Nothing
print.summaryCountsEPPM <-
function(x, ...) {
if (x$data.type==TRUE) {
cat("\n","Dependent variable a vector of counts.","\n")
} else {
cat("\n","Dependent variable is a list of frequency distributions for counts","\n")
} # end of data.type==
# Checking for truncation i.e. ltvalue and utvalue are not NA
if (is.na(x$ltvalue)==FALSE) { cat("\n","distribution truncated below at ",x$ltvalue) }
if (is.na(x$utvalue)==FALSE) { cat("\n","distribution truncated above at ",x$utvalue,"\n") }
if (is.null(x$converged)==FALSE) {
cat("\nCall:", deparse(x$call, width.cutoff = floor(getOption("width") *
0.85)), "", sep = "\n")
cat("Model type :",x$model.type,"\n")
cat("Model name :",x$model.name,"\n")
if (x$model.type=="mean and scale-factor") {
cat("Link scale-factor : log","\n") }
offsetid.mean <- sum(x$offset.mean)
offsetid.scalef <- sum(x$offset.scalef)
if ((offsetid.mean!=0) | (offsetid.scalef!=0)) {
cat("non zero offsets in linear predictors","\n") }
npar <- length(x$optim$par)
if ((x$model.name=="Faddy distribution") |
(x$model.name=="Faddy distribution fixed b")) {
if (x$model.name=="Faddy distribution") { wks <- npar - 2
} else { wks <- npar - 1 }
if (abs(x$optim$par[wks]-1)<1.e-6) {
cat("Boundary for Faddy distribution c of 1 has been reached","\n")
cat("hence its se is set to NA.","\n") }}
cat(paste("\n","Coefficients (model for mean with", x$link,"link)\n", sep = " "))
print(x$coeff.table.mean)
if (is.null(x$coeff.table.scalef)==FALSE) {
cat(paste("\n","Coefficients (model for scale-factor with log link)\n"))
print(x$coeff.table.scalef) } # end if is.null
if ((x$model.name=="negative binomial fixed b") |
(x$model.name=="Faddy distribution fixed b")) {
cat(paste("\n","Value of fixed b", x$fixed.b,"\n", sep = " "))
} # end of if model.name
if (is.null(x$weights)==FALSE) {
cat("\n","Maximum weighted likelihood regression.")
if (x$data.type==TRUE) {
cat("\n","Vector of weights used.","\n")
} else {
cat("\n","List of weights used.","\n") }
if (is.null(attr(x$weights, which="normalize"))==FALSE) {
if (attr(x$weights, which="normalize")==TRUE) {
cat("Normalization to a value of",
attr(x$weights, which="norm.to.n"),".\n", sep = " ") }}
} # end of is.null(weights)
if (is.na(x$loglik)==TRUE) { cat("Log-likelihood is NA","\n")
} else {
cat("\n","Type of estimator: ML (maximum likelihood)")
cat("\n","Log-likelihood:",x$loglik,"on",length(x$optim$par),"Df", sep=" ")
if (length(x$optim$par)==1) {
cat("\n","Single parameter Poisson so no use of optim", sep=" ")
} else {
optim.method <- x$method
if (optim.method=="Nelder-Mead") {
cat("\n","Number of iterations:",x$optim$counts[1],"of optim method",optim.method,sep=" ","\n")
} else { gradient.method <- attr(x$method,which="grad.method")
cat("\n","Number of iterations:",x$optim$counts[1],"of optim method",optim.method,
"gradient method",gradient.method,sep=" ","\n")
cat("\n final gradients of parameters \n")
print(x$gradient) } } # end of if length(x$optim$par)=1
code <- list(c("successful"),
c("iteration limit max has been reached"),
c(" "),c(" "),c(" "),c(" "),
c(" "),c(" "),c(" "),c(" "),
c("degeneracy of the Nelder-Mead"))
wks <- attr(x$converged, which="code") + 1
cat("\n","return code",attr(x$converged, which="code"),code[[as.numeric(wks)]],"\n", sep=" ")
} # end of if is.na(x$loglik
} else {
cat("\n","Failure of checks on entry arguments to CountsEPPM")
cat("\n","or numerical derivative calculations failed.")
} # end of if (is.null(converged)
}
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