Summaries expose stored settings and diagnostics without recomputing geometry, fitting, or selection. Console output is bounded; the summary's structured fields retain selected controls, criterion tables, local diagnostic counts, solver information, and density accounting for closer inspection.
Usage
# S3 method for class 'summary.geosmooth_fit'
print(x, ...)
# S3 method for class 'lps'
summary(object, ...)
# S3 method for class 'malps'
summary(object, ...)
# S3 method for class 'ps_lps'
summary(object, ...)
# S3 method for class 'chart_kernel'
summary(object, ...)
# S3 method for class 'local_likelihood'
summary(object, ...)
# S3 method for class 'lpl_tf'
summary(object, ...)
# S3 method for class 'slpl_tf'
summary(object, ...)
# S3 method for class 'metric.graph.lowpass.fit'
summary(object, ...)
# S3 method for class 'metric.graph.lowpass.refit'
summary(object, ...)
# S3 method for class 'ssrhe.hessian.fit'
summary(object, ...)
# S3 method for class 'ssrhe.hessian.refit'
summary(object, ...)
# S3 method for class 'ssrhe.hessian.l1.fit'
summary(object, ...)
# S3 method for class 'graph.trend.filtering.fit'
summary(object, ...)
# S3 method for class 'pttf.trend.filtering.fit'
summary(object, ...)
# S3 method for class 'density_fit'
summary(object, ...)
# S3 method for class 'lpl_tf'
print(x, ...)
# S3 method for class 'slpl_tf'
print(x, ...)
# S3 method for class 'density_fit'
print(x, ...)
# S3 method for class 'ps_lps'
print(x, ...)
# S3 method for class 'chart_kernel'
print(x, ...)
# S3 method for class 'local_likelihood'
print(x, ...)Value
summary() returns a summary.geosmooth_fit list with model,
n.training, n.evaluation, n.responses, n.observed, nonfinite,
status, convergence, prediction.domain, selected, criteria,
diagnostics, solver, mass, accounting, and warnings fields.
Print methods return their input invisibly.
Details
A finite result is not a convergence certificate. Where the algorithm records no stopping certificate, convergence is explicitly unavailable. Local fallback counts and failed evaluations are reported separately. Selection criteria are conditional tuning scores; they are not an independent assessment of prediction error. GCV, CV MSE, RMSE, and likelihood losses have different interpretations.
Examples
X <- matrix(seq(0, 1, length.out = 20), ncol = 1)
fit <- fit.chart.kernel(X, X[, 1]^2, support.size = 8L)
fit
#> Chart-kernel smoothing
#> Training rows: 20 ; evaluation rows: 20 ; responses: 1
#> Observed responses: 20 ; nonfinite fitted entries: 0
#> Output: finite output
#> Convergence: unavailable (no stored stopping certificate)
#> Selected: support.size=8, kernel=gaussian, cv.rmse.observed=NA
#> Prediction scope: stored evaluation rows; supply X.eval when fitting
summary(fit)
#> Chart-kernel smoothing
#> Training rows: 20 ; evaluation rows: 20 ; responses: 1
#> Observed responses: 20 ; nonfinite fitted entries: 0
#> Output: finite output
#> Convergence: unavailable (no stored stopping certificate)
#> Selected: support.size=8, kernel=gaussian, cv.rmse.observed=NA
#> Prediction scope: stored evaluation rows; supply X.eval when fitting
mass <- normalize.density(c(1, 2, 3))
mass
#> Probability mass
#> Training rows: NA ; evaluation rows: 3 ; responses: 1
#> Observed responses: NA ; nonfinite fitted entries: 0
#> Output: ok
#> Convergence: unavailable (no stored stopping certificate)
#> Total mass: 1 ; positive support: 3
#> Accounting: mass=1.000e+00, min.rho=1.667e-01, max.rho=5.000e-01,
#> raw.mass=6.000e+00, neg.mass=0.000e+00, clip.mass=0.000e+00,
#> normalization.constant=6.000e+00, mass.tol=1.000e-08, neg.tol=1.000e-12
#> Prediction scope: mass on the stored support
summary(mass)$accounting
#> $mass
#> [1] 1
#>
#> $min.rho
#> [1] 0.1666667
#>
#> $max.rho
#> [1] 0.5
#>
#> $raw.mass
#> [1] 6
#>
#> $neg.mass
#> [1] 0
#>
#> $clip.mass
#> [1] 0
#>
#> $normalization.constant
#> [1] 6
#>
#> $mass.tol
#> [1] 1e-08
#>
#> $neg.tol
#> [1] 1e-12
#>