1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
|
\name{predict.clm}
\alias{predict.clm}
\title{Predict Method for CLM fits}
\description{
Obtains predictions from a cumulative link model.
}
\usage{
\method{predict}{clm}(object, newdata, se.fit = FALSE, interval = FALSE,
level = 0.95,
type = c("prob", "class", "cum.prob", "linear.predictor"),
na.action = na.pass, ...)
}
\arguments{
\item{object}{a fitted object of class inheriting from
\code{clm}.}
\item{newdata}{optionally, a data frame in which to look for variables
with which to predict. Note that all predictor variables should be
present having the same names as the variables used to fit the
model. If the response variable is present in \code{newdata}
predictions are obtained for the levels of the response as given by
\code{newdata}. If the response variable is omitted from
\code{newdata} predictions are obtained for all levels of the
response variable for each of the rows of \code{newdata}.
}
\item{se.fit}{should standard errors of the predictions be provided?
Not applicable and ignored when \code{type = "class"}.
}
\item{interval}{should confidence intervals for the predictions be
provided? Not applicable and ignored when \code{type = "class"}.
}
\item{level}{the confidence level.
}
\item{type}{the type of predictions. \code{"prob"} gives
probabilities, \code{"class"} gives predicted response class
membership defined as highest probability prediction,
\code{"cum.prob"} gives cumulative probabilities (see details)
and \code{"linear.predictor"} gives predictions on the scale of the
linear predictor including the boundary categories.
}
\item{na.action}{function determining what should be done with missing
values in \code{newdata}. The default is to predict \code{NA}.
}
\item{\dots}{further arguments passed to or from other methods.
}
}
\details{
If \code{newdata} is omitted and \code{type = "prob"} a vector of
fitted probabilities are returned identical to the result from
\code{fitted}.
If \code{newdata} is supplied and the response
variable is omitted, then predictions, standard errors and intervals
are matrices rather than vectors with the same number of rows as
\code{newdata} and with one column for each response class. If
\code{type = "class"} predictions are always a vector.
If \code{newdata} is omitted, the way missing values in the original fit are handled
is determined by the \code{na.action} argument of that fit. If
\code{na.action = na.omit} omitted cases will not appear in the
residuals, whereas if \code{na.action = na.exclude}
they will appear (in predictions, standard
errors or interval limits), with residual value \code{NA}. See also
\code{\link{napredict}}.
If \code{type = "cum.prob"} or \code{type = "linear.predictor"} there
will be two sets of predictions, standard errors and intervals; one
for j and one for j-1 (in the usual notation) where j = 1, ..., J index
the response classes.
If newdata is supplied and the response variable is omitted, then
\code{predict.clm} returns much the same thing as \code{predict.polr}
(matrices of predictions). Similarly, if \code{type = "class"}.
If the fit is rank-deficient, some of the columns of the design matrix
will have been dropped. Prediction from such a fit only makes sense if
newdata is contained in the same subspace as the original data. That
cannot be checked accurately, so a warning is issued
(cf. \code{\link{predict.lm}}).
If a flexible link function is used (\code{Aranda-Ordaz} or \code{log-gamma})
standard errors and confidence intervals of predictions do not take the
uncertainty in the link-parameter into account.
}
\value{
A list containing the following components
\item{fit}{predictions or fitted values if \code{newdata} is not
supplied.
}
\item{se.fit}{if \code{se.fit=TRUE} standard errors of the predictions
otherwise \code{NULL}.
}
\item{upr, lwr}{if \code{interval=TRUE} lower and upper confidence
limits.}
}
\author{Rune Haubo B Christensen}
\seealso{
\code{\link[ordinal]{clm}}, \code{\link[ordinal]{clmm}}.
}
\examples{
## simple model:
fm1 <- clm(rating ~ contact + temp, data=wine)
summary(fm1)
## Fitted values with standard errors and confidence intervals:
predict(fm1, se.fit=TRUE, interval=TRUE) # type="prob"
## class predictions for the observations:
predict(fm1, type="class")
newData <- expand.grid(temp = c("cold", "warm"),
contact = c("no", "yes"))
## Predicted probabilities in all five response categories for each of
## the four cases in newData:
predict(fm1, newdata=newData, type="prob")
## now include standard errors and intervals:
predict(fm1, newdata=newData, se.fit=TRUE, interval=TRUE, type="prob")
}
\keyword{models}
|
