residualDiagnostics methods for lme objects
Usage
# S3 method for lme
residualDiagnostics(
object,
ev.perc = 0.001,
robust = FALSE,
distr = "normal",
standardized = TRUE,
...
)Arguments
- object
An object with class
lme.- ev.perc
The extreme value percentile to use. Defaults to .001.
- robust
A logical value, whether to use robust estimates or not. Defaults to
FALSE.- distr
A character string specifying the assumed distribution. Currently “normal”, but future options may be supported in the future.
- standardized
A logical value whether to use standardized pearson residual values or not. Defaults to
TRUE.- ...
Additional arguments. Not currently used.
Value
A logical (is.residualDiagnostics) or
a residualDiagnostics object (list) for
as.residualDiagnostics and residualDiagnostics.
Examples
library(JWileymisc)
sleep[1,1] <- NA
m <- nlme::lme(extra ~ group, data = sleep, random = ~ 1 | ID,
na.action = na.omit)
residualDiagnostics(m)$Residuals
#> Residuals Predicted isEV Index
#> <num> <num> <fctr> <int>
#> 1: -0.700128686 -0.9621668 No 2
#> 2: 0.021691421 -0.2197614 No 3
#> 3: -0.117253346 -1.0931795 No 4
#> 4: 0.658755591 -0.7001414 No 5
#> 5: 0.665700971 2.7935312 No 6
#> 6: 0.323895359 3.4049239 No 7
#> 7: 0.400316839 0.4353022 No 8
#> 8: -1.532411926 1.3960622 No 9
#> 9: 0.279433777 1.7454295 No 10
#> 10: -0.106066416 1.9966289 No 11
#> 11: 0.165397761 0.6493189 No 12
#> 12: -0.320216242 1.3917243 No 13
#> 13: -0.459161009 0.5183061 No 14
#> 14: -1.110119656 0.9113443 No 15
#> 15: -0.005506903 4.4050169 No 16
#> 16: 0.530821382 5.0164096 No 17
#> 17: -0.490424510 2.0467879 No 18
#> 18: 1.747982740 3.0075479 No 19
#> 19: 0.047292851 3.3569151 No 20
m <- nlme::lme(hp ~ mpg, data = mtcars, random = ~ 1 | cyl,
na.action = na.omit)
residualDiagnostics(m)$Residuals
#> Residuals Predicted isEV Index
#> <num> <num> <fctr> <int>
#> 1: -0.23664029 118.76861 No 1
#> 2: -0.23664029 118.76861 No 2
#> 3: -0.22511638 101.34160 No 3
#> 4: -0.18972936 117.03034 No 4
#> 5: -0.43428124 191.09211 No 5
#> 6: -0.71168060 131.37103 No 6
#> 7: 0.93880308 210.21303 No 7
#> 8: -0.87407610 94.38853 No 8
#> 9: -0.17114196 101.34160 No 9
#> 10: -0.09690578 126.59080 No 10
#> 11: -0.26109405 132.67473 No 11
#> 12: -0.56908306 201.08714 No 12
#> 13: -0.46353346 197.17604 No 13
#> 14: -0.70981586 206.30194 No 14
#> 15: -0.59806685 227.16112 No 15
#> 16: -0.32819477 227.16112 No 16
#> 17: 0.58090589 208.47477 No 17
#> 18: 0.17209140 59.62322 No 18
#> 19: -0.44028418 68.31455 No 19
#> 20: 0.32102019 53.10473 No 20
#> 21: -0.26962808 106.99096 No 21
#> 22: -1.48424891 204.99824 No 22
#> 23: -1.51943211 206.30194 No 23
#> 24: 0.82152575 214.55870 No 24
#> 25: -0.37564257 188.91928 No 25
#> 26: -0.42602300 81.78611 No 26
#> 27: 0.09619667 87.43547 No 27
#> 28: 1.20593552 68.31455 No 28
#> 29: 1.62747604 203.69454 No 29
#> 30: 1.36506772 124.41797 No 30
#> 31: 3.44974596 207.17107 Yes 31
#> 32: 0.04249069 107.42552 No 32
#> Residuals Predicted isEV Index
rm(m, sleep)