| Title: | Calibration Performance |
|---|---|
| Description: | Plots calibration curves and computes statistics for assessing calibration performance. See De Cock Campo (2023) <doi:10.48550/arXiv.2309.08559> and Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>. |
| Authors: | De Cock Bavo [aut, cre], Nieboer Daan [aut], Van Calster Ben [aut], Steyerberg Ewout [aut], Vergouwe Yvonne [aut] |
| Maintainer: | De Cock Bavo <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 2.0.3 |
| Built: | 2024-11-17 06:11:42 UTC |
| Source: | https://github.com/cran/CalibrationCurves |
Adjusted version of the rcspline.plot function where only the output is returned and no plot is made
.rcspline.plot( x, y, model = c("logistic", "cox", "ols"), xrange, event, nk = 5, knots = NULL, show = c("xbeta", "prob"), adj = NULL, xlab, ylab, ylim, plim = c(0, 1), plotcl = TRUE, showknots = TRUE, add = FALSE, plot = TRUE, subset, lty = 1, noprint = FALSE, m, smooth = FALSE, bass = 1, main = "auto", statloc ).rcspline.plot( x, y, model = c("logistic", "cox", "ols"), xrange, event, nk = 5, knots = NULL, show = c("xbeta", "prob"), adj = NULL, xlab, ylab, ylim, plim = c(0, 1), plotcl = TRUE, showknots = TRUE, add = FALSE, plot = TRUE, subset, lty = 1, noprint = FALSE, m, smooth = FALSE, bass = 1, main = "auto", statloc )
x |
a numeric predictor |
y |
a numeric response. For binary logistic regression, |
model |
|
xrange |
range for evaluating |
event |
event/censoring indicator if |
nk |
number of knots |
knots |
knot locations, default based on quantiles of |
show |
|
adj |
optional matrix of adjustment variables |
xlab |
|
ylab |
|
ylim |
|
plim |
|
plotcl |
plot confidence limits |
showknots |
show knot locations with arrows |
add |
add this plot to an already existing plot |
plot |
logical to indicate whether a plot has to be made. |
subset |
subset of observations to process, e.g. |
lty |
line type for plotting estimated spline function |
noprint |
suppress printing regression coefficients and standard errors |
m |
for |
smooth |
plot nonparametric estimate if |
bass |
smoothing parameter (see |
main |
main title, default is |
statloc |
location of summary statistics. Default positioning by clicking left mouse button where upper left corner of statistics should appear.
Alternative is |
list with components (‘knots’, ‘x’, ‘xbeta’, ‘lower’, ‘upper’) which are respectively the knot locations, design matrix, linear predictor, and lower and upper confidence limits
lrm, cph, rcspline.eval, plot, supsmu,
coxph.fit, lrm.fit
This infix operator can be used to create a background job for a block of code in RStudio/Posit and, once completed, all objects created in the block of code are imported into the global environment.
lhs %{}% rhslhs %{}% rhs
lhs |
not used, see details and examples |
rhs |
the block of code that you want to run |
You can use this infix operator in two different ways. Either you set the left-hand side to NULL or you use the syntax
`%{}%` ({BlockOfCode})
prints the ID of the background job in the console and, once completed, the objects created in the block of code are imported into the global environment
# Can only be executed in Rstudio ## Not run: NULL %{}% { x = rnorm(1e7) y = rnorm(1e7) } `%{}%` ({ x = rnorm(1e7) y = rnorm(1e7) }) ## End(Not run)# Can only be executed in Rstudio ## Not run: NULL %{}% { x = rnorm(1e7) y = rnorm(1e7) } `%{}%` ({ x = rnorm(1e7) y = rnorm(1e7) }) ## End(Not run)
This infix operator can be used to create a background job in RStudio/Posit and, once completed, the value of rhs is assigned to lhs.
lhs %<=% rhslhs %<=% rhs
lhs |
the object that the rhs value is assigned to |
rhs |
the value you want to assign to lhs |
prints the ID of the background job in the console and, once completed, the value of lhs is assigned to rhs
# Can only be executed in Rstudio ## Not run: x %<=% rnorm(1e7)# Can only be executed in Rstudio ## Not run: x %<=% rnorm(1e7)
Obtain the point estimate and the confidence interval of the AUC by various methods based on the Mann-Whitney statistic.
auc.nonpara.mw(x, y, conf.level=0.95, method=c("newcombe", "pepe", "delong", "jackknife", "bootstrapP", "bootstrapBCa"), nboot)auc.nonpara.mw(x, y, conf.level=0.95, method=c("newcombe", "pepe", "delong", "jackknife", "bootstrapP", "bootstrapBCa"), nboot)
x |
a vector of observations from class P. |
y |
a vector of observations from class N. |
conf.level |
confidence level of the interval. The default is 0.95. |
method |
a method used to construct the CI. |
nboot |
number of bootstrap iterations. |
The function implements various methods based on the Mann-Whitney statistic.
Point estimate and lower and upper bounds of the CI of the AUC.
The observations from class P tend to have larger values than that from class N.
This help-file is a copy of the original help-file of the function auc.nonpara.mw from the auRoc-package. It is important to note
that, when using method="pepe", the confidence interval is computed as documented in Qin and Hotilovac (2008) and that this is
different from the original function.
Elizabeth R Delong, David M Delong, and Daniel L Clarke-Pearson (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 837-845
Dai Feng, Giuliana Cortese, and Richard Baumgartner (2015) A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size. Statistical Methods in Medical Research DOI: 10.1177/0962280215602040
Robert G Newcombe (2006) Confidence intervals for an effect size measure based on the Mann-Whitney statistic. Part 2: asymptotic methods and evaluation. Statistics in medicine 25(4) 559-573
Margaret Sullivan Pepe (2003) The statistical evaluation of medical tests for classification and prediction. Oxford University Press
Qin, G., & Hotilovac, L. (2008). Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test. Statistical Methods in Medical Research, 17(2), pp. 207-21
Using this package, you can assess the calibration performance of your prediction model. That is, to which extent the predictions and correspond with what we observe empirically.
To assess the calibration of model with a binary outcome, you can use the val.prob.ci.2 or the valProbggplot function. If the outcome of your prediction model
is not binary but follows a different distribution of the exponential family, you can employ the genCalCurve function.
If you are not familiar with the theory and/or application of calibration, you can consult the vignette of the package. This vignette provides a comprehensive overview of the theory and contains a tutorial with some practical examples. Further, we suggest the reader to consult the paper on generalized calibration curves on arXiv. In this paper, we provide the theoretical background on the generalized calibration framework and illustrate its applicability with some prototypical examples of both statistical and machine learning prediction models that are well-calibrated, overfit and underfit.
Originally, the package only contained functions to assess the calibration of prediction models with a binary outcome. The details section provides some background information on the history of the package's development.
Some years ago, Yvonne Vergouwe and Ewout Steyerberg adapted the function val.prob from the rms-package (https://cran.r-project.org/package=rms) into val.prob.ci and added the following functions to val.prob:
Scaled Brier score by relating to max for average calibrated Null model
Risk distribution according to outcome
0 and 1 to indicate outcome label; set with d1lab="..", d0lab=".."
Labels: y axis: "Observed Frequency"; Triangle: "Grouped observations"
Confidence intervals around triangles
A cut-off can be plotted; set x coordinate
In December 2015, Bavo De Cock, Daan Nieboer, and Ben Van Calster adapted
this to val.prob.ci.2:
Flexible calibration curves can be obtained using loess (default) or restricted cubic splines, with pointwise 95% confidence intervals. Flexible calibration curves are now given by default and this decision is based on the findings reported in Van Calster et al. (2016).
Loess: confidence intervals can be obtained in closed form or using bootstrapping (CL.BT=T will do bootstrapping with 2000 bootstrap samples, however this will take a while)
RCS: 3 to 5 knots can be used
the knot locations will be estimated using default quantiles of
x (by rcspline.eval, see rcspline.plot and rcspline.eval)
if estimation problems occur at the specified number of knots (nr.knots, default is 5), the analysis is repeated with nr.knots-1 until the problem has disappeared and the function stops if there is still an estimation problem with 3 knots
You can now adjust the plot through use of normal plot commands
(cex.axis etcetera), and the size of the legend now has to be specified in
cex.leg
Label y-axis: "Observed proportion"
Stats: added the Estimated Calibration Index (ECI), a statistical measure to quantify lack of calibration (Van Hoorde et al., 2015)
Stats to be shown in the plot: by default we show the "abc" of model performance (Steyerberg et al., 2011). That is, calibration intercept (calibration-in-the-large), calibration slope and c-
statistic. Alternatively, the user can select the statistics of
choice (e.g. dostats=c("C (ROC)","R2") or dostats=c(2,3).
Vectors p, y and logit no longer have to be sorted
In 2023, Bavo De Cock (Campo) published a paper that introduces the generalized calibration framework. This framework is an extension of the logistic calibration framework to prediction models where the outcome's distribution is a member of the exponential family. As such, we are able to assess the calibration of a wider range of prediction models. The methods in this paper are implemented in the CalibrationCurves package.
The most current version of this package can always be found on
https://github.com/BavoDC and can easily be installed using the following code: install.packages("devtools") # if not yet installed require(devtools) install_github("BavoDC/CalibrationCurves", dependencies = TRUE, build_vignettes = TRUE)
De Cock Campo, B. (2023). Towards reliable predictive analytics: a generalized calibration framework. arXiv:2309.08559, available at https://arxiv.org/abs/2309.08559.
Steyerberg, E.W.Van Calster, B., Pencina, M.J. (2011). Performance measures for prediction models and markers : evaluation of predictions and classifications. Revista Espanola de Cardiologia, 64(9), pp. 788-794
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina M., Steyerberg E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of Clinical Epidemiology, 74, pp. 167-176
Van Hoorde, K., Van Huffel, S., Timmerman, D., Bourne, T., Van Calster, B. (2015). A spline-based tool to assess and visualize the calibration of multiclass risk predictions. Journal of Biomedical Informatics, 54, pp. 283-93
Function to assess the calibration performance of a prediction model where the outcome's distribution is a member of the exponential family (De Cock Campo, 2023). The function plots the generalized calibration curve and computes the generalized calibration slope and intercept.
genCalCurve( y, yHat, family, plot = TRUE, Smooth = FALSE, GLMCal = TRUE, lwdIdeal = 2, colIdeal = "gray", ltyIdeal = 1, lwdSmooth = 1, colSmooth = "blue", ltySmooth = 1, argzSmooth = alist(degree = 2), lwdGLMCal = 1, colGLMCal = "red", ltyGLMCal = 1, AddStats = T, Digits = 3, cexStats = 1, lwdLeg = 1.5, Legend = TRUE, legendPos = "bottomright", xLim = NULL, yLim = NULL, posStats = NULL, confLimitsSmooth = c("none", "bootstrap", "pointwise"), confLevel = 0.95, Title = "Calibration plot", xlab = "Predicted value", ylab = "Empirical average", EmpiricalDistribution = TRUE, length.seg = 1, ... )genCalCurve( y, yHat, family, plot = TRUE, Smooth = FALSE, GLMCal = TRUE, lwdIdeal = 2, colIdeal = "gray", ltyIdeal = 1, lwdSmooth = 1, colSmooth = "blue", ltySmooth = 1, argzSmooth = alist(degree = 2), lwdGLMCal = 1, colGLMCal = "red", ltyGLMCal = 1, AddStats = T, Digits = 3, cexStats = 1, lwdLeg = 1.5, Legend = TRUE, legendPos = "bottomright", xLim = NULL, yLim = NULL, posStats = NULL, confLimitsSmooth = c("none", "bootstrap", "pointwise"), confLevel = 0.95, Title = "Calibration plot", xlab = "Predicted value", ylab = "Empirical average", EmpiricalDistribution = TRUE, length.seg = 1, ... )
y |
a vector with the values for the response variable |
yHat |
a vector with the predicted values |
family |
a description of the type of distribution and link function in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.) |
plot |
logical, indicating if a plot should be made or not. |
Smooth |
logical, indicating if the flexible calibration curve should be estimated. |
GLMCal |
logical, indicating if the GLM calibration curve has to be estimated. |
lwdIdeal |
the line width of the ideal line. |
colIdeal |
the color of the ideal line. |
ltyIdeal |
the line type of the ideal line. |
lwdSmooth |
the line width of the flexible calibration curve. |
colSmooth |
the color of the flexible calibration curve. |
ltySmooth |
the line type of the flexible calibration curve. |
argzSmooth |
arguments passed to |
lwdGLMCal |
the line width of the GLM calibration curve. |
colGLMCal |
the color of the GLM calibration curve. |
ltyGLMCal |
the line type of the GLM calibration curve. |
AddStats |
logical, indicating whether to add the values of the generalized calibration slope and intercept to the plot. |
Digits |
the number of digits of the generalized calibration slope and intercept. |
cexStats |
the font size of the statistics shown on the plot. |
lwdLeg |
the line width in the legend. |
Legend |
logical, indicating whether the legend has to be added. |
legendPos |
the position of the legend on the plot. |
xLim, yLim
|
numeric vectors of length 2, giving the x and y coordinates ranges (see |
posStats |
numeric vector of length 2, specifying the x and y coordinates of the statistics (generalized calibration curve and intercept) printed on the plot. Default is |
confLimitsSmooth |
character vector to indicate if and how the confidence limits for the flexible calibration curve have to be computed. |
confLevel |
the confidence level for the calculation of the pointwise confidence limits of the flexible calibration curve. |
Title |
the title of the plot |
xlab |
x-axis label, default is |
ylab |
y-axis label, default is |
EmpiricalDistribution |
logical, indicating if the empirical distribution of the predicted values has to be added to the bottom of the plot. |
length.seg |
controls the length of the histogram lines. Default is |
... |
An object of type GeneralizedCalibrationCurve with the following slots:
call |
the matched call. |
ggPlot |
the ggplot object. |
stats |
a vector containing performance measures of calibration. |
cl.level |
the confidence level used. |
Calibration |
contains the calibration intercept and slope, together with their confidence intervals. |
Cindex |
the value of the c-statistic, together with its confidence interval. |
warningMessages |
if any, the warning messages that were printed while running the function. |
CalibrationCurves |
The coordinates for plotting the calibration curves. |
De Cock Campo, B. (2023). Towards reliable predictive analytics: a generalized calibration framework. arXiv:2309.08559, available at https://arxiv.org/abs/2309.08559.
library(CalibrationCurves) library(mgcv) data("poissontraindata") data("poissontestdata") glmFit = glm(Y ~ ., data = poissontraindata, family = poisson) # Example of a well calibrated poisson prediction model yOOS = poissontestdata$Y yHat = predict(glmFit, newdata = poissontestdata, type = "response") genCalCurve(yOOS, yHat, family = "poisson", plot = TRUE) # Example of an overfit poisson prediction model gamFit = gam(Y ~ x1 + x3 + x1:x3 + s(x5), data = poissontraindata, family = poisson) yHat = as.vector(predict(gamFit, newdata = poissontestdata, type = "response")) genCalCurve(yOOS, yHat, family = "poisson", plot = TRUE) # Example of an underfit poisson prediction model glmFit = glm(Y ~ x2, data = poissontraindata, family = poisson) yOOS = poissontestdata$Y yHat = predict(glmFit, newdata = poissontestdata, type = "response") genCalCurve(yOOS, yHat, family = "poisson", plot = TRUE)library(CalibrationCurves) library(mgcv) data("poissontraindata") data("poissontestdata") glmFit = glm(Y ~ ., data = poissontraindata, family = poisson) # Example of a well calibrated poisson prediction model yOOS = poissontestdata$Y yHat = predict(glmFit, newdata = poissontestdata, type = "response") genCalCurve(yOOS, yHat, family = "poisson", plot = TRUE) # Example of an overfit poisson prediction model gamFit = gam(Y ~ x1 + x3 + x1:x3 + s(x5), data = poissontraindata, family = poisson) yHat = as.vector(predict(gamFit, newdata = poissontestdata, type = "response")) genCalCurve(yOOS, yHat, family = "poisson", plot = TRUE) # Example of an underfit poisson prediction model glmFit = glm(Y ~ x2, data = poissontraindata, family = poisson) yOOS = poissontestdata$Y yHat = predict(glmFit, newdata = poissontestdata, type = "response") genCalCurve(yOOS, yHat, family = "poisson", plot = TRUE)
Function to load multiple packages at once
LibraryM(...)LibraryM(...)
... |
the packages that you want to load |
invisible NULL
LibraryM(CalibrationCurves)LibraryM(CalibrationCurves)
Prints the call, confidence level and values for the performance measures.
## S3 method for class 'CalibrationCurve' print(x, ...)## S3 method for class 'CalibrationCurve' print(x, ...)
x |
an object of type CalibrationCurve, resulting from |
... |
arguments passed to |
The original CalibrationCurve object is returned.
Prints the call, confidence level and values for the performance measures.
## S3 method for class 'GeneralizedCalibrationCurve' print(x, ...)## S3 method for class 'GeneralizedCalibrationCurve' print(x, ...)
x |
an object of type GeneralizedCalibrationCurve, resulting from |
... |
arguments passed to |
The original GeneralizedCalibrationCurve object is returned.
Prints the ggplot, call, confidence level and values for the performance measures.
## S3 method for class 'ggplotCalibrationCurve' print(x, ...)## S3 method for class 'ggplotCalibrationCurve' print(x, ...)
x |
an object of type ggplotCalibrationCurve, resulting from |
... |
arguments passed to |
The original ggplotCalibrationCurve object is returned.
Both the traindata and testdata dataframe are synthetically generated data sets to illustrate the functionality of the package. The traindata has 1000 observations and the testdata has 500 observations. The same settings were used to generate both data sets.
data(traindata) data(testdata)data(traindata) data(testdata)
ythe binary outcome variable
x1covariate 1
x2covariate 2
x3covariate 3
x4covariate 4
See the examples for how the data sets were generated.
# The data sets were generated as follows set.seed(1782) # Simulate training data nTrain = 1000 B = c(0.1, 0.5, 1.2, -0.75, 0.8) X = replicate(4, rnorm(nTrain)) p0true = binomial()$linkinv(cbind(1, X) %*% B) y = rbinom(nTrain, 1, p0true) colnames(X) = paste0("x", seq_len(ncol(X))) traindata = data.frame(y, X) # Simulate validation data nTest = 500 X = replicate(4, rnorm(nTest)) p0true = binomial()$linkinv(cbind(1, X) %*% B) y = rbinom(nTest, 1, p0true) colnames(X) = paste0("x", seq_len(ncol(X))) testdata = data.frame(y, X)# The data sets were generated as follows set.seed(1782) # Simulate training data nTrain = 1000 B = c(0.1, 0.5, 1.2, -0.75, 0.8) X = replicate(4, rnorm(nTrain)) p0true = binomial()$linkinv(cbind(1, X) %*% B) y = rbinom(nTrain, 1, p0true) colnames(X) = paste0("x", seq_len(ncol(X))) traindata = data.frame(y, X) # Simulate validation data nTest = 500 X = replicate(4, rnorm(nTest)) p0true = binomial()$linkinv(cbind(1, X) %*% B) y = rbinom(nTest, 1, p0true) colnames(X) = paste0("x", seq_len(ncol(X))) testdata = data.frame(y, X)
Both the traindata and testdata dataframe are synthetically generated data sets to illustrate the functionality of the package. The traindata has 5000 observations and the testdata has 1000 observations. The same settings were used to generate both data sets.
data(poissontraindata) data(poissontestdata)data(poissontraindata) data(poissontestdata)
ythe poisson distributed outcome variable
x1covariate 1
x2covariate 2
x3covariate 3
x4covariate 4
x5covariate 5
See the examples for how the data sets were generated.
# The data sets were generated as follows library(MASS) library(magrittr) ScaleRange <- function(x, xmin = -1, xmax = 1) { xRange = range(x) (x - xRange[1]) / diff(xRange) * (xmax - xmin) + xmin } set.seed(144) p = 5 N = 1e6 n = 5e3 nOOS = 1e3 S = matrix(NA, 5, 5) rho = c(0.025, 0, 0, 0.05, 0.075, 0, 0, 0.025, 0, 0) S[upper.tri(S)] = rho S[lower.tri(S)] = t(S)[lower.tri(S)] diag(S) = 1 Matrix::isSymmetric(S) X = mvrnorm(N, rep(0, p), Sigma = S, empirical = TRUE) X = apply(X, 2, ScaleRange) B = c(-2.3, 1.5, 2, -1, -2, -1.5) mu = poisson()$linkinv(cbind(1, X) %*% B) Y = rpois(N, mu) Df = data.frame(Y, X) colnames(Df)[-1] %<>% tolower() set.seed(2) DfS = Df[sample(1:nrow(Df), n, FALSE), ] DfOOS = Df[sample(1:nrow(Df), nOOS, FALSE), ] poissontraindata = DfS poissontestdata = DfOOS# The data sets were generated as follows library(MASS) library(magrittr) ScaleRange <- function(x, xmin = -1, xmax = 1) { xRange = range(x) (x - xRange[1]) / diff(xRange) * (xmax - xmin) + xmin } set.seed(144) p = 5 N = 1e6 n = 5e3 nOOS = 1e3 S = matrix(NA, 5, 5) rho = c(0.025, 0, 0, 0.05, 0.075, 0, 0, 0.025, 0, 0) S[upper.tri(S)] = rho S[lower.tri(S)] = t(S)[lower.tri(S)] diag(S) = 1 Matrix::isSymmetric(S) X = mvrnorm(N, rep(0, p), Sigma = S, empirical = TRUE) X = apply(X, 2, ScaleRange) B = c(-2.3, 1.5, 2, -1, -2, -1.5) mu = poisson()$linkinv(cbind(1, X) %*% B) Y = rpois(N, mu) Df = data.frame(Y, X) colnames(Df)[-1] %<>% tolower() set.seed(2) DfS = Df[sample(1:nrow(Df), n, FALSE), ] DfOOS = Df[sample(1:nrow(Df), nOOS, FALSE), ] poissontraindata = DfS poissontestdata = DfOOS
The function val.prob.ci.2 is an adaptation of val.prob from Frank Harrell's rms package,
https://cran.r-project.org/package=rms. Hence, the description of some of the functions of val.prob.ci.2
come from the the original val.prob.
The key feature of val.prob.ci.2 is the generation of logistic and flexible calibration curves and related statistics.
When using this code, please cite: Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M.J., Steyerberg,
E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of Clinical Epidemiology,
74, pp. 167-176
val.prob.ci.2( p, y, logit, group, weights = rep(1, length(y)), normwt = FALSE, pl = TRUE, smooth = c("loess", "rcs", "none"), CL.smooth = "fill", CL.BT = FALSE, lty.smooth = 1, col.smooth = "black", lwd.smooth = 1, nr.knots = 5, logistic.cal = FALSE, lty.log = 1, col.log = "black", lwd.log = 1, xlab = "Predicted probability", ylab = "Observed proportion", xlim = c(-0.02, 1), ylim = c(-0.15, 1), m, g, cuts, emax.lim = c(0, 1), legendloc = c(0.5, 0.27), statloc = c(0, 0.85), dostats = TRUE, cl.level = 0.95, method.ci = "pepe", roundstats = 2, riskdist = "predicted", cex = 0.75, cex.leg = 0.75, connect.group = FALSE, connect.smooth = TRUE, g.group = 4, evaluate = 100, nmin = 0, d0lab = "0", d1lab = "1", cex.d01 = 0.7, dist.label = 0.04, line.bins = -0.05, dist.label2 = 0.03, cutoff, las = 1, length.seg = 1, y.intersp = 1, lty.ideal = 1, col.ideal = "red", lwd.ideal = 1, allowPerfectPredictions = FALSE, argzLoess = alist(degree = 2), ... )val.prob.ci.2( p, y, logit, group, weights = rep(1, length(y)), normwt = FALSE, pl = TRUE, smooth = c("loess", "rcs", "none"), CL.smooth = "fill", CL.BT = FALSE, lty.smooth = 1, col.smooth = "black", lwd.smooth = 1, nr.knots = 5, logistic.cal = FALSE, lty.log = 1, col.log = "black", lwd.log = 1, xlab = "Predicted probability", ylab = "Observed proportion", xlim = c(-0.02, 1), ylim = c(-0.15, 1), m, g, cuts, emax.lim = c(0, 1), legendloc = c(0.5, 0.27), statloc = c(0, 0.85), dostats = TRUE, cl.level = 0.95, method.ci = "pepe", roundstats = 2, riskdist = "predicted", cex = 0.75, cex.leg = 0.75, connect.group = FALSE, connect.smooth = TRUE, g.group = 4, evaluate = 100, nmin = 0, d0lab = "0", d1lab = "1", cex.d01 = 0.7, dist.label = 0.04, line.bins = -0.05, dist.label2 = 0.03, cutoff, las = 1, length.seg = 1, y.intersp = 1, lty.ideal = 1, col.ideal = "red", lwd.ideal = 1, allowPerfectPredictions = FALSE, argzLoess = alist(degree = 2), ... )
p |
predicted probability |
y |
vector of binary outcomes |
logit |
predicted log odds of outcome. Specify either |
group |
a grouping variable. If numeric this variable is grouped into
|
weights |
an optional numeric vector of per-observation weights (usually frequencies),
used only if |
normwt |
set to |
pl |
|
smooth |
|
CL.smooth |
|
CL.BT |
|
lty.smooth |
the linetype of the flexible calibration curve. Default is |
col.smooth |
the color of the flexible calibration curve. Default is |
lwd.smooth |
the line width of the flexible calibration curve. Default is |
nr.knots |
specifies the number of knots for rcs-based calibration curve. The default as well as the highest allowed value is 5. In case the specified number of knots leads to estimation problems, then the number of knots is automatically reduced to the closest value without estimation problems. |
logistic.cal |
|
lty.log |
if |
col.log |
if |
lwd.log |
if |
xlab |
x-axis label, default is |
ylab |
y-axis label, default is |
xlim, ylim
|
numeric vectors of length 2, giving the x and y coordinates ranges (see |
m |
If grouped proportions are desired, average no. observations per group |
g |
If grouped proportions are desired, number of quantile groups |
cuts |
If grouped proportions are desired, actual cut points for constructing
intervals, e.g. |
emax.lim |
Vector containing lowest and highest predicted probability over which to
compute |
legendloc |
if |
statloc |
the "abc" of model performance (Steyerberg et al., 2011)-calibration intercept, calibration slope,
and c statistic-will be added to the plot, using statloc as the upper left corner of a box (default is c(0,.85).
You can specify a list or a vector. Use locator(1) for the mouse, |
dostats |
specifies whether and which performance measures are shown in the figure.
|
cl.level |
if |
method.ci |
method to calculate the confidence interval of the c-statistic. The argument is passed to |
roundstats |
specifies the number of decimals to which the statistics are rounded when shown in the plot. Default is 2. |
riskdist |
Use |
cex, cex.leg
|
controls the font size of the statistics ( |
connect.group |
Defaults to |
connect.smooth |
Defaults to |
g.group |
number of quantile groups to use when |
evaluate |
number of points at which to store the |
nmin |
applies when |
d0lab, d1lab
|
controls the labels for events and non-events (i.e. outcome y) for the histograms.
Defaults are |
cex.d01 |
controls the size of the labels for events and non-events. Default is 0.7. |
dist.label |
controls the horizontal position of the labels for events and non-events. Default is 0.04. |
line.bins |
controls the horizontal (y-axis) position of the histograms. Default is -0.05. |
dist.label2 |
controls the vertical distance between the labels for events and non-events. Default is 0.03. |
cutoff |
puts an arrow at the specified risk cut-off(s). Default is none. |
las |
controls whether y-axis values are shown horizontally (1) or vertically (0). |
length.seg |
controls the length of the histogram lines. Default is |
y.intersp |
character interspacing for vertical line distances of the legend ( |
lty.ideal |
linetype of the ideal line. Default is |
col.ideal |
controls the color of the ideal line on the plot. Default is |
lwd.ideal |
controls the line width of the ideal line on the plot. Default is |
allowPerfectPredictions |
Logical, indicates whether perfect predictions (i.e. values of either 0 or 1) are allowed. Default is |
argzLoess |
a list with arguments passed to the |
... |
When using the predicted probabilities of an uninformative model (i.e. equal probabilities for all observations), the model has no predictive value. Consequently, where applicable, the value of the performance measure corresponds to the worst possible theoretical value. For the ECI, for example, this equals 1 (Edlinger et al., 2022).
An object of type CalibrationCurve with the following slots:
call |
the matched call. |
stats |
a vector containing performance measures of calibration. |
cl.level |
the confidence level used. |
Calibration |
contains the calibration intercept and slope, together with their confidence intervals. |
Cindex |
the value of the c-statistic, together with its confidence interval. |
warningMessages |
if any, the warning messages that were printed while running the function. |
CalibrationCurves |
The coordinates for plotting the calibration curves. |
In order to make use (of the functions) of the package auRoc, the user needs to install JAGS. However, since our package only uses the
auc.nonpara.mw function which does not depend on the use of JAGS, we therefore copied the code and slightly adjusted it when
method="pepe".
Edlinger, M, van Smeden, M, Alber, HF, Wanitschek, M, Van Calster, B. (2022). Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption. Statistics in Medicine, 41( 8), pp. 1334– 1360
Qin, G., & Hotilovac, L. (2008). Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test. Statistical Methods in Medical Research, 17(2), pp. 207-21
Steyerberg, E.W., Van Calster, B., Pencina, M.J. (2011). Performance measures for prediction models and markers : evaluation of predictions and classifications. Revista Espanola de Cardiologia, 64(9), pp. 788-794
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina M., Steyerberg E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of Clinical Epidemiology, 74, pp. 167-176
Van Hoorde, K., Van Huffel, S., Timmerman, D., Bourne, T., Van Calster, B. (2015). A spline-based tool to assess and visualize the calibration of multiclass risk predictions. Journal of Biomedical Informatics, 54, pp. 283-93
# Load package library(CalibrationCurves) set.seed(1783) # Simulate training data X = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, X) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) y = rbinom(5e2, 1, p0true) Df = data.frame(y, X) # Fit logistic model FitLog = lrm(y ~ ., Df) # Simulate validation data Xval = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, Xval) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) yval = rbinom(5e2, 1, p0true) Pred = binomial()$linkinv(cbind(1, Xval) %*% coef(FitLog)) # Default calibration plot val.prob.ci.2(Pred, yval) # Adding logistic calibration curves and other additional features val.prob.ci.2(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 2, col.log = "red", lwd.log = 1.5) val.prob.ci.2(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 9, col.log = "red", lwd.log = 1.5, col.ideal = colors()[10], lwd.ideal = 0.5)# Load package library(CalibrationCurves) set.seed(1783) # Simulate training data X = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, X) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) y = rbinom(5e2, 1, p0true) Df = data.frame(y, X) # Fit logistic model FitLog = lrm(y ~ ., Df) # Simulate validation data Xval = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, Xval) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) yval = rbinom(5e2, 1, p0true) Pred = binomial()$linkinv(cbind(1, Xval) %*% coef(FitLog)) # Default calibration plot val.prob.ci.2(Pred, yval) # Adding logistic calibration curves and other additional features val.prob.ci.2(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 2, col.log = "red", lwd.log = 1.5) val.prob.ci.2(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 9, col.log = "red", lwd.log = 1.5, col.ideal = colors()[10], lwd.ideal = 0.5)
The function valProbggplot is an adaptation of val.prob from Frank Harrell's rms package,
https://cran.r-project.org/package=rms. Hence, the description of some of the functions of valProbggplot
come from the the original val.prob.
The key feature of valProbggplot is the generation of logistic and flexible calibration curves and related statistics.
When using this code, please cite: Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M.J., Steyerberg,
E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of Clinical Epidemiology,
74, pp. 167-176
valProbggplot( p, y, logit, group, weights = rep(1, length(y)), normwt = FALSE, pl = TRUE, smooth = c("loess", "rcs", "none"), CL.smooth = "fill", CL.BT = FALSE, lty.smooth = 1, col.smooth = "black", lwd.smooth = 1, nr.knots = 5, logistic.cal = FALSE, lty.log = 1, col.log = "black", lwd.log = 1, xlab = "Predicted probability", ylab = "Observed proportion", xlim = c(-0.02, 1), ylim = c(-0.15, 1), m, g, cuts, emax.lim = c(0, 1), legendloc = c(0.5, 0.27), statloc = c(0, 0.85), dostats = TRUE, cl.level = 0.95, method.ci = "pepe", roundstats = 2, riskdist = "predicted", size = 3, size.leg = 5, connect.group = FALSE, connect.smooth = TRUE, g.group = 4, evaluate = 100, nmin = 0, d0lab = "0", d1lab = "1", size.d01 = 5, dist.label = 0.01, line.bins = -0.05, dist.label2 = 0.04, cutoff, length.seg = 0.85, lty.ideal = 1, col.ideal = "red", lwd.ideal = 1, allowPerfectPredictions = FALSE, argzLoess = alist(degree = 2) )valProbggplot( p, y, logit, group, weights = rep(1, length(y)), normwt = FALSE, pl = TRUE, smooth = c("loess", "rcs", "none"), CL.smooth = "fill", CL.BT = FALSE, lty.smooth = 1, col.smooth = "black", lwd.smooth = 1, nr.knots = 5, logistic.cal = FALSE, lty.log = 1, col.log = "black", lwd.log = 1, xlab = "Predicted probability", ylab = "Observed proportion", xlim = c(-0.02, 1), ylim = c(-0.15, 1), m, g, cuts, emax.lim = c(0, 1), legendloc = c(0.5, 0.27), statloc = c(0, 0.85), dostats = TRUE, cl.level = 0.95, method.ci = "pepe", roundstats = 2, riskdist = "predicted", size = 3, size.leg = 5, connect.group = FALSE, connect.smooth = TRUE, g.group = 4, evaluate = 100, nmin = 0, d0lab = "0", d1lab = "1", size.d01 = 5, dist.label = 0.01, line.bins = -0.05, dist.label2 = 0.04, cutoff, length.seg = 0.85, lty.ideal = 1, col.ideal = "red", lwd.ideal = 1, allowPerfectPredictions = FALSE, argzLoess = alist(degree = 2) )
p |
predicted probability |
y |
vector of binary outcomes |
logit |
predicted log odds of outcome. Specify either |
group |
a grouping variable. If numeric this variable is grouped into
|
weights |
an optional numeric vector of per-observation weights (usually frequencies),
used only if |
normwt |
set to |
pl |
|
smooth |
|
CL.smooth |
|
CL.BT |
|
lty.smooth |
the linetype of the flexible calibration curve. Default is |
col.smooth |
the color of the flexible calibration curve. Default is |
lwd.smooth |
the line width of the flexible calibration curve. Default is |
nr.knots |
specifies the number of knots for rcs-based calibration curve. The default as well as the highest allowed value is 5. In case the specified number of knots leads to estimation problems, then the number of knots is automatically reduced to the closest value without estimation problems. |
logistic.cal |
|
lty.log |
if |
col.log |
if |
lwd.log |
if |
xlab |
x-axis label, default is |
ylab |
y-axis label, default is |
xlim, ylim
|
numeric vectors of length 2, giving the x and y coordinates ranges (see |
m |
If grouped proportions are desired, average no. observations per group |
g |
If grouped proportions are desired, number of quantile groups |
cuts |
If grouped proportions are desired, actual cut points for constructing
intervals, e.g. |
emax.lim |
Vector containing lowest and highest predicted probability over which to
compute |
legendloc |
if |
statloc |
the "abc" of model performance (Steyerberg et al., 2011)-calibration intercept, calibration slope,
and c statistic-will be added to the plot, using statloc as the upper left corner of a box (default is c(0,.85).
You can specify a list or a vector. Use locator(1) for the mouse, |
dostats |
specifies whether and which performance measures are shown in the figure.
|
cl.level |
if |
method.ci |
method to calculate the confidence interval of the c-statistic. The argument is passed to |
roundstats |
specifies the number of decimals to which the statistics are rounded when shown in the plot. Default is 2. |
riskdist |
Use |
size, size.leg
|
controls the font size of the statistics ( |
connect.group |
Defaults to |
connect.smooth |
Defaults to |
g.group |
number of quantile groups to use when |
evaluate |
number of points at which to store the |
nmin |
applies when |
d0lab, d1lab
|
controls the labels for events and non-events (i.e. outcome y) for the histograms.
Defaults are |
size.d01 |
controls the size of the labels for events and non-events. Default is 5. |
dist.label |
controls the horizontal position of the labels for events and non-events. Default is 0.01. |
line.bins |
controls the horizontal (y-axis) position of the histograms. Default is -0.05. |
dist.label2 |
controls the vertical distance between the labels for events and non-events. Default is 0.03. |
cutoff |
puts an arrow at the specified risk cut-off(s). Default is none. |
length.seg |
controls the length of the histogram lines. Default is |
lty.ideal |
linetype of the ideal line. Default is |
col.ideal |
controls the color of the ideal line on the plot. Default is |
lwd.ideal |
controls the line width of the ideal line on the plot. Default is |
allowPerfectPredictions |
Logical, indicates whether perfect predictions (i.e. values of either 0 or 1) are allowed. Default is |
argzLoess |
a list with arguments passed to the |
When using the predicted probabilities of an uninformative model (i.e. equal probabilities for all observations), the model has no predictive value. Consequently, where applicable, the value of the performance measure corresponds to the worst possible theoretical value. For the ECI, for example, this equals 1 (Edlinger et al., 2022).
An object of type ggplotCalibrationCurve with the following slots:
call |
the matched call. |
ggPlot |
the ggplot object. |
stats |
a vector containing performance measures of calibration. |
cl.level |
the confidence level used. |
Calibration |
contains the calibration intercept and slope, together with their confidence intervals. |
Cindex |
the value of the c-statistic, together with its confidence interval. |
warningMessages |
if any, the warning messages that were printed while running the function. |
CalibrationCurves |
The coordinates for plotting the calibration curves. |
In order to make use (of the functions) of the package auRoc, the user needs to install JAGS. However, since our package only uses the
auc.nonpara.mw function which does not depend on the use of JAGS, we therefore copied the code and slightly adjusted it when
method="pepe".
Edlinger, M, van Smeden, M, Alber, HF, Wanitschek, M, Van Calster, B. (2022). Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption. Statistics in Medicine, 41( 8), pp. 1334– 1360
Qin, G., & Hotilovac, L. (2008). Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test. Statistical Methods in Medical Research, 17(2), pp. 207-21
Steyerberg, E.W., Van Calster, B., Pencina, M.J. (2011). Performance measures for prediction models and markers : evaluation of predictions and classifications. Revista Espanola de Cardiologia, 64(9), pp. 788-794
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina M., Steyerberg E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of Clinical Epidemiology, 74, pp. 167-176
Van Hoorde, K., Van Huffel, S., Timmerman, D., Bourne, T., Van Calster, B. (2015). A spline-based tool to assess and visualize the calibration of multiclass risk predictions. Journal of Biomedical Informatics, 54, pp. 283-93
# Load package library(CalibrationCurves) set.seed(1783) # Simulate training data X = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, X) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) y = rbinom(5e2, 1, p0true) Df = data.frame(y, X) # Fit logistic model FitLog = lrm(y ~ ., Df) # Simulate validation data Xval = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, Xval) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) yval = rbinom(5e2, 1, p0true) Pred = binomial()$linkinv(cbind(1, Xval) %*% coef(FitLog)) # Default calibration plot valProbggplot(Pred, yval) # Adding logistic calibration curves and other additional features valProbggplot(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 2, col.log = "red", lwd.log = 1.5) valProbggplot(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 9, col.log = "red", lwd.log = 1.5, col.ideal = colors()[10], lwd.ideal = 0.5)# Load package library(CalibrationCurves) set.seed(1783) # Simulate training data X = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, X) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) y = rbinom(5e2, 1, p0true) Df = data.frame(y, X) # Fit logistic model FitLog = lrm(y ~ ., Df) # Simulate validation data Xval = replicate(4, rnorm(5e2)) p0true = binomial()$linkinv(cbind(1, Xval) %*% c(0.1, 0.5, 1.2, -0.75, 0.8)) yval = rbinom(5e2, 1, p0true) Pred = binomial()$linkinv(cbind(1, Xval) %*% coef(FitLog)) # Default calibration plot valProbggplot(Pred, yval) # Adding logistic calibration curves and other additional features valProbggplot(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 2, col.log = "red", lwd.log = 1.5) valProbggplot(Pred, yval, CL.smooth = TRUE, logistic.cal = TRUE, lty.log = 9, col.log = "red", lwd.log = 1.5, col.ideal = colors()[10], lwd.ideal = 0.5)