Computes residuals for models fit with galamm() using the definitions in
Chapter 8 of Dunn and Smyth (2018)
.
Define \(y\) as the response and \(\hat{\mu}\) as the model fit. Importantly,
\(\hat{\mu}\) includes all random effects. Also define \(V(\cdot)\) as the
variance function of the model family, and \(w\) as the weight. The Pearson
residual is then
$$r_{P} = (y - \hat{\mu})/\sqrt{V(\hat{\mu}) / w}.$$
Furthermore, let \(sgn(\cdot)\) be the function which returns the sign of its
argument and let \(d(y, \hat{\mu})\) be the model deviance. The deviance
residual is then
$$r_{D} = sgn(y - \hat{\mu}) \sqrt{w d(y, \hat{\mu})}.$$
Arguments
- object
An object of class
galammreturned fromgalamm.- type
Character of length one describing the type of residuals to be returned. One of
"pearson"and"deviance". Argument is case sensitive.- ...
Optional arguments passed on to other methods. Currently not used.
References
Dunn PK, Smyth GK (2018). Generalized Linear Models With Examples in R, Springer Texts in Statistics. Springer, New York, NY. ISBN 978-1-4419-0117-0 978-1-4419-0118-7, doi:10.1007/978-1-4419-0118-7 .
See also
fitted.galamm() for model fitted values, predict.galamm() for
model predictions, and plot.galamm() for diagnostic plots. The generic
function is residuals().
Other details of model fit:
VarCorr(),
coef.galamm(),
confint.galamm(),
deviance.galamm(),
factor_loadings.galamm(),
family.galamm(),
fitted.galamm(),
fixef(),
formula.galamm(),
llikAIC(),
logLik.galamm(),
nobs.galamm(),
predict.galamm(),
print.VarCorr.galamm(),
ranef.galamm(),
response(),
sigma.galamm(),
vcov.galamm()
Examples
# Poisson GLMM
count_mod <- galamm(
formula = y ~ lbas * treat + lage + v4 + (1 | subj),
data = epilep, family = poisson
)
# Extract residuals
residuals(count_mod)
#> [1] 0.75134825 -0.30588389 -0.30588389 -0.02647642 -0.29969105 0.75942563
#> [7] -0.29969105 -0.02030696 -0.31982773 0.94348448 -1.58313993 1.96255720
#> [13] 0.43844044 0.43844044 -1.23495860 0.76431835 -1.92290673 0.98985619
#> [19] -1.39331347 2.54290384 -0.37901975 -1.61176436 0.85372485 0.87239664
#> [25] 1.53250415 0.42157251 -1.80029078 -0.45686210 3.20970074 -0.85522137
#> [31] -0.24548305 -1.89587259 -0.37109232 0.04054904 0.04054904 -0.01046534
#> [37] 2.32295391 1.96014958 -0.57948072 -2.54300871 2.19282080 -1.20593505
#> [43] -2.66254471 1.98848496 1.50225502 -0.64560897 0.07034569 -1.02528638
#> [49] -0.11018739 -0.11018739 0.86264510 -0.84232934 -1.20333646 -0.60001269
#> [55] 0.30497297 1.51905875 0.11484915 2.14376752 -1.40683963 -1.16387436
#> [61] 2.65545389 -2.24478741 -2.24478741 0.34313328 -1.52938364 -1.52938364
#> [67] 0.43219090 0.71507238 0.93383050 -0.48618501 -0.66368695 0.38154208
#> [73] -0.56482022 0.41685518 -1.05565792 0.78036317 -0.61348596 -2.06574589
#> [79] 0.83877398 1.76694279 -0.32319792 0.20279343 -0.32319792 0.52638912
#> [85] -0.15886014 0.39262021 -0.15886014 0.71796768 -0.88312263 -0.36313065
#> [91] -0.36313065 1.04374857 0.11210337 1.55462045 -2.05167224 0.56867360
#> [97] -3.01755708 -2.01950534 6.63027648 -1.03914103 -0.02145063 -0.72321509
#> [103] -0.02145063 -0.55408107 0.27795300 -0.97310964 0.90348433 -0.11893239
#> [109] -0.13340625 0.41112684 -0.13340625 0.15259422 -0.06770284 0.82764610
#> [115] -0.66460214 -0.50349772 0.20892340 -0.15792875 0.57577555 -0.92446003
#> [121] -1.46575702 1.26320826 0.58096694 -1.35232878 0.10795686 1.89482700
#> [127] -1.08328990 0.38775621 -1.16421688 0.73022624 1.20383702 0.10529799
#> [133] 0.46538682 -0.09615929 -1.21925153 0.18294961 0.90033033 -0.27288190
#> [139] 0.19640299 0.13717193 0.17355958 -0.29134663 1.10337200 0.03108554
#> [145] -0.20516397 1.11018319 -1.52051113 1.44850131 -1.49633342 0.02175214
#> [151] 0.02175214 0.44848458 -1.29984938 3.78574203 -2.02636244 -0.57121746
#> [157] 0.95126708 -0.03257705 -0.03257705 -0.93776498 -1.46467211 -0.09917878
#> [163] 1.26631454 -1.35132783 1.17204252 0.59252146 -1.72556281 0.29235793
#> [169] -1.34839757 -0.60923644 2.10102101 0.26122160 1.25864008 -0.66816476
#> [175] -1.43888670 0.94731198 2.83566561 -0.95477231 -1.27064214 -0.52432300
#> [181] -0.35539104 -0.35539104 0.48257860 1.62375415 -0.76701612 0.63019867
#> [187] -0.06840872 0.38751946 0.77476967 -0.15207809 -1.07892584 -0.99543270
#> [193] 2.75568498 -1.44160278 -0.64752131 -0.38687286 0.21353124 -0.31387209
#> [199] -0.84127542 0.53720677 0.39735678 -0.36116194 -0.74042129 0.44481689
#> [205] -1.29929849 -0.21360713 -1.29929849 1.24229858 0.06700104 -1.59599420
#> [211] 2.44270852 -0.53607134 1.14333078 -0.40015859 0.11433786 -1.79323794
#> [217] -0.35757659 0.68406717 0.16324529 -0.07793957 -3.19183238 3.13136837
#> [223] 1.98169550 -0.71780112 -0.15016611 0.52039502 -1.49128836 -0.64907910
#> [229] -0.98405084 -0.98405084 -0.98405084 -0.90789964 -0.94826426 0.94946156
#> [235] 0.31688629 -0.08723898