modavgShrink {AICcmodavg} | R Documentation |

This function computes an alternative version of model-averaging
parameter estimates that consists in shrinking estimates toward 0 to
reduce model selection bias as in Burnham and Anderson (2002, p. 152),
Anderson (2008, pp. 130-132) and Lukacs et al. (2010). Specifically,
models without the parameter of interest have an estimate and variance
of 0. `modavgShrink`

also returns unconditional standard errors
and unconditional confidence intervals as described in Buckland et
al. (1997) and Burnham and Anderson (2002).

modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICaov.lm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICbetareg' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICsclm.clm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICclm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICclmm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICcoxme' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICcoxph' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICglm.lm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, gamdisp = NULL, ...) ## S3 method for class 'AICgls' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICglmmTMB' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, ...) ## S3 method for class 'AIChurdle' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AIClm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AIClme' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AIClmekin' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICmer' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICglmerMod' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AIClmerMod' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AIClmerModLmerTest' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICmaxlikeFit.list' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, ...) ## S3 method for class 'AICmultinom.nnet' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, ...) ## S3 method for class 'AICnegbin.glm.lm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICpolr' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICrlm.lm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICsurvreg' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICvglm' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, ...) ## S3 method for class 'AICzeroinfl' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, ...) ## S3 method for class 'AICunmarkedFitOccu' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitColExt' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuRN' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitPCount' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitPCO' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitDS' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitGDS' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuFP' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitMPois' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitGMM' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitGPC' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuMulti' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuMS' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuTTD' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitMMO' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitDSO' modavgShrink(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95, c.hat = 1, parm.type = NULL, ...)

`cand.set` |
a list storing each of the models in the candidate model set. |

`parm` |
the parameter of interest, enclosed between quotes, for which a model-averaged estimate is required. For a categorical variable, the label of the estimate must be included as it appears in the output (see 'Details' below). |

`modnames` |
a character vector of model names to facilitate the identification of
each model in the model selection table. If |

`second.ord` |
logical. If |

`nobs` |
this argument allows to specify a numeric value other than total
sample size to compute the AICc (i.e., |

`uncond.se` |
either, |

`conf.level` |
the confidence level ( |

`c.hat` |
value of overdispersion parameter (i.e., variance inflation factor) such
as that obtained from |

`gamdisp` |
if gamma GLM is used, the dispersion parameter should be specified here to apply the same value to each model. |

`parm.type` |
this argument specifies the parameter type on which the
model-averaged estimate of a predictor will be computed and is only
relevant for models of |

`...` |
additional arguments passed to the function. |

The parameter for which a model-averaged estimate is requested must be
specified with the `parm`

argument and must be identical to its
label in the model output (e.g., from `summary`

). For factors, one
must specify the name of the variable and the level of interest. The
shrinkage version of model averaging is only appropriate for cases where
each parameter is given an equal weighting in the model (i.e., each
parameter must appear the same number of times in the models) and has
the same interpretation across all models. As a result, models with
interaction terms or polynomial terms are not supported by
`modavgShrink`

.

`modavgShrink`

is implemented for a list containing objects of
`aov`

, `betareg`

, `clm`

, `clmm`

, `clogit`

,
`coxme`

, `coxph`

, `glm`

, `glmmTMB`

, `gls`

,
`hurdle`

, `lm`

, `lme`

, `lmekin`

, `maxlikeFit`

,
`mer`

, `glmerMod`

, `lmerMod`

, `lmerModLmerTest`

,
`multinom`

, `polr`

, `rlm`

, `survreg`

, `vglm`

,
`zeroinfl`

classes as well as various models of `unmarkedFit`

classes.

`modavgShrink`

creates an object of class `modavgShrink`

with the following components:

`Parameter` |
the parameter for which a model-averaged estimate with shrinkage was obtained. |

`Mod.avg.table` |
the model selection table based on models including the parameter of interest. |

`Mod.avg.beta` |
the model-averaged estimate based on all models. |

`Uncond.SE` |
the unconditional standard error for the model-averaged estimate (as opposed to the conditional SE based on a single model). |

`Conf.level` |
the confidence level used to compute the confidence interval. |

`Lower.CL` |
the lower confidence limit. |

`Upper.CL` |
the upper confidence limit. |

Marc J. Mazerolle

Anderson, D. R. (2008) *Model-based Inference in the Life Sciences:
a primer on evidence*. Springer: New York.

Buckland, S. T., Burnham, K. P., Augustin, N. H. (1997) Model selection:
an integral part of inference. *Biometrics* **53**, 603–618.

Burnham, K. P., Anderson, D. R. (2002) *Model Selection and
Multimodel Inference: a practical information-theoretic
approach*. Second edition. Springer: New York.

Burnham, K. P., Anderson, D. R. (2004) Multimodel inference:
understanding AIC and BIC in model selection. *Sociological
Methods and Research* **33**, 261–304.

Dail, D., Madsen, L. (2011) Models for estimating abundance from
repeated counts of an open population. *Biometrics* **67**,
577–587.

Lukacs, P. M., Burnham, K. P., Anderson, D. R. (2010) Model selection
bias and Freedman's paradox. *Annals of the Institute of
Statistical Mathematics* **62**, 117–125.

MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle,
J. A., Langtimm, C. A. (2002) Estimating site occupancy rates when
detection probabilities are less than one. *Ecology* **83**,
2248–2255.

MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G.,
Franklin, A. B. (2003) Estimating site occupancy, colonization, and
local extinction when a species is detected imperfectly. *Ecology*
**84**, 2200–2207.

Mazerolle, M. J. (2006) Improving data analysis in herpetology: using
Akaike's Information Criterion (AIC) to assess the strength of
biological hypotheses. *Amphibia-Reptilia* **27**, 169–180.

Royle, J. A. (2004) *N*-mixture models for estimating population
size from spatially replicated counts. *Biometrics* **60**,
108–115.

`AICc`

, `aictab`

, `c_hat`

,
`importance`

, `confset`

, `evidence`

,
`modavg`

, `modavgCustom`

,
`modavgPred`

##cement example in Burnham and Anderson 2002 data(cement) ##setup same model set as in Table 3.2, p. 102 Cand.models <- list( ) Cand.models[[1]] <- lm(y ~ x1 + x2, data = cement) Cand.models[[2]] <- lm(y ~ x1 + x2 + x4, data = cement) Cand.models[[3]] <- lm(y ~ x1 + x2 + x3, data = cement) Cand.models[[4]] <- lm(y ~ x1 + x4, data = cement) Cand.models[[5]] <- lm(y ~ x1 + x3 + x4, data = cement) Cand.models[[6]] <- lm(y ~ x2 + x3 + x4, data = cement) Cand.models[[7]] <- lm(y ~ x1 + x2 + x3 + x4, data = cement) Cand.models[[8]] <- lm(y ~ x3 + x4, data = cement) Cand.models[[9]] <- lm(y ~ x2 + x3, data = cement) Cand.models[[10]] <- lm(y ~ x4, data = cement) Cand.models[[11]] <- lm(y ~ x2, data = cement) Cand.models[[12]] <- lm(y ~ x2 + x4, data = cement) Cand.models[[13]] <- lm(y ~ x1, data = cement) Cand.models[[14]] <- lm(y ~ x1 + x3, data = cement) Cand.models[[15]] <- lm(y ~ x3, data = cement) ##vector of model names Modnames <- paste("mod", 1:15, sep="") ##AICc aictab(cand.set = Cand.models, modnames = Modnames) ##compute model-averaged estimate with shrinkage - each parameter ##appears 8 times in the models modavgShrink(cand.set = Cand.models, modnames = Modnames, parm = "x1") ##compare against classic model-averaging modavg(cand.set = Cand.models, modnames = Modnames, parm = "x1") ##note that model-averaged estimate with shrinkage is closer to 0 than ##with the classic version ##remove a few models from the set and run again Cand.unbalanced <- Cand.models[-c(3, 14, 15)] ##set up model names Modnames <- paste("mod", 1:length(Cand.unbalanced), sep="") ##issues an error because some parameters appear more often than others ## Not run: modavgShrink(cand.set = Cand.unbalanced, modnames = Modnames, parm = "x1") ## End(Not run) ##example on Orthodont data set in nlme ## Not run: require(nlme) ##set up candidate model list ##age and sex parameters appear in the same number of models ##same number of models with and without these parameters Cand.models <- list( ) Cand.models[[1]] <- lme(distance ~ age, data = Orthodont, method = "ML") ##random is ~ age | Subject as it is a grouped data frame Cand.models[[2]] <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1, method = "ML") Cand.models[[3]] <- lme(distance ~ 1, data = Orthodont, random = ~ 1, method = "ML") Cand.models[[4]] <- lme(distance ~ Sex, data = Orthodont, random = ~ 1, method = "ML") ##create a vector of model names Modnames <- paste("mod", 1:length(Cand.models), sep = "") ##compute importance values for age imp.age <- importance(cand.set = Cand.models, parm = "age", modnames = Modnames, second.ord = TRUE, nobs = NULL) ##compute shrinkage version of model averaging on age mod.avg.age.shrink <- modavgShrink(cand.set = Cand.models, parm = "age", modnames = Modnames, second.ord = TRUE, nobs = NULL) ##compute classic version of model averaging on age mod.avg.age.classic <- modavg(cand.set = Cand.models, parm = "age", modnames = Modnames, second.ord = TRUE, nobs = NULL) ##correspondence between shrinkage version and classic version of ##model averaging mod.avg.age.shrink$Mod.avg.beta/imp.age$w.plus mod.avg.age.classic$Mod.avg.beta detach(package:nlme) ## End(Not run) ##example of N-mixture model modified from ?pcount ## Not run: require(unmarked) data(mallard) mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs = mallard.site, obsCovs = mallard.obs) ##set up models so that each variable on abundance appears twice fm.mall.one <- pcount(~ ivel + date ~ length + forest, mallardUMF, K = 30) fm.mall.two <- pcount(~ ivel + date ~ elev + forest, mallardUMF, K = 30) fm.mall.three <- pcount(~ ivel + date ~ length + elev, mallardUMF, K = 30) ##model list and names Cands <- list(fm.mall.one, fm.mall.two, fm.mall.three) Modnames <- c("length + forest", "elev + forest", "length + elev") ##compute model-averaged estimate with shrinkage for elev on abundance modavgShrink(cand.set = Cands, modnames = Modnames, parm = "elev", parm.type = "lambda") detach(package:unmarked) ## End(Not run)

[Package *AICcmodavg* version 2.3-1 Index]