AICcmodavg (version 2.3-3)

checkConv: Check Convergence of Fitted Model

Description

This function checks the convergence information contained in models of various classes.

Usage

checkConv(mod, ...)
# S3 method for betareg
checkConv(mod, ...)
# S3 method for clm
checkConv(mod, ...)
# S3 method for clmm
checkConv(mod, ...)
# S3 method for glm
checkConv(mod, ...)
# S3 method for glmmTMB
checkConv(mod, ...)
# S3 method for hurdle
checkConv(mod, ...)
# S3 method for lavaan
checkConv(mod, ...)
# S3 method for maxlikeFit
checkConv(mod, ...)
# S3 method for merMod
checkConv(mod, ...)
# S3 method for lmerModLmerTest
checkConv(mod, ...)
# S3 method for multinom
checkConv(mod, ...)
# S3 method for nls
checkConv(mod, ...)
# S3 method for polr
checkConv(mod, ...)
# S3 method for unmarkedFit
checkConv(mod, ...)
# S3 method for zeroinfl
checkConv(mod, ...)

Value

checkConv returns a list with the following components:

converged

a logical value indicating whether the algorithm converged or not.

message

a string containing the message from the optimization function.

Arguments

mod

an object containing the output of a model of the classes mentioned above.

...

additional arguments passed to the function.

Author

Marc J. Mazerolle

Details

This function checks the element of a model object that contains the convergence information from the optimization function. The function is currently implemented for models of classes betareg, clm, clmm, glm, glmmTMB, hurdle, lavaan, maxlikeFit, merMod, lmerModLmerTest, multinom, nls, polr, unmarkedFit, and zeroinfl. The function is particularly useful for functions with several groups of parameters, such as those of the unmarked package (Fiske and Chandler, 2011).

References

Fiske, I., Chandler, R. (2011) unmarked: An R Package for fitting hierarchical models of wildlife occurrence and abundance. Journal of Statistical Software 43, 1--23.

See Also

checkParms, covDiag, mb.gof.test, Nmix.gof.test

Examples

##example modified from ?pcount
if (FALSE) {
if(require(unmarked)){
##Simulate data
set.seed(3)
nSites <- 100
nVisits <- 3
##covariate
x <- rnorm(nSites)               
beta0 <- 0
beta1 <- 1
##expected counts
lambda <- exp(beta0 + beta1*x)   
N <- rpois(nSites, lambda)      
y <- matrix(NA, nSites, nVisits)
p <- c(0.3, 0.6, 0.8)           
for(j in 1:nVisits) {
  y[,j] <- rbinom(nSites, N, p[j])
}
## Organize data
visitMat <- matrix(as.character(1:nVisits),
                   nSites, nVisits, byrow=TRUE)
     
umf <- unmarkedFramePCount(y=y, siteCovs=data.frame(x=x),
                           obsCovs=list(visit=visitMat))
## Fit model
fm1 <- pcount(~ visit ~ 1, umf, K=50)
checkConv(fm1)
}
}