Predict capture probabilities from an efp fit

Predictions of capture probability from a fitted efp model, for the data used to fit the model or for new data.

Usage

# S3 method for class 'efp'
predict(
  object,
  newdata = NULL,
  type = c("link", "response", "lpmatrix"),
  se.fit = FALSE,
  exclude = NULL,
  ...
)

Arguments

object

an efp fitted model

newdata

a data.frame containing the covariates in the model formula. If NULL, predictions are for the data used to fit the model.

type

the type of prediction: "link" for the logit capture probability, "response" for the capture probability and "lpmatrix" for the design matrix which gives the logit capture probability when multiplied by the model parameters.

se.fit

if TRUE standard errors of the predictions are also returned

exclude

a character vector of terms to leave out of the prediction, named as in the model output, for example "s(visitID)". This is the way to predict for levels of a random effect that were not in the data used to fit the model. Variables that are only used by excluded smooth or random effect terms need not be supplied in newdata.

...

further arguments, ignored

Value

a vector of predictions, or if se.fit = TRUE a list with elements fit and se.fit. If type = "lpmatrix" a matrix.

Details

Predictions are conditional on the estimated random effects, unless these are removed using exclude. For a random walk smooth, predictions can be made for any level in the penalty, including levels with no data, which are interpolated. Standard errors on the response scale are calculated using the delta method.

The offset supplied to efp is included in the predictions only when newdata is NULL.

Examples

data(ef_data)
ef_data$sampleID <- factor(paste(ef_data$date, ef_data$siteID,
                                 ef_data$lifestage, ef_data$species))
m1 <- efp(count ~ lifestage, data = ef_data, pass = pass, id = sampleID)

newdata <- data.frame(lifestage = c("Fry", "Parr"))
predict(m1, newdata = newdata, type = "response", se.fit = TRUE)
#> $fit
#> [1] 0.6641846 0.7863670
#> 
#> $se.fit
#> [1] 0.01587840 0.01248373
#>