Nothing
print.summaryBinaryEPPM <-
function(x, ...) {
if (x$data.type==TRUE) {
cat("\n","\n","Dependent variable a vector of numerator / denominator.","\n")
} else {
cat("\n","\n","Dependent variable is a list of binomial frequency distributions","\n")
} # end of data.type==
if (is.null(x$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$model.name,"\n")
if (x$link=="powerlogit") {
cat("Link p :",x$link,"power",attr(x$link, which="power"),"\n",sep=" ")
} else {
cat("Link p :",x$link,"\n")
} # end if link
if (x$model.type=="p and scale-factor") {
cat("Link scale-factor : log","\n") }
offsetid.p <- sum(x$offset.p)
offsetid.scalef <- sum(x$offset.scalef)
if ((offsetid.p!=0) | (offsetid.scalef!=0)) {
cat("non zero offsets in linear predictors","\n") }
if (x$link=="powerlogit") {
cat(paste("\n","Coefficients (model for p with", x$link,
"link power",attr(x$link, which="power"),")\n", sep = " "))
} else {
cat(paste("\n","Coefficients (model for p with", x$link,
" link)\n", sep = " ")) } # end if link
if ((x$model.type=="p only") & (x$model.name=="EPPM extended binomial")) {
wks <- length(x$optim$par)
cat(paste("Coefficient of",names(x$optim$par)[wks],"has 1 subtracted from it\n", sep=" "))
cat(paste("so the test is against 1 i.e., a binomial.\n")) }
print(x$coeff.table.p)
if (is.null(x$coeff.table.scalef)==FALSE) {
cat(paste("\n","Coefficients (model for scale factor with log link)\n"))
print(x$coeff.table.scalef) } # end if is.null
if (is.null(x$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=" ")
cat("\n","Pseudo R-squared:",x$pseudo.r.squared, "type",
attr(x$pseudo.r.squared, which="names"), sep=" ")
if (length(x$optim$par)==1) {
cat("\n","Single parameter binomial so no iteration", sep=" ")
} else {
optim.method <- x$method
if (optim.method=="Nelder-Mead") {
cat("\n","Number of iterations:",x$optim$counts[1],"of optim method",optim.method, sep=" ")
} else { gradient.method <- attr(x$method,which="grad.method")
cat("\n","Number of iterations:",x$optim$counts[1],"of optim method",optim.method,
"gradient method",gradient.method,sep=" ") }
} # 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 BinaryEPPM")
cat("\n","or numerical derivative calculations failed.")
} # end of if (is.null(converged)
}
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