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Predict from a fitted timesift

Source: R/timesift.R
predict.timesift.Rd

Rebuilds every member's representation for the new targets from the settings its own arm was built with, predicts with the model refitted on all targets, and combines them where the ensemble is asked for.

Usage

# S3 method for class 'timesift'
predict(
  object,
  targets,
  series = NULL,
  candidate = "ensemble",
  type = c("response", "binary", "spread"),
  rule = c("youden", "kappa", "prevalence", "mpa"),
  alpha = 0.05,
  ...
)

Arguments

object

A timesift() fit.

targets

A data frame of targets, carrying the identifier, the anchor and the static columns the fit was given.

series

The long table of readings for those targets, or NULL for a targets-only fit.

candidate

"ensemble", the stack refitted on every target; "selected", the candidate the rule chose on every target (object$choice); or the name of one candidate.

type

"response" for the prediction on the scale of the response head, a probability of presence under the shipped one, "binary" for presence and absence, or "spread" for how far the ensemble's members disagree.

rule

With type = "binary", the rule of decision_threshold() the cut is learned by.

alpha

With type = "spread", one minus the coverage of the interval.

...

Ignored.

Value

A [target, response] matrix of predictions, named by target and in the order the fit carries its own targets: sorted by identifier, or the targets' own order where target_time anchors them. Under type = "binary" an integer matrix of 0 and 1, NA for a response whose held-out predictions give no cut. Under type = "spread" the [target, response, statistic] array of ensemble_spread().

Details

With type = "binary" each response is cut into presence and absence at the threshold decision_threshold() learns from the same candidate's out-of-fold predictions of the fit's own targets: never from predictions of units the model was fitted on, and never from the new targets, which carry no response. rule is the rule that picks it. A binary map of a species is this prediction on one target per map cell.

With type = "spread" the ensemble's members are read side by side rather than combined: ensemble_spread() gives their weighted mean, standard deviation, coefficient of variation and interval at alpha, which is biomod2's EMcv and EMci and, on one target per map cell, an uncertainty map.

See also

decision_threshold() for the cuts themselves.

Examples

set.seed(1)
t <- seq(as.POSIXct("2021-09-01", tz = "UTC"), by = "hour", length.out = 24 * 90)
units <- sprintf("p%02d", 1:30)
warmth <- rnorm(30)
logger <- data.frame(
  plot = rep(units, each = length(t)), datetime = rep(t, 30),
  temp = as.numeric(vapply(warmth, function(w) w + sin(seq_along(t) / 300), numeric(length(t)))))
plots <- data.frame(plot = units,
                    sp_a = rbinom(30, 1, plogis(2 * warmth)),
                    sp_b = rbinom(30, 1, plogis(-2 * warmth)))
fit <- timesift(plots, logger, y = starts_with("sp_"), id = plot, time = datetime,
                sift = grains("week", "month"), resampling = cv(v = 3L), verbose = FALSE)
head(predict(fit, plots, logger), 3)
predict(fit, plots, logger, type = "binary", rule = "prevalence")[1:3, ]