residualDiagnostics methods for lme objects
Usage
# S3 method for class 'lme'
residualDiagnostics(
object,
ev.perc = 0.001,
robust = FALSE,
distr = "normal",
standardized = TRUE,
cut = 8L,
quantiles = 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.- cut
An integer, how many unique predicted values there have to be at least for predicted values to be treated continuously, otherwise they are treated as discrete values. Defaults to 8.
- quantiles
A logical whether to calculate quantiles for the residuals. Defaults to
TRUE. IfFALSE, then do not calculate them. These are based on simple quantiles for each predicted value if the predicted values are few enough to be treated discretely. Seecutargument. Otherwise they are based on quantile regression. First trying smoothing splines, and falling back to linear quantil regression if the splines fail. You may also want to turn these off if they are not working well, or are not of value in your diagnostics.- ...
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)