unmarkedFit-class {unmarked}R Documentation

Class "unmarkedFit"

Description

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

Slots

fitType:
Object of class "character"
call:
Object of class "call"
formula:
Object of class "formula"
data:
Object of class "unmarkedFrame"
sitesRemoved:
Object of class "numeric"
estimates:
Object of class "unmarkedEstimateList"
AIC:
Object of class "numeric"
opt:
Object of class "list" containing results from optim
negLogLike:
Object of class "numeric"
nllFun:
Object of class "function"
knownOcc:
unmarkedFitOccu only: sites known to be occupied
K:
unmarkedFitPCount only: upper bound used in integration
mixture:
unmarkedFitPCount only: Mixing distribution
keyfun:
unmarkedFitDS only: detection function used by distsamp
unitsOut:
unmarkedFitDS only: density units

Methods

[
signature(x = "unmarkedFit", i = "ANY", j = "ANY", drop = "ANY"): extract either 'det' or 'state' coefficient information
backTransform
signature(obj = "unmarkedFit"): back-transform parameters to original scale when no covariate effects are modeled
coef
signature(object = "unmarkedFit"): returns parameter estimates. type can be 'state' or 'det'. If altNames=T estimate names are more specific.
confint
signature(object = "unmarkedFit"): Returns confidence intervals. Must specify type and method (either "normal" or "profile")
fitted
signature(object = "unmarkedFit"): returns expected values of Y
getData
signature(object = "unmarkedFit"): extracts data
getP
signature(object = "unmarkedFit"): calculates and extracts expected detection probabilities
hessian
signature(object = "unmarkedFit"): Returns hessian matrix
linearComb
signature(obj = "unmarkedFit", coefficients = "matrixOrVector"): Returns estimate and SE on original scale when covariates are present
mle
signature(object = "unmarkedFit"): Same as coef(fit)?
names
signature(x = "unmarkedFit"): Names of parameter levels
nllFun
signature(object = "unmarkedFit"): returns negative log-likelihood used to estimate parameters
parboot
signature(object = "unmarkedFit"): Parametric bootstrapping method to assess goodness-of-fit
plot
signature(x = "unmarkedFit", y = "missing"): Plots expected vs. observed values
predict
signature(object = "unmarkedFit"): Returns predictions and standard errors for original data or for covariates in a new data.frame
profile
signature(fitted = "unmarkedFit"): used by confint method='profile'
residuals
signature(object = "unmarkedFit"): returns residuals
sampleSize
signature(object = "unmarkedFit"): returns number of sites in sample
SE
signature(obj = "unmarkedFit"): returns standard errors
show
signature(object = "unmarkedFit"): concise results
summary
signature(object = "unmarkedFit"): results with more details
update
signature(object = "unmarkedFit"): refit model with changes to one or more arguments
vcov
signature(object = "unmarkedFit"): returns variance-covariance matrix

Note

This is a superclass with child classes for each fit type

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 model
(fm1 <- multinomPois(~ 1 ~ ufp + trba, ovenFrame))

# Apply a bunch of methods to the fitted model

names(fm1)
fm1['state']
fm1['det']

backTransform(fm1, whichEstimate ='det')

coef(fm1, type='state')

confint(fm1, type='state', method='profile')

fitted(fm1)

getData(fm1)

getP(fm1)

# Return predicted abundance at specified covariate values
linearComb(fm1, c(Int = 1, ufp = 0, trba = 0), type='state')

# 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)

sampleSize(fm1)

summary(fm1)

(fmNull <- update(fm1, formula = ~1 ~1))

vcov(fm1, type='state')


[Package unmarked version 0.8-1 Index]