unmarkedFit-class {unmarked} | R Documentation |
Contains fitted model information which can be manipulated or extracted using the methods described below.
fitType
:"character"
call
:"call"
formula
:"formula"
data
:"unmarkedFrame"
sitesRemoved
:"numeric"
estimates
:"unmarkedEstimateList"
AIC
:"numeric"
opt
:"list"
containing results from
optim
negLogLike
:"numeric"
nllFun
:"function"
knownOcc
:K
:mixture
:keyfun
:unitsOut
:signature(x = "unmarkedFit", i = "ANY", j = "ANY",
drop = "ANY")
: extract either 'det' or 'state' coefficient information signature(obj = "unmarkedFit")
: back-transform
parameters to original scale when no covariate effects are modeled signature(object = "unmarkedFit")
: returns parameter
estimates. type can be 'state' or 'det'. If altNames=T estimate names
are more specific. signature(object = "unmarkedFit")
: Returns confidence
intervals. Must specify type and method (either "normal" or "profile") signature(object = "unmarkedFit")
: returns expected
values of Y signature(object = "unmarkedFit")
: extracts data signature(object = "unmarkedFit")
: calculates and extracts
expected detection probabilities signature(object = "unmarkedFit")
: Returns hessian
matrix signature(obj = "unmarkedFit",
coefficients = "matrixOrVector")
: Returns estimate and SE on original
scale when covariates are present signature(object = "unmarkedFit")
: Same as coef(fit)? signature(x = "unmarkedFit")
: Names of parameter levels signature(object = "unmarkedFit")
: returns negative
log-likelihood used to estimate parameters signature(object = "unmarkedFit")
: Parametric
bootstrapping method to assess goodness-of-fit signature(x = "unmarkedFit", y = "missing")
: Plots
expected vs. observed values signature(object = "unmarkedFit")
: Returns predictions
and standard errors for original data or for covariates in a new
data.frame signature(fitted = "unmarkedFit")
: used by confint
method='profile' signature(object = "unmarkedFit")
: returns residuals signature(object = "unmarkedFit")
: returns number
of sites in sample signature(obj = "unmarkedFit")
: returns standard errors signature(object = "unmarkedFit")
: concise results signature(object = "unmarkedFit")
: results with more
details signature(object = "unmarkedFit")
: refit model with
changes to one or more arguments signature(object = "unmarkedFit")
: returns
variance-covariance matrix This is a superclass with child classes for each fit type
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')