| Title: | Optimal Categorisation of Continuous Variables in Prediction Models |
|---|---|
| Description: | Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio, MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf>. |
| Authors: | Irantzu Barrio [aut, cre], Maria Xose Rodriguez-Alvarez [aut], Inmaculada Arostegui [ctb] |
| Maintainer: | Irantzu Barrio <[email protected]> |
| License: | GPL |
| Version: | 1.3 |
| Built: | 2024-11-13 04:59:26 UTC |
| Source: | https://github.com/cran/CatPredi |
Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. The categorisation can be done either in a univariate or a multivariate setting.
Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui
Maintainer: Irantzu Barrio <[email protected]>
I Barrio, I Arostegui, M.X Rodriguez-Alvarez and J.M Quintana (2015). A new approach to categorising continuous variables in prediction models: proposal and validation. Statistical Methods in Medical Research, in press.
I Barrio, M.X Rodriguez-Alvarez, L Meira-Machado, C Esteban and I Arostegui (2017). Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. SORT, 41:73-92
Returns an object with the optimal cut points to categorise a continuous predictor variable in a logistic regression model
catpredi(formula, cat.var, cat.points = 1, data, method = c("addfor", "genetic"), range = NULL, correct.AUC = TRUE, control = controlcatpredi(), ...)catpredi(formula, cat.var, cat.points = 1, data, method = c("addfor", "genetic"), range = NULL, correct.AUC = TRUE, control = controlcatpredi(), ...)
formula |
An object of class |
cat.var |
Name of the continuous variable to categorise. |
cat.points |
Number of cut points to look for. |
data |
Data frame containing all needed variables. |
method |
The algorithm selected to search for the optimal cut points. "addfor" if the AddFor algorithm is choosen and "genetic" otherwise. |
range |
The range of the continuous variable in which to look for the cut points. By default NULL, i.e, all the range. |
correct.AUC |
A logical value. If TRUE the bias corrected AUC is estimated. |
control |
Output of the controlcatpredi() function. |
... |
Further arguments for passing on to the function |
Returns an object of class "catpredi" with the following components:
call |
the matched call. |
method |
the algorithm selected in the call. |
formula |
an object of class |
cat.var |
name of the continuous variable to categorise. |
data |
the data frame with the variables used in the call. |
correct.AUC |
The logical value used in the call. |
results |
a list with the estimated cut points, AUC and bias corrected AUC. |
control |
the control parameters used in the call. |
For each of the methods used in the call, a list with the following components is obtained:
"cutpoints" |
Estimated optimal cut points. |
"AUC" |
Estimated AUC. |
"AUC.cor" |
Estimated bias corrected AUC. |
Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui
I Barrio, I Arostegui, M.X Rodriguez-Alvarez and J.M Quintana (2015). A new approach to categorising continuous variables in prediction models: proposal and validation. Statistical Methods in Medical Research (in press).
S.N Wood (2006). Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.
See Also as controlcatpredi, comp.cutpoints, plot.catpredi,
summary.catpredi.
library(CatPredi) set.seed(127) #Simulate data n = 200 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) #Covariate zh <- rnorm(n, mean=1.5, sd=1) zd <- rnorm(n, mean=1, sd=1) z <- c(zh, zd) # Data frame df <- data.frame(y = y, x = x, z = z) # Select optimal cut points using the AddFor algorithm res.addfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 3, data = df, method = "addfor", range=NULL, correct.AUC=FALSE)library(CatPredi) set.seed(127) #Simulate data n = 200 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) #Covariate zh <- rnorm(n, mean=1.5, sd=1) zd <- rnorm(n, mean=1, sd=1) z <- c(zh, zd) # Data frame df <- data.frame(y = y, x = x, z = z) # Select optimal cut points using the AddFor algorithm res.addfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 3, data = df, method = "addfor", range=NULL, correct.AUC=FALSE)
Returns an object with the optimal cut points to categorise a continuous predictor variable in a Cox proportional hazards regression model
catpredi.survival(formula, cat.var, cat.points = 1, data, method = c("addfor", "genetic"), conc.index = c("cindex", "cpe"), range = NULL, correct.index = TRUE, control = controlcatpredi.survival(), ...)catpredi.survival(formula, cat.var, cat.points = 1, data, method = c("addfor", "genetic"), conc.index = c("cindex", "cpe"), range = NULL, correct.index = TRUE, control = controlcatpredi.survival(), ...)
formula |
An object of class |
cat.var |
Name of the continuous variable to categorise. |
cat.points |
Number of cut points to look for. |
data |
Data frame containing all needed variables. |
method |
The algorithm selected to search for the optimal cut points. "addfor" if the AddFor algorithm is choosen and "genetic" otherwise. |
conc.index |
The concordance probability estimator selected for maximisation purposes. "cindex" if the c-index concordance probability is choosen and "cpe" otherwise.
The c-index and CPE are estimated using the |
range |
The range of the continuous variable in which to look for the cut points. By default NULL, i.e, all the range. |
correct.index |
A logical value. If TRUE the bias corrected concordance probability is estimated. |
control |
Output of the controlcatpredi.survival() function. |
... |
Further arguments for passing on to the function |
Returns an object of class "catpredi.survival" with the following components:
call |
the matched call. |
method |
the algorithm selected in the call. |
formula |
an object of class |
cat.var |
name of the continuous variable to categorise. |
data |
the data frame with the variables used in the call. |
correct.index |
The logical value used in the call. |
results |
a list with the estimated cut points, concordance probability and bias corrected concordance probability. |
control |
the control parameters used in the call. |
When the c-index concordance probability is choosen, a list with the following components is obtained for each of the methods used in the call:
"cutpoints" |
Estimated optimal cut points. |
"Cindex" |
Estimated c-index. |
"Cindex.cor" |
Estimated bias corrected c-index. |
When the CPE concordance probability is choosen, a list with the following components is obtained for each of the methods used in the call:
"cutpoints" |
Estimated optimal cut points. |
"CPE" |
Estimated CPE. |
"CPE.cor" |
Estimated bias corrected CPE. |
Irantzu Barrio and Maria Xose Rodriguez-Alvarez
I Barrio, M.X Rodriguez-Alvarez, L Meira-Machado, C Esteban and I Arostegui (2017). Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. SORT, 41:73-92
M Gonen and G Heller (2005). Concordance probability and discriminatory power in proportional hazards regression. Biometrika, 92:965-970.
F Harrell (2001). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.
See Also controlcatpredi.survival, comp.cutpoints.survival, plot.catpredi.survival, catpredi.
library(CatPredi) library(survival) set.seed(123) #Simulate data n = 500 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select optimal cut points using the AddFor algorithm res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = FALSE)library(CatPredi) library(survival) set.seed(123) #Simulate data n = 500 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select optimal cut points using the AddFor algorithm res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = FALSE)
Compares two objects of class "catpredi".
comp.cutpoints(obj1, obj2, V = 100)comp.cutpoints(obj1, obj2, V = 100)
obj1 |
an object inheriting from class "catpredi" for k number of cut points |
obj2 |
an object inheriting from class "catpredi" for k+1 number of cut points |
V |
Number of bootstrap resamples. By default V=100 |
This function returns an object of class "comp.cutpoints" with the following components:
AUC.cor.diff |
the difference of the bias corrected AUCs for the two categorical variables. |
icb.auc.diff |
bootstrap based confidence interval for the bias corrected AUC difference. |
Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui
I Barrio, I Arostegui, M.X Rodriguez-Alvarez and J.M Quintana (2015). A new approach to categorising continuous variables in prediction models: proposal and validation. Statistical Methods in Medical Research (in press).
See Also as catpredi.
library(CatPredi) set.seed(127) #Simulate data n = 100 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) # Data frame df <- data.frame(y = y, x = x) # Select 2 optimal cut points using the AddFor algorithm. Correct the AUC res.addfor.k2 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 2, data = df, method = "addfor", range=NULL, correct.AUC=TRUE, control=controlcatpredi(addfor.g=100)) # Select 3 optimal cut points using the AddFor algorithm. Correct the AUC res.addfor.k3 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, data = df, method = "addfor", range=NULL, correct.AUC=TRUE, control=controlcatpredi(addfor.g=100)) # Select optimal number of cut points comp <- comp.cutpoints(res.addfor.k2, res.addfor.k3, V = 100)library(CatPredi) set.seed(127) #Simulate data n = 100 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) # Data frame df <- data.frame(y = y, x = x) # Select 2 optimal cut points using the AddFor algorithm. Correct the AUC res.addfor.k2 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 2, data = df, method = "addfor", range=NULL, correct.AUC=TRUE, control=controlcatpredi(addfor.g=100)) # Select 3 optimal cut points using the AddFor algorithm. Correct the AUC res.addfor.k3 <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, data = df, method = "addfor", range=NULL, correct.AUC=TRUE, control=controlcatpredi(addfor.g=100)) # Select optimal number of cut points comp <- comp.cutpoints(res.addfor.k2, res.addfor.k3, V = 100)
Compares two objects of class "catpredi.survival"
comp.cutpoints.survival(obj1, obj2, V = 100)comp.cutpoints.survival(obj1, obj2, V = 100)
obj1 |
an object inheriting from class "catpredi.survival" for k number of cut points |
obj2 |
an object inheriting from class "catpredi.survival" for k+1 number of cut points |
V |
Number of bootstrap resamples. By default V=100 |
This function returns an object of class "comp.cutpoints.survival" with the following components:
CI.cor.diff |
the difference of the bias corrected concordance probability for the two categorical variables. |
icb.CI.diff |
bootstrap based confidence interval for the bias corrected concordance probability difference. |
Irantzu Barrio and Maria Xose Rodriguez-Alvarez
I Barrio, M.X Rodriguez-Alvarez, L Meira-Machado, C Esteban and I Arostegui (2017). Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. SORT, 41:73-92
See Also as catpredi.survival.
library(CatPredi) library(survival) set.seed(123) #Simulate data n = 300 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select 2 optimal cut points using the AddFor algorithm. Correct the c-index res.k2 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE) # Select 3 optimal cut points using the AddFor algorithm. Correct the c-index res.k3 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 3, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE) # Select optimal number of cut points comp <- comp.cutpoints.survival(res.k2, res.k3, V = 100)library(CatPredi) library(survival) set.seed(123) #Simulate data n = 300 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select 2 optimal cut points using the AddFor algorithm. Correct the c-index res.k2 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE) # Select 3 optimal cut points using the AddFor algorithm. Correct the c-index res.k3 <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 3, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = TRUE) # Select optimal number of cut points comp <- comp.cutpoints.survival(res.k2, res.k3, V = 100)
Function used to set several parameters to control the selection of the optimal cut points in a logistic regression model
controlcatpredi(min.p.cat = 1, addfor.g = 100, B = 50, b.method = c("ncoutcome", "coutcome"), print.gen = 0)controlcatpredi(min.p.cat = 1, addfor.g = 100, B = 50, b.method = c("ncoutcome", "coutcome"), print.gen = 0)
min.p.cat |
Set the minimun number of individuals in each category |
addfor.g |
Grid size for the AddFor algorithm |
B |
Number of bootstrap replicates for the AUC bias correction procedure |
b.method |
Allows to specify whether the bootstrap resampling should be done considering or not the outcome variable. The option "ncoutcome" indicates that the data is resampled without taking into account the response variable, while "coutcome" indicates that the data is resampled in regard to the response variable |
print.gen |
corresponds to the argument print.level of the |
A list with components for each of the possible arguments.
Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui
Mebane Jr, W. R., & Sekhon, J. S. (2011). Genetic optimization using derivatives: the rgenoud package for R. Journal of Statistical Software 4211, 1-26.
See Also as catpredi.
Function used to set several parameters to control the selection of the optimal cut points in a Cox proportional hazards regression model
controlcatpredi.survival(min.p.cat = 5, addfor.g = 100, B = 50, b.method = c("ncoutcome", "coutcome"), print.gen = 0)controlcatpredi.survival(min.p.cat = 5, addfor.g = 100, B = 50, b.method = c("ncoutcome", "coutcome"), print.gen = 0)
min.p.cat |
Set the minimun number of individuals in each category. |
addfor.g |
Grid size for the AddFor algorithm. |
B |
Number of bootstrap replicates for the AUC bias correction procedure |
b.method |
Allows to specify whether the bootstrap resampling should be done considering or not the outcome variable. The option "ncoutcome" indicates that the data is resampled without taking into account the response variable, while "coutcome" indicates that the data is resampled in regard to the response variable. |
print.gen |
Corresponds to the argument print.level of the |
A list with components for each of the possible arguments.
Irantzu Barrio and Maria Xose Rodriguez-Alvarez
Mebane Jr, W. R., & Sekhon, J. S. (2011). Genetic optimization using derivatives: the rgenoud package for R. Journal of Statistical Software 4211, 1-26.
See Also as catpredi.survival.
Plots the relationship between the predictor variable is aimed to categorise and the response variable based on a GAM model. Additionally, the optimal cut points obtained with the catpredi() function are drawn on the graph.
## S3 method for class 'catpredi' plot(x, ...)## S3 method for class 'catpredi' plot(x, ...)
x |
An object of type catpredi. |
... |
Additional arguments to be passed on to other functions. Not yet implemented. |
This function returns the plot of the relationship between the predictor variable and the outcome.
Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui
I Barrio, I Arostegui, M.X Rodriguez-Alvarez and J.M Quintana (2015). A new approach to categorising continuous variables in prediction models: proposal and validation. Statistical Methods in Medical Research (in press).
See Also as catpredi.
library(CatPredi) set.seed(127) #Simulate data n = 100 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) # Data frame df <- data.frame(y = y, x = x) # Select optimal cut points using the AddFor algorithm res.addfor <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, data = df, method = "addfor", range = NULL, correct.AUC = FALSE) # Plot plot(res.addfor)library(CatPredi) set.seed(127) #Simulate data n = 100 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) # Data frame df <- data.frame(y = y, x = x) # Select optimal cut points using the AddFor algorithm res.addfor <- catpredi(formula = y ~ 1, cat.var = "x", cat.points = 3, data = df, method = "addfor", range = NULL, correct.AUC = FALSE) # Plot plot(res.addfor)
Plots the functional form of the predictor variable we want to categorise. Additionally, the optimal cut points obtained with the catpredi.survival() function are drawn on the graph.
## S3 method for class 'catpredi.survival' plot(x, ...)## S3 method for class 'catpredi.survival' plot(x, ...)
x |
An object of type catpredi.survival . |
... |
Additional arguments to be passed on to other functions. Not yet implemented. |
This function returns the plot of the relationship between the predictor variable and the outcome.
Irantzu Barrio and Maria Xose Rodriguez-Alvarez
I Barrio, M.X Rodriguez-Alvarez, L Meira-Machado, C Esteban and I Arostegui (2017). Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. SORT, 41:73-92
See Also as catpredi.survival.
library(CatPredi) library(survival) set.seed(123) #Simulate data n = 500 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select optimal cut points using the AddFor algorithm res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = FALSE) # Plot plot(res)library(CatPredi) library(survival) set.seed(123) #Simulate data n = 500 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select optimal cut points using the AddFor algorithm res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = FALSE) # Plot plot(res)
Produces a summary of a catpredi object. The following are printed: the call to the catpredi() function; the estimated optimal cut points obtained with the method selected and the estimated AUC and bias corrected AUC (if the argument correct.AUC is TRUE) for the categorised variable.
## S3 method for class 'catpredi' summary(object, digits = 4, ...)## S3 method for class 'catpredi' summary(object, digits = 4, ...)
object |
an object of class catpredi as produced by catpredi() |
digits |
. |
... |
further arguments passed to or from other methods. |
Returns an object of class "summary.catpredi" with the same components as the catpredi
function (see catpredi). plus:
fit.gam |
fitted model according to the model specified in the call, based on the function |
Irantzu Barrio, Maria Xose Rodriguez-Alvarez and Inmaculada Arostegui
I Barrio, I Arostegui, M.X Rodriguez-Alvarez and J.M Quintana (2015). A new approach to categorising continuous variables in prediction models: proposal and validation. Statistical Methods in Medical Research (in press).
See Also as catpredi.
library(CatPredi) set.seed(127) #Simulate data n = 200 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) #Covariate zh <- rnorm(n, mean=1.5, sd=1) zd <- rnorm(n, mean=1, sd=1) z <- c(zh, zd) # Data frame df <- data.frame(y = y, x = x, z = z) # Select optimal cut points using the AddFor algorithm res.addfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 2, data = df, method = "addfor", range=NULL, correct.AUC=FALSE) # Summary summary(res.addfor)library(CatPredi) set.seed(127) #Simulate data n = 200 #Predictor variable xh <- rnorm(n, mean = 0, sd = 1) xd <- rnorm(n, mean = 1.5, sd = 1) x <- c(xh, xd) #Response y <- c(rep(0,n), rep(1,n)) #Covariate zh <- rnorm(n, mean=1.5, sd=1) zd <- rnorm(n, mean=1, sd=1) z <- c(zh, zd) # Data frame df <- data.frame(y = y, x = x, z = z) # Select optimal cut points using the AddFor algorithm res.addfor <- catpredi(formula = y ~ z, cat.var = "x", cat.points = 2, data = df, method = "addfor", range=NULL, correct.AUC=FALSE) # Summary summary(res.addfor)
Produces a summary of a "catpredi.survival" object. The following are printed: the call to the catpredi.survival() function; the estimated optimal cut points obtained with the method and concordance probability estimator selected and the estimated and bias corrected concordance probability for the categorised variable (whenever the argument correct.index is set to TRUE) .
## S3 method for class 'catpredi.survival' summary(object, digits = 4, ...)## S3 method for class 'catpredi.survival' summary(object, digits = 4, ...)
object |
an object of class "catpredi.survival" as produced by catpredi.survival() |
digits |
. |
... |
further arguments passed to or from other methods. |
Returns an object of class "summary.catpredi.survival" with the same components as the catpredi.survival
function (see catpredi.survival).
Irantzu Barrio and Maria Xose Rodriguez-Alvarez
I Barrio, M.X Rodriguez-Alvarez, L Meira-Machado, C Esteban and I Arostegui (2017). Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. SORT, 41:73-92
See Also as catpredi.survival.
library(CatPredi) library(survival) set.seed(123) #Simulate data n = 500 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select optimal cut points using the AddFor algorithm res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = FALSE) # Summary summary(res)library(CatPredi) library(survival) set.seed(123) #Simulate data n = 500 tauc = 1 X <- rnorm(n=n, mean=0, sd=2) SurvT <- exp(2*X + rweibull(n = n, shape=1, scale = 1)) + rnorm(n, mean=0, sd=0.25) # Censoring time CensTime <- runif(n=n, min=0, max=tauc) # Status SurvS <- as.numeric(SurvT <= CensTime) # Data frame dat <- data.frame(X = X, SurvT = pmin(SurvT, CensTime), SurvS = SurvS) # Select optimal cut points using the AddFor algorithm res <- catpredi.survival (formula= Surv(SurvT,SurvS)~1, cat.var="X", cat.points = 2, data = dat, method = "addfor", conc.index = "cindex", range = NULL, correct.index = FALSE) # Summary summary(res)