What a run found

Two kinds of row. A candidate row is the candidate's mean score on the outer folds, how many responses it scored highest on, and whether one fitted model covered those responses or one was fitted per response. These rows are the comparison: they share folds and cells, so their shape is read across grains and learners, but the highest of them was picked out on the folds it is scored on. The selected and ensemble rows are the procedure's held-out score, the choice and the weights made inside every outer training fold, and they are the level to quote.

Usage

# S3 method for class 'timesift'
summary(object, ...)

# S3 method for class 'timesift'
print(x, ...)

# S3 method for class 'timesift_summary'
print(x, ...)

Arguments

object

A timesift result.

...

Ignored, so that the methods take the arguments their generics declare.

x

A timesift result, or the table this returns.

Value

A data frame of class timesift_summary, one row per candidate and, where the run made an estimate, one for the selected candidate and one for the stack. It carries the mean score, its standard error across responses for the two procedure rows, the responses won, how the responses were covered, and scored: "outer folds" for a candidate, "nested" for the procedure. The weights fitted on every target are in the weights attribute and the candidate chosen on every target in choice.

Details

Both columns beside a candidate's mean are worth reading. A candidate can carry the ensemble without winning a single response, which is what won shows and a mean alone hides; and a joint model and a per-response one reach the same [target, response] matrix by different routes, which is what responses records.

A candidate the run built no representation for, because its learner cannot read the representation it was paired with, is listed with no mean rather than dropped, so the report says what was asked for as well as what ran.

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)
report <- summary(fit)
report
attr(report, "weights")