# 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 ```r 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, ...) ``` ## Arguments - `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. ## Details 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). ## Returns `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. - **data**: a data frame or, in the case of `unmarkedFit` objects, a list of data frames (e.g., obsCovs, siteCovs, yearlySiteCovs). ## References 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. ## Author(s) Marc J. Mazerolle ## See Also `extractCN`, `extractSE`, `modavgPred`, `modavgCustom`, `modavgEffect`, `predict`, `predictSE` ## Examples ```r ##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) ```

extractX function