augPred.RdPredicted values are obtained at the specified values of
primary. If object has a grouping structure
(i.e. getGroups(object) is not NULL), predicted values
are obtained for each group. If level has more than one
element, predictions are obtained for each level of the
max(level) grouping factor. If other covariates besides
primary are used in the prediction model, their average
(numeric covariates) or most frequent value (categorical covariates)
are used to obtain the predicted values. The original observations
are also included in the returned object.
augPred(object, primary, minimum, maximum, length.out, ...)
<!-- %% extra argument 'level': -->
# S3 method for class 'lme'
augPred(object, primary = NULL,
minimum = min(primary), maximum = max(primary),
length.out = 51, level = Q, ...)a fitted model object from which predictions can be
extracted, using a predict method.
an optional one-sided formula specifying the primary
covariate to be used to generate the augmented predictions. By
default, if a covariate can be extracted from the data used to generate
object (using getCovariate), it will be used as
primary.
an optional lower limit for the primary
covariate. Defaults to min(primary).
an optional upper limit for the primary
covariate. Defaults to max(primary).
an optional integer with the number of primary covariate values at which to evaluate the predictions. Defaults to 51.
an optional integer vector specifying the desired prediction levels. Levels increase from outermost to innermost grouping, with level 0 representing the population (fixed effects) predictions. Defaults to the innermost level.
some methods for the generic may require additional arguments.
a data frame with four columns representing, respectively, the values
of the primary covariate, the groups (if object does not have a
grouping structure, all elements will be 1), the predicted or
observed values, and the type of value in the third column:
original for the observed values and predicted (single
or no grouping factor) or predict.groupVar (multiple levels of
grouping), with groupVar replaced by the actual grouping
variable name (fixed is used for population predictions). The
returned object inherits from class "augPred".
Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York.
This function is generic; method functions can be written to handle
specific classes of objects. Classes which already have methods for
this function include: gls, lme, and lmList.
fm1 <- lme(Orthodont, random = ~1)
augPred(fm1, length.out = 2, level = c(0,1))
#> age .groups distance .type
#> 1 8 M01 26.00000 original
#> 2 10 M01 25.00000 original
#> 3 12 M01 29.00000 original
#> 4 14 M01 31.00000 original
#> 5 8 M02 21.50000 original
#> 6 10 M02 22.50000 original
#> 7 12 M02 23.00000 original
#> 8 14 M02 26.50000 original
#> 9 8 M03 23.00000 original
#> 10 10 M03 22.50000 original
#> 11 12 M03 24.00000 original
#> 12 14 M03 27.50000 original
#> 13 8 M04 25.50000 original
#> 14 10 M04 27.50000 original
#> 15 12 M04 26.50000 original
#> 16 14 M04 27.00000 original
#> 17 8 M05 20.00000 original
#> 18 10 M05 23.50000 original
#> 19 12 M05 22.50000 original
#> 20 14 M05 26.00000 original
#> 21 8 M06 24.50000 original
#> 22 10 M06 25.50000 original
#> 23 12 M06 27.00000 original
#> 24 14 M06 28.50000 original
#> 25 8 M07 22.00000 original
#> 26 10 M07 22.00000 original
#> 27 12 M07 24.50000 original
#> 28 14 M07 26.50000 original
#> 29 8 M08 24.00000 original
#> 30 10 M08 21.50000 original
#> 31 12 M08 24.50000 original
#> 32 14 M08 25.50000 original
#> 33 8 M09 23.00000 original
#> 34 10 M09 20.50000 original
#> 35 12 M09 31.00000 original
#> 36 14 M09 26.00000 original
#> 37 8 M10 27.50000 original
#> 38 10 M10 28.00000 original
#> 39 12 M10 31.00000 original
#> 40 14 M10 31.50000 original
#> 41 8 M11 23.00000 original
#> 42 10 M11 23.00000 original
#> 43 12 M11 23.50000 original
#> 44 14 M11 25.00000 original
#> 45 8 M12 21.50000 original
#> 46 10 M12 23.50000 original
#> 47 12 M12 24.00000 original
#> 48 14 M12 28.00000 original
#> 49 8 M13 17.00000 original
#> 50 10 M13 24.50000 original
#> 51 12 M13 26.00000 original
#> 52 14 M13 29.50000 original
#> 53 8 M14 22.50000 original
#> 54 10 M14 25.50000 original
#> 55 12 M14 25.50000 original
#> 56 14 M14 26.00000 original
#> 57 8 M15 23.00000 original
#> 58 10 M15 24.50000 original
#> 59 12 M15 26.00000 original
#> 60 14 M15 30.00000 original
#> 61 8 M16 22.00000 original
#> 62 10 M16 21.50000 original
#> 63 12 M16 23.50000 original
#> 64 14 M16 25.00000 original
#> 65 8 F01 21.00000 original
#> 66 10 F01 20.00000 original
#> 67 12 F01 21.50000 original
#> 68 14 F01 23.00000 original
#> 69 8 F02 21.00000 original
#> 70 10 F02 21.50000 original
#> 71 12 F02 24.00000 original
#> 72 14 F02 25.50000 original
#> 73 8 F03 20.50000 original
#> 74 10 F03 24.00000 original
#> 75 12 F03 24.50000 original
#> 76 14 F03 26.00000 original
#> 77 8 F04 23.50000 original
#> 78 10 F04 24.50000 original
#> 79 12 F04 25.00000 original
#> 80 14 F04 26.50000 original
#> 81 8 F05 21.50000 original
#> 82 10 F05 23.00000 original
#> 83 12 F05 22.50000 original
#> 84 14 F05 23.50000 original
#> 85 8 F06 20.00000 original
#> 86 10 F06 21.00000 original
#> 87 12 F06 21.00000 original
#> 88 14 F06 22.50000 original
#> 89 8 F07 21.50000 original
#> 90 10 F07 22.50000 original
#> 91 12 F07 23.00000 original
#> 92 14 F07 25.00000 original
#> 93 8 F08 23.00000 original
#> 94 10 F08 23.00000 original
#> 95 12 F08 23.50000 original
#> 96 14 F08 24.00000 original
#> 97 8 F09 20.00000 original
#> 98 10 F09 21.00000 original
#> 99 12 F09 22.00000 original
#> 100 14 F09 21.50000 original
#> 101 8 F10 16.50000 original
#> 102 10 F10 19.00000 original
#> 103 12 F10 19.00000 original
#> 104 14 F10 19.50000 original
#> 105 8 F11 24.50000 original
#> 106 10 F11 25.00000 original
#> 107 12 F11 28.00000 original
#> 108 14 F11 28.00000 original
#> predict.fixed1 8 M01 22.04259 predict.fixed
#> predict.fixed2 14 M01 26.00370 predict.fixed
#> predict.fixed3 8 M02 22.04259 predict.fixed
#> predict.fixed4 14 M02 26.00370 predict.fixed
#> predict.fixed5 8 M03 22.04259 predict.fixed
#> predict.fixed6 14 M03 26.00370 predict.fixed
#> predict.fixed7 8 M04 22.04259 predict.fixed
#> predict.fixed8 14 M04 26.00370 predict.fixed
#> predict.fixed9 8 M05 22.04259 predict.fixed
#> predict.fixed10 14 M05 26.00370 predict.fixed
#> predict.fixed11 8 M06 22.04259 predict.fixed
#> predict.fixed12 14 M06 26.00370 predict.fixed
#> predict.fixed13 8 M07 22.04259 predict.fixed
#> predict.fixed14 14 M07 26.00370 predict.fixed
#> predict.fixed15 8 M08 22.04259 predict.fixed
#> predict.fixed16 14 M08 26.00370 predict.fixed
#> predict.fixed17 8 M09 22.04259 predict.fixed
#> predict.fixed18 14 M09 26.00370 predict.fixed
#> predict.fixed19 8 M10 22.04259 predict.fixed
#> predict.fixed20 14 M10 26.00370 predict.fixed
#> predict.fixed21 8 M11 22.04259 predict.fixed
#> predict.fixed22 14 M11 26.00370 predict.fixed
#> predict.fixed23 8 M12 22.04259 predict.fixed
#> predict.fixed24 14 M12 26.00370 predict.fixed
#> predict.fixed25 8 M13 22.04259 predict.fixed
#> predict.fixed26 14 M13 26.00370 predict.fixed
#> predict.fixed27 8 M14 22.04259 predict.fixed
#> predict.fixed28 14 M14 26.00370 predict.fixed
#> predict.fixed29 8 M15 22.04259 predict.fixed
#> predict.fixed30 14 M15 26.00370 predict.fixed
#> predict.fixed31 8 M16 22.04259 predict.fixed
#> predict.fixed32 14 M16 26.00370 predict.fixed
#> predict.fixed33 8 F01 22.04259 predict.fixed
#> predict.fixed34 14 F01 26.00370 predict.fixed
#> predict.fixed35 8 F02 22.04259 predict.fixed
#> predict.fixed36 14 F02 26.00370 predict.fixed
#> predict.fixed37 8 F03 22.04259 predict.fixed
#> predict.fixed38 14 F03 26.00370 predict.fixed
#> predict.fixed39 8 F04 22.04259 predict.fixed
#> predict.fixed40 14 F04 26.00370 predict.fixed
#> predict.fixed41 8 F05 22.04259 predict.fixed
#> predict.fixed42 14 F05 26.00370 predict.fixed
#> predict.fixed43 8 F06 22.04259 predict.fixed
#> predict.fixed44 14 F06 26.00370 predict.fixed
#> predict.fixed45 8 F07 22.04259 predict.fixed
#> predict.fixed46 14 F07 26.00370 predict.fixed
#> predict.fixed47 8 F08 22.04259 predict.fixed
#> predict.fixed48 14 F08 26.00370 predict.fixed
#> predict.fixed49 8 F09 22.04259 predict.fixed
#> predict.fixed50 14 F09 26.00370 predict.fixed
#> predict.fixed51 8 F10 22.04259 predict.fixed
#> predict.fixed52 14 F10 26.00370 predict.fixed
#> predict.fixed53 8 F11 22.04259 predict.fixed
#> predict.fixed54 14 F11 26.00370 predict.fixed
#> predict.Subject1 8 M01 25.38635 predict.Subject
#> predict.Subject2 14 M01 29.34746 predict.Subject
#> predict.Subject3 8 M02 21.46107 predict.Subject
#> predict.Subject4 14 M02 25.42218 predict.Subject
#> predict.Subject5 8 M03 22.24613 predict.Subject
#> predict.Subject6 14 M03 26.20724 predict.Subject
#> predict.Subject7 8 M04 24.37699 predict.Subject
#> predict.Subject8 14 M04 28.33810 predict.Subject
#> predict.Subject9 8 M05 21.12462 predict.Subject
#> predict.Subject10 14 M05 25.08573 predict.Subject
#> predict.Subject11 8 M06 24.15269 predict.Subject
#> predict.Subject12 14 M06 28.11380 predict.Subject
#> predict.Subject13 8 M07 21.79752 predict.Subject
#> predict.Subject14 14 M07 25.75863 predict.Subject
#> predict.Subject15 8 M08 21.90967 predict.Subject
#> predict.Subject16 14 M08 25.87078 predict.Subject
#> predict.Subject17 8 M09 23.03118 predict.Subject
#> predict.Subject18 14 M09 26.99229 predict.Subject
#> predict.Subject19 8 M10 26.95646 predict.Subject
#> predict.Subject20 14 M10 30.91757 predict.Subject
#> predict.Subject21 8 M11 21.68537 predict.Subject
#> predict.Subject22 14 M11 25.64648 predict.Subject
#> predict.Subject23 8 M12 22.24613 predict.Subject
#> predict.Subject24 14 M12 26.20724 predict.Subject
#> predict.Subject25 8 M13 22.24613 predict.Subject
#> predict.Subject26 14 M13 26.20724 predict.Subject
#> predict.Subject27 8 M14 22.80688 predict.Subject
#> predict.Subject28 14 M14 26.76799 predict.Subject
#> predict.Subject29 8 M15 23.70409 predict.Subject
#> predict.Subject30 14 M15 27.66520 predict.Subject
#> predict.Subject31 8 M16 21.12462 predict.Subject
#> predict.Subject32 14 M16 25.08573 predict.Subject
#> predict.Subject33 8 F01 19.66666 predict.Subject
#> predict.Subject34 14 F01 23.62777 predict.Subject
#> predict.Subject35 8 F02 21.12462 predict.Subject
#> predict.Subject36 14 F02 25.08573 predict.Subject
#> predict.Subject37 8 F03 21.79752 predict.Subject
#> predict.Subject38 14 F03 25.75863 predict.Subject
#> predict.Subject39 8 F04 22.80688 predict.Subject
#> predict.Subject40 14 F04 26.76799 predict.Subject
#> predict.Subject41 8 F05 20.78816 predict.Subject
#> predict.Subject42 14 F05 24.74928 predict.Subject
#> predict.Subject43 8 F06 19.44235 predict.Subject
#> predict.Subject44 14 F06 23.40347 predict.Subject
#> predict.Subject45 8 F07 21.12462 predict.Subject
#> predict.Subject46 14 F07 25.08573 predict.Subject
#> predict.Subject47 8 F08 21.46107 predict.Subject
#> predict.Subject48 14 F08 25.42218 predict.Subject
#> predict.Subject49 8 F09 19.44235 predict.Subject
#> predict.Subject50 14 F09 23.40347 predict.Subject
#> predict.Subject51 8 F10 17.08719 predict.Subject
#> predict.Subject52 14 F10 21.04830 predict.Subject
#> predict.Subject53 8 F11 24.15269 predict.Subject
#> predict.Subject54 14 F11 28.11380 predict.Subject