Predicting responses or linear parts of the baseline-category and conditional logit models
predict.RdThe predict() methods allow to obtain within-sample and
out-of-sample predictions from models
fitted with mclogit() and mblogit().
For models with random effecs fitted using the PQL-method, it is possible to obtain responses that are conditional on the reconstructed random effects.
Usage
# S3 method for class 'mblogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE, ...)
# S3 method for class 'mclogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE, ...)
# S3 method for class 'mmblogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE,
conditional=TRUE, ...)
# S3 method for class 'mmclogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE,
conditional=TRUE, ...)Arguments
- object
an object in class "mblogit", "mmblogit", "mclogit", or "mmclogit"
- newdata
an optional data frame with new data
- type
a character string specifying the kind of prediction
- se.fit
a logical value; whether predictions should be accompanied with standard errors
- conditional
a logical value; whether predictions should be made conditional on the random effects (or whether they are set to zero, i.e. their expectation). This argument is consequential only if the "mmblogit" or "mmclogit" object was created with
method="PQL".- ...
other arguments, ignored.
Value
The predict methods return either a matrix (unless called with
se.fit=TRUE) or a list with two matrix-valued elements
"fit" and "se.fit".
Examples
library(MASS)
(house.mblogit <- mblogit(Sat ~ Infl + Type + Cont,
data = housing,
weights=Freq))
#>
#> Iteration 1 - deviance = 3493.764 - criterion = 0.9614469
#> Iteration 2 - deviance = 3470.111 - criterion = 0.00681597
#> Iteration 3 - deviance = 3470.084 - criterion = 7.82437e-06
#> Iteration 4 - deviance = 3470.084 - criterion = 7.469596e-11
#> converged
#>
#> Call: mblogit(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq)
#>
#> Coefficients:
#> Predictors
#> Logit eqn. (Intercept) InflMedium InflHigh TypeApartment TypeAtrium
#> Medium/Low -0.4192 0.4464 0.6649 -0.4357 0.1314
#> High/Low -0.1387 0.7349 1.6126 -0.7356 -0.4080
#> Predictors
#> Logit eqn. TypeTerrace ContHigh
#> Medium/Low -0.6666 0.3609
#> High/Low -1.4123 0.4818
#>
#> Null Deviance: 3694
#> Residual Deviance: 3470
head(pred.house.mblogit <- predict(house.mblogit))
#> Medium High
#> 1 -0.41922874 -0.1387428
#> 2 -0.41922874 -0.1387428
#> 3 -0.41922874 -0.1387428
#> 4 0.02716715 0.5961205
#> 5 0.02716715 0.5961205
#> 6 0.02716715 0.5961205
str(pred.house.mblogit <- predict(house.mblogit,se=TRUE))
#> List of 2
#> $ fit : num [1:72, 1:2] -0.4192 -0.4192 -0.4192 0.0272 0.0272 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:72] "1" "2" "3" "4" ...
#> .. ..$ : chr [1:2] "Medium" "High"
#> $ se.fit: num [1:72, 1:2] 0.173 0.173 0.173 0.17 0.17 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:2] "Medium" "High"
head(pred.house.mblogit <- predict(house.mblogit,
type="response"))
#> Low Medium High
#> 1 0.3955687 0.2601077 0.3443236
#> 2 0.3955687 0.2601077 0.3443236
#> 3 0.3955687 0.2601077 0.3443236
#> 4 0.2602403 0.2674072 0.4723526
#> 5 0.2602403 0.2674072 0.4723526
#> 6 0.2602403 0.2674072 0.4723526
str(pred.house.mblogit <- predict(house.mblogit,se=TRUE,
type="response"))
#> List of 2
#> $ fit : num [1:72, 1:3] 0.396 0.396 0.396 0.26 0.26 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:72] "1" "2" "3" "4" ...
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
#> $ se.fit: num [1:72, 1:3] 0.0343 0.0343 0.0343 0.0273 0.0273 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:72] "1" "2" "3" "4" ...
#> .. ..$ : chr [1:3] "Low" "Medium" "High"
# This takes a bit longer.
data(electors)
(mcre <- mclogit(
cbind(Freq,interaction(time,class))~econ.left/class+welfare/class+auth/class,
random=~1|party.time,
data=within(electors,party.time<-interaction(party,time))))
#>
#> Iteration 1 - deviance = 1070.463 - criterion = 0.1596265
#> Iteration 2 - deviance = 965.7808 - criterion = 0.0253941
#> Iteration 3 - deviance = 949.163 - criterion = 0.005112973
#> Iteration 4 - deviance = 947.9638 - criterion = 0.0002016051
#> Iteration 5 - deviance = 947.8468 - criterion = 2.469648e-07
#> Iteration 6 - deviance = 947.8431 - criterion = 4.94197e-13
#> converged
#> mclogit(formula = cbind(Freq, interaction(time, class)) ~ econ.left/class +
#> welfare/class + auth/class, data = within(electors, party.time <- interaction(party,
#> time)), random = ~1 | party.time)
#>
#> Coefficients:
#> econ.left welfare
#> -0.17380 2.05525
#> auth econ.left:classnew.middle
#> 0.08059 -1.66428
#> econ.left:classold.middle classnew.middle:welfare
#> -2.96667 -0.99252
#> classold.middle:welfare classnew.middle:auth
#> -1.62032 -1.39064
#> classold.middle:auth
#> 1.45728
#>
#> (Co-)Variances:
#> Grouping level: party.time
#> (Const.)
#> (Const.) 1.604
#>
#> Approximate residual deviance: 947.8
str(predict(mcre))
#> num [1:450] 1.168 4.367 0.148 -1.285 -1.378 ...
str(predict(mcre,type="response"))
#> num [1:450] 0.03789 0.92862 0.01366 0.00326 0.00297 ...
str(predict(mcre,se.fit=TRUE))
#> List of 2
#> $ fit : num [1:450] 1.168 4.367 0.148 -1.285 -1.378 ...
#> $ se.fit: Named num [1:450] 0.533 0.531 0.609 0.536 0.539 ...
#> ..- attr(*, "names")= chr [1:450] "1" "2" "3" "4" ...
str(predict(mcre,type="response",se.fit=TRUE))
#> List of 2
#> $ fit : num [1:450] 0.03789 0.92862 0.01366 0.00326 0.00297 ...
#> $ se.fit: num [1:450] 0.004138 0.007417 0.004969 0.000562 0.000509 ...