| Type: | Package |
| Title: | Univariate Bootstrapping and Other Things |
| Version: | 0.1.5 |
| Author: | Patrick O'Keefe |
| Maintainer: | Patrick O'Keefe <okeefep@ohsu.edu> |
| Description: | Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi:10.1037/1082-989X.12.4.414>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| Imports: | MASS, base, stats, psych, copula |
| Suggests: | lavaan |
| NeedsCompilation: | no |
| Packaged: | 2022-08-08 19:52:27 UTC; pgok1 |
| Repository: | CRAN |
| Date/Publication: | 2022-08-09 14:10:02 UTC |
AllBootResults
Description
AllBootResults
Usage
AllBootResults(boot, lower = 0.025, upper = 0.975, data, FUN, ...)
Arguments
boot |
A matrix of bootstrap results |
lower |
the lower alpha |
upper |
the upper alpha |
data |
the data used for analysis |
FUN |
the function used for analysis |
... |
additional arguments to pass to FUN |
Value
a matrix of results. Includes the baseline results, all output from standardBootIntervals, all results from BCa for both the jackknife and bootstrap accelleration methods. The bootstrap accelleration method is experimental.
Examples
data<-DFSimulated()
boots<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boots<-bootAnalysis(boots, cbind, DFanalysis, 1,2,3, robust=FALSE)
AllBootResults(boots, .025,.975, data, DFanalysis, 1,2,3, robust=FALSE)
BCa
Description
BCa
Usage
BCa(
boot,
data,
alphalower = 0.025,
alphaupper = 0.975,
accelleration = "jack",
FUN,
...
)
Arguments
boot |
A vector of bootstrap estimates of Theta |
data |
The data that was analyzed via the bootstrap |
alphalower |
The lower alpha for CI creation |
alphaupper |
The upper alpha for CI creation |
accelleration |
can currently take two values, "jack" and "bootstrap". "jack" returns the jackknife estimate of the accelleration parameter. "boot" is an experimental function that uses the bootstrap estimates in the calculation of the accelleration parameter. "boot" is many times faster (approximately n times faster where n is the number of observations). |
FUN |
The function used to get estimates of Theta |
... |
Additional arguments to FUN |
Value
A matrix of BCa bootstrap CI's, the bias parameter and the accellation parameter
Examples
data<-DFSimulated()
boot<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boot<-bootAnalysis(boot, cbind, DFanalysis, 1,2,3, robust=FALSE)
BCa(boot, data, .025,.975, accelleration="bootstrap", DFanalysis, 1,2,3, robust=FALSE)
BarebonesBetas
Description
Gives just the beta weights from a linear model.
Usage
BarebonesBetas(data, Y = NULL, RHS = NULL)
Arguments
data |
Data to be analyzed. Dependent variable MUST BE THE FIRST VARIABLE. |
Y |
optional. The dependent variable |
RHS |
option. The right hand side of the model, in R's model formulation (i.e., ~ X1+X2+etc) |
Value
A vector of beta coefficients
Examples
Data<-TestData()
BarebonesBetas(Data)
DFSimulated
Description
DFSimulated
Usage
DFSimulated(MZ = 250, DZ = 250, a2 = 0.3, c2 = 0.3)
Arguments
MZ |
Number of MZ twins to simulate |
DZ |
Number of DZ twins to simulate |
a2 |
Heritability (proportion of variance) |
c2 |
Shared environment (proportion of variance) |
Value
A dataframe
Examples
TwinData<-DFSimulated(200,200,.3,.3)
DFSimulatedChisq
Description
DFSimulatedChisq
Usage
DFSimulatedChisq(MZ = 250, DZ = 250, a2 = 0.3, c2 = 0.3, df = 10)
Arguments
MZ |
Number of MZ twins to simulate |
DZ |
Number of DZ twins to simulate |
a2 |
Heritability (proportion of variance) |
c2 |
Shared environment (proportion of variance) |
df |
Total degrees of freedom for the Chi-Square variable |
Value
A dataframe of Chi-Square distributed outcome observations for MZ and DZ twins
Examples
TwinData<-DFSimulatedChisq(200,200,.3,.3, 10)
DFSimulatedChisqNew
Description
DFSimulatedChisqNew
Usage
DFSimulatedChisqNew(MZ = 250, DZ = 250, a2 = 0.3, c2 = 0.3, df = 5)
Arguments
MZ |
Number of MZ twins to simulate |
DZ |
Number of DZ twins to simulate |
a2 |
Heritability (proportion of variance) |
c2 |
Shared environment (proportion of variance) |
df |
Total degrees of freedom for the Chi-Square variable |
Value
A dataframe of Chi-Square distributed outcome observations for MZ and DZ twins
Examples
TwinData<-DFSimulatedChisqNew(200,200,.3,.3, 10)
DFanalysis
Description
There are three possible models to be fit. The default is the Rodgers and Kohler formulation of the DF model (Rodgers & Kohler, 2005). The non-default (if RK=F), is to fit the original DeFries-Fulker model. The third option is only used when dominance coefficients are provided, and is based on the formulation by Waller (Waller 1994).
Usage
DFanalysis(
data = NULL,
proband,
sibling,
Rs,
Ds = NULL,
RK = T,
robust = T,
DE = T,
betasonly = F,
typicalSE = F
)
Arguments
data |
A dataframe. This is not necessary as the variables can be passed directly via the other arguments. |
proband |
Called "proband" for historical reasons this is the variable on the left hand side of the regression. |
sibling |
The right hand side version of proband. This would be the matched sibling scores. |
Rs |
This is the vector of relatedness coefficients |
Ds |
A vector of dominance coefficients. 1 for MZ twins, .25 for DZ twins and full siblings. The default is null, and no value should be provided if using the ACE model. This should only have a non-null value when fitting an ADE model. There is an RK version of this model, however it is not based on published work. The RK version uses double entered (and mean centered) data in order to drop the intercept term and the extraneous regression coefficient (both of which can be constrained to 0 when the phenotypic mean is 0). Initial simulations suggest that this formulation provides accurate parameter estimates, however the original formulation can be used by simply setting RK=F. It is assumed that, if RK=T, that DE=T (i.e., do NOT double enter data prior to analysis if using the ADE model). |
RK |
Use the Rodgers and Kohler simplified version of the DF model (recommended). Data should not be double entered prior to analysis. |
robust |
Use the Kohler and Rodgers robust standard errors (recommeneded when using double entered data) |
DE |
Will the data need to be double entered? |
betasonly |
If TRUE only the beta weights from the regression analysis will be returned. |
typicalSE |
Should the typical regression standard errors be used? Default is false. |
Value
The results from MyLM
Examples
TwinData<-DFSimulated(2000,2000,.3,.3)
p<-TwinData[,1]
s<-TwinData[,2]
r<-TwinData[,3]
DFanalysis(data=NULL, p,s,r)
Grouping_function
Description
originally from the ParallelTree package. If data argument is Null, takes a variable "x" and a matrix or dataframe of level identifiers (e.g., mother and then child IDs). Level variables should be included in order from highest level to the lowest. Listwise deletes missing data. Otherwise grabs variables from entered dataframe Group_function
Usage
Group_function(
data = NULL,
x,
levels,
func = mean,
center = FALSE,
nested = TRUE,
append = FALSE,
funcName = "Mean"
)
Arguments
data |
a data frame with the x and level variables included. Default is NULL. |
x |
If data = NULL a dataframe of scores to have the function applied to. If data != NULL, a vector of string(s) naming the variable(s) in data to use. |
levels |
If data = NULL, a dataframe of grouping variables. If data != NULL, a vector of strings naming the variables in data to use. levels should be ordered from the highest level to the lowest. Group and case identifiers should be unique, if they are not unique, cases with non-unique identifiers will be grouped together. |
func |
A function to apply at each group. Default is mean. |
center |
If set to true variables will be group/person mean centered. Note that the grand mean remains unchanged by this operation. If this output is to be passed directly to Parallel_Tree the grand mean should be set to 0. |
nested |
Are level variables nested? Default is TRUE. If set to FALSE means will be calculated for level variable independently. FALSE may be useful in cases of crossed designs. Note that if data are nested but all identifiers are unique both within and across groups nested = FALSE and nested = TRUE will return the same result. |
append |
If set to true, the original data will be returned along with all created variables. |
funcName |
Provides way to name function used. This is used when creating names for created variables. Default is "Mean". |
Value
This function returns a dataframe with variables labeled according to the level at which the function was applied. Assumed function is mean, and all variables are labeled accordingly. If an alternative function is used labels should be manually changed to reflect function used.
Examples
#the ChickWeight data is from base R
#nested is set to false because Chick and Time are crossed
Means_Chick<-Group_function(data=ChickWeight,x="weight", levels =c("Diet","Chick","Time"),
nested = FALSE, append=TRUE)
HoffPseudoStandard
Description
HoffPseudoStandard
Usage
HoffPseudoStandard(betas, SDX, interceptvar)
Arguments
betas |
A vector of betas from a multilevel model |
SDX |
A vector of the standard deviations of the X value for each of the X's associated with the bets |
interceptvar |
A vector of the intercept variances at the level associated with the betas |
Value
A vector of pseudostandardized coefficients
Examples
print("none")
MyLM
Description
MyLM
Usage
MyLM(Y, X, robust = F, betasonly = F, typicalSE = T)
Arguments
Y |
The Y variable |
X |
A matrix of X variables |
robust |
Should robust standard errors be calculated? Assumes a double entered twin dataset with twins evenly spaced in the dataset. |
betasonly |
Should only the betas be returned? Good for bootstrapping |
typicalSE |
Should the typical standard errors be included? Default is true. Can be true when robust is True. |
Value
Returns a matrix of betas and standard errors
Examples
X<-DFSimulated(100,100,.4,.4)
Y<-RK(X[,1],X[,2],X[,3])
MyLM(Y[,1],Y[,c(2:3)],TRUE)
The Naive Bootstrap
Description
The Naive Bootstrap
Usage
NaiveBoot(data, B = 1000, groups = NULL, keepgroups = F, size = 1)
Arguments
data |
data to be bootstrapped |
B |
number of bootstrap samples to take |
groups |
grouping variable if there is one |
keepgroups |
keep the grouping variable? |
size |
size of the bootstrap resamples relative to the original sample |
Value
a list of bootstrap resamples
Examples
X<-TestData()
Y<-NaiveBoot(X)
The Naive Bootstrap
Description
The Naive Bootstrap
Usage
NaiveBoot_dep(data, B = 1000, groups = NULL, keepgroups = F, size = 1)
Arguments
data |
data to be bootstrapped |
B |
number of bootstrap samples to take |
groups |
grouping variable if there is one |
keepgroups |
keep the grouping variable? |
size |
size of the bootstrap resamples relative to the original sample |
Value
a list of bootstrap resamples
Examples
X<-TestData()
Y<-NaiveBoot(X)
RK
Description
RK
Usage
RK(proband, sibling, Rs, DE = T)
Arguments
proband |
column name or number of the proband |
sibling |
column name or number of the siblings |
Rs |
column name or number of the relatedness coefficients |
DE |
Should the data be double entered? |
Value
A dataframe
Examples
X<-DFSimulated(100,100,.3,.3)
Y<-RK(X[,1],X[,2],X[,3])
Sfunc
Description
function for calculating the matrices for the Kohler Rodgers SE
Usage
Sfunc(X, e)
Arguments
X |
A matrix of X variables |
e |
A matrix of error terms |
Value
A matrix
Examples
print("Nah")
TestData
Description
Simple function for creating a dataset of two related variables.
Usage
TestData(nobs = 1000, intercept = 0, beta = 5, meanX = 0, sdX = 1, sdYerr = 1)
Arguments
nobs |
Number of observations, defaults to 1000 |
intercept |
Intercept of the regression. Defaults to 0 |
beta |
Beta for the regression equation, defaults to 5 |
meanX |
Mean of X, defaults to 0 |
sdX |
Standard deviation of X, defaults to 1 |
sdYerr |
Variance of the error term of Y, defaults to 1 |
Value
A dataframe with an X and Y variable produced by the entered parameters
Examples
X<-TestData()
aCalc
Description
This function calculates the actual "a" estimate from the jackknife approximation of a used in BCa CI's
Usage
aCalc(X)
Arguments
X |
A vector of jackknife results |
Value
An estimate of a for use in BCa.
Examples
X<-rchisq(100,2)
aCalc(X)
Title
Description
Title
Usage
aboot(boot)
Arguments
boot |
a vector of bootstrap resample statistics to use to calculate the accelleration parameter. |
Value
a vector of accelleration parameters for use in BCa bootstrap intervals
Examples
data<-DFSimulated()
boots<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boots<-bootAnalysis(boots, cbind, DFanalysis, 1,2,3, robust=FALSE)
boots<-t(boots)
aboot(boots)
add
Description
add
Usage
add(x)
Arguments
x |
a list to be summed. Useful for doing elementwise summation of a list of matrices. |
Value
returns a single summed object (e.g., a matrix)
Examples
x<-list(matrix(c(1:4),nrow=2),matrix(c(1:4),nrow=2))
add(x)
ajack
Description
ajack
Usage
ajack(data, FUN, ...)
Arguments
data |
data to get the bias parameter (a) for |
FUN |
a function to be applied to the data |
... |
additional arguments passed to FUN |
Value
a vector of accelleration parameters for use in BCa bootstrap intervals
Examples
data<-DFSimulated()
ajack(data,DFanalysis, betasonly=TRUE, robust=FALSE)
Title
Description
Title
Usage
bias(boot, theta)
Arguments
boot |
A vector of bootstrap estimates of theta |
theta |
the sample estimate of theta |
Value
z0 the bias parameter for BCa CI
Examples
X<-data.frame(rnorm(1000))
theta<-mean(X)
boot<-NaiveBoot(X)
boot<-lapply(boot, mean)
boot<-do.call(rbind, boot)
bias(boot, theta)
bootAnalysis
Description
bootAnalysis
Usage
bootAnalysis(boot, collapse = cbind, FUN, ...)
Arguments
boot |
A list of bootstrap resamples from NaiveBoot or uniboot. |
collapse |
Should the results be collapsed from list form. Can take values of NULL, cbind or rbind |
FUN |
The function to apply to the bootstrap resamples |
... |
additional arguments to be passed to FUN |
Value
A list or matrix of results
Examples
data<-DFSimulated()
data<-doubleEnter(data[,1],data[,2],data[,3])
boots<-uniboot(data, 1000, "Rs", TRUE, .5, NULL)
results<-bootAnalysis(boots, cbind, FUN=DFanalysis, 1,2,3,TRUE,FALSE,FALSE,TRUE,FALSE)
bootsample
Description
bootsample
Usage
bootsample(data, size = 1)
Arguments
data |
a dataset to be bootstrapped |
size |
the size of the bootstrap sample relative to the original sample |
Value
a dataset
Examples
X<-TestData()
Y<-bootsample(X)
cent
Description
cent
Usage
cent(X)
Arguments
X |
vector to be centered |
Value
Returns a centered vector
Examples
X<-c(1:10)
cent(X)
centerData
Description
centerData
Usage
centerData(data)
Arguments
data |
The data to be centered |
Value
The centered data
Examples
X<-data.frame(X=c(1:4),Y=c(6:9))
centerData(X)
cholcors
Description
cholcors
Usage
cholcors(X, use = "everything")
Arguments
X |
A matrix of data. |
use |
the missing data type to use for the correlation. Default is R's default "everything". |
Value
This function returns the cholesky decomposition of the correlation matrix of the data
Examples
X<-stats::rnorm(100)
Y<-stats::rnorm(100)+X
Z<-cbind(X,Y)
cholcors(Z)
cholcovs
Description
cholcovs
Usage
cholcovs(X, use = "everything")
Arguments
X |
A matrix of data. |
use |
the missing data type to use for the correlation. Default is R's default "everything". |
Value
This function returns the cholesky decomposition of the correlation matrix of the data
Examples
X<-stats::rnorm(100)
Y<-stats::rnorm(100)+X
Z<-cbind(X,Y)
cholcovs(Z)
DoubleEnter
Description
DoubleEnter
Usage
doubleEnter(proband, sibling, Rs)
Arguments
proband |
The proband scores |
sibling |
The matched sibling scores |
Rs |
The relatedness coefficients |
Value
A dataframe
Examples
X<-DFSimulated(10,10,.2,.2)
Y<-doubleEnter(X[,"proband"], X[,"sibling"], X[,"Rs"])
findSa
Description
This is an implementation of the YHY bootstrap covariance matrix.
Usage
findSa(S, fitted, p, a = 0.5, df, n, tau = NULL, tol = 1e-07)
Arguments
S |
Sample covariance matrix |
fitted |
The fitted covariance matrix |
p |
the number of columns in the covariance matrix |
a |
the starting value for the a parameter |
df |
the degrees of freedom in the model |
n |
the number of participants in the model |
tau |
the population tau. If no tau is provided, the estimated tau from the model will be used |
tol |
the difference between ga and tau at which the function will converge |
Value
a list of the "a" adjusted covariance matrix, Sa, the tau, ga, and the number of interations.
Examples
require(Omisc)
require(lavaan)
set.seed(2^7-1)
modelTest<-'
LV1=~ .7*x1+.8*x2+.75*x3+.6*x4
LV2=~ .7*y1+.8*y2+.75*y3+.6*y4
LV1~~.3*LV2
LV1~~1*LV1
LV2~~1*LV2
'
modelFit<-'
LV1=~ x1+x2+x3+x4
LV2=~ y1+y2+y3+y4
LV1~~start(.5)*LV2
LV1~~1*LV1
LV2~~1*LV2
'
testdata<-simulateData(modelTest, sample.nobs = 250)
fit<-cfa(modelFit, testdata)
fitted<-fitted(fit)$cov
fitted<-fitted[,1:ncol(fitted)]
S<-cov(testdata)
p<-8
a<-.5
n<-250
df<-21
findSa(S, fitted, p, .5, df, n)
jackknife
Description
jackknife
Usage
jackknife(data)
Arguments
data |
The data to jackknife |
Value
a list of jackknife datasets
Examples
data<-cbind(1:10,1:10)
result<-jackknife(data)
lapply(result,mean)
justBetas
Description
justBetas
Usage
justBetas(data, Y, X)
Arguments
data |
A data frame |
Y |
The name or column number of the Y variable |
X |
The name(s) or column number(s) of the X variables |
Value
A vector of unstandardized beta weights
Examples
X<-stats::rnorm(100)
Y<-stats::rnorm(100)+5*(X)
data<-cbind(Y,X)
justBetas(data,1,2)
#if you want an intercept
Y<-stats::rnorm(100)+5*(X)+5
data<-cbind(Y,X,1)
justBetas(data,1,c(2:3))
lbind
Description
lbind is meant to be used in conjuction with lapply to combine elements of lists using rbind.
Usage
lbind(index, alist, n)
Arguments
index |
a list of indexes. This should count the number of items to return in the final list |
alist |
a list of objects to be passed to rbind. They should be grouped according to which objects will be combined (e.g., if 1,2,3 are to be passed to cbind then they should be adjacent to eachother). |
n |
The number of objects in each group. Currently each group must consist of the same number of objects. |
Value
a list
Examples
alist<-list(c(1,1),c(2,2),c(3,3))
index<-list(1)
n<-3
lapply(index,lbind,alist,3)
leave1out
Description
leave1out
Usage
leave1out(x, data)
Arguments
x |
Which row(s) of data to leave out |
data |
A dataframe or matrix. |
Value
The reduced dataframe or matrix
Examples
data<-cbind(1:10,1:10)
leave1out(5,data)
resample
Description
resample
Usage
resample(X, size)
Arguments
X |
A vector to be resamples |
size |
The size of the resulting vector. Should be a number such that size*nrow(X) is a whole number |
Value
A vector of resampled X values
Examples
X<-c(1:10)
resample(X,.5)
standardBootIntervals
Description
This returns the quantiles of the bootstrap samples specified by the user. The quantiles uses the type=4 argument of the quantile function, which appears to function best.
Usage
standardBootIntervals(boot, lower = 0.025, upper = 0.975)
Arguments
boot |
A vector of bootstrap results |
lower |
the lower alpha |
upper |
the upper alpha |
Value
A matrix of the mean, median, min, max, lower and upper CI values
Examples
data<-DFSimulated()
boots<-NaiveBoot(data, groups="Rs", keepgroups=TRUE, B=100)
boots<-bootAnalysis(boots, cbind, DFanalysis,1,2,3,TRUE,FALSE,TRUE,TRUE,FALSE)
apply(boots,1, standardBootIntervals)
DFanalysis(data,1,2,3)
Univariate Bootstrap
Description
WARNING: This function can't be used with data that is already fed through the RK function. The correlation matrix will not be positive definite.
Usage
uniboot(
data,
B = 1000,
groups = NULL,
keepgroups = F,
size = 1,
HIcor = NULL,
samplefrom = "group",
use = "everything",
standardized = T
)
Arguments
data |
The data frame to be resampled |
B |
The number of bootstrap samples. |
groups |
A grouping variable name |
keepgroups |
Should the grouping variable be kept in the final datasets? |
size |
The size of the bootstrap sample to be returned. Should be as a proportion and must be evenly divided into nrow(data). |
HIcor |
If a hypothesis imposed correlation matrix is to be used, this argument takes a list of hypothesized correlation matrices. IT MUST BE A LIST OF ONE OR MORE MATRICES. Multiple matrices can be entered in the case of grouped data (one for each group). If the nil-null correlation is to be used an identity matrix can be entered here (the same size as the appropriate correlation matrix). |
samplefrom |
Takes one of either "group" or "whole". When doing bootstrapping of grouped data this tells uniboot if the whole sample should be used as the sampling frame for each group (whole), or not (group). "group" should be used unless it is believed that all groups share the same underlying marginal distribution for each variable (e.g., the same mean and variance in the case of normally distributed data). |
use |
The missing data method for cor. Default is R's default "everything". |
standardized |
should the resampled data be standardized? The default is TRUE. This is computationally more efficient (the data are standardized as a step during the diagonalization procedure). |
Value
A list of bootstrap samples
Examples
data<-TestData()
X<-uniboot(data,1000)
unibootVar
Description
unibootVar
Usage
unibootVar(X, times)
Arguments
X |
The variable |
times |
The number of times the variable is repeated in the univariate sampling frame. This is going to be equal to the number of variables being univariately sampled |
Value
The variance of the variable in the univariate sampling frame
Examples
X<-c(1,2)
times<-100
unibootVar(X,times)
var(X)
Univariate Bootstrap
Description
WARNING: This function can't be used with data that is already fed through the RK function. The correlation matrix will not be positive definite.
Usage
uniboot_dep(
data,
B = 1000,
groups = NULL,
keepgroups = F,
size = 1,
HIcor = NULL,
samplefrom = "group",
use = "everything"
)
Arguments
data |
The data frame to be resampled |
B |
The number of bootstrap samples. Alternatively "sampleframe" which will return the univariate sampling frame. "samplefrom" is not advised when there are many observations and/or many variables as the returned dataframe will be quite large. |
groups |
A grouping variable name |
keepgroups |
Should the grouping variable be kept in the final datasets? |
size |
The size of the bootstrap sample to be returned. Should be as a proportion and must be evenly divided into nrow(data). |
HIcor |
If a hypothesis imposed correlation matrix is to be used, this argument takes a list of hypothesized correlation matrices. IT MUST BE A LIST OF ONE OR MORE MATRICES. Multiple matrices can be entered in the case of grouped data (one for each group). If the nil-null correlation is to be used an identity matrix can be entered here (the same size as the appropriate correlation matrix). |
samplefrom |
Takes one of either "group" or "whole". When doing bootstrapping of grouped data this tells uniboot if the whole sample should be used as the sampling frame for each group (whole), or not (group). "group" should be used unless it is believed that all groups share the same underlying marginal distribution for each variable (e.g., the same mean and variance in the case of normally distributed data). |
use |
The missing data method for cor. Default is R's default "everything". |
Value
A list of bootstrap samples
Examples
data<-TestData()
X<-uniboot(data,1000)
unibootsample
Description
unibootsample
Usage
unibootsample(data, size)
Arguments
data |
A dataframe or matrix to be univariately bootstrapped |
size |
size of each bootstrap sample as a fraction of the total sample size. Total sample size must be evenly divisible by "size". |
Value
A matrix or dataframe with nrow=nrow(X)*size
Examples
X<-c(0:9)
Y<-c(20:29)
Z<-cbind(X,Y)
unibootsample(Z,1)
Title
Description
Title
Usage
zScore(X, reps = 1)
Arguments
X |
The vector to be turned into z scores |
reps |
The number of reps the vector is to be repeated. This will only be used in univariate bootstrapping. The default is 1. |
Value
A vector of z scores.
Examples
X<-c(1:10)
zScore(X)
centerData
Description
centerData
Usage
zScoreData(data)
Arguments
data |
The data to be converted to z scores |
Value
Data converted to z scores
Examples
X<-data.frame(X=c(1:4),Y=c(6:9))
zScoreData(X)