validate.cph {rms} | R Documentation |
This is the version of the validate
function specific to models
fitted with cph
or psm
.
# fit <- cph(formula=Surv(ftime,event) ~ terms, x=TRUE, y=TRUE, \dots) ## S3 method for class 'cph' validate(fit, method="boot", B=40, bw=FALSE, rule="aic", type="residual", sls=.05, aics=0, force=NULL, pr=FALSE, dxy=FALSE, u, tol=1e-9, ...) ## S3 method for class 'psm' validate(fit, method="boot",B=40, bw=FALSE, rule="aic", type="residual", sls=.05, aics=0, force=NULL, pr=FALSE, dxy=FALSE, tol=1e-12, rel.tolerance=1e-5, maxiter=15, ...)
fit |
a fit derived |
method |
see |
B |
number of repetitions. For |
rel.tolerance,maxiter,bw |
|
rule |
Applies if |
type |
|
sls |
significance level for a factor to be kept in a model, or for judging the residual chi-square. |
aics |
cutoff on AIC when |
force |
see |
pr |
|
tol,... |
see |
dxy |
set to |
u |
must be specified if the model has any stratification factors and |
Statistics validated include the Nagelkerke R^2,
Dxy, slope shrinkage, the discrimination index D
[(model L.R. chi-square - 1)/L], the unreliability index
U = (difference in -2 log likelihood between uncalibrated
X beta and
X beta with overall slope calibrated to test sample) / L,
and the overall quality index Q = D - U. g is the
g-index on the log relative hazard (linear predictor) scale.
L is -2 log likelihood with beta=0. The "corrected" slope
can be thought of as shrinkage factor that takes into account overfitting.
See predab.resample
for the list of resampling methods.
matrix with rows corresponding to Dxy, Slope, D,
U, and Q, 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.
The values corresponting to the row Dxy are equal to 2 * (C - 0.5) where C is the C-index or concordance probability. If the user is correlating the linear predictor (predicted log hazard) with survival time and she wishes to get the more usual correlation using predicted survival time or predicted survival probability, Dxy should be negated.
prints a summary, and optionally statistics for each re-fit (if pr=TRUE
)
Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu
validate
, predab.resample
,
fastbw
, rms
, rms.trans
,
calibrate
, rcorr.cens
,
cph
, survival-internal
,
gIndex
n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" sex <- factor(sample(c('Male','Female'), n, TRUE)) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) dt <- -log(runif(n))/h e <- ifelse(dt <= cens,1,0) dt <- pmin(dt, cens) units(dt) <- "Year" S <- Surv(dt,e) f <- cph(S ~ age*sex, x=TRUE, y=TRUE) # Validate full model fit validate(f, B=10) # normally B=150 # Validate a model with stratification. Dxy is the only # discrimination measure for such models, by Dxy requires # one to choose a single time at which to predict S(t|X) f <- cph(S ~ rcs(age)*strat(sex), x=TRUE, y=TRUE, surv=TRUE, time.inc=2) validate(f, dxy=TRUE, u=2, B=10) # normally B=150 # Note u=time.inc