# R/print.summary.dsmodel.R In Distance: Distance Sampling Detection Function and Abundance Estimation

#### Documented in print.summary.dsmodel

```#' Print summary of distance detection function model object
#'
#' Provides a brief summary of a distance sampling analysis. Including: detection
#' function parameters, model selection criterion, and optionally abundance in the
#' covered (sampled) region and its standard error.
#'
#' @aliases print.summary.dsmodel
#' @param x a summary of distance sampling analysis
#' @param \dots unspecified and unused arguments for S3 consistency
#' @return Nothing, just prints the summary.
#' @author David L. Miller and Jeff Laake
#' @keywords utility
#' @export
print.summary.dsmodel <- function (x,...){

# split up the object
dht.obj <- x\$dht
model <- x\$ddf
x <- x\$ds

# routine from dht to print the tables...
# stolen from print.dht, removed vcmatrices and cor arguments
print.tables <- function(x,bysample){
cat("\nSummary statistics:\n")
print(x\$summary)
if("N" %in% names(x))
{
cat("\nAbundance:\n")
print(x\$N)
}
cat("\nDensity:\n")
print(x\$D)
if(bysample)
{
cat("\nEstimates by sample:\n")
print(x\$bysample)
}
}

cat("\nSummary for distance analysis \n")
cat("Number of observations : ", x\$n,"\n")
cat("Distance range         : ", x\$left, " - ",x\$width,"\n")

cat("\nModel :",model.description(model),"\n")
# Remind the user that monotonicity constraints were enforced
if(x\$mono & x\$mono.strict){
cat("\nStrict monotonicity constraints were enforced.\n")
}else if(x\$mono){
cat("\nMonotonicity constraints were enforced.\n")
}
cat("AIC   :", x\$aic, "\n")

# parameter summaries
cat("\nDetection function parameters\n")
cat("Scale coefficient(s): ", "\n")
print(x\$coeff\$key.scale)
if(x\$key %in% c("gamma","hr")) {
cat("\nShape coefficient(s): ", "\n")
print(x\$coeff\$key.shape)
}
}
cat("\n")
if(!is.null(x\$Nhat)){
parameters=data.frame(Estimate=c(x\$average.p,x\$Nhat))
row.names(parameters)=c("Average p", "N in covered region")
if(!is.null(x\$average.p.se)){
parameters\$SE=c(x\$average.p.se,x\$Nhat.se)
parameters\$CV=parameters\$SE/parameters\$Estimate
}
}else{
parameters=data.frame(Estimate=c(x\$average.p))
row.names(parameters)=c("Average p")
if(!is.null(x\$average.p.se)){
parameters\$SE=c(x\$average.p.se)
parameters\$CV=parameters\$SE/parameters\$Estimate
}
}
print(parameters)

## dht stuff
x<-dht.obj
if(!is.null(x)){
bysample<-FALSE # for now!
if(is.null(x\$clusters)){
print.tables(x\$individuals,bysample)
}else{
cat("\nSummary for clusters\n")
print.tables(x\$clusters,bysample)
cat("\nSummary for individuals\n")
print.tables(x\$individuals,bysample)
cat("\nExpected cluster size\n")
# Added CV as an output LJT 14/10/09
S<-x\$Expected.S
if(!is.null(S\$se.Expected.S)){
S\$cv.Expected.S<-S\$se.Expected.S/S\$Expected.S
S\$cv.Expected.S[S\$Expected.S==0]<-0
}

print(S)
}
}

invisible()
}
```

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Distance documentation built on July 4, 2017, 9:29 a.m.