#' impute missing data
#' @param data a \code{data.frame} containing the variables \code{Year}, \code{Month}, \code{Site} and \code{Observed}. The missing values of \code{Observed} are imputed by the algorithm.
#' @param formula A formula defining the model to use for the imputation
#' @param initial the initial value by which the missing values are replaced
#' @inheritParams imputeINLA
#' @export
#' @return A list with two elements: \code{data} with imputed values and \code{iterations} which is the number of iterations
#' @importFrom MASS glm.nb
#' @template deprecated
imputeUnderhill <- function(
data,
formula = Observed ~ Year + Month + Site,
initial = 0,
family = c("nbinomial", "poisson")
){
# nocov start
.Deprecated(
new = "impute"
)
family <- match.arg(family)
missing.data <- which(is.na(data[, as.character(formula[2])]))
data$Observed[missing.data] <- initial
do.loop <- TRUE
iterations <- 1
while (do.loop) {
if (family == "nbinomial") {
model <- glm.nb(formula, data = data)
} else {
model <- glm(formula, data = data, family = poisson)
}
new.values <- round(
predict(model, newdata = data[missing.data, ], type = "response")
)
if (any(data[missing.data, as.character(formula[2])] < new.values)) {
data[missing.data, as.character(formula[2])] <- pmax(
data[missing.data, as.character(formula[2])],
new.values
)
iterations <- iterations + 1
} else {
break
}
}
return(list(data = data, iterations = iterations))
# nocov end
}
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