unmarkedFit-class {unmarked}

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 one of names(obj), eg 'state' or 'det'

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 one of names(obj), eg 'state' or 'det'. If altNames=TRUE 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

getFP

signature(object = "unmarkedFit"): calculates and extracts expected false positive detection probabilities

getB

signature(object = "unmarkedFit"): calculates and extracts expected probabilities a true positive detection was classified as certain

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

smoothed

signature(object="unmarkedFitColExt"): Returns the smoothed trajectory from a colonization-extinction model fit. Takes additional logical argument mean which specifies whether or not to return the average over sites.

projected

signature(object="unmarkedFitColExt"): Returns the projected trajectory from a colonization-extinction model fit. Takes additional logical argument mean which specifies whether or not to return the average over sites.

logLik

signature(object="unmarkedFit"): Returns the log-likelihood.

LRT

signature(m1="unmarkedFit", m2="unmarkedFit"): Returns the chi-squared statistic, degrees-of-freedom, and p-value from a Likelihood Ratio Test.

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 couple of models
(fm1 <- multinomPois(~ 1 ~ ufc + 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, ufc = 0, trba = 0), type='state'))
backTransform(lc)

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

# Predict abundance at specified covariate values.
newdat <- data.frame(ufc = 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)