france)
# }# NOT RUN {ordAveEffPlot(polr.mod, "lrself", data=france)
# }# NOT RUN {# }