AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c)
This package includes functions to create model selection
tables based on Akaike's information criterion (AIC) and the
second-order AIC (AICc), as well as their quasi-likelihood
counterparts (QAIC, QAICc). Tables are printed with delta AIC
and Akaike weights. The package also features functions to
conduct classic model averaging (multimodel inference) for a
given parameter of interest and predicted values, as well as a
shrinkage version of model averaging parameter estimates.
Other handy functions enable the computation of relative
variable importance, evidence ratios, and confidence sets for
the best model. The present version works with Cox regression
('coxph' class), linear models ('lm' class), generalized linear
models ('glm' class), linear models fit by generalized least
squares ('gls' class), linear mixed models ('lme' class),
generalized linear mixed models ('mer' class), multinomial and
ordinal logistic regressions ('multinom', 'polr', 'clm', and
'clmm' classes), robust regression models ('rlm' class),
nonlinear models ('nls' class), and nonlinear mixed models
('nlme' class). The package also supports various models
incorporating detection probabilities such as single-season
occupancy models ('unmarkedFitOccu' and 'unmarkedFitOccuFP
classes), multiple-season occupancy models ('unmarkedFitColExt'
class), single-season heterogeneity models ('unmarkedFitOccuRN'
class), single-season and multiple-season N-mixture models for
repeated counts ('unmarkedFitPCount' and 'unmarkedFitPCO'
classes, respectively), and distance sampling models
('unmarkedFitDS' and 'unmarkedFitGDS' classes).
||lme4, MASS, Matrix, nlme, nnet, ordinal, stats4, survival, unmarked|
||Marc J. Mazerolle. Special thanks to
T. Ergon for the original idea of storing candidate models in a
||Marc J. Mazerolle <marc.mazerolle at uqat.ca>|
||GPL (≥ 2)|