Check results for 'AICcmodavg'

using R version 3.0.1 Patched (2013-05-18 r62762)
using platform: sparc-sun-solaris2.10 (32-bit)
using session charset: UTF-8
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this is package ‘AICcmodavg’ version ‘1.30’
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checking examples ... ERROR
Running examples in ‘AICcmodavg-Ex.R’ failed
The error most likely occurred in:

> ### Name: AICcmodavg-package
> ### Title: Model Selection and Multimodel Inference Based on (Q)AIC(c)
> ### Aliases: AICcmodavg-package AICcmodavg
> ### Keywords: models
>
> ### ** Examples
>
> ##anuran larvae example from Mazerolle (2006) - Poisson GLM with offset
> data(min.trap)
> ##assign "UPLAND" as the reference level as in Mazerolle (2006)
> min.trap$Type <- relevel(min.trap$Type, ref = "UPLAND")
>
> ##set up candidate models
> Cand.mod <- list()
> ##global model
> Cand.mod[[1]] <- glm(Num_anura ~ Type + log.Perimeter + Num_ranatra,
+ family = poisson, offset = log(Effort),
+ data = min.trap)
> Cand.mod[[2]] <- glm(Num_anura ~ Type + log.Perimeter, family = poisson,
+ offset = log(Effort), data = min.trap)
> Cand.mod[[3]] <- glm(Num_anura ~ Type + Num_ranatra, family = poisson,
+ offset = log(Effort), data = min.trap)
> Cand.mod[[4]] <- glm(Num_anura ~ Type, family = poisson,
+ offset = log(Effort), data = min.trap)
> Cand.mod[[5]] <- glm(Num_anura ~ log.Perimeter + Num_ranatra,
+ family = poisson, offset = log(Effort),
+ data = min.trap)
> Cand.mod[[6]] <- glm(Num_anura ~ log.Perimeter, family = poisson,
+ offset = log(Effort), data = min.trap)
> Cand.mod[[7]] <- glm(Num_anura ~ Num_ranatra, family = poisson,
+ offset = log(Effort), data = min.trap)
> Cand.mod[[8]] <- glm(Num_anura ~ 1, family = poisson,
+ offset = log(Effort), data = min.trap)
>
> ##check c-hat for global model
> c_hat(Cand.mod[[1]]) #uses Pearson's chi-square/df
[1] 1.037769
> ##note the very low overdispersion: in this case, the analysis could be
> ##conducted without correcting for c-hat as its value is reasonably close
> ##to 1
>
> ##assign names to each model
> Modnames <- c("type + logperim + invertpred", "type + logperim",
+ "type + invertpred", "type", "logperim + invertpred",
+ "logperim", "invertpred", "intercept only")
>
> ##model selection table based on AICc
> aictab(cand.set = Cand.mod, modnames = Modnames)

Model selection based on AICc :

K AICc Delta_AICc AICcWt Cum.Wt LL
type + invertpred 3 54.03 0.00 0.60 0.60 -23.42
type + logperim + invertpred 4 56.57 2.54 0.17 0.77 -23.23
logperim + invertpred 3 57.91 3.88 0.09 0.86 -25.35
invertpred 2 58.63 4.60 0.06 0.92 -27.03
type + logperim 3 59.38 5.35 0.04 0.96 -26.09
type 2 59.74 5.71 0.03 1.00 -27.58
intercept only 1 65.47 11.44 0.00 1.00 -31.65
logperim 2 67.27 13.24 0.00 1.00 -31.35

>
> ##compute evidence ratio
> evidence(aictab(cand.set = Cand.mod, modnames = Modnames))

Evidence ratio between models ' type + invertpred ' and ' type + logperim + invertpred ':
3.56

>
> ##compute confidence set based on 'raw' method
> confset(cand.set = Cand.mod, modnames = Modnames, second.ord = TRUE,
+ method = "raw")

Confidence set for the best model

Method: raw sum of model probabilities

95% confidence set:
K AICc Delta_AICc AICcWt
type + invertpred 3 54.03 0.00 0.60
type + logperim + invertpred 4 56.57 2.54 0.17
logperim + invertpred 3 57.91 3.88 0.09
invertpred 2 58.63 4.60 0.06
type + logperim 3 59.38 5.35 0.04

Model probabilities sum to 0.96

>
> ##compute importance value for "TypeBOG" - same number of models
> ##with vs without variable
> importance(cand.set = Cand.mod, modnames = Modnames, parm = "TypeBOG")

Importance values of ' TypeBOG ' :

w+ (models including parameter): 0.85
w- (models excluding parameter): 0.15

>
> ##compute model-averaged estimate of "TypeBOG"
> modavg(cand.set = Cand.mod, modnames = Modnames, parm = "TypeBOG")

Multimodel inference on " TypeBOG " based on AICc

AICc table used to obtain model-averaged estimate:

K AICc Delta_AICc AICcWt Estimate SE
type + logperim + invertpred 4 56.57 2.54 0.20 -1.19 0.62
type + logperim 3 59.38 5.35 0.05 -1.70 0.59
type + invertpred 3 54.03 0.00 0.71 -1.35 0.56
type 2 59.74 5.71 0.04 -1.42 0.56

Model-averaged estimate: -1.34
Unconditional SE: 0.58
95 % Unconditional confidence interval: -2.48 , -0.19

>
> ##compute model-averaged estimate of "TypeBOG" with shrinkage
> ##same number of models with vs without variable
> modavg.shrink(cand.set = Cand.mod, modnames = Modnames,
+ parm = "TypeBOG")

Multimodel inference on " TypeBOG " based on AICc

AICc table used to obtain model-averaged estimate with shrinkage:

K AICc Delta_AICc AICcWt Estimate SE
type + logperim + invertpred 4 56.57 2.54 0.17 -1.19 0.62
type + logperim 3 59.38 5.35 0.04 -1.70 0.59
type + invertpred 3 54.03 0.00 0.60 -1.35 0.56
type 2 59.74 5.71 0.03 -1.42 0.56
logperim + invertpred 3 57.91 3.88 0.09 0.00 0.00
logperim 2 67.27 13.24 0.00 0.00 0.00
invertpred 2 58.63 4.60 0.06 0.00 0.00
intercept only 1 65.47 11.44 0.00 0.00 0.00

Model-averaged estimate with shrinkage: -1.14
Unconditional SE: 0.72
95 % Unconditional confidence interval: -2.54 , 0.27

>
> ##compute model-average predictions for two types of ponds
> ##create a data set for predictions
> dat.pred <- data.frame(Type = factor(c("BOG", "UPLAND")),
+ log.Perimeter = mean(min.trap$log.Perimeter),
+ Num_ranatra = mean(min.trap$Num_ranatra),
+ Effort = mean(min.trap$Effort))
>
> ##model-averaged predictions across entire model set
> modavgpred(cand.set = Cand.mod, modnames = Modnames,
+ newdata = dat.pred)

Model-averaged predictions on the response scale based on entire model set:

mod.avg.pred uncond.se
1 0.28 0.18
2 0.86 0.35

>
>
>
> ##single-season occupancy model example modified from ?occu
> require(unmarked)
Loading required package: unmarked
Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
there is no package called ‘unmarked’
> ##single season
> data(frogs)
Warning in data(frogs) : data set ‘frogs’ not found
> pferUMF <- unmarkedFrameOccu(pfer.bin)
Error: could not find function "unmarkedFrameOccu"
Execution halted