| Type: | Package |
| Title: | Patient Subgroup Identification for Clinical Drug Development |
| Version: | 0.12 |
| Description: | Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Imports: | glmnet, MASS, rpart, survival, Matrix, ggplot2 |
| NeedsCompilation: | no |
| Packaged: | 2024-02-02 15:31:31 UTC; huangxx13 |
| Author: | Xin Huang [aut, cre, cph], Yan Sun [aut], Saptarshi Chatterjee [aut], Paul Trow [aut] |
| Maintainer: | Xin Huang <xin.huang@abbvie.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-03 12:20:10 UTC |
SubgrpID
Description
Exploratory Subgroup Identification main function
Usage
SubgrpID(
data.train,
data.test = NULL,
yvar,
censorvar = NULL,
trtvar = NULL,
trtref = NULL,
xvars,
type = "c",
n.boot = 25,
des.res = "larger",
min.sigp.prcnt = 0.2,
pre.filter = NULL,
filter.method = NULL,
k.fold = 5,
cv.iter = 20,
max.iter = 500,
mc.iter = 20,
method = c("Seq.BT"),
do.cv = FALSE,
out.file = NULL,
file.path = "",
plots = FALSE
)
Arguments
data.train |
data frame for training dataset |
data.test |
data frame for testing dataset, default = NULL |
yvar |
variable (column) name for response variable |
censorvar |
variable name for censoring (1: event; 0: censor), default = NULL |
trtvar |
variable name for treatment variable, default = NULL (prognostic signature) |
trtref |
coding (in the column of trtvar) for treatment arm |
xvars |
vector of variable names for predictors (covariates) |
type |
type of response variable: "c" continuous; "s" survival; "b" binary |
n.boot |
number of bootstrap for batting procedure, or the variable selection procedure for PRIM; for PRIM, when n.boot=0, bootstrapping for variable selection is not conducted |
des.res |
the desired response. "larger": prefer larger response. "smaller": prefer smaller response |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff |
pre.filter |
NULL (default), no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected |
filter.method |
NULL (default), no prefiltering; "univariate", univaraite filtering; "glmnet", glmnet filtering; "unicart", univariate rpart filtering for prognostic case |
k.fold |
cross-validation folds |
cv.iter |
Algotithm terminates after cv.iter successful iterations of cross-validation, or after max.iter total iterations, whichever occurs first |
max.iter |
total iterations, whichever occurs first |
mc.iter |
number of iterations for the Monte Carlo procedure to get a stable "best number of predictors" |
method |
current version only supports sequential-BATTing ("Seq.BT") for subgroup identification |
do.cv |
whether to perform cross validation for performance evaluation. TRUE or FALSE (Default) |
out.file |
Name of output result files excluding method name. If NULL no output file would be saved |
file.path |
default: current working directory. When specifying a dir, use "/" at the end. e.g. "TEMP/" |
plots |
default: FALSE. whether to save plots |
Details
Function for SubgrpID
Value
A list with SubgrpID output
- res
list of all results from the algorithm
- train.stat
list of subgroup statistics on training dataset
- test.stat
list of subgroup statistics on testing dataset
- cv.res
list of all results from cross-validation on training dataset
- train.plot
interaction plot for training dataset
- test.plot
interaction plot for testing dataset
Examples
# no run
n <- 40
k <- 5
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
yvar="y.binary"
xvars=paste("x", c(1:k), sep="")
trtvar="treatment"
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
set.seed(888)
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
sig2=sig2, y.sig2=y.sig2, rho=rho,
rhos.bt.real=rhos.bt.real, a.constent=a.constent)
TestData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
sig2=sig2, y.sig2=y.sig2, rho=rho,
rhos.bt.real=rhos.bt.real, a.constent=a.constent)
subgrp <- SubgrpID(data.train=ObsData$data,
data.test=TestData$data,
yvar=yvar,
trtvar=trtvar,
trtref="1",
xvars=xvars,
type="b",
n.boot=5, # suggest n.boot > 25, depends on sample size
des.res = "larger",
# do.cv = TRUE,
# cv.iter = 2, # uncomment to run CV
method="Seq.BT")
subgrp$res
subgrp$train.stat
subgrp$test.stat
subgrp$train.plot
subgrp$test.plot
#subgrp$cv.res$stats.summary #CV estimates of all results
balanced.folds
Description
Create balanced folds for cross-validation.
Usage
balanced.folds(y, nfolds = min(min(table(y)), 10))
Arguments
y |
the response vector |
nfolds |
number of folds |
Details
Create balanced folds for cross-validation.
Value
This function returns balanced folds
batting.pred
Description
Main predictive BATTing function
Usage
batting.pred(
dataset,
ids,
yvar,
censorvar,
trtvar,
type,
class.wt,
xvar,
n.boot,
des.res,
min.sigp.prcnt
)
Arguments
dataset |
input dataset in data frame |
ids |
training indices |
yvar |
response variable name |
censorvar |
censoring variable name 1:event; 0: censor. |
trtvar |
treatment variable name |
type |
"c" continuous; "s" survival; "b" binary |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1) |
xvar |
name of predictor for which cutpoint needs to be obtained |
n.boot |
number of bootstraps for BATTing step. |
des.res |
the desired response. "larger": prefer larger response. "smaller": prefer smaller response. |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
Details
Main predictive BATTing function
Value
a signature rule consisting of variable name, direction, optimal cutpoint and the corresponding p-value.
batting.prog
Description
Main prognostic BATTing function
Usage
batting.prog(
dataset,
ids,
yvar,
censorvar,
type,
class.wt,
xvar,
n.boot,
des.res,
min.sigp.prcnt
)
Arguments
dataset |
input dataset in data frame |
ids |
training indices |
yvar |
response variable name |
censorvar |
censoring variable name 1:event; 0: censor. |
type |
"c" continuous; "s" survival; "b" binary |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1) |
xvar |
name of predictor for which cutpoint needs to be obtained |
n.boot |
number of bootstraps for BATTing step. |
des.res |
the desired response. "larger": prefer larger response. "smaller": prefer smaller response. |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
Details
Main prognostic BATTing function
Value
a signature rule consisting of variable name, direction, optimal cutpoint and the corresponding p-value.
binary.stats
Description
A function for binary statistics
Usage
binary.stats(pred.class, y.vec)
Arguments
pred.class |
predicted output for each subject |
y.vec |
response vector |
Details
A function for binary statistics
Value
a data frame with sensitivity, specificity, NPV, PPV and accuracy
cv.folds
Description
Cross-validation folds.
Usage
cv.folds(n, folds = 10)
Arguments
n |
number of observations. |
folds |
number of folds. |
Details
Cross-validation folds.
Value
a list containing the observation numbers for each fold.
cv.pval
Description
p-value calculation for each iteration of cross validation.
Usage
cv.pval(yvar, censorvar = NULL, trtvar = NULL, data, type = "s")
Arguments
yvar |
response variable name. |
censorvar |
censor-variable name. |
trtvar |
treatment variable name. For prognostic case trtvar=NULL. |
data |
dataset containing response and predicted output. |
type |
data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c". |
Details
p-value calculation for each iteration of cross validation.
Value
p-value based on response and prediction vector for each iteration.
cv.seqlr.batting
Description
Cross Validation for Sequential BATTing
Usage
cv.seqlr.batting(
y,
x,
censor.vec = NULL,
trt.vec = NULL,
trtref = NULL,
type = "c",
n.boot = 50,
des.res = "larger",
class.wt = c(1, 1),
min.sigp.prcnt = 0.2,
pre.filter = NULL,
filter.method = NULL,
k.fold = 5,
cv.iter = 50,
max.iter = 500
)
Arguments
y |
data frame containing the response |
x |
data frame containing the predictors |
censor.vec |
vector giving the censor status (only for TTE data , censor=0,event=1) : default = NULL |
trt.vec |
vector containing values of treatment variable ( for predictive signature). Set trt.vec to NULL for prognostic signature. |
trtref |
code for treatment arm. |
type |
data type. "c" - continuous , "b" - binary, "s" - time to event : default = "c". |
n.boot |
number of bootstraps in BATTing step. |
des.res |
the desired response. "larger": prefer larger response. "smaller": prefer smaller response |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1) |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
pre.filter |
NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected. |
filter.method |
NULL, no prefiltering, "univariate", univaraite filtering; "glmnet", glmnet filtering, "unicart": univariate rpart filtering for prognostic case. |
k.fold |
number of folds for CV. |
cv.iter |
algorithm terminates after cv.iter successful iterations of cross-validation. |
max.iter |
total number of iterations allowed (including unsuccessful ones). |
Details
Cross Validation for Sequential BATTing
Value
a list containing with following entries:
- stats.summary
Summary of performance statistics.
- pred.classes
Data frame containing the predictive clases (TRUE/FALSE) for each iteration.
- folds
Data frame containing the fold indices (index of the fold for each row) for each iteration.
- sig.list
List of length cv.iter * k.fold containing the signature generated at each of the k folds, for all iterations.
- error.log
List of any error messages that are returned at an iteration.
- interplot
Treatment*subgroup interaction plot for predictive case
data.gen
Description
Function for simulated data generation
Usage
data.gen(
n,
k,
prevalence = sqrt(0.5),
prog.eff = 1,
sig2,
y.sig2,
rho,
rhos.bt.real,
a.constent
)
Arguments
n |
Total sample size |
k |
Number of markers |
prevalence |
prevalence of predictive biomarkers with values above the cutoff |
prog.eff |
effect size |
sig2 |
standard deviation of each marker |
y.sig2 |
Standard Deviation of the error term in the linear component |
rho |
rho*sig2 is the entries for covariance matrix between pairs of different k markers |
rhos.bt.real |
correlation between each prognostic and predictive markers |
a.constent |
a constant is set such that there is no overall treatment effect |
Details
Function for simulated data generation
Value
A list of simulated clinical trial data with heterogeneous prognostic and predictive biomarkers
Examples
n <- 500
k <- 10
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
sig2=sig2, y.sig2=y.sig2, rho=rho,
rhos.bt.real=rhos.bt.real, a.constent=a.constent)
evaluate.cv.results
Description
Take the raw output of kfold.cv and calculate performance statistics for each iteration of the cross-validation.
Usage
evaluate.cv.results(cv.data, y, censor.vec, trt.vec, type)
Arguments
cv.data |
output of prediction function from kfold.cv |
y |
data frame of the response variable from CV data. |
censor.vec |
data frame indicating censoring for survival data. For binary or continuous data, set censor.vec <- NULL. |
trt.vec |
data frame indicating whether or not the patient was treated. For the pronostic case, set trt.vec <- NULL. |
type |
data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c" |
Details
Cross-validation Performance Evaluation
Value
a list containing raw statistics and fold information
evaluate.results
Description
Get statistics for a single set of predictions.
Usage
evaluate.results(
y,
predict.data,
censor.vec = NULL,
trt.vec = NULL,
trtref = NULL,
type
)
Arguments
y |
data frame of the response variable. |
predict.data |
output of prediction function from kfold.cv. |
censor.vec |
data frame indicating censoring for survival data. For binary or continuous data, set censor.vec <- NULL. |
trt.vec |
data frame indicating whether or not the patient was treated. For the pronostic case, set trt.vec <- NULL. |
trtref |
treatment reference. |
type |
data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c". |
Details
Get statistics for a single set of predictions.
Value
a list containing p-value and group statistics.
filter
Description
Filter function for Prognostic and preditive biomarker signature development for Exploratory Subgroup Identification in Randomized Clinical Trials
Usage
filter(
data,
type = "c",
yvar,
xvars,
censorvar = NULL,
trtvar = NULL,
trtref = 1,
n.boot = 50,
cv.iter = 20,
pre.filter = length(xvars),
filter.method = NULL
)
Arguments
data |
input data frame |
type |
type of response variable: "c" continuous; "s" survival; "b" binary |
yvar |
variable (column) name for response variable |
xvars |
vector of variable names for predictors (covariates) |
censorvar |
variable name for censoring (1: event; 0: censor), default = NULL |
trtvar |
variable name for treatment variable, default = NULL (prognostic signature) |
trtref |
coding (in the column of trtvar) for treatment arm, default = 1 (no use for prognostic signature) |
n.boot |
number of bootstrap for the BATTing procedure |
cv.iter |
Algotithm terminates after cv.iter successful iterations of cross-validation, or after max.iter total iterations, whichever occurs first |
pre.filter |
NULL (default), no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected |
filter.method |
NULL (default), no prefiltering; "univariate", univaraite filtering; "glmnet", glmnet filtering |
Details
Filter function for predictive/prognostic biomarker candidates for signature development
The function contains two algorithms for filtering high-dimentional multivariate (prognostic/predictive) biomarker candidates via univariate fitering (used p-values of group difference for prognostic case, p-values of interaction term for predictive case); LASSO/Elastic Net method. (Tian L. et al 2012)
Value
var |
a vector of filter results of variable names |
References
Tian L, Alizadeh A, Gentles A, Tibshirani R (2012) A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates. J Am Stat Assoc. 2014 Oct; 109(508): 1517-1532.
Examples
# no run
filter.glmnet
Description
Flitering using MC glmnet
Usage
filter.glmnet(
data,
type,
yvar,
xvars,
censorvar,
trtvar,
trtref,
n.boot = 50,
cv.iter = 20,
pre.filter = length(xvars)
)
Arguments
data |
input data frame |
type |
"c" continuous; "s" survival; "b" binary |
yvar |
response variable name |
xvars |
covariates variable name |
censorvar |
censoring variable name 1:event; 0: censor. |
trtvar |
treatment variable name |
trtref |
code for treatment arm |
n.boot |
number of bootstrap for filtering |
cv.iter |
number of iterations required for MC glmnet filtering |
pre.filter |
NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected |
Details
Flitering using MC glmnet
Value
variables selected after glmnet filtering
filter.unicart
Description
rpart filtering
Usage
filter.unicart(
data,
type,
yvar,
xvars,
censorvar,
trtvar,
trtref = 1,
pre.filter = length(xvars)
)
Arguments
data |
input data frame |
type |
"c" continuous; "s" survival; "b" binary |
yvar |
response variable name |
xvars |
covariates variable name |
censorvar |
censoring variable name 1:event; 0: censor. |
trtvar |
treatment variable name |
trtref |
code for treatment arm |
pre.filter |
NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected |
Details
rpart filtering (only for prognostic case)
Value
selected covariates after rpart filtering
filter.univariate
Description
Univariate Filtering
Usage
filter.univariate(
data,
type,
yvar,
xvars,
censorvar,
trtvar,
trtref = 1,
pre.filter = length(xvars)
)
Arguments
data |
input data frame |
type |
"c" continuous; "s" survival; "b" binary |
yvar |
response variable name |
xvars |
covariates variable name |
censorvar |
censoring variable name 1:event; 0: censor. |
trtvar |
treatment variable name |
trtref |
code for treatment arm |
pre.filter |
NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected |
Details
Univariate Filtering
Value
covariate names after univariate filtering.
find.pred.stats
Description
Find predictive stats from response and prediction vector
Usage
find.pred.stats(data, yvar, trtvar, type, censorvar)
Arguments
data |
data frame with response and prediction vector |
yvar |
response variable name |
trtvar |
treatment variable name |
type |
data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c". |
censorvar |
censoring variable name |
Details
Find predictive stats from response and prediction vector
Value
a data frame of predictive statistics
find.prog.stats
Description
Find prognostic stats from response and prediction vector
Usage
find.prog.stats(data, yvar, type, censorvar)
Arguments
data |
data frame with response and prediction vector |
yvar |
response variable name |
type |
data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c". |
censorvar |
censoring variable name |
Details
Find prognostic stats from response and prediction vector
Value
a data frame of predictive statistics
get.var.counts.seq
Description
Get signature variables from output of seqlr.batting.
Usage
get.var.counts.seq(sig.list, xvars)
Arguments
sig.list |
signature list returned by seqlr.batting. |
xvars |
predictor variable names |
Value
the variables included in signature rules returned by seqlr.batting
interaction.plot
Description
A function for interaction plot
Usage
interaction.plot(
data.eval,
type,
main = "Interaction Plot",
trt.lab = c("Trt.", "Ctrl.")
)
Arguments
data.eval |
output of evaluate.results or summarize.cv.stats |
type |
data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c". |
main |
title of the plot |
trt.lab |
treatment label |
Details
A function for interaction plot
Value
A ggplot object.
kfold.cv
Description
Perform k-fold cross-validation of a model.
Usage
kfold.cv(
data,
model.Rfunc,
model.Rfunc.args,
predict.Rfunc,
predict.Rfunc.args,
k.fold = 5,
cv.iter = 50,
strata,
max.iter = 500
)
Arguments
data |
the CV data |
model.Rfunc |
Name of the model function. |
model.Rfunc.args |
List of input arguments to model.Rfunc. |
predict.Rfunc |
Name of the prediction function, which takes the prediction rule returned by model.Rfunc along with any input data (not necessarily the input data to kfold.cv) and returns a TRUE-FALSE predictionvector specifying the positive and negative classes for the data. |
predict.Rfunc.args |
List containing input arguments to predict.Rfunc, except for data and predict.rule. |
k.fold |
Number of folds of the cross-validation. |
cv.iter |
Number of iterations of the cross-validation. If model.Rfunc returns an error at any of the k.fold calls, the current iteration is aborted. Iterations are repeated until cv.iter successful iterations have occurred. |
strata |
Stratification vector of length the number of rows of data, usually corresponding to the vector of events. |
max.iter |
Function stops after max.iter iterations even if cv.iter successful iterations have not occurred. |
Details
Perform k-fold cross-validation of a model.
Value
List of length 2 with the following fields:
cv.data - List of length cv.iter. Entry i contains the output of predict.Rfunc at the ith iteration.
sig.list - list of length cv.iter * k.fold, whose entries are the prediction.rules (signatures) returned by model.Rfunc at each k.fold iteration.
make.arg.list
Description
Create a list of variables corresponding to the arguments of the function func.name and assigns values.
Usage
make.arg.list(func.name)
Arguments
func.name |
function name |
Details
Create a list of variables corresponding to the arguments of the function func.name and assigns values.
Value
list of variables corresponding to the arguments of the function
permute.rows
Description
Randomly permute the rows of a matrix.
Usage
permute.rows(A)
Arguments
A |
a matrix for which its rows have to be permuted. |
Details
Randomly permute the rows of a matrix.
Value
the matrix with permuted rows.
permute.vector
Description
Randomly permute the entries of a vector.
Usage
permute.vector(x)
Arguments
x |
the vector for which its entries have to be permuted |
Details
Randomly permute the entries of a vector.
Value
the permuted vector
pred.seqlr
Description
Assign positive and negative groups based on predict.rule, the output of seqlr.batting.
Usage
pred.seqlr(x, predict.rule)
Arguments
x |
input predictors matrix |
predict.rule |
Prediction rule returned by seqlr.batting. |
Details
Prediction function for Sequential BATTing
Value
a logical vector indicating the prediction for each row of data.
pred.seqlr.cv
Description
Assign positive and negative groups for cross-validation data given prediction rule in predict.rule.
Usage
pred.seqlr.cv(data, predict.rule, args)
Arguments
data |
input data frame |
predict.rule |
Prediction rule returned by seqlr.batting. |
args |
Prediction rule arguments |
Details
Prediction function for CV Sequential BATTing
Value
a logical vector indicating the prediction for each row of data.
query.data
Description
internal function used in seqlr.batting
Usage
query.data(data, rule)
Arguments
data |
the given dataset |
rule |
rule is a vector of the form [x-variable, direction, cutoff, p-value] |
Details
internal function used in seqlr.batting
Value
a logical variable indicating whether rules are satisfied or not.
resample
Description
Creates a permutation of given size.
Usage
resample(x, size, ...)
Arguments
x |
the x vector. |
size |
resampling size. |
... |
optional argument. |
Details
Creates a permutation of given size.
Value
A resample of x is returned.
seqlr.batting
Description
Perform sequential BATTing method.
Usage
seqlr.batting(
y,
x,
censor.vec = NULL,
trt.vec = NULL,
trtref = NULL,
type = "c",
n.boot = 50,
des.res = "larger",
class.wt = c(1, 1),
min.sigp.prcnt = 0.2,
pre.filter = NULL,
filter.method = NULL
)
Arguments
y |
data frame containing the response. |
x |
data frame containing the predictors. |
censor.vec |
vector containing the censor status (only for TTE data , censor=0,event=1) - default = NULL. |
trt.vec |
vector containing values of treatment variable ( for predictive signature). Set trt.vec to NULL for prognostic signature. |
trtref |
code for treatment arm. |
type |
data type. "c" - continuous , "b" - binary, "s" - time to event : default = "c". |
n.boot |
number of bootstraps in BATTing step. |
des.res |
the desired response. "larger": prefer larger response. "smaller": prefer smaller response |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1) |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
pre.filter |
NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected |
filter.method |
NULL, no prefiltering, "univariate", univaraite filtering; "glmnet", glmnet filtering, "unicart": univariate rpart filtering for prognostic case. |
Details
Perform sequential BATTing method.
Value
it returns a list of signature rules consisting of variable names, directions, thresholds and the loglikelihood at each step the signatures are applied.
seqlr.batting.wrapper
Description
Wrapper function for seqlr.batting, to be passed to kfold.cv.
Usage
seqlr.batting.wrapper(data, args)
Arguments
data |
data frame equal to cbind(y, x, trt, censor), where y and x are inputs to seqlr.batting. |
args |
list containing all other input arguments to seq.batting except for x and y. Also contains xvars=names(x) and yvar=names(y). |
Details
Wrapper function for seqlr.batting, to be passed to kfold.cv.
Value
prediction rule returned by seqlr.batting.
seqlr.find.cutoff.pred
Description
Find cutoff for predictive case.
Usage
seqlr.find.cutoff.pred(
data,
yvar,
censorvar,
xvar,
trtvar,
type,
class.wt,
dir,
nsubj,
min.sigp.prcnt
)
Arguments
data |
input data frame. |
yvar |
response variable name. |
censorvar |
censoring variable name. |
xvar |
name of predictor for which cutpoint needs to be obtained. |
trtvar |
treatment variable name. |
type |
"c" continuous; "s" survival; "b" binary. |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1). |
dir |
direction of cut. |
nsubj |
number of subjects. |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
Details
Find cutoff for predictive case.
Value
the optimal score (p-value of subgroup*treatment interaction) for a predictor variable.
seqlr.find.cutoff.prog
Description
Find cutoff for prognostic case.
Usage
seqlr.find.cutoff.prog(
data,
yvar,
censorvar,
xvar,
type,
class.wt,
dir,
nsubj,
min.sigp.prcnt
)
Arguments
data |
input data frame. |
yvar |
response variable name. |
censorvar |
censoring variable name. |
xvar |
name of predictor for which cutpoint needs to be obtained. |
type |
"c" continuous; "s" survival; "b" binary. |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1). |
dir |
direction of cut. |
nsubj |
number of subjects. |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
Details
Find cutoff for prognostic case.
Value
the optimal score (p-value of main effect) for a predictor variable.
seqlr.score.pred
Description
Compute score of cutoff for predictive case
Usage
seqlr.score.pred(
data,
yvar,
censorvar,
xvar,
trtvar,
cutoff,
type,
class.wt,
dir,
nsubj,
min.sigp.prcnt
)
Arguments
data |
input data frame. |
yvar |
response variable name. |
censorvar |
censoring variable name. |
xvar |
name of predictor for which cutpoint needs to be obtained. |
trtvar |
treatment variable name. |
cutoff |
a specific cutpoint for which the score needs to be computed. |
type |
"c" continuous; "s" survival; "b" binary. |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1). |
dir |
direction of cut. |
nsubj |
number of subjects. |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
Details
Compute score of cutoff for predictive case
Value
score (p-value of treatment*subgroup interaction) for the given cutoff.
seqlr.score.prog
Description
Compute score of cutoff for prognostic case
Usage
seqlr.score.prog(
data,
yvar,
censorvar,
xvar,
cutoff,
type,
class.wt,
dir,
nsubj,
min.sigp.prcnt
)
Arguments
data |
input data frame. |
yvar |
response variable name. |
censorvar |
censoring variable name. |
xvar |
name of predictor for which cutpoint needs to be obtained. |
cutoff |
a specific cutpoint for which the score needs to be computed. |
type |
"c" continuous; "s" survival; "b" binary. |
class.wt |
vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1). |
dir |
direction of cut. |
nsubj |
number of subjects. |
min.sigp.prcnt |
desired proportion of signature positive group size for a given cutoff. |
Details
Compute score of cutoff for prognostic case
Value
score (p-value of main effect) for the given cutoff.
summarize.cv.stats
Description
Calculate summary statistics from raw statistics returned by evaluate.cv.results.
Usage
summarize.cv.stats(raw.stats, trtvar, type)
Arguments
raw.stats |
raw statistics from evaluate.cv.results |
trtvar |
treatment variable name |
type |
data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c" |
Details
Calculate summary statistics from raw statistics returned by evaluate.cv.results.
Value
a list containing p-values, summary statistics and group statistics.