wiqid (version 0.3.3)

predict.wiqid: Predict method for objects of class 'wiqid'

Description

Obtains predictions, with estimates, standard errors and confidence intervals, from a fitted model object of class wiqid, as produced by frequentist estimation functions in the wiqid package. Not all functions produce objects that enable predictions to be made; see Details. Please treat this as a 'beta' version and check output carefully.

Usage

# S3 method for wiqid
predict(object, newdata, parameter, ci, type=c("link", "response"), ...)

Value

Returns a matrix with four columns (estimate, SE, lower and upper confidence limits) and a row for each row in newdata. If newdata has row names, these will be used for the output. Note that for an intercept-only submodel, all rows will have identical values. Attributes give information on the link used and the confidence level.

Arguments

object

an object of class wiqid.

newdata

a data frame with columns for each of the covariates in the model. Unused columns are ignored. Missing values are not allowed. See Details.

parameter

character; the name of the parameter to predict; this will appear on the left hand side of one of the formulae in the model.

ci

the confidence interval to use; the default is to use object$ci or, if that is NULL, 0.95.

type

the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus if the parameter is a probability, the default predictions are on the logit or probit scale and type = "response" gives the predicted probabilities. May be abbreviated.

...

further arguments for other methods.

Author

Mike Meredith.

Details

Most wiqid functions have models with multiple submodels, corresponding to the formulae in the model argument. Check object$formulae for a list of the available submodels.

The argument newdata is required (even for intercept-only models), and must be a data frame with named columns for each of the covariates in the submodel. For factors, the levels must be (a subset of) the levels in the original data; check object$xlev for possible levels.

predict is not yet implemented for the following functions:

occSStime and occSScovSite	: use occSS instead.
occMStime and occMScovSite	: use occMS instead.
closedCap* functions	: these models have no covariates.
surv* functions	: these have no covariates.

Examples

# Generate some simulated occupancy data for 300 sites:
set.seed(2017)
original.data <- data.frame(
  elev = runif(300, 0, 1000),
  forType = factor(sample(c("dry", "swamp", "mangrove"), size=300, replace=TRUE, prob=3:1)))
modMat <- model.matrix( ~ elev + forType, data = original.data)
psiCoef <- c(3, -0.003, -3, -1) # declines with 'elev'; highest for 'dry', lowest 'mangrove'
psi <- plogis(modMat %*% psiCoef)
hist(psi, breaks=20)
z <- rbinom(300, 1, psi)
mean(z)  # true realized occupancy
# detection history for 3 replicates, constant p = 0.6:
DH <- matrix(rbinom(300*3, 1, 0.6*z), nrow=300)
# fit models
m0 <- occSS(DH)
mE <- occSS(DH, psi ~ elev, data = original.data)
mEF <- occSS(DH, psi ~ elev + forType, data = original.data)

# now try predictions:
newdata <- expand.grid(elev=c(200, 500, 800), forType=c("dry", "swamp"))
predict(mEF, newdata, "psi")
cbind(newdata, predict(mEF, newdata, "psi", type='res'))
cbind(newdata, predict(mE, newdata, "psi", type='res'))
cbind(newdata, predict(m0, newdata, "psi", type='res'))

# do a nice plot
xx <- seq(0, 1000, length=51)
plotdata <- expand.grid(elev=xx, forType=c("dry", "swamp", "mangrove"))
toPlot <- predict(mEF, plotdata, "psi", type='res')
plot(xx, rep(0.5, 51), type='n', las=1, ylim=range(toPlot),
  xlab="Elevation", ylab="Occupancy probability")
ciCols <- adjustcolor(c('lightgreen', 'skyblue', 'pink'), 0.5)
estCols <- c('darkgreen', 'blue', 'red')
for(i in 1:3) {
  this1 <- toPlot[plotdata$forType == levels(plotdata$forType)[i], ]
  polygon(c(xx, rev(xx)), c(this1[, 3], rev(this1[, 4])), col=ciCols[i])
  lines(xx, this1[, 1], col=estCols[i])
}
legend('topright', levels(plotdata$forType), lty=1, col=estCols, bty='n')

# Add a survey-level covariate: observer ID with different detection probabilities
observer <- c(sample(1:2, size=300, replace=TRUE),  # A and B on first survey occasion
              sample(1:3, size=300, replace=TRUE),  # A, B and C for second
              sample(2:3, size=300, replace=TRUE))  # only B and C for third
obsID <- matrix(LETTERS[observer], nrow=300)
colnames(obsID) <- c("obs1", "obs2", "obs3")
original.data <- cbind(original.data, as.data.frame(obsID))
str(original.data)
p <- c(0.4, 0.6, 0.8)[observer]
DH <- matrix(rbinom(300*3, 1, p*z), nrow=300)
mEFO <- occSS(DH, list(psi ~ elev + forType, p ~ obs), data = original.data)
# Check the categorical covariate names and levels:
mEFO$xlev
predict(mEFO, data.frame(obs=c("A", "B", "C")), "p")
predict(mEFO, data.frame(obs=c("A", "B", "C")), "p", type="resp")