Glance accepts a model object and returns a
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an
of the appropriate type.
## S3 method for class 'ridgelm' glance(x, ...)
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
This is similar to the output of
select.ridgelm, but it is
returned rather than printed.
tibble::tibble() with exactly one row and columns:
modified HKB estimate of the ridge constant
modified L-W estimate of the ridge constant
choice of lambda that minimizes GCV
Other ridgelm tidiers:
# load libraries for models and data library(MASS) names(longley) <- "y" # fit model and summarizd results fit1 <- lm.ridge(y ~ ., longley) tidy(fit1) fit2 <- lm.ridge(y ~ ., longley, lambda = seq(0.001, .05, .001)) td2 <- tidy(fit2) g2 <- glance(fit2) # coefficient plot library(ggplot2) ggplot(td2, aes(lambda, estimate, color = term)) + geom_line() # GCV plot ggplot(td2, aes(lambda, GCV)) + geom_line() # add line for the GCV minimizing estimate ggplot(td2, aes(lambda, GCV)) + geom_line() + geom_vline(xintercept = g2$lambdaGCV, col = "red", lty = 2)
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