R: Extract Predictors from Candidate Model List

extractX {AICcmodavg}

R Documentation

Extract Predictors from Candidate Model List

Description

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.

Usage

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, ...)

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` and `unmarkedFitOccuMulti` 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.
`...`	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).

Value

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).

Author(s)

Marc J. Mazerolle

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.

Examples

##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)

[Package AICcmodavg version 2.3-3 Index]