predict.BinaryEPPM | R Documentation |
Extract various types of predictions from BinaryEPPM regression models.
## S3 method for class 'BinaryEPPM'
predict(object, newdata = NULL, type = c("response",
"linear.predictor.p", "linear.predictor.scale.factor",
"p", "scale.factor", "scale.factor.limits", "mean",
"variance", "distribution", "distribution.parameters"), na.action = na.pass, ...)
object |
fitted model object of class "BinaryEPPM". |
newdata |
optionally, a data frame in which to look for variables with which to predict. If omitted, the original observations are used. |
type |
character indicating type of predictions: fitted means of responses ("response"), linear predictors ("linear.predictor.p", "linear.predictor.scale.factor"), fitted value of probability of success ("p"), fitted value of scale-factor ("scale.factor"), fitted value of mean ("mean"), scale factor limits ("scale.factor.limits"), fitted value of variance ("variance"), fitted probability distribution ("distribution"), parameters of fitted distributions ("distribution.parameters") |
na.action |
function determining what should be done with missing values in newdata. The default is to predict NA. |
... |
some methods for this generic function require additional arguments. |
A vector or list of the predicted values from the fitted model object.
David M. Smith <dmccsmith@verizon.net>
Cribari-Neto F, Zeileis A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1-24. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v034.i02")}.
predict.betareg
data("ropespores.case")
output.fn <- BinaryEPPM(data = ropespores.case,
number.spores / number.tested ~ 1 + offset(logdilution),
model.type = 'p only', model.name = 'binomial')
predict(output.fn, type = "response")
predict(output.fn, type = "linear.predictor.p")
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