This function calculates the relative importance of variables (w+) based on the sum of Akaike weights (model probabilities) of the models that include the variable. Note that this measure of evidence is only appropriate when the variable appears in the same number of models as those that do not include the variable.
importance(cand.set, parm, modnames = NULL, second.ord = TRUE,
nobs = NULL, ...)# S3 method for AICaov.lm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICbetareg
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICsclm.clm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICclm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICclmm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICclogit.coxph
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICcoxme
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICcoxph
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICglm.lm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, ...)
# S3 method for AICglmerMod
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AIClmerModLmerTest
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICglmmTMB
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, ...)
# S3 method for AICgls
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AIClm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AIClme
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AIClmekin
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICmaxlikeFit.list
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, ...)
# S3 method for AICmer
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICmultinom.nnet
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, ...)
# S3 method for AICnegbin.glm.lm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICnlmerMod
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICpolr
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICrlm.lm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICsurvreg
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
# S3 method for AICunmarkedFitColExt
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitOccu
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitOccuFP
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitOccuRN
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitPCount
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitPCO
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitDS
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitGDS
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitMPois
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitGMM
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitGPC
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitOccuMulti
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL, ...)
# S3 method for AICunmarkedFitOccuMS
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL,
...)
# S3 method for AICunmarkedFitOccuTTD
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL,
...)
# S3 method for AICunmarkedFitMMO
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL,
...)
# S3 method for AICunmarkedFitDSO
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, parm.type = NULL,
...)
# S3 method for AICvglm
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, c.hat = 1, ...)
# S3 method for AICzeroinfl
importance(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, ...)
importance returns an object of class importance
consisting of the following components:
the parameter for which an importance value is required.
the sum of Akaike weights for the models that include the parameter of interest.
the sum of Akaike weights for the models that exclude the parameter of interest.
a list storing each of the models in the candidate model set.
the parameter of interest for which a measure of relative importance is required.
a character vector of model names to facilitate the identification of
each model in the model selection table. If NULL, the function
uses the names in the cand.set list of candidate models. If no names
appear in the list, generic names (e.g., Mod1, Mod2) are
supplied in the table in the same order as in the list of candidate models.
logical. If TRUE, the function returns the second-order Akaike
information criterion (i.e., AICc).
this argument allows to specify a numeric value other than total sample
size to compute the AICc (i.e., nobs defaults to total number of
observations). This is relevant only for mixed models or various models
of unmarkedFit classes where sample size is not straightforward. In
such cases, one might use total number of observations or number of
independent clusters (e.g., sites) as the value of nobs.
value of overdispersion parameter (i.e., variance inflation factor) such
as that obtained from c_hat. Note that values of c.hat different
from 1 are only appropriate for binomial GLM's with trials > 1 (i.e.,
success/trial or cbind(success, failure) syntax), with Poisson GLM's,
single-season occupancy models (MacKenzie et al. 2002), dynamic
occupancy models (MacKenzie et al. 2003), or N-mixture models
(Royle 2004, Dail and Madsen 2011). If c.hat > 1,
importance will return the quasi-likelihood analogue of the
information criteria requested and multiply the variance-covariance
matrix of the estimates by this value (i.e., SE's are multiplied by
sqrt(c.hat)). This option is not supported for generalized
linear mixed models of the mer or merMod classes.
this argument specifies the parameter type on which
the variable of interest will be extracted and is only relevant for
models of unmarkedFit classes. The character strings supported
vary with the type of model fitted. For unmarkedFitOccu and
unmarkedFitOccuMulti objects, either psi or
detect can be supplied to indicate whether the parameter is on
occupancy or detectability, respectively. For
unmarkedFitColExt objects, possible values are psi,
gamma, epsilon, and detect, for parameters on
occupancy in the inital year, colonization, extinction, and
detectability, respectively. For unmarkedFitOccuTTD objects,
possible values are psi, gamma, epsilon, and
detect, for parameters on occupancy in the inital year,
colonization, extinction, and time-to-dection (lambda rate parameter),
respectively. For unmarkedFitOccuFP objects, one can specify
psi, detect, falsepos, and certain, for
occupancy, detectability, probability of assigning false-positives,
and probability detections are certain, respectively. For
unmarkedFitOccuMS objects, possible values are psi,
phi, or detect, denoting occupancy, transition, and
detection probabilities, respectively. For unmarkedFitOccuRN
objects, either lambda or detect can be entered for
abundance and detectability parameters, respectively. For
unmarkedFitPCount and unmarkedFitMPois objects,
lambda or detect denote parameters on abundance and
detectability, respectively. For unmarkedFitPCO,
unmarkedFitMMO, and unmarkedFitDSO objects, one can
enter lambda, gamma, omega, iota, or
detect, to specify parameters on abundance, recruitment,
apparent survival, immigration, and detectability, respectively. For
unmarkedFitDS objects, lambda and detect are
supported. For unmarkedFitGDS, lambda, phi, and
detect denote abundance, availability, and detection
probability, respectively. For unmarkedFitGMM and
unmarkedFitGPC objects, lambda, phi, and
detect denote abundance, availability, and detectability,
respectively.
additional arguments passed to the function.
Marc J. Mazerolle
Burnham, K. P., and Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.
Dail, D., Madsen, L. (2011) Models for estimating abundance from repeated counts of an open population. Biometrics 67, 577--587.
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.
Royle, J. A. (2004) N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108--115.
AICc, aictab, c_hat,
confset, evidence, modavg,
modavgShrink, modavgPred