calibrate {rms} | R Documentation

## Resampling Model Calibration

### Description

Uses bootstrapping or cross-validation to get bias-corrected (overfitting- corrected) estimates of predicted vs. observed values based on subsetting predictions into intervals (for survival models) or on nonparametric smoothers (for other models). There are calibration functions for Cox (`cph`), parametric survival models (`psm`), binary and ordinal logistic models (`lrm`) and ordinary least squares (`ols`). For survival models, "predicted" means predicted survival probability at a single time point, and "observed" refers to the corresponding Kaplan-Meier survival estimate, stratifying on intervals of predicted survival, or, if the `polspline` package is installed, the predicted survival probability as a function of transformed predicted survival probability using the flexible hazard regression approach (see the `val.surv` function for details). For logistic and linear models, a nonparametric calibration curve is estimated over a sequence of predicted values. The fit must have specified `x=TRUE, y=TRUE`. The `print` and `plot` methods for `lrm` and `ols` models (which use `calibrate.default`) print the mean absolute error in predictions, the mean squared error, and the 0.9 quantile of the absolute error. Here, error refers to the difference between the predicted values and the corresponding bias-corrected calibrated values.

Below, the second, third, and fourth invocations of `calibrate` are, respectively, for `ols` and `lrm`, `cph`, and `psm`. The first and second `plot` invocation are respectively for `lrm` and `ols` fits or all other fits.

### Usage

    calibrate(fit, ...)
    ## Default S3 method:
    calibrate(fit, predy,
      method=c("boot","crossvalidation",".632","randomization"),
      B=40, bw=FALSE, rule=c("aic","p"),
      type=c("residual","individual"),
      sls=.05, aics=0, force=NULL, estimates=TRUE, pr=FALSE, kint,
      smoother="lowess", digits=NULL, ...)
    ## S3 method for class 'cph'
    calibrate(fit, cmethod=c('hare', 'KM'),
      method="boot", u, m=150, pred, cuts, B=40,
      bw=FALSE, rule="aic", type="residual", sls=0.05, aics=0, force=NULL,
      estimates=TRUE,
      pr=FALSE, what="observed-predicted", tol=1e-12, maxdim=5, ...)
    ## S3 method for class 'psm'
    calibrate(fit, cmethod=c('hare', 'KM'),
      method="boot", u, m=150, pred, cuts, B=40,
      bw=FALSE,rule="aic",
      type="residual", sls=.05, aics=0, force=NULL, estimates=TRUE,
      pr=FALSE, what="observed-predicted", tol=1e-12, maxiter=15,
      rel.tolerance=1e-5, maxdim=5, ...)

    ## S3 method for class 'calibrate'
    print(x, B=Inf, ...)
    ## S3 method for class 'calibrate.default'
    print(x, B=Inf, ...)

    ## S3 method for class 'calibrate'
    plot(x, xlab, ylab, subtitles=TRUE, conf.int=TRUE,
     cex.subtitles=.75, riskdist=TRUE, add=FALSE,
     scat1d.opts=list(nhistSpike=200), par.corrected=NULL, ...)

    ## S3 method for class 'calibrate.default'
    plot(x, xlab, ylab, xlim, ylim,
      legend=TRUE, subtitles=TRUE, scat1d.opts=NULL, ...)

### Arguments

`fit` | a fit from `ols`, `lrm`, `cph` or `psm`
`x` | an object created by `calibrate`
`method, B, bw, rule, type, sls, aics, force, estimates` | see `validate`. For `print.calibrate`, `B` is an upper limit on the number of resamples for which information is printed about which variables were selected in each model re-fit. Specify zero to suppress printing. Default is to print all re-samples.
`cmethod` | method for validating survival predictions using right-censored data. The default is `cmethod='hare'` to use the `hare` function in the `polspline` package. Specify `cmethod='KM'` to use less precision stratified Kaplan-Meier estimates. If the `polspline` package is not available, the procedure reverts to `cmethod='KM'`.
`u` | the time point for which to validate predictions for survival models. For `cph` fits, you must have specified `surv=TRUE, time.inc=u`, where `u` is the constant specifying the time to predict.
`m` | group predicted `u`-time units survival into intervals containing `m` subjects on the average (for survival models only)
`pred` | vector of predicted survival probabilities at which to evaluate the calibration curve. By default, the low and high prediction values from `datadist` are used, which for large sample size is the 10th smallest to the 10th largest predicted probability.
`cuts` | actual cut points for predicted survival probabilities. You may specify only one of `m` and `cuts` (for survival models only)
`pr` | set to `TRUE` to print intermediate results for each re-sample
`what` | The default is `"observed-predicted"`, meaning to estimate optimism in this difference. This is preferred as it accounts for skewed distributions of predicted probabilities in outer intervals. You can also specify `"observed"`. This argument applies to survival models only.
`tol` | criterion for matrix singularity (default is `1e-12`)
`maxdim` | see `hare`
`maxiter` | for `psm`, this is passed to `survreg.control` (default is 15 iterations)
`rel.tolerance` | parameter passed to `survreg.control` for `psm` (default is 1e-5).
`predy` | a scalar or vector of predicted values to calibrate (for `lrm`, `ols`). Default is 50 equally spaced points between the 5th smallest and the 5th largest predicted values. For `lrm` the predicted values are probabilities (see `kint`).
`kint` | For an ordinal logistic model the default predicted probability that Y≥q the middle level. Specify `kint` to specify the intercept to use, e.g., `kint=2` means to calibrate Prob(Y≥q b), where b is the second level of Y.
`smoother` | a function in two variables which produces x- and y-coordinates by smoothing the input `y`. The default is to use `lowess(x, y, iter=0)`.
`digits` | If specified, predicted values are rounded to `digits` digits before passing to the smoother. Occasionally, large predicted values on the logit scale will lead to predicted probabilities very near 1 that should be treated as 1, and the `round` function will fix that. Applies to `calibrate.default`.
`...` | other arguments to pass to `predab.resample`, such as `group`, `cluster`, and `subset`. Also, other arguments for `plot`.
`xlab` | defaults to "Predicted x-units Survival" or to a suitable label for other models
`ylab` | defaults to "Fraction Surviving x-units" or to a suitable label for other models
`xlim,ylim` | 2-vectors specifying x- and y-axis limits, if not using defaults
`subtitles` | set to `FALSE` to suppress subtitles in plot describing method and for `lrm` and `ols` the mean absolute error and original sample size
`conf.int` | set to `FALSE` to suppress plotting 0.95 confidence intervals for Kaplan-Meier estimates
`cex.subtitles` | character size for plotting subtitles
`riskdist` | set to `FALSE` to suppress the distribution of predicted risks (survival probabilities) from being plotted
`add` | set to `TRUE` to add the calibration plot to an existing plot
`scat1d.opts` | a list specifying options to send to `scat1d` if `riskdist=TRUE`. See `scat1d`.
`par.corrected` | a list specifying graphics parameters `col`, `lty`, `lwd`, `pch` to be used in drawing overfitting-corrected estimates. Default is `col="blue"`, `lty=1`, `lwd=1`, `pch=4`.
`legend` | set to `FALSE` to suppress legends (for `lrm`, `ols` only) on the calibration plot, or specify a list with elements `x` and `y` containing the coordinates of the upper left corner of the legend. By default, a legend will be drawn in the lower right 1/16th of the plot.

### Details

If the fit was created using penalized maximum likelihood estimation, the same `penalty` and `penalty.scale` parameters are used during validation.

### Value

matrix specifying mean predicted survival in each interval, the corresponding estimated bias-corrected Kaplan-Meier estimates, number of subjects, and other statistics. For linear and logistic models, the matrix instead has rows corresponding to the prediction points, and the vector of predicted values being validated is returned as an attribute. The returned object has class `"calibrate"` or `"calibrate.default"`. `plot.calibrate.default` invisibly returns the vector of estimated prediction errors corresponding to the dataset used to fit the model.

### Side Effects

prints, and stores an object `pred.obs` or `.orig.cal`

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu

### See Also

`validate`, `predab.resample`, `groupkm`, `errbar`, `scat1d`, `cph`, `psm`, `lowess`

### Examples

    set.seed(1)
    n <- 200
    d.time <- rexp(n)
    x1 <- runif(n)
    x2 <- factor(sample(c('a', 'b', 'c'), n, TRUE))
    f <- cph(Surv(d.time) ~ pol(x1,2) * x2, x=TRUE, y=TRUE, surv=TRUE, time.inc=1.5)
    #or f <- psm(S ~ \dots)
    pa <- 'polspline' %in% row.names(installed.packages())
    if(pa) {
     cal <- calibrate(f, u=1.5, B=20)  # cmethod='hare'
     plot(cal)
    }
    cal <- calibrate(f, u=1.5, cmethod='KM', m=50, B=20)  # usually B=200 or 300
    plot(cal, add=pa)

    set.seed(1)
    y <- sample(0:2, n, TRUE)
    x1 <- runif(n)
    x2 <- runif(n)
    x3 <- runif(n)
    x4 <- runif(n)
    f <- lrm(y ~ x1 + x2 + x3 * x4, x=TRUE, y=TRUE)
    cal <- calibrate(f, kint=2, predy=seq(.2, .8, length=60),
                     group=y)
    # group= does k-sample validation: make resamples have same
    # numbers of subjects in each level of y as original sample

    plot(cal)
    #See the example for the validate function for a method of validating
    #continuation ratio ordinal logistic models.  You can do the same
    #thing for calibrate

[Package rms version 5.1-2 Index]