GLMM.Select: Perform Model Selection Using Generalized Linear Mixed Models...

In ballengerj/FishyR: Functions to Facilitate Standard Analyses Conducted in Fisheries Science

Perform Model Selection Using Generalized Linear Mixed Models (GLMMs)

Description

GLMM.Select generates a vector that contains an AIC, an AICc, a BIC, and a dispersion parameter estimate. Intent is to use this function in a loop to extract model information criterion for a range of model formulations, facilitating the automated selection of *best* model.

Usage

GLMM.Select(
  formula,
  data,
  family = gaussian(),
  ziformula = ~0,
  dispformula = ~1,
  weights = NULL,
  offset = NULL,
  contrasts = NULL,
  na.action = na.fail,
  se = TRUE,
  verbose = FALSE,
  doFit = TRUE,
  control = glmmTMBControl(profile = TRUE, collect = TRUE),
  REML = FALSE,
  start = NULL,
  map = NULL,
  sparseX = NULL
)

Arguments

formula	combined fixed and random effects formula, following lme4 syntax.
data	data frame (tibbles are OK) containing model variables. Not required, but strongly recommended; if data is not specified, downstream methods such as prediction with new data (predict(fitted_model, newdata = ...)) will fail. If it is necessary to call glmmTMB with model variables taken from the environment rather than from a data frame, specifying data=NULL will suppress the warning message.
family	a family function, a character string naming a family function, or the result of a call to a family function (variance/link function) information. See family for a generic discussion of families or family_glmmTMB for details of glmmTMB-specific families.
ziformula	a one-sided (i.e., no response variable) formula for zero-inflation combining fixed and random effects: the default ~0 specifies no zero-inflation. Specifying ~. sets the zero-inflation formula identical to the right-hand side of formula (i.e., the conditional effects formula); terms can also be added or subtracted. When using ~. as the zero-inflation formula in models where the conditional effects formula contains an offset term, the offset term will automatically be dropped. The zero-inflation model uses a logit link.
dispformula	a one-sided formula for dispersion containing only fixed effects: the default ~1 specifies the standard dispersion given any family. The argument is ignored for families that do not have a dispersion parameter. For an explanation of the dispersion parameter for each family, see sigma. The dispersion model uses a log link. In Gaussian mixed models, dispformula=~0 fixes the residual variance to be 0 (actually a small non-zero value), forcing variance into the random effects. The precise value can be controlled via control=glmmTMBControl(zero_dispval=...); the default value is sqrt(.Machine$double.eps).
weights	weights, as in glm. Not automatically scaled to have sum 1.
offset	offset for conditional model (only).
contrasts	an optional list, e.g., list(fac1="contr.sum"). See the contrasts.arg of model.matrix.default.
na.action	a function that specifies how to handle observations containing NAs. The default action (na.omit, inherited from the 'factory fresh' value of getOption("na.action")) strips any observations with any missing values in any variables. Using na.action = na.exclude will similarly drop observations with missing values while fitting the model, but will fill in NA values for the predicted and residual values for cases that were excluded during the fitting process because of missingness.
se	whether to return standard errors.
verbose	whether progress indication should be printed to the console.
doFit	whether to fit the full model, or (if FALSE) return the pre-processed data and parameter objects, without fitting the model.
control	control parameters, see glmmTMBControl.
REML	whether to use REML estimation rather than maximum likelihood.
start	starting values, expressed as a list with possible components beta, betazi, betad (fixed-effect parameters for conditional, zero-inflation, dispersion models); b, bzi (conditional modes for conditional and zero-inflation models); theta, thetazi (random-effect parameters, on the standard deviation/Cholesky scale, for conditional and z-i models); thetaf (extra family parameters, e.g., shape for Tweedie models).
map	a list specifying which parameter values should be fixed to a constant value rather than estimated. map should be a named list containing factors corresponding to a subset of the internal parameter names (see start parameter). Distinct factor values are fitted as separate parameter values, NA values are held fixed: e.g., map=list(beta=factor(c(1,2,3,NA))) would fit the first three fixed-effect parameters of the conditional model and fix the fourth parameter to its starting value. In general, users will probably want to use start to specify non-default starting values for fixed parameters. See MakeADFun for more details.
sparseX	a named logical vector containing (possibly) elements named "cond", "zi", "disp" to indicate whether fixed-effect model matrices for particular model components should be generated as sparse matrices, e.g. c(cond=TRUE). Default is all FALSE

Details

See glmmTMB

Note

For more information about the glmmTMB package, see Brooks et al. (2017) and the vignette(package="glmmTMB") collection. For the underlying TMB package that performs the model estimation, see Kristensen et al. (2016).

References

Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M. and Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400.

Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H. and Bell, B. (2016). TMB: Automatic differentiation and Laplace approximation. Journal of Statistical Software, 70, 1–21.

Millar, R. B. (2011). Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB. Wiley, New York.

ballengerj/FishyR documentation built on June 17, 2022, 10:33 p.m.