This function calculates predicted probabilities for the selected covariate profiles.
1 2 3 4 
obj 
object of 
vars 
vector selecting a set of covariates from the fitted
model. This can be a character vector of covariate names (as output from

newdata 
data.frame with the same structure as model.matrix(boolean). 
k 
integer indicating the number of points at which the predicted probability should be calculated. 
conf.int 
logical; should confidence intervals be simulated. 
n 
number of draws to take from the estimated parameter space. 
as.table 
logical (default 
scales 
list of settings for the scales argument passed to

between 
numeric specifying the space between panels. 
xlab 
string, the 
ylab 
string, the 
... 
Additional arguments to pass to 
Returns an object of boolprobclass
, the default
action being to present the default plot.
Jason W. Morgan (morgan.746@osu.edu)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## Not run:
## Note: This example assumes a boolean model has already been fit.
## Plot predicted probabilities for a fitted model. Either vars or
## newdata *must* be specified.
boolprob(fit, vars = c("x1_a", "x4_b"))
boolprob(fit, vars = c(2, 3, 4, 6))
## Specifying conf.int = TRUE produces simulated confidence intervals.
## The number of samples to pull from the distribution of the estimated
## coefficients is controlled by n; n=100 is default. This can take a
## while.
(prob < boolprob(fit, vars = c(2, 3, 4, 6), n = 1000, conf.int = TRUE))
## End(Not run)

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