Extract Predictors from Candidate Model List
This function extracts the predictors used in candidate models. The function is currently implemented for glm, glmmTMB, gls, lm, lme, merMod, lmerModLmerTest, rlm, survreg object classes that are stored in a list as well as various models of unmarkedFit
classes. 1.1
extractX(cand.set, ...) ## S3 method for class 'AICaov.lm' extractX(cand.set, ...) ## S3 method for class 'AICglm.lm' extractX(cand.set, ...) ## S3 method for class 'AICglmmTMB' extractX(cand.set, ...) ## S3 method for class 'AIClm' extractX(cand.set, ...) ## S3 method for class 'AICgls' extractX(cand.set, ...) ## S3 method for class 'AIClme' extractX(cand.set, ...) ## S3 method for class 'AICglmerMod' extractX(cand.set, ...) ## S3 method for class 'AIClmerMod' extractX(cand.set, ...) ## S3 method for class 'AIClmerModLmerTest' extractX(cand.set, ...) ## S3 method for class 'AICrlm.lm' extractX(cand.set, ...) ## S3 method for class 'AICsurvreg' extractX(cand.set, ...) ## S3 method for class 'AICunmarkedFitOccu' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitColExt' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuRN' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitPCount' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitPCO' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitDS' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitGDS' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuFP' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitMPois' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitGMM' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitGPC' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuMulti' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuMS' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuTTD' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitMMO' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitDSO' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitGOccu' extractX(cand.set, parm.type = NULL, ...) ## S3 method for class 'AICunmarkedFitOccuComm' extractX(cand.set, parm.type = NULL, ...)
cand.set: a list storing each of the models in the candidate model set.
parm.type: this argument specifies the parameter type on which the predictors 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, unmarkedFitOccuMulti, and unmarkedFitOccuComm objects, either psi or detect can be supplied to indicate whether the parameter is on occupancy or detectability, respectively. For unmarkedFitColExt and unmarkedFitOccuTTD, possible values are psi, gamma, epsilon, and detect, for parameters on occupancy in the inital year, colonization, extinction, and detectability, 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. For unmarkedFitGOccu objects, possible values are psi, phi, or detect, denoting occupancy, availability, and detection probabilities, respectively.
...: additional arguments passed to the function.
The candidate models must be stored in a list. The results of extractX are useful in preparing a newdata
data frame to use in computing model-averaged predictions with modavgPred or differences between groups with modavgEffect (Burnham and Anderson 2002, Anderson 2008, Burnham et al. 2011).
extractX returns an object of class extractX with the following components: - predictors: a character vector of the names of the predictors included in the model, excluding the intercept term.
unmarkedFit objects, a list of data frames (e.g., obsCovs, siteCovs, yearlySiteCovs).Anderson, D. R. (2008) Model-based Inference in the Life Sciences: a primer on evidence. Springer: New York.
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., Huyvaert, K. P. (2011) AIC model selection and multimodel inference in behaviorial ecology: some background, observations and comparisons. Behavioral Ecology and Sociobiology 65 , 23--25.
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.
Pinheiro, J. C., Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. Springer Verlag: New York.
Royle, J. A. (2004) N-mixture models for estimating population size from spatially replicated counts. Biometrics 60 , 108--115.
Marc J. Mazerolle
extractCN, extractSE, modavgPred, modavgCustom, modavgEffect, predict, predictSE
##example from subset of models in Table 1 in Mazerolle (2006) data(dry.frog) Cand.models <- list( ) Cand.models[[1]] <- lm(log_Mass_lost ~ Shade + Substrate + cent_Initial_mass + Initial_mass2, data = dry.frog) Cand.models[[2]] <- lm(log_Mass_lost ~ Shade + Substrate + cent_Initial_mass + Initial_mass2 + Shade:Substrate, data = dry.frog) Cand.models[[3]] <- lm(log_Mass_lost ~ cent_Initial_mass + Initial_mass2, data = dry.frog) Cand.models[[4]] <- lm(log_Mass_lost ~ Shade + cent_Initial_mass + Initial_mass2, data = dry.frog) Cand.models[[4]] <- lm(log_Mass_lost ~ Shade + cent_Initial_mass + Initial_mass2, data = dry.frog) Cand.models[[5]] <- lm(log_Mass_lost ~ Substrate + cent_Initial_mass + Initial_mass2, data = dry.frog) ##assign names names(Cand.models) <- paste(1:length(Cand.models)) ##extract predictors from candidate model set orig.data <- extractX(cand.set = Cand.models) orig.data str(orig.data) ## Not run: ##model-averaged prediction with original variables modavgPred(Cand.models, newdata = orig.data$data) ## End(Not run) ##example of model-averaged predictions from N-mixture model (e.g., Royle 2004) ##modified from ?pcount ##each variable appears twice on lambda in the models ## Not run: require(unmarked) data(mallard) mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs = mallard.site, obsCovs = mallard.obs) ##set up models so that each variable on abundance appears twice fm.mall.one <- pcount(~ ivel + date ~ length + forest, mallardUMF, K = 30) fm.mall.two <- pcount(~ ivel + date ~ elev + forest, mallardUMF, K = 30) fm.mall.three <- pcount(~ ivel + date ~ length + elev, mallardUMF, K = 30) fm.mall.four <- pcount(~ ivel + date ~ 1, mallardUMF, K = 30) ##model list Cands <- list(fm.mall.one, fm.mall.two, fm.mall.three, fm.mall.four) names(Cands) <- c("length + forest", "elev + forest", "length + elev", "null") ##extract predictors on lambda lam.dat <- extractX(cand.set = Cands, parm.type = "lambda") lam.dat str(lam.dat) ##extract predictors on detectability extractX(cand.set = Cands, parm.type = "detect") ##model-averaged predictions on lambda ##extract data siteCovs <- lam.dat$data$siteCovs ##create vector of forest values forest <- seq(min(siteCovs$forest), max(siteCovs$forest), length.out = 40) dframe <- data.frame(forest = forest, length = mean(siteCovs$length), elev = mean(siteCovs$elev)) modavgPred(Cands, parm.type = "lambda", newdata = dframe) detach(package:unmarked) ## End(Not run) ##example of model-averaged abundance from distance model ## Not run: require(unmarked) data(linetran) #example from ?distsamp ltUMF <- with(linetran, { unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4), siteCovs = data.frame(Length, area, habitat), dist.breaks = c(0, 5, 10, 15, 20), tlength = linetran$Length * 1000, survey = "line", unitsIn = "m") }) ## Half-normal detection function. Density output (log scale). No covariates. fm1 <- distsamp(~ 1 ~ 1, ltUMF) ## Halfnormal. Covariates affecting both density and and detection. fm2 <- distsamp(~area + habitat ~ habitat, ltUMF) ## Hazard function. Covariates affecting both density and and detection. fm3 <- distsamp(~area + habitat ~ habitat, ltUMF, keyfun="hazard") ##assemble model list Cands <- list(fm1, fm2, fm3) ##model-average predictions on abundance extractX(cand.set = Cands, parm.type = "lambda") detach(package:unmarked) ## End(Not run) ##example using Orthodont data set from Pinheiro and Bates (2000) ## Not run: require(nlme) ##set up candidate models m1 <- gls(distance ~ age, correlation = corCompSymm(value = 0.5, form = ~ 1 | Subject), data = Orthodont, method = "ML") m2 <- gls(distance ~ 1, correlation = corCompSymm(value = 0.5, form = ~ 1 | Subject), data = Orthodont, method = "ML") ##assemble in list Cand.models <- list("age effect" = m1, "null model" = m2) ##model-averaged predictions extractX(cand.set = Cand.models) detach(package:nlme) ## End(Not run)
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