The validate function when used on an object created by
Rq does resampling validation of a quantile regression
model, with or without backward step-down variable deletion. Uses
resampling to estimate the optimism in various measures of predictive
accuracy which include mean absolute prediction error (MAD), Spearman
rho, the \(g\)-index, and the intercept and slope
of an overall
calibration \(a + b\hat{y}\). The "corrected"
slope can be thought of as shrinkage factor that takes into account
overfitting. validate.Rq can also be used when a model for a
continuous response is going to be applied to a binary response. A
Somers' \(D_{xy}\) for this case is computed for each resample by
dichotomizing y. This can be used to obtain an ordinary receiver
operating characteristic curve area using the formula \(0.5(D_{xy} +
1)\). See predab.resample for the list of
resampling methods.
The LaTeX needspace package must be in effect to use the
latex method.
# fit <- fitting.function(formula=response ~ terms, x=TRUE, y=TRUE)
# S3 method for Rq
validate(fit, method="boot", B=40,
bw=FALSE, rule="aic", type="residual", sls=0.05, aics=0,
force=NULL, estimates=TRUE, pr=FALSE, u=NULL, rel=">",
tolerance=1e-7, …)a fit derived by Rq. The options x=TRUE and y=TRUE
must have been specified. See validate for a description of
arguments method - pr.
see
validate and predab.resample and
fastbw
If specifed, y is also dichotomized at the cutoff u for
the purpose of getting a bias-corrected estimate of \(D_{xy}\).
relationship for dichotomizing predicted y. Defaults to
">" to use y>u. rel can also be "<",
">=", and "<=".
ignored
other arguments to pass to predab.resample, such as group, cluster, and subset
matrix with rows corresponding to various indexes, and optionally \(D_{xy}\), and columns for the original index, resample estimates, indexes applied to whole or omitted sample using model derived from resample, average optimism, corrected index, and number of successful resamples.
prints a summary, and optionally statistics for each re-fit
# NOT RUN {
set.seed(1)
x1 <- runif(200)
x2 <- sample(0:3, 200, TRUE)
x3 <- rnorm(200)
distance <- (x1 + x2/3 + rnorm(200))^2
f <- Rq(sqrt(distance) ~ rcs(x1,4) + scored(x2) + x3, x=TRUE, y=TRUE)
#Validate full model fit (from all observations) but for x1 < .75
validate(f, B=20, subset=x1 < .75) # normally B=300
#Validate stepwise model with typical (not so good) stopping rule
validate(f, B=20, bw=TRUE, rule="p", sls=.1, type="individual")
# }