Package 'iBST' reference manual

Title:	Improper Bagging Survival Tree
Description:	Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See 'Cyprien Mbogning' and 'Philippe Broet' (2016)<doi:10.1186/s12859-016-1090-x> for an overview about the methods implemented in this package.
Authors:	Cyprien Mbogning and Philippe Broet
Maintainer:	Cyprien Mbogning <[email protected]>
License:	GPL (>= 2)
Version:	1.2
Built:	2024-11-22 04:36:31 UTC
Source:	https://github.com/cran/iBST

improper Bagging Subsample Survival Tree

Description

Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible)using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importanceare computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See Cyprien Mbogning and Philippe Broet (2016)<doi:10.1186/s12859-016-1090-x> for an overview about the methods implemented in this package.

Details

Package:	iBST
Type:	Package
Version:	1.2
Date:	2023-01-12
License:	GPL(>=2.0)

Author(s)

Cyprien Mbogning and Philippe Broet

Maintainer: Cyprien Mbogning <[email protected]>

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

Duhaze Julianne et al. (2020). A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort. Frontiers in Immunology, 11.

Examples

## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 feat_samp = length(T.names)
 set.seed(5000)
 
 ## fit an improper survival tree
 burn.tree <- suppressWarnings(improper_tree(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg))
    
 plot(burn.tree)
 text(burn.tree, cex = .7, xpd = TRUE)
 
 ## fit an improper Bagging survival tree with the adjusted Logrank criterion
 burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "LR", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))
 
 ## fit an improper Bagging survival tree with the pseudo R2 criterion
 burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))

 ## Plot the variable importance scores
 par(mfrow=c(1,3))
barplot(burn.BagEssai1$IIS, 
   main = 'IIS', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'lightblue')
   
barplot(burn.BagEssai1$DIIS, 
   main = 'DIIS', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'grey') 
   
barplot(burn.BagEssai1$DEPTH, 
   main = 'MinDepth', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'purple')


 ## evaluation of the Bagging predictors 
pred0 <- suppressWarnings(Bagg_pred_Surv(burn, 
   Y.names, 
   P.names, 
   burn.BagEssai0, 
   args.parallel = list(numWorkers = 1), 
   OOB = TRUE)) 
 
 
 pred1 <- suppressWarnings(Bagg_pred_Surv(burn, 
   Y.names, 
   P.names, 
   burn.BagEssai1, 
   args.parallel = list(numWorkers = 1), 
   OOB = TRUE)) 
 
## End(Not run)
## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 feat_samp = length(T.names)
 set.seed(5000)
 
 ## fit an improper survival tree
 burn.tree <- suppressWarnings(improper_tree(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg))
    
 plot(burn.tree)
 text(burn.tree, cex = .7, xpd = TRUE)
 
 ## fit an improper Bagging survival tree with the adjusted Logrank criterion
 burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "LR", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))
 
 ## fit an improper Bagging survival tree with the pseudo R2 criterion
 burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))

 ## Plot the variable importance scores
 par(mfrow=c(1,3))
barplot(burn.BagEssai1$IIS, 
   main = 'IIS', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'lightblue')
   
barplot(burn.BagEssai1$DIIS, 
   main = 'DIIS', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'grey') 
   
barplot(burn.BagEssai1$DEPTH, 
   main = 'MinDepth', 
   horiz = TRUE, 
   las = 1,
   cex.names = .8, 
   col = 'purple')


 ## evaluation of the Bagging predictors 
pred0 <- suppressWarnings(Bagg_pred_Surv(burn, 
   Y.names, 
   P.names, 
   burn.BagEssai0, 
   args.parallel = list(numWorkers = 1), 
   OOB = TRUE)) 
 
 
 pred1 <- suppressWarnings(Bagg_pred_Surv(burn, 
   Y.names, 
   P.names, 
   burn.BagEssai1, 
   args.parallel = list(numWorkers = 1), 
   OOB = TRUE)) 
 
## End(Not run)

Bagging survival tree prediction

Description

Use the Bagging improper survival tree to predict on new features and to evaluate the predictor using Out Of Bag Integrated Brier Scores with either the Nelson Aalen estimator or the Breslow estimator. A permutation importance score is also computed using OOB observations.

Usage

Bagg_pred_Surv(xdata, Y.names, P.names, resBag, args.parallel = list(numWorkers = 1), 
               new_data = data.frame(), OOB = FALSE)
Bagg_pred_Surv(xdata, Y.names, P.names, resBag, args.parallel = list(numWorkers = 1), 
               new_data = data.frame(), OOB = FALSE)

Arguments

`xdata`	The learning data frame
`Y.names`	A vector of the names of the two variables of interest (the time-to-event is follow by the event indicator)
`P.names`	The names of independant variables acting on the non-susceptible population (the plateau)
`resBag`	The result of the `Bagg_Surv` function
`args.parallel`	a list containing the number of parallel computing arguments: The number of workers, the type of parallelization to achieve, ... see `mclapply` for further details.
`new_data`	An optional data frame to validate the bagging procedure (the test dataset)
`OOB`	A value of `TRUE` or `FALSE` with `TRUE` indicating the computation of the OOB error using the Integrated Brier Score and also the computation of the permutation importance score.

Value

`PREDNA`	A matrix with Nelson Aalen predictions on all individuals of the learning sample
`PREDBRE`	A matrix with Breslow predictions on all individuals of the learning sample
`tabhazNAa`	A list of matrix with Nelson Aalen prediction of each tree of the bagging sequence with the leaf node prediction in each column
`tabhazBRe`	A list of matrix with Breslow prediction of each tree of the bagging sequence with the leaf node prediction in each column
`OOB`	A value of `NULL` if `OOB` is `FALSE`. A list of twelve elements otherwise: `IBSKM`: The Kaplan-Meier estimation of the Integrated Brier Score; `IBSNAOOB`: The OOB error using the Nelson-Aalen estimator; `IBSBREOOB`: The OOB error using the Breslow estimator; `vimpoobpbpna`: The permutation variable importance using the Nelson-Aalen estimator; `vimpoobpbpbre`: The permutation variable importance using the Breslow estimator; `oobibspbpna`: The mean OOB error predictor by predictor using the Nelson-Aalen estimator; `oobibspbpbre`: The mean OOB error predictor by predictor using the Breslow estimator; `SURVNAOOB`: A matrix with the predicted OOB survival using the Nelson-Aalen estimator; `SURVBREOOB`: A matrix with the predicted OOB survival using the Breslow estimator; `BSTKM`: The vector of Brier scores using the KM estimator; `BSTNAOOB`: The vector of Brier scores using the NA estimator; `BSTBREOOB`: The vector of Brier scores using the BRE estimator.
`Timediff`	The execution time of the prediction procedure
`TEST`	A value of `NULL` if `new_data` is not available. A list of seven elements otherwise: `IBSNAKMnew`: The IBS using the NA estimator on the new dataset; `IBSBRKMnew`: The IBS using the BRE estimator on the new dataset; `IBSKMnew`: The IBS using the KM estimator on the new dataset; `SURVNAnew`: A matrix of predicted survival on the new dataset using the NA estimator; `SURVBREnew`: A matrix of predicted survival on the new dataset using the BRE estimator; `SURV_NAnew`: a vector of survival prediction on the testing dataset using the NA estimator; `SURV_BREnew`: a vector of survival prediction on the testing dataset using the BRE estimator.

Author(s)

Cyprien Mbogning and Philippe Broet

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

Examples

## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 feat_samp = length(T.names)
 set.seed(5000)
 
 burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn, 
        Y.names, 
        P.names, 
        T.names, 
        method = "LR", 
        args.rpart = myarg, 
        args.parallel = list(numWorkers = 1), 
        Bag = mybag, feat = feat_samp))
 
 burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn, 
        Y.names, 
        P.names, 
        T.names, 
        method = "R2", 
        args.rpart = myarg, 
        args.parallel = list(numWorkers = 1), 
        Bag = mybag, feat = feat_samp))

pred0 <- Bagg_pred_Surv(burn, 
    Y.names, 
    P.names, 
    burn.BagEssai0, 
    args.parallel = list(numWorkers = 1), 
    OOB = TRUE) 
 
 
 pred1 <- Bagg_pred_Surv(burn, 
    Y.names, 
    P.names, 
    burn.BagEssai1, 
    args.parallel = list(numWorkers = 1), 
    OOB = TRUE) 
 
## End(Not run)
## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 feat_samp = length(T.names)
 set.seed(5000)
 
 burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn, 
        Y.names, 
        P.names, 
        T.names, 
        method = "LR", 
        args.rpart = myarg, 
        args.parallel = list(numWorkers = 1), 
        Bag = mybag, feat = feat_samp))
 
 burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn, 
        Y.names, 
        P.names, 
        T.names, 
        method = "R2", 
        args.rpart = myarg, 
        args.parallel = list(numWorkers = 1), 
        Bag = mybag, feat = feat_samp))

pred0 <- Bagg_pred_Surv(burn, 
    Y.names, 
    P.names, 
    burn.BagEssai0, 
    args.parallel = list(numWorkers = 1), 
    OOB = TRUE) 
 
 
 pred1 <- Bagg_pred_Surv(burn, 
    Y.names, 
    P.names, 
    burn.BagEssai1, 
    args.parallel = list(numWorkers = 1), 
    OOB = TRUE) 
 
## End(Not run)

Bagging improper survival trees

Description

Bagging sunbsampling procedure to aggregate several improper trees using either the pseudo-R2 procedure or the adjusted Logrank procedure. Several scores for variables importance are computed.

Usage

Bagg_Surv(xdata, 
     Y.names, 
     P.names, 
     T.names, 
     method = "R2", 
     args.rpart, 
     args.parallel = list(numWorkers = 1), 
     Bag = 100, feat = 5)
Bagg_Surv(xdata, 
     Y.names, 
     P.names, 
     T.names, 
     method = "R2", 
     args.rpart, 
     args.parallel = list(numWorkers = 1), 
     Bag = 100, feat = 5)

Arguments

`xdata`	The learning data frame
`Y.names`	A vector of the names of the two variables of interest (the time-to-event is follow by the event indicator)
`P.names`	The names of independant variables acting on the non-susceptible population (the plateau)
`T.names`	The names of independant variables acting on the survival of the susceptible population
`method`	The choosen method (either `"LR"` for the Logrank or `"R2"` for the proposed pseudo-R2 criterion)
`args.rpart`	The improper survival tree parameters: a list of options that control details of the rpart algorithm. `minbucket`: the minimum number of observations in any terminal <leaf> node; `cp`: complexity parameter (Any split that does not decrease the overall lack of fit by a factor of cp is not attempted); `maxdepth`: the maximum depth of any node of the final tree, with the root node counted as depth 0. ... See `rpart.control` for further details
`args.parallel`	a list containing the number of parallel computing arguments: The number of workers, the type of parallelization to achieve, ... see `mclapply` for further details.
`Bag`	The number of Bagging samples to consider
`feat`	The size of features subsample. A full baging when feat is the total number of features.

Details

For the Bagging procedure, it is mendatory to set maxcompete = 0 and maxsurrogate = 0 within the args.rpart arguments. This will ensured the correct calculation of the importance of variables and also a better computation time.

Value

A list of ten elements

`MaxTreeList`	The list of improper survival trees computed during the bagging procedure
`IIS`	The Index Importance Score
`DIIS`	The Depth Index Importance Score
`DEPTH`	The minimum depth importance Score
`IND_OOB`	A list of length `Bag` containing the Out Of Bag (OOB) individuals for improper survival tree model
`IIND_SAMP`	The final list of length `Bag` of sample individuals used for each improper survival tree
`IIND_SAMP`	The initial list of sample individuals used for each improper survival tree at teh beginning
`Bag`	The number of bagging samples retained at the end of the procedure after removing the trees without leaves
`indrpart`	a vector of `TRUE` or `FALSE` with the value FALSE when the corresponding tree is removed from the final bagged predictor
`Timediff`	The ellapsed time of the Bagging procedure

Note

This version of the code allows for the moment only one variable to have an impact on the cured population.The next version will allow more than one variable.

Author(s)

Cyprien Mbogning and Philippe Broet

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

Examples

## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 feat_samp = length(T.names)
 set.seed(5000)
 
 burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "LR", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))
 
 burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))


## End(Not run)
## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 feat_samp = length(T.names)
 set.seed(5000)
 
 burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "LR", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))
 
 burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg, 
    args.parallel = list(numWorkers = 1), 
    Bag = mybag, feat = feat_samp))


## End(Not run)

burn dataset

Description

The burn data frame has 154 rows and 17 columns.

Usage

data(burn)data(burn)

Format

A data frame with 154 observations on the following 17 variables.

Obs: Observation number
Z1: Treatment: 0-routine bathing 1-Body cleansing
Z2: Gender (0=male 1=female)
Z3: Race: 0=nonwhite 1=white
Z4: Percentage of total surface area burned
Z5: Burn site indicator: head 1=yes, 0=no
Z6: Burn site indicator: buttock 1=yes, 0=no
Z7: Burn site indicator: trunk 1=yes, 0=no
Z8: Burn site indicator: upper leg 1=yes, 0=no
Z9: Burn site indicator: lower leg 1=yes, 0=no
Z10: Burn site indicator: respiratory tract 1=yes, 0=no
Z11: Type of burn: 1=chemical, 2=scald, 3=electric, 4=flame
T1: Time to excision or on study time
D1: Excision indicator: 1=yes 0=no
T2: Time to prophylactic antibiotic treatment or on study time
D2: Prophylactic antibiotic treatment: 1=yes 0=no
T3: Time to straphylocous aureaus infection or on study time
D3: Straphylocous aureaus infection: 1=yes 0=no

Source

Klein and Moeschberger (1997) Survival Analysis Techniques for Censored and truncated data, Springer.

Ichida et al. Stat. Med. 12 (1993): 301-310.

Examples

data(burn)
## maybe str(burn) ;
data(burn)
## maybe str(burn) ;

imprper survival tree

Description

Fit an improper survival tree for the mixed population (susceptible and nonsusceptible) using either the proposed pseudo R2 criterion or an adjusted Logrank criterion

Usage

improper_tree(xdata, 
     Y.names, 
     P.names, 
     T.names, 
     method = "R2", 
     args.rpart)
improper_tree(xdata, 
     Y.names, 
     P.names, 
     T.names, 
     method = "R2", 
     args.rpart)

Arguments

`xdata`	The learning data frame
`Y.names`	A vector of the names of the two variables of interest (the time-to-event is follow by the event indicator)
`P.names`	The names of independant variables acting on the non-susceptible population (the plateau)
`T.names`	The names of independant variables acting on the survival of the susceptible population
`method`	The choosen method (either `"LR"` for the Logrank or `"R2"` for the proposed pseudo-R2 criterion)
`args.rpart`	The improper survival tree parameters: a list of options that control details of the rpart algorithm. `minbucket`: the minimum number of observations in any terminal <leaf> node; `cp`: complexity parameter (Any split that does not decrease the overall lack of fit by a factor of cp is not attempted); `maxdepth`: the maximum depth of any node of the final tree, with the root node counted as depth 0. ... See `rpart.control` for further details

Value

An unprunned improper survival tree

Author(s)

Cyprien Mbogning and Philippe Broet

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

Examples

## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 3)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 burn.tree <- suppressWarnings(improper_tree(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg))
    
 plot(burn.tree)
 text(burn.tree, cex = .7, xpd = TRUE)
 
## End(Not run)
## Not run: 
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 3)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 burn.tree <- suppressWarnings(improper_tree(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg))
    
 plot(burn.tree)
 text(burn.tree, cex = .7, xpd = TRUE)
 
## End(Not run)

permutation variable selection

Description

Variable selection using the permutation test on several scores of importance: IIS, DIIS and DEPTH.

Usage

permute_select_surv(xdata, 
    Y.names, 
    P.names, 
    T.names, 
    importance = "IIS", 
    method = "R2",
    Bag, 
    args.rpart, 
    args.parallel = list(numWorkers = 1), 
    nperm = 50)
permute_select_surv(xdata, 
    Y.names, 
    P.names, 
    T.names, 
    importance = "IIS", 
    method = "R2",
    Bag, 
    args.rpart, 
    args.parallel = list(numWorkers = 1), 
    nperm = 50)

Arguments

`xdata`	The learning data frame
`Y.names`	A vector of the names of the two variables of interest (the time-to-event is follow by the event indicator)
`P.names`	The names of independant variables acting on the non-susceptible population (the plateau)
`T.names`	The names of independant variables acting on the survival of the susceptible population
`importance`	The importance score to consider: either `IIS`, `DIIS` or `DEPTH`
`method`	The splitting method: either `"R2"` for the proposed pseudo-R2 criterion or `"LR"` for the adjusted Logrank criterion
`Bag`	The number of Bagging samples to consider
`args.rpart`	The improper survival tree parameters: a list of options that control details of the rpart algorithm. `minbucket`: the minimum number of observations in any terminal <leaf> node; `cp`: complexity parameter (Any split that does not decrease the overall lack of fit by a factor of cp is not attempted); `maxdepth`: the maximum depth of any node of the final tree, with the root node counted as depth 0. ... See `rpart.control` for further details
`args.parallel`	a list containing the number of parallel computing arguments: The number of workers, the type of parallelization to achieve, ... see `mclapply` for further details.
`nperm`	The number of permutation samples to consider for the permutation test

Details

Testing weither the importance score is null or not.

Value

A list of five elements:

`pvalperm1`	The permutation test P-values ranking in decreasing order
`pvalperm2`	The permutation test P-values ranking in decreasing order considering an approximate gaussian distribution under the null hypothesis
`pvalKS`	The Kolmogorov-Smirnov P-values of the comparisons between the observed importance under the null hypothesis and a theoretical gaussian distribution
`IMPH1`	The observed importance score
`PERMH0`	A matrix with the importance scores for each permutation sample in each column

Author(s)

Cyprien Mbogning and Philippe Broet

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

Examples

## Not run: 
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 set.seed(5000)
 
 data(burn)
 resperm0 <- suppressWarnings(permute_select_surv(xdata = burn, 
       Y.names, 
       P.names, 
       T.names, 
       method = "LR", 
       Bag = mybag, 
       args.rpart = myarg, 
       args.parallel = list(numWorkers = 1), 
       nperm = 150))
 
## End(Not run)
## Not run: 
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 set.seed(5000)
 
 data(burn)
 resperm0 <- suppressWarnings(permute_select_surv(xdata = burn, 
       Y.names, 
       P.names, 
       T.names, 
       method = "LR", 
       Bag = mybag, 
       args.rpart = myarg, 
       args.parallel = list(numWorkers = 1), 
       nperm = 150))
 
## End(Not run)

Pseudo R2 criterion

Description

Pseudo R2 criterion for a mixture of population (susceptible and nonsusceptible populations)

Usage

PseudoR2.Cure(ygene, ydelai, yetat, strate, ordered = FALSE)
PseudoR2.Cure(ygene, ydelai, yetat, strate, ordered = FALSE)

Arguments

`ygene`	The main variable of interest
`ydelai`	The right censored delay until the event
`yetat`	The censoring indicator
`strate`	The varaiables acting on the nonsusceptible or cured population
`ordered`	A value of `TRUE` or `FALSE` indicating weither or not the times to event are ordered

Value

A pseudo R2 value lying between 0 and 1.

Author(s)

Cyprien Mbogning and Philippe Broet

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

Examples

data(burn)
PseudoR2.Cure(ygene = burn$Z3, 
   ydelai = burn$T3, 
   yetat = burn$D3, 
   strate = burn$Z2)
   
PseudoR2.Cure(ygene = burn$Z2, 
   ydelai = burn$T3, 
   yetat = burn$D3, 
   strate = burn$Z2)
data(burn)
PseudoR2.Cure(ygene = burn$Z3, 
   ydelai = burn$T3, 
   yetat = burn$D3, 
   strate = burn$Z2)
   
PseudoR2.Cure(ygene = burn$Z2, 
   ydelai = burn$T3, 
   yetat = burn$D3, 
   strate = burn$Z2)

Simple function using Rcpp

Description

Simple function using Rcpp

Usage

rcpp_hello_world()	
rcpp_hello_world()

Examples

## Not run: 
rcpp_hello_world()

## End(Not run)
## Not run: 
rcpp_hello_world()

## End(Not run)

From a tree to indicators (or dummy variables)

Description

Coerces a given tree structure inheriting from rpart to binary covariates.

Usage

tree2indicators(fit)
tree2indicators(fit)

Arguments

fit

a tree structure inheriting to the rpart method

Value

a list of indicators defining the leaf nodes of the fitted tree from left to right

Author(s)

Cyprien Mbogning

Examples

fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
tree2indicators(fit)
fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
tree2indicators(fit)

Package 'iBST'

Help Index

improper Bagging Subsample Survival Tree

Description

Details

Author(s)

References

See Also

Examples

Bagging survival tree prediction

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Bagging improper survival trees

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

burn dataset

Description

Usage

Format

Source

Examples

imprper survival tree

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

permutation variable selection

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Pseudo R2 criterion

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Simple function using Rcpp

Description

Usage

Examples

From a tree to indicators (or dummy variables)

Description

Usage

Arguments

Value

Author(s)

Examples