R: Class "unmarkedFit"

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 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)

[Package unmarked version 1.0.0 Index]