R/createNormalParameters.R
In MikeKSmith/MSToolkit: The MSToolkit Library For Clinical Trial Design
#' Create parameters based on a multivariate normal distribution
#'
#' Creates parameter variables by sampling from a multivariate normal
#' distribution. Parameter values can vary between simulation replicates, but
#' also between subjects within each simulation replicate (between subject
#' variability).
#'
#' The function first samples a new value for the parameter. This parameter
#' value is used for each subject within the trial replicate. These values are
#' generated from a multivariate normal distribution with mean and covariance
#' matrix specified within the function arguments. These can be viewed as the
#' "fixed effects" for the trial replicate.
#'
#' If "betNames" argument has been supplied ("betNames" must be a subset of the
#' parameter names supplied in the "names" argument) then another sample will be
#' taken to generate subject-specific parameter values. If the "betMean" input
#' has not been supplied, the mean of the between effects distribution will be
#' 0. Samples for each subject will be taken from a multivariate normal
#' distribution with mean "betMean" and covariate matrix "betCov". "betCov"
#' specifies between subject variability in the parameters.
#'
#' If fixed and between subject effects have been generated, the "errStruc"
#' input will specify how the between subject effects are to be applied:
#'
#' * None = Keep between subject effect separate (and use the "suffix"
#' supplied to name the variables)
#' * Additive = Add each between subject effect to the corresponding fixed
#' effect
#' * Proportional = Multiply the fixed effect by the exponentiated between
#' subject effect
#' * Log-Normal = Add "1 + each between subject effect" to the corresponding
#' fixed effect
#'
#' If the "range" argument is provided, the output from the above algorithm
#' (fixed + between) is checked against the acceptance range. If the data is
#' not within range, the function will take "maxDraws" more attempts at the
#' above algorithm to try and generate "in range" data. If that is not
#' possible, the function will stop and return an error.
#'
#' @param subjects (Required) Subjects for which to create parameters
#' @param names (Required) Names of parameters to generate
#' @param mean (Required) Means for fixed parameters
#' @param covariance (Optional) Covariance matrix for fixed parameters. By
#' default, this is a matrix of zeros, representing no error to be included
#' @param range (Optional) Range of acceptable values. Missing by default,
#' resulting in no "range" of values is applied to the data
#' @param betNames (Optional) Between subject effects to create. Default is to
#' not create between subject data
#' @param betMean (Optional) Means for the between subject effects. Default is
#' to not create between subject data
#' @param betCov (Optional) Covariance matrix for the between subject effects.
#' Default is to not create between subject data
#' @param errStruc (Optional) (None) Function to map between subject effects:
#' Additive, Proportional, Log-Normal or None
#' @param suffix (Optional) Suffix to use for retain between subject effects
#' (default is ".Between")
#' @param idCol (Optional) Subject variable name for return data ("SUBJ" by
#' default)
#' @param maxDraws (Optional) Maximum number of iterations for valid parameters
#' (10 by default)
#' @param seed (Optional) Random number generation seed. By default, this is
#' dervied from the current random seed
#' @param flagName (Optional) Flag name for parameters out of bounds ("PAROMIT"
#' by default)
#' @param digits (Optional) Number of digits to which to round generated
#' continuous parameters. Can be an atomic integer, in which case all
#' variables are rounded according to it. Can be a numeric vector of the same
#' length of the number of fixed parameters, in which case each parameter is
#' rounded according to its value. The default is 3
#' @param parRangeTolerance (Optional, default .9) Proportion of subjects with
#' parameters in specified range that is acceptable for us to continue
#' @return A data frame containing parameter data
#' @note If earlier versions of MSToolkit, the "Proportional" error structure
#' was implemented as exp(fixed + between subject)
#' @author Rich Pugh
# @seealso \code{\link{createParameters}} and
# \code{\link{createExternalParameters}}
#' @keywords datagen
#' @examples
#'
#' createNormalParameters(5, "E0,ED50,EMAX",
#' mean = c(0, 50, 100),
#' covariance = diag(c(1, 5, 10)))
#' # SUBJ E0 ED50 EMAX PAROMIT
#' #1 1 -0.8356286 50.41064 98.01898 0
#' #2 2 -0.8356286 50.41064 98.01898 0
#' #3 3 -0.8356286 50.41064 98.01898 0
#' #4 4 -0.8356286 50.41064 98.01898 0
#' #5 5 -0.8356286 50.41064 98.01898 0
#'
#' createNormalParameters(5, "E0,ED50,EMAX",
#' mean = c(0, 50, 100),
#' covariance = diag(c(1, 5, 10)),
#' betNames = c("E0", "EMAX"),
#' betCov = diag(2),
#' errStruc = "Additive")
#' # SUBJ E0 ED50 EMAX PAROMIT
#' #1 1 0.7596522 50.41064 98.59476 0
#' #2 2 -0.5061208 50.41064 97.71359 0
#' #3 3 -1.6560970 50.41064 99.53076 0
#' #4 4 -0.3481996 50.41064 98.40882 0
#' #5 5 -0.0973039 50.41064 97.39774 0
#'
#' # no covariance by default
#' createNormalParameters(5, "E0,ED50,EMAX", mean = c(0, 50, 100) )
#' # SUBJ E0 ED50 EMAX PAROMIT
#' #1 1 0 50 100 0
#' #2 2 0 50 100 0
#' #3 3 0 50 100 0
#' #4 4 0 50 100 0
#' #5 5 0 50 100 0
#'
#' @export
"createNormalParameters" <- function(
subjects,
names,
mean,
covariance = 0,
range,
betNames,
betMean = 0,
betCov = 0,
errStruc = c("None", "Proportional", "Additive", "Log-Normal"),
suffix = ".Between",
idCol = getEctdColName("Subject"),
maxDraws = 10,
seed = .deriveFromMasterSeed( ),
flagName = getEctdColName("ParOmit"),
digits = 3,
parRangeTolerance = .5
) {
##############################################################################
# Mango Solutions, Chippenham SN15 1BN 2009
# createNormalParameters.R Wed Jun 20 11:39:52 BST 2007 @486 /Internet Time/
#
# Author: Rich P
##############################################################################
# DESCRIPTION: create parameters from a normal distribution
# KEYWORDS: datagen, component:data:allocate
##############################################################################
# Set the seed
set.seed(seed)
## initial tests
subjects <- .expandSubjects( subjects )
nSubjects <- length(subjects)
mean <- parseCharInput( mean )
nFixed <- length(mean)
idCol <- parseCharInput( idCol,
expected = 1,
convertToNumeric = FALSE,
valid = TRUE)
flagName <- parseCharInput( flagName,
expected = 1,
convertToNumeric = FALSE,
valid = TRUE)
maxDraws <- parseCharInput( maxDraws,
expected = 1,
convertToNumeric = TRUE)
digits <- parseCharInput( digits,
convertToNumeric = TRUE)
names <- if(missing(names)) {
"X" %.% 1:nFixed
} else {
parseCharInput( names,
convertToNumeric = FALSE,
checkdup = TRUE,
expected = nFixed,
valid = TRUE )
}
covariance <- parseCovMatrix( covariance, nFixed )
# Checks on digits, maxDraws and errStruc
getErrStruc <- try(match.arg(errStruc))
if (class(getErrStruc) == "try-error") {
errStruc <- casefold(substring(errStruc, 1, 1),
upper = TRUE)
getErrStruc <- try(match.arg(errStruc))
if (class(getErrStruc) == "try-error") ectdStop(
"Error Structure input should be 'None', 'Additive', 'Log-Normal' or
'Proportional'")
}
if (maxDraws < 1) ectdStop(
"The maximum number of draws should be a positive integer")
if (any(digits < 0)) ectdStop(
"The `digits` argument must be positive")
## Generate parameters depending on presence of "range" input
if (missing(range)) {
# Generate 1 set of fixed effects
fixed <- .generateFixedPars(mean, covariance, nSubjects, names)
## Now deal with the between subject effects
if ( !missing(betNames) ) {
# Generate a set of between subject parameters
betNames <- parseCharInput( betNames,
convertToNumeric = FALSE,
checkdup = TRUE,
valid = TRUE )
between <- .generateBetweenPars(betNames,
names,
betMean,
betCov,
nSubjects,
suffix)
omitFlag <- rep(0, nSubjects)
out <- .combineFixedAndBetween(fixed,
between,
betNames,
getErrStruc,
suffix,
digits)
}
else { # no between
out <- .roundIt( fixed, digits )
}
# Combine data
out <- data.frame( subjects, out, rep(0, nSubjects))
names(out)[c(1, length(out))] <- c(idCol, flagName)
}
else {
# Loop over draws allowed
for (i in 1:maxDraws) {
fixed <- .generateFixedPars(mean, covariance, nSubjects, names)
## Now deal with the between subject effects
if ( !missing(betNames) ) {
# Generate a set of between subject parameters
betNames <- parseCharInput( betNames,
convertToNumeric = FALSE,
checkdup = TRUE,
valid = TRUE )
between <- .generateBetweenPars(betNames,
names,
betMean,
betCov,
nSubjects,
suffix)
omitFlag <- rep(0, nSubjects)
out <- .combineFixedAndBetween(fixed,
between,
betNames,
getErrStruc,
suffix,
digits)
}
else { # no between
out <- .roundIt( fixed, digits )
}
# Combine data
out <- data.frame( subjects, out, rep(0, nSubjects))
names(out)[c(1, length(out))] <- c(idCol, flagName)
# Check to see if the data is in range
out <- .checkParameterRange(out, range, flagName)
if (all(out[[flagName]] == 0)) break
# Stop if we have exhausted our number of draws
if (i == maxDraws) {
subjectFlags <- out[[flagName]][!duplicated(out[[idCol]])]
propOk <- 1 - sum(subjectFlags) / length(subjectFlags)
roundPercent <- round(100 * propOk, 1)
basicMessage <- paste("After ",
maxDraws,
" attempts, ",
roundPercent,
"% of subjects have parameters in acceptable
range",
sep="")
if (propOk >= parRangeTolerance) ectdWarning( basicMessage )
else ectdStop( basicMessage )
}
}
}
rownames(out) <- 1:nSubjects
out
}
".generateFixedPars" <- function(mean, cov, N, colNames) {
fixed <- as.data.frame( matrix( MASS::mvrnorm( n = 1,
mu = mean,
Sigma = cov ),
nrow = 1 ) )
names(fixed) <- colNames
fixed[rep(1, N), , drop = FALSE]
}
".generateBetweenPars" <- function(betNames, fixedNames, mean, cov, N, suffix) {
myTest <- betNames %in% fixedNames
if ( !all(myTest)) {
outMessage <- paste(
"Some between subject effects don't have a corresponding fixed parameter:",
paste(betNames[!myTest],
collapse=", "))
ectdStop( outMessage )
}
# Get the covariance matrix + means
nBetween <- length( betNames )
betCov <- parseCovMatrix(cov, nBetween )
betMean <- rep(parseCharInput(mean), length.out = nBetween)
# Generate the data once
between <- as.data.frame( MASS::mvrnorm( N, mu = betMean, Sigma = betCov ) )
## apply the suffix to the between data and return it
names( between ) <- paste(betNames, suffix, sep="")
between
}
".combineFixedAndBetween" <- function(fixed, between, betNames, errStruc, suffix, digits) {
switch( errStruc,
"Additive" = {
out <- fixed
for ( bn in betNames ) out[[ bn ]] <- out[[ bn ]] +
between[[ paste(bn, suffix, sep="") ]]
out <- .roundIt( out, digits )
},
"Log-Normal" = {
out <- fixed
for( bn in betNames ) out[[ bn ]] <- out[[ bn ]] *
exp( between[[ paste(bn, suffix, sep="") ]] )
out <- .roundIt( out, digits )
},
"Proportional" = {
out <- fixed
for( bn in betNames ) out[[ bn ]] <- out[[ bn ]] *
( 1 + between[[ paste(bn, suffix, sep="") ]] )
out <- .roundIt( out, digits )
},
"None" = {
digits <- parseCharInput( digits, convertToNumeric = TRUE)
fixed <- .roundIt( fixed, digits )
if ( length(digits) == 1 ||
ncol(fixed) == ncol(between) ) between <- .roundIt( between, digits)
else between <- .roundIt( between,
digits = digits[ names(fixed) %in% betNames ] )
out <- data.frame( fixed, between)
}
)
out
}
".checkParameterRange" <- function(out, range, flagName) {
range <- parseRangeCode( range ) # Parse the range code
alright <- try( eval( range, out ) )
if (class(alright) == "try-error" ||
length(alright) != nrow(out)) out[[flagName]] <- rep(1, nrow(out))
else out[[flagName]] <- as.numeric(!alright)
out
}
MikeKSmith/MSToolkit documentation built on Feb. 15, 2024, 5:32 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' Create parameters based on a multivariate normal distribution
#'
#' Creates parameter variables by sampling from a multivariate normal
#' distribution. Parameter values can vary between simulation replicates, but
#' also between subjects within each simulation replicate (between subject
#' variability).
#'
#' The function first samples a new value for the parameter. This parameter
#' value is used for each subject within the trial replicate. These values are
#' generated from a multivariate normal distribution with mean and covariance
#' matrix specified within the function arguments. These can be viewed as the
#' "fixed effects" for the trial replicate.
#'
#' If "betNames" argument has been supplied ("betNames" must be a subset of the
#' parameter names supplied in the "names" argument) then another sample will be
#' taken to generate subject-specific parameter values. If the "betMean" input
#' has not been supplied, the mean of the between effects distribution will be
#' 0. Samples for each subject will be taken from a multivariate normal
#' distribution with mean "betMean" and covariate matrix "betCov". "betCov"
#' specifies between subject variability in the parameters.
#'
#' If fixed and between subject effects have been generated, the "errStruc"
#' input will specify how the between subject effects are to be applied:
#'
#' * None = Keep between subject effect separate (and use the "suffix"
#' supplied to name the variables)
#' * Additive = Add each between subject effect to the corresponding fixed
#' effect
#' * Proportional = Multiply the fixed effect by the exponentiated between
#' subject effect
#' * Log-Normal = Add "1 + each between subject effect" to the corresponding
#' fixed effect
#'
#' If the "range" argument is provided, the output from the above algorithm
#' (fixed + between) is checked against the acceptance range. If the data is
#' not within range, the function will take "maxDraws" more attempts at the
#' above algorithm to try and generate "in range" data. If that is not
#' possible, the function will stop and return an error.
#'
#' @param subjects (Required) Subjects for which to create parameters
#' @param names (Required) Names of parameters to generate
#' @param mean (Required) Means for fixed parameters
#' @param covariance (Optional) Covariance matrix for fixed parameters. By
#' default, this is a matrix of zeros, representing no error to be included
#' @param range (Optional) Range of acceptable values. Missing by default,
#' resulting in no "range" of values is applied to the data
#' @param betNames (Optional) Between subject effects to create. Default is to
#' not create between subject data
#' @param betMean (Optional) Means for the between subject effects. Default is
#' to not create between subject data
#' @param betCov (Optional) Covariance matrix for the between subject effects.
#' Default is to not create between subject data
#' @param errStruc (Optional) (None) Function to map between subject effects:
#' Additive, Proportional, Log-Normal or None
#' @param suffix (Optional) Suffix to use for retain between subject effects
#' (default is ".Between")
#' @param idCol (Optional) Subject variable name for return data ("SUBJ" by
#' default)
#' @param maxDraws (Optional) Maximum number of iterations for valid parameters
#' (10 by default)
#' @param seed (Optional) Random number generation seed. By default, this is
#' dervied from the current random seed
#' @param flagName (Optional) Flag name for parameters out of bounds ("PAROMIT"
#' by default)
#' @param digits (Optional) Number of digits to which to round generated
#' continuous parameters. Can be an atomic integer, in which case all
#' variables are rounded according to it. Can be a numeric vector of the same
#' length of the number of fixed parameters, in which case each parameter is
#' rounded according to its value. The default is 3
#' @param parRangeTolerance (Optional, default .9) Proportion of subjects with
#' parameters in specified range that is acceptable for us to continue
#' @return A data frame containing parameter data
#' @note If earlier versions of MSToolkit, the "Proportional" error structure
#' was implemented as exp(fixed + between subject)
#' @author Rich Pugh
# @seealso \code{\link{createParameters}} and
# \code{\link{createExternalParameters}}
#' @keywords datagen
#' @examples
#'
#' createNormalParameters(5, "E0,ED50,EMAX",
#' mean = c(0, 50, 100),
#' covariance = diag(c(1, 5, 10)))
#' # SUBJ E0 ED50 EMAX PAROMIT
#' #1 1 -0.8356286 50.41064 98.01898 0
#' #2 2 -0.8356286 50.41064 98.01898 0
#' #3 3 -0.8356286 50.41064 98.01898 0
#' #4 4 -0.8356286 50.41064 98.01898 0
#' #5 5 -0.8356286 50.41064 98.01898 0
#'
#' createNormalParameters(5, "E0,ED50,EMAX",
#' mean = c(0, 50, 100),
#' covariance = diag(c(1, 5, 10)),
#' betNames = c("E0", "EMAX"),
#' betCov = diag(2),
#' errStruc = "Additive")
#' # SUBJ E0 ED50 EMAX PAROMIT
#' #1 1 0.7596522 50.41064 98.59476 0
#' #2 2 -0.5061208 50.41064 97.71359 0
#' #3 3 -1.6560970 50.41064 99.53076 0
#' #4 4 -0.3481996 50.41064 98.40882 0
#' #5 5 -0.0973039 50.41064 97.39774 0
#'
#' # no covariance by default
#' createNormalParameters(5, "E0,ED50,EMAX", mean = c(0, 50, 100) )
#' # SUBJ E0 ED50 EMAX PAROMIT
#' #1 1 0 50 100 0
#' #2 2 0 50 100 0
#' #3 3 0 50 100 0
#' #4 4 0 50 100 0
#' #5 5 0 50 100 0
#'
#' @export
"createNormalParameters" <- function(
subjects,
names,
mean,
covariance = 0,
range,
betNames,
betMean = 0,
betCov = 0,
errStruc = c("None", "Proportional", "Additive", "Log-Normal"),
suffix = ".Between",
idCol = getEctdColName("Subject"),
maxDraws = 10,
seed = .deriveFromMasterSeed( ),
flagName = getEctdColName("ParOmit"),
digits = 3,
parRangeTolerance = .5
) {
##############################################################################
# Mango Solutions, Chippenham SN15 1BN 2009
# createNormalParameters.R Wed Jun 20 11:39:52 BST 2007 @486 /Internet Time/
#
# Author: Rich P
##############################################################################
# DESCRIPTION: create parameters from a normal distribution
# KEYWORDS: datagen, component:data:allocate
##############################################################################
# Set the seed
set.seed(seed)
## initial tests
subjects <- .expandSubjects( subjects )
nSubjects <- length(subjects)
mean <- parseCharInput( mean )
nFixed <- length(mean)
idCol <- parseCharInput( idCol,
expected = 1,
convertToNumeric = FALSE,
valid = TRUE)
flagName <- parseCharInput( flagName,
expected = 1,
convertToNumeric = FALSE,
valid = TRUE)
maxDraws <- parseCharInput( maxDraws,
expected = 1,
convertToNumeric = TRUE)
digits <- parseCharInput( digits,
convertToNumeric = TRUE)
names <- if(missing(names)) {
"X" %.% 1:nFixed
} else {
parseCharInput( names,
convertToNumeric = FALSE,
checkdup = TRUE,
expected = nFixed,
valid = TRUE )
}
covariance <- parseCovMatrix( covariance, nFixed )
# Checks on digits, maxDraws and errStruc
getErrStruc <- try(match.arg(errStruc))
if (class(getErrStruc) == "try-error") {
errStruc <- casefold(substring(errStruc, 1, 1),
upper = TRUE)
getErrStruc <- try(match.arg(errStruc))
if (class(getErrStruc) == "try-error") ectdStop(
"Error Structure input should be 'None', 'Additive', 'Log-Normal' or
'Proportional'")
}
if (maxDraws < 1) ectdStop(
"The maximum number of draws should be a positive integer")
if (any(digits < 0)) ectdStop(
"The `digits` argument must be positive")
## Generate parameters depending on presence of "range" input
if (missing(range)) {
# Generate 1 set of fixed effects
fixed <- .generateFixedPars(mean, covariance, nSubjects, names)
## Now deal with the between subject effects
if ( !missing(betNames) ) {
# Generate a set of between subject parameters
betNames <- parseCharInput( betNames,
convertToNumeric = FALSE,
checkdup = TRUE,
valid = TRUE )
between <- .generateBetweenPars(betNames,
names,
betMean,
betCov,
nSubjects,
suffix)
omitFlag <- rep(0, nSubjects)
out <- .combineFixedAndBetween(fixed,
between,
betNames,
getErrStruc,
suffix,
digits)
}
else { # no between
out <- .roundIt( fixed, digits )
}
# Combine data
out <- data.frame( subjects, out, rep(0, nSubjects))
names(out)[c(1, length(out))] <- c(idCol, flagName)
}
else {
# Loop over draws allowed
for (i in 1:maxDraws) {
fixed <- .generateFixedPars(mean, covariance, nSubjects, names)
## Now deal with the between subject effects
if ( !missing(betNames) ) {
# Generate a set of between subject parameters
betNames <- parseCharInput( betNames,
convertToNumeric = FALSE,
checkdup = TRUE,
valid = TRUE )
between <- .generateBetweenPars(betNames,
names,
betMean,
betCov,
nSubjects,
suffix)
omitFlag <- rep(0, nSubjects)
out <- .combineFixedAndBetween(fixed,
between,
betNames,
getErrStruc,
suffix,
digits)
}
else { # no between
out <- .roundIt( fixed, digits )
}
# Combine data
out <- data.frame( subjects, out, rep(0, nSubjects))
names(out)[c(1, length(out))] <- c(idCol, flagName)
# Check to see if the data is in range
out <- .checkParameterRange(out, range, flagName)
if (all(out[[flagName]] == 0)) break
# Stop if we have exhausted our number of draws
if (i == maxDraws) {
subjectFlags <- out[[flagName]][!duplicated(out[[idCol]])]
propOk <- 1 - sum(subjectFlags) / length(subjectFlags)
roundPercent <- round(100 * propOk, 1)
basicMessage <- paste("After ",
maxDraws,
" attempts, ",
roundPercent,
"% of subjects have parameters in acceptable
range",
sep="")
if (propOk >= parRangeTolerance) ectdWarning( basicMessage )
else ectdStop( basicMessage )
}
}
}
rownames(out) <- 1:nSubjects
out
}
".generateFixedPars" <- function(mean, cov, N, colNames) {
fixed <- as.data.frame( matrix( MASS::mvrnorm( n = 1,
mu = mean,
Sigma = cov ),
nrow = 1 ) )
names(fixed) <- colNames
fixed[rep(1, N), , drop = FALSE]
}
".generateBetweenPars" <- function(betNames, fixedNames, mean, cov, N, suffix) {
myTest <- betNames %in% fixedNames
if ( !all(myTest)) {
outMessage <- paste(
"Some between subject effects don't have a corresponding fixed parameter:",
paste(betNames[!myTest],
collapse=", "))
ectdStop( outMessage )
}
# Get the covariance matrix + means
nBetween <- length( betNames )
betCov <- parseCovMatrix(cov, nBetween )
betMean <- rep(parseCharInput(mean), length.out = nBetween)
# Generate the data once
between <- as.data.frame( MASS::mvrnorm( N, mu = betMean, Sigma = betCov ) )
## apply the suffix to the between data and return it
names( between ) <- paste(betNames, suffix, sep="")
between
}
".combineFixedAndBetween" <- function(fixed, between, betNames, errStruc, suffix, digits) {
switch( errStruc,
"Additive" = {
out <- fixed
for ( bn in betNames ) out[[ bn ]] <- out[[ bn ]] +
between[[ paste(bn, suffix, sep="") ]]
out <- .roundIt( out, digits )
},
"Log-Normal" = {
out <- fixed
for( bn in betNames ) out[[ bn ]] <- out[[ bn ]] *
exp( between[[ paste(bn, suffix, sep="") ]] )
out <- .roundIt( out, digits )
},
"Proportional" = {
out <- fixed
for( bn in betNames ) out[[ bn ]] <- out[[ bn ]] *
( 1 + between[[ paste(bn, suffix, sep="") ]] )
out <- .roundIt( out, digits )
},
"None" = {
digits <- parseCharInput( digits, convertToNumeric = TRUE)
fixed <- .roundIt( fixed, digits )
if ( length(digits) == 1 ||
ncol(fixed) == ncol(between) ) between <- .roundIt( between, digits)
else between <- .roundIt( between,
digits = digits[ names(fixed) %in% betNames ] )
out <- data.frame( fixed, between)
}
)
out
}
".checkParameterRange" <- function(out, range, flagName) {
range <- parseRangeCode( range ) # Parse the range code
alright <- try( eval( range, out ) )
if (class(alright) == "try-error" ||
length(alright) != nrow(out)) out[[flagName]] <- rep(1, nrow(out))
else out[[flagName]] <- as.numeric(!alright)
out
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.