print_rfmstate_objects: Print RFmstate Result Objects

In RFmstate: Random Forest-Based Multistate Survival Analysis

Print RFmstate Result Objects

Description

Concise print methods for fitted models, predictions, direct semi-Markov probability results, Aalen–Johansen point estimates, importance results, diagnostics, and fitted-model summaries.

Usage

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

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

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

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

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

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

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

Arguments

x	An object of the class required by the selected print method: rfmstate, rfmstate_pred, trans_prob, aj_estimate, rfmstate_importance, rfmstate_diag, or summary.rfmstate.
...	Ignored.

Details

Printed values are deliberately concise and rounded only for display. Stored numerical results are unchanged. The fitted-model and summary methods identify edge-level ranger OOB quantities; prediction and direct-probability methods state their fresh-entry conditioning; the AJ method states that it returns point estimates only; and the diagnostic method distinguishes edge OOB output from patient-level cross-validation. The example is in a donttest block because it fits survival forests to exercise every result class.

Value

x, invisibly.

Limitations

Print methods are descriptive and do not revalidate, refit, or add uncertainty. Rounded importance or probability values must not be used in downstream calculations. Edge OOB statistics are not full-state validation, negative permutation importance is not a protective/causal effect, forest confidence intervals are unavailable, and AJ confidence intervals are not returned.

Examples

ms <- define_multistate(c("A", "B"), "B", list(A = "B"))
dat <- data.frame(id = 1:60, x = seq(-1, 1, length.out = 60),
                  time_B = 1:60, censor = NA_real_)
long <- prepare_data(dat, "id", ms, list(B = "time_B"), "censor", "x")
fit <- rfmstate(long, num.trees = 20, min.node.size = 3,
                min_events = 3, sparse_warning = Inf, seed = 42)
pred <- predict(fit, data.frame(x = 0), times = c(0, 10),
                target_grid_points = 64, max_grid_points = 1025)
tt <- seq(0, 10, length.out = 1001)
tp <- compute_trans_prob(
  list("A->B" = data.frame(time = tt, hazard = 0.1 * tt)),
  ms, times = c(0, 5, 10), target_grid_points = 128
)
aj <- aalen_johansen(long)
print(fit)
print(pred)
print(tp)
print(aj)
print(importance(fit))
print(diagnose(fit))
print(summary(fit))

RFmstate documentation built on Sept. 10, 2026, 1:09 a.m.