R/R2MCDS-internal.R
In RoyChristian/GeoAviR: Calling MCDS from R.
Defines functions
.abundancetable
.chisquaretable
.detectiontable
.modeltable
.abundancetable <-
function(model){
#extract lambda value, se and ci
lambda.val <-round(coef(model, type="state"),3)
lambda.se<- round(SE(model, type="state"),3)
lambda.ci <- round(confint(model, type="state"),3)
#extract pred values
predicted<-round(unlist(predict(model, type="state")[1,]),3)
estimates<-c(lambda.val, predicted[1])
se <- c(lambda.se, predicted[2])
coef.var <- round(se/estimates*100,3)
down.CI<- c(lambda.ci[1], predicted[3])
up.CI<- c(lambda.ci[2], predicted[4])
#combine all values in a dataframe
out<-data.frame(estimates, se, coef.var, down.CI, up.CI)
names(out) <- c("Estimates", "SE*", "% of var.", "95% Lower", "95% Upper")
row.names(out)<-c("lambda", "Density")
return(out)
#END of the function
}
.chisquaretable <-
function(model){
breaks.values<-model@data@dist.breaks
cuts <- paste(breaks.values[-length(breaks.values)], breaks.values[-1], sep="-")
#extract observed values
observed <-colSums(getY(model@data))
#extract predicted values
predicted <-colSums(fitted(model))
#Calculate chi-square values
chi.values <- (observed - predicted)^2 / predicted
out<-data.frame(cuts,observed, predicted, chi.values)
return(out)
#END of the function
}
.detectiontable <-
function(model){
d.val <-round(coef(model, type="det"),3)
d.se<- round(SE(model, type="det"),3)
d.ci <- round(confint(model, type="det"),3)
predicted<-round(unlist(predict(model, type="det")[1,]),3)
effect.half <- round(sapply(1:4, function(i){integrate(gxhn, 0, max(model@data@dist.breaks), sigma=predicted[i])$value }),3)[-2]
effect.se <-round(((effect.half[1]-effect.half[2])/((predicted[1]-predicted[3])/predicted[2])+(effect.half[3]-effect.half[1])/((predicted[4]-predicted[1])/predicted[2]))/2,3)
detection <- round(effect.half/max(model@data@dist.breaks),3)
detection.se <-round(((detection[1]-detection[2])/((predicted[1]-predicted[3])/predicted[2])+(detection[3]-detection[1])/((predicted[4]-predicted[1])/predicted[2]))/2,3)
estimates<-c(d.val, detection[1], effect.half[1])
se <- c(d.se, detection.se, effect.se)
coef.var <- round(se/estimates*100,3)
down.CI<- c(d.ci[1], detection[2], effect.half[2])
up.CI<- c(d.ci[2], detection[3], effect.half[3])
out<-data.frame(estimates, se, coef.var, down.CI, up.CI)
names(out) <- c("Estimates", "SE*", "% of var.", "95% Lower", "95% Upper")
if(model@keyfun=="halfnorm"){param="sigma"}
if(model@keyfun=="exp"){param="rate"}
if(model@keyfun=="hazard"){param="shape"}
row.names(out)<-c(param, "p_hat", "EHWS")
return(out)
#END of the function
}
.modeltable <-
function(model, sampling.units="Watches", obs.units="Birds"){
if(model@data@unitsIn=="m"){
effort = sum(model@data@tlength)/1000
diameter =max(model@data@dist.breaks)
}else{
effort = sum(model@data@tlength)
diameter = max(model@data@dist.breaks)*1000
}
samples <- nrow(getY(model@data))
observations <- sum(getY(model@data))
if(model@keyfun=="halfnorm"){key = "Halfnormal"}
if(model@keyfun=="exp"){key = "Exponential"}
if(model@keyfun=="hazard"){key = "Hazard"}
Descriptive<-c("Effort", " Samples", "Width", "# Observations","Key")
Values<-rbind(effort, samples, diameter, observations, key, deparse.level = 0)
units <- c("km", sampling.units, "m",obs.units, "")
out<-data.frame(Descriptive, Values, units)
return(out)
#END of the function
}
.Random.seed <-
c(403L, 2L, 809312574L, -1635933832L, -354641317L, -1667060295L,
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)
RoyChristian/GeoAviR documentation built on Jan. 13, 2020, 8:16 p.m.
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Defines functions .abundancetable .chisquaretable .detectiontable .modeltable
.abundancetable <-
function(model){
#extract lambda value, se and ci
lambda.val <-round(coef(model, type="state"),3)
lambda.se<- round(SE(model, type="state"),3)
lambda.ci <- round(confint(model, type="state"),3)
#extract pred values
predicted<-round(unlist(predict(model, type="state")[1,]),3)
estimates<-c(lambda.val, predicted[1])
se <- c(lambda.se, predicted[2])
coef.var <- round(se/estimates*100,3)
down.CI<- c(lambda.ci[1], predicted[3])
up.CI<- c(lambda.ci[2], predicted[4])
#combine all values in a dataframe
out<-data.frame(estimates, se, coef.var, down.CI, up.CI)
names(out) <- c("Estimates", "SE*", "% of var.", "95% Lower", "95% Upper")
row.names(out)<-c("lambda", "Density")
return(out)
#END of the function
}
.chisquaretable <-
function(model){
breaks.values<-model@data@dist.breaks
cuts <- paste(breaks.values[-length(breaks.values)], breaks.values[-1], sep="-")
#extract observed values
observed <-colSums(getY(model@data))
#extract predicted values
predicted <-colSums(fitted(model))
#Calculate chi-square values
chi.values <- (observed - predicted)^2 / predicted
out<-data.frame(cuts,observed, predicted, chi.values)
return(out)
#END of the function
}
.detectiontable <-
function(model){
d.val <-round(coef(model, type="det"),3)
d.se<- round(SE(model, type="det"),3)
d.ci <- round(confint(model, type="det"),3)
predicted<-round(unlist(predict(model, type="det")[1,]),3)
effect.half <- round(sapply(1:4, function(i){integrate(gxhn, 0, max(model@data@dist.breaks), sigma=predicted[i])$value }),3)[-2]
effect.se <-round(((effect.half[1]-effect.half[2])/((predicted[1]-predicted[3])/predicted[2])+(effect.half[3]-effect.half[1])/((predicted[4]-predicted[1])/predicted[2]))/2,3)
detection <- round(effect.half/max(model@data@dist.breaks),3)
detection.se <-round(((detection[1]-detection[2])/((predicted[1]-predicted[3])/predicted[2])+(detection[3]-detection[1])/((predicted[4]-predicted[1])/predicted[2]))/2,3)
estimates<-c(d.val, detection[1], effect.half[1])
se <- c(d.se, detection.se, effect.se)
coef.var <- round(se/estimates*100,3)
down.CI<- c(d.ci[1], detection[2], effect.half[2])
up.CI<- c(d.ci[2], detection[3], effect.half[3])
out<-data.frame(estimates, se, coef.var, down.CI, up.CI)
names(out) <- c("Estimates", "SE*", "% of var.", "95% Lower", "95% Upper")
if(model@keyfun=="halfnorm"){param="sigma"}
if(model@keyfun=="exp"){param="rate"}
if(model@keyfun=="hazard"){param="shape"}
row.names(out)<-c(param, "p_hat", "EHWS")
return(out)
#END of the function
}
.modeltable <-
function(model, sampling.units="Watches", obs.units="Birds"){
if(model@data@unitsIn=="m"){
effort = sum(model@data@tlength)/1000
diameter =max(model@data@dist.breaks)
}else{
effort = sum(model@data@tlength)
diameter = max(model@data@dist.breaks)*1000
}
samples <- nrow(getY(model@data))
observations <- sum(getY(model@data))
if(model@keyfun=="halfnorm"){key = "Halfnormal"}
if(model@keyfun=="exp"){key = "Exponential"}
if(model@keyfun=="hazard"){key = "Hazard"}
Descriptive<-c("Effort", " Samples", "Width", "# Observations","Key")
Values<-rbind(effort, samples, diameter, observations, key, deparse.level = 0)
units <- c("km", sampling.units, "m",obs.units, "")
out<-data.frame(Descriptive, Values, units)
return(out)
#END of the function
}
.Random.seed <-
c(403L, 2L, 809312574L, -1635933832L, -354641317L, -1667060295L,
2098679816L, -38292810L, -1067480591L, -871251933L, 1597702258L,
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