Check Convergence of Fitted Model
This function checks the convergence information contained in models of various classes.
checkConv(mod, ...) ## S3 method for class 'betareg' checkConv(mod, ...) ## S3 method for class 'clm' checkConv(mod, ...) ## S3 method for class 'clmm' checkConv(mod, ...) ## S3 method for class 'glm' checkConv(mod, ...) ## S3 method for class 'glmmTMB' checkConv(mod, ...) ## S3 method for class 'hurdle' checkConv(mod, ...) ## S3 method for class 'lavaan' checkConv(mod, ...) ## S3 method for class 'maxlikeFit' checkConv(mod, ...) ## S3 method for class 'merMod' checkConv(mod, ...) ## S3 method for class 'lmerModLmerTest' checkConv(mod, ...) ## S3 method for class 'multinom' checkConv(mod, ...) ## S3 method for class 'nls' checkConv(mod, ...) ## S3 method for class 'polr' checkConv(mod, ...) ## S3 method for class 'unmarkedFit' checkConv(mod, ...) ## S3 method for class 'zeroinfl' checkConv(mod, ...)
mod |
an object containing the output of a model of the classes mentioned above. |
... |
additional arguments passed to the function. |
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).
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. |
Marc J. Mazerolle
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.
##example modified from ?pcount
## Not run:
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)
}
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