predict.glmmPQL: Predict Method for glmmPQL Fits

Description Usage Arguments Value See Also Examples

View source: R/glmmPQL.R

Description

Obtains predictions from a fitted generalized linear model with random effects.

Usage

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## S3 method for class 'glmmPQL'
predict(object, newdata = NULL, type = c("link", "response"),
       level, na.action = na.pass, ...)

Arguments

object

a fitted object of class inheriting from "glmmPQL".

newdata

optionally, a data frame in which to look for variables with which to predict.

type

the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities.

level

an optional integer vector giving the level(s) of grouping to be used in obtaining the predictions. Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions. Defaults to the highest or innermost level of grouping.

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.

Value

If level is a single integer, a vector otherwise a data frame.

See Also

glmmPQL, predict.lme.

Examples

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fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 |  ID,
               family = binomial, data = bacteria)
predict(fit, bacteria, level = 0, type="response")
predict(fit, bacteria, level = 1, type="response")

Example output

iteration 1
iteration 2
iteration 3
iteration 4
iteration 5
iteration 6
  [1] 0.9680779 0.9680779 0.8587270 0.8587270 0.9344832 0.9344832 0.7408574
  [8] 0.7408574 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511 0.9680779
 [15] 0.9680779 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270
 [22] 0.8587270 0.8587270 0.8970307 0.8970307 0.6358511 0.6358511 0.9344832
 [29] 0.9344832 0.7408574 0.7408574 0.7408574 0.9680779 0.9680779 0.8587270
 [36] 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270
 [43] 0.9344832 0.7408574 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270
 [50] 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511 0.9680779 0.9680779
 [57] 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270 0.8970307
 [64] 0.8970307 0.6358511 0.6358511 0.6358511 0.9344832 0.9344832 0.7408574
 [71] 0.7408574 0.7408574 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270
 [78] 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511 0.9680779 0.9680779
 [85] 0.8587270 0.8587270 0.8587270 0.9344832 0.9344832 0.7408574 0.7408574
 [92] 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779
 [99] 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270 0.8587270
[106] 0.8587270 0.9344832 0.9344832 0.7408574 0.7408574 0.7408574 0.8970307
[113] 0.8970307 0.6358511 0.6358511 0.9680779 0.9680779 0.8587270 0.9680779
[120] 0.9680779 0.8587270 0.8587270 0.8970307 0.8970307 0.6358511 0.6358511
[127] 0.6358511 0.9344832 0.7408574 0.7408574 0.7408574 0.9680779 0.8587270
[134] 0.8587270 0.8587270 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511
[141] 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 0.9344832 0.7408574
[148] 0.8970307 0.8970307 0.6358511 0.6358511 0.9680779 0.9680779 0.8587270
[155] 0.8970307 0.8970307 0.6358511 0.9680779 0.9680779 0.8587270 0.8587270
[162] 0.8587270 0.9344832 0.9344832 0.7408574 0.7408574 0.7408574 0.9680779
[169] 0.9680779 0.8587270 0.8587270 0.8587270 0.9344832 0.7408574 0.8970307
[176] 0.8970307 0.6358511 0.6358511 0.6358511 0.9344832 0.9344832 0.7408574
[183] 0.7408574 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 0.8970307
[190] 0.8970307 0.6358511 0.6358511 0.6358511 0.9344832 0.9344832 0.7408574
[197] 0.7408574 0.7408574 0.8970307 0.6358511 0.6358511 0.9344832 0.9344832
[204] 0.7408574 0.7408574 0.7408574 0.8970307 0.8970307 0.6358511 0.6358511
[211] 0.9344832 0.9344832 0.7408574 0.7408574 0.7408574 0.9344832 0.9344832
[218] 0.7408574 0.7408574 0.7408574
attr(,"label")
[1] "Predicted values"
      X01       X01       X01       X01       X02       X02       X02       X02 
0.9828449 0.9828449 0.9198935 0.9198935 0.9050782 0.9050782 0.6564944 0.6564944 
      X03       X03       X03       X03       X03       X04       X04       X04 
0.9724022 0.9724022 0.8759665 0.8759665 0.8759665 0.9851548 0.9851548 0.9300763 
      X04       X04       X05       X05       X05       X05       X05       X06 
0.9300763 0.9300763 0.9851548 0.9851548 0.9300763 0.9300763 0.9300763 0.9662755 
      X06       X06       X06       X07       X07       X07       X07       X07 
0.9662755 0.8516962 0.8516962 0.7291679 0.7291679 0.3504978 0.3504978 0.3504978 
      X08       X08       X08       X08       X08       X09       X09       X09 
0.9426815 0.9426815 0.7672499 0.7672499 0.7672499 0.9851548 0.9851548 0.9300763 
      X09       X09       X10       X10       X11       X11       X11       X11 
0.9300763 0.9300763 0.9640326 0.8430706 0.9851548 0.9851548 0.9300763 0.9300763 
      X11       X12       X12       X12       X12       X12       X13       X13 
0.9300763 0.8334870 0.8334870 0.5008219 0.5008219 0.5008219 0.9851548 0.9851548 
      X13       X13       X13       X14       X14       X14       X15       X15 
0.9300763 0.9300763 0.9300763 0.8907227 0.8907227 0.6203155 0.9724022 0.9724022 
      X15       X15       X15       X16       X16       X16       X16       X16 
0.8759665 0.8759665 0.8759665 0.9287777 0.9287777 0.7232833 0.7232833 0.7232833 
      X17       X17       X17       X17       X17       X18       X18       X18 
0.9426815 0.9426815 0.7672499 0.7672499 0.7672499 0.7070916 0.7070916 0.3260827 
      X18       X18       X19       X19       X19       X19       X19       X20 
0.3260827 0.3260827 0.8702991 0.8702991 0.5735499 0.5735499 0.5735499 0.9736293 
      X20       X20       X20       X21       X21       X21       X21       X21 
0.9736293 0.8809564 0.8809564 0.9851548 0.9851548 0.9300763 0.9300763 0.9300763 
      Y01       Y01       Y01       Y01       Y01       Y02       Y02       Y02 
0.9851548 0.9851548 0.9300763 0.9300763 0.9300763 0.7607971 0.7607971 0.3893126 
      Y02       Y02       Y03       Y03       Y03       Y03       Y03       Y04 
0.3893126 0.3893126 0.8487181 0.8487181 0.5292976 0.5292976 0.5292976 0.5734482 
      Y04       Y04       Y04       Y05       Y05       Y05       Y06       Y06 
0.5734482 0.2122655 0.2122655 0.7144523 0.7144523 0.3339997 0.9828449 0.9828449 
      Y06       Y06       Y07       Y07       Y07       Y07       Y07       Y08 
0.9198935 0.9198935 0.8334870 0.8334870 0.5008219 0.5008219 0.5008219 0.9238389 
      Y08       Y08       Y08       Y09       Y09       Y09       Y09       Y10 
0.7085660 0.7085660 0.7085660 0.9847299 0.9281899 0.9281899 0.9281899 0.9188296 
      Y10       Y10       Y10       Y10       Y11       Y11       Y11       Y11 
0.9188296 0.6940862 0.6940862 0.6940862 0.9851548 0.9851548 0.9300763 0.9300763 
      Y11       Y12       Y12       Y13       Y13       Y13       Y13       Y14 
0.9300763 0.9640326 0.8430706 0.5734482 0.5734482 0.2122655 0.2122655 0.9793383 
      Y14       Y14       Z01       Z01       Z01       Z02       Z02       Z02 
0.9793383 0.9047659 0.9556329 0.9556329 0.8119328 0.9851548 0.9851548 0.9300763 
      Z02       Z02       Z03       Z03       Z03       Z03       Z03       Z05 
0.9300763 0.9300763 0.9779690 0.9779690 0.8989642 0.8989642 0.8989642 0.8702991 
      Z05       Z05       Z05       Z05       Z06       Z06       Z07       Z07 
0.8702991 0.5735499 0.5735499 0.5735499 0.8306525 0.4957505 0.8334870 0.8334870 
      Z07       Z07       Z07       Z09       Z09       Z09       Z09       Z10 
0.5008219 0.5008219 0.5008219 0.9736293 0.9736293 0.8809564 0.8809564 0.9851548 
      Z10       Z10       Z10       Z10       Z11       Z11       Z11       Z11 
0.9851548 0.9300763 0.9300763 0.9300763 0.9724022 0.9724022 0.8759665 0.8759665 
      Z11       Z14       Z14       Z14       Z14       Z14       Z15       Z15 
0.8759665 0.9287777 0.9287777 0.7232833 0.7232833 0.7232833 0.9643851 0.8444172 
      Z15       Z19       Z19       Z19       Z19       Z19       Z20       Z20 
0.8444172 0.9779690 0.9779690 0.8989642 0.8989642 0.8989642 0.7620490 0.7620490 
      Z20       Z20       Z24       Z24       Z24       Z24       Z24       Z26 
0.3909523 0.3909523 0.8487181 0.8487181 0.5292976 0.5292976 0.5292976 0.9287777 
      Z26       Z26       Z26       Z26 
0.9287777 0.7232833 0.7232833 0.7232833 
attr(,"label")
[1] "Predicted values"

MASS documentation built on Nov. 1, 2018, 5:06 p.m.