unmarked (version 0.8-9)

unmarkedFit-class: Class "unmarkedFit"

Description

Contains fitted model information which can be manipulated or extracted using the methods described below.

Examples

showClass("unmarkedFit")

# Format removal data for multinomPois 
data(ovendata)
ovenFrame <- unmarkedFrameMPois(y = ovendata.list$data,
	siteCovs = as.data.frame(scale(ovendata.list$covariates[,-1])), 
	type = "removal")

# Fit a couple of models
(fm1 <- multinomPois(~ 1 ~ ufp + trba, ovenFrame))
summary(fm1)

# Apply a bunch of methods to the fitted model

# Look at the different parameter types
names(fm1)
fm1['state']
fm1['det']

# Coefficients from abundance part of the model
coef(fm1, type='state')

# Variance-covariance matrix
vcov(fm1, type='state')

# Confidence intervals using profiled likelihood
confint(fm1, type='state', method='profile')

# Expected values
fitted(fm1)

# Original data
getData(fm1)

# Detection probabilities
getP(fm1)

# log-likelihood
logLik(fm1)

# Back-transform detection probability to original scale
# backTransform only works on models with no covariates or 
#     in conjunction with linearComb (next example)
backTransform(fm1, type ='det')

# Predicted abundance at specified covariate values
(lc <- linearComb(fm1, c(Int = 1, ufp = 0, trba = 0), type='state'))
backTransform(lc)

# Assess goodness-of-fit
parboot(fm1)
plot(fm1)

# Predict abundance at specified covariate values.
newdat <- data.frame(ufp = 0, trba = seq(-1, 1, length=10))
predict(fm1, type='state', newdata=newdat)

# Number of sites in the sample
sampleSize(fm1)

# Fit a new model without covariates
(fmNull <- update(fm1, formula = ~1 ~1))

# Likelihood ratio test
LRT(fm1, fmNull)