predict.zeroinfl {pscl} | R Documentation |
Generate predicted counts from zero-inflated regression models for
count data, fit with the zeroinfl
function, producing
objects of class zeroinfl
.
## S3 method for class 'zeroinfl': predict(object, newdata, se.fit = FALSE, conf = 0.95, MC = 1000, type = c("response", "prob"), na.action = na.pass, ...)
object |
an object of class zeroinfl |
newdata |
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. |
se.fit |
logical switch indicating if standard errors on predicted values are to be computed |
conf |
a proportion, width of confidence intervals computed if
se.fit=TRUE , defaults to .95 |
MC |
number of Monte Carlo iterates for computing standard errors and confidence intervals around predicted values |
type |
the type of prediction required. The default is
"response" , generating predictions on the scale of the
observed counts. Choosing type="prob" generates a matrix of
predicted probabilities over the range of counts observed in the data,
as documented in predprob.zeroinfl ; no standard errors or
confidence intervals are
produced for predicted probabilities. The value of the type
argument can be abbreviated. |
na.action |
function determining what should be done with missing
values in newdata. The default is to predict NA . |
... |
further arguments passed to or from other methods. |
Monte Carlo methods are used to generate standard errors and
confidence intervals as follows: MC
samples are drawn from a
multivariate normal distribution centered on the MLEs of the
parameters and variance-covariance matrix equal to the MLE's
variance-covariance matrix. With sampled set of parameter values,
predicted values are generated. The standard deviation of the
predicted values is returned as se.fit
along with the lower and
upper quantiles corresponding to confidence level conf
.
a list, with components
yhat |
a vector of predicted counts |
mu |
predictions from the count part of the model, on the scale of the counts |
phi |
predicted probabilities from the zero-inflated component of the model, the probability of zero count |
se |
if se.fit=T , the standard errors of the predicted
counts |
lower,uppper |
if se.fit=T , the lower/upper bounds of confidence
intervals of content conf |
prob |
if type="prob" , a matrix of predicted
probabilities, with each row containing predicted probabilities over
the range of counts observed in the data, i.e., ncol(prob) = length(min(y):max(y)) |
Variables are first looked for in newdata and then searched for in the usual way (which will include the environment of the formula used in the fit).
Simon Jackman <jackman@stanford.edu>
zeroinfl
, predict.glm
, predprob.zeroinfl
data(bioChemists) zip <- zeroinfl(count=art ~ ., x = ~ fem + mar + kid5 + phd + ment, z = ~ fem + mar + kid5 + phd + ment, data=bioChemists,trace=TRUE) newdata <- expand.grid(list(fem="Men", mar="Married", kid5=1, phd=3.103, ment=0:77)) yhat <- predict(zip,newdata=newdata, se.fit=TRUE,MC=2500) ## Not run: plot(x=newdata$ment, y=yhat$yhat, xlab="Mentor Articles", ylab="Predicted Counts", ylim=range(zip$y), type="n") polygon(x=c(newdata$ment,rev(newdata$ment)), y=c(yhat$lower,rev(yhat$upper)), border=FALSE, col=gray(.75)) lines(x=newdata$ment, y=yhat$yhat, lwd=2) rug(quantile(bioChemists$ment,c(.05,.50,.95))) title("Predicted Counts and 95 ## End(Not run)