| predict.BTm {BradleyTerry2} | R Documentation |
Predict Method for Bradley-Terry Models
Description
Obtain predictions and optionally standard errors of those predictions from a fitted Bradley-Terry model.
Usage
## S3 method for class 'BTm'
predict(
object,
newdata = NULL,
level = ifelse(is.null(object$random), 0, 1),
type = c("link", "response", "terms"),
se.fit = FALSE,
dispersion = NULL,
terms = NULL,
na.action = na.pass,
...
)
Arguments
object |
a fitted object of class |
newdata |
(optional) a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. |
level |
for models with random effects: an integer vector giving the
level(s) at which predictions are required. Level zero corresponds to
population-level predictions (fixed effects only), whilst level one
corresponds to the player-level predictions (full model) which are NA for
contests involving players not in the original data. By default, |
type |
the type of prediction required. The default is on the scale of
the linear predictors; the alternative |
se.fit |
logical switch indicating if standard errors are required. |
dispersion |
a value for the dispersion, not used for models with
random effects. If omitted, that returned by |
terms |
with |
na.action |
function determining what should be done with missing
values in |
... |
further arguments passed to or from other methods. |
Details
If newdata is omitted the predictions are based on the data used for
the fit. In that case how cases with missing values in the original fit are
treated is determined by the na.action argument of that fit. If
na.action = na.omit omitted cases will not appear in the residuals,
whereas if na.action = na.exclude they will appear (in predictions
and standard errors), with residual value NA. See also
napredict.
Value
If se.fit = FALSE, a vector or matrix of predictions. If
se = TRUE, a list with components
fit |
Predictions |
se.fit |
Estimated standard errors |
Author(s)
Heather Turner
See Also
predict.glm(), predict.glmmPQL()
Examples
## The final model in example(flatlizards)
result <- rep(1, nrow(flatlizards$contests))
Whiting.model3 <- BTm(1, winner, loser, ~ throat.PC1[..] + throat.PC3[..] +
head.length[..] + SVL[..] + (1|..),
family = binomial(link = "probit"),
data = flatlizards, trace = TRUE)
## `new' data for contests between four of the original lizards
## factor levels must correspond to original levels, but unused levels
## can be dropped - levels must match rows of predictors
newdata <- list(contests = data.frame(
winner = factor(c("lizard048", "lizard060"),
levels = c("lizard006", "lizard011",
"lizard048", "lizard060")),
loser = factor(c("lizard006", "lizard011"),
levels = c("lizard006", "lizard011",
"lizard048", "lizard060"))
),
predictors = flatlizards$predictors[c(3, 6, 27, 33), ])
predict(Whiting.model3, level = 1, newdata = newdata)
## same as
predict(Whiting.model3, level = 1)[1:2]
## introducing a new lizard
newpred <- rbind(flatlizards$predictors[c(3, 6, 27),
c("throat.PC1","throat.PC3", "SVL", "head.length")],
c(-5, 1.5, 1, 0.1))
rownames(newpred)[4] <- "lizard059"
newdata <- list(contests = data.frame(
winner = factor(c("lizard048", "lizard059"),
levels = c("lizard006", "lizard011",
"lizard048", "lizard059")),
loser = factor(c("lizard006", "lizard011"),
levels = c("lizard006", "lizard011",
"lizard048", "lizard059"))
),
predictors = newpred)
## can only predict at population level for contest with new lizard
predict(Whiting.model3, level = 0:1, se.fit = TRUE, newdata = newdata)
## predicting at specific levels of covariates
## consider a model from example(CEMS)
table6.model <- BTm(outcome = cbind(win1.adj, win2.adj),
player1 = school1, player2 = school2,
formula = ~ .. +
WOR[student] * Paris[..] +
WOR[student] * Milano[..] +
WOR[student] * Barcelona[..] +
DEG[student] * St.Gallen[..] +
STUD[student] * Paris[..] +
STUD[student] * St.Gallen[..] +
ENG[student] * St.Gallen[..] +
FRA[student] * London[..] +
FRA[student] * Paris[..] +
SPA[student] * Barcelona[..] +
ITA[student] * London[..] +
ITA[student] * Milano[..] +
SEX[student] * Milano[..],
refcat = "Stockholm",
data = CEMS)
## estimate abilities for a combination not seen in the original data
## same schools
schools <- levels(CEMS$preferences$school1)
## new student data
students <- data.frame(STUD = "other", ENG = "good", FRA = "good",
SPA = "good", ITA = "good", WOR = "yes", DEG = "no",
SEX = "female", stringsAsFactors = FALSE)
## set levels to be the same as original data
for (i in seq_len(ncol(students))){
students[,i] <- factor(students[,i], levels(CEMS$students[,i]))
}
newdata <- list(preferences =
data.frame(student = factor(500), # new id matching with `students[1,]`
school1 = factor("London", levels = schools),
school2 = factor("Paris", levels = schools)),
students = students,
schools = CEMS$schools)
## warning can be ignored as model specification was over-parameterized
predict(table6.model, newdata = newdata)
## if treatment contrasts are use (i.e. one player is set as the reference
## category), then predicting the outcome of contests against the reference
## is equivalent to estimating abilities with specific covariate values
## add student with all values at reference levels
students <- rbind(students,
data.frame(STUD = "other", ENG = "good", FRA = "good",
SPA = "good", ITA = "good", WOR = "no", DEG = "no",
SEX = "female", stringsAsFactors = FALSE))
## set levels to be the same as original data
for (i in seq_len(ncol(students))){
students[,i] <- factor(students[,i], levels(CEMS$students[,i]))
}
newdata <- list(preferences =
data.frame(student = factor(rep(c(500, 502), each = 6)),
school1 = factor(schools, levels = schools),
school2 = factor("Stockholm", levels = schools)),
students = students,
schools = CEMS$schools)
predict(table6.model, newdata = newdata, se.fit = TRUE)
## the second set of predictions (elements 7-12) are equivalent to the output
## of BTabilities; the first set are adjust for `WOR` being equal to "yes"
BTabilities(table6.model)