Package 'Omisc' reference manual

Title:	DeFries-Fulker Analysis and Univariate Bootstrapping
Description:	Implements the Univariate Bootstrap and the Traditional (Naive) Bootstrap for resampling multivariate data while preserving covariance structure. Also provides functions for DeFries-Fulker behavioral genetics models, including the Rodgers-Kohler formulation with robust standard errors.
Authors:	Patrick O'Keefe [aut, cre]
Maintainer:	Patrick O'Keefe <[email protected]>
License:	GPL-3
Version:	0.2.0
Built:	2026-05-31 08:07:30 UTC
Source:	https://github.com/cran/Omisc

Title

Description

Title

Usage

aboot(boot)
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)
data<-DFSimulated()
boots<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boots<-bootAnalysis(boots, cbind, DFanalysis, 1,2,3, robust=FALSE)
boots<-t(boots)
aboot(boots)

aCalc

Description

This function calculates the actual "a" estimate from the jackknife approximation of a used in BCa CI's

Usage

aCalc(X)
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)

X<-rchisq(100,2)
aCalc(X)

add

Description

add

Usage

add(x)
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)
x<-list(matrix(c(1:4),nrow=2),matrix(c(1:4),nrow=2))
add(x)

ajack

Description

ajack

Usage

ajack(data, FUN, ...)
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)
data<-DFSimulated()
ajack(data,DFanalysis, betasonly=TRUE, robust=FALSE)

AllBootResults

Description

AllBootResults

Usage

AllBootResults(boot, lower = 0.025, upper = 0.975, data, FUN, ...)
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)
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)

BarebonesBetas

Description

Gives just the beta weights from a linear model.

Usage

BarebonesBetas(data)
BarebonesBetas(data)

Arguments

data

Data to be analyzed. Dependent variable MUST BE THE FIRST VARIABLE.

Value

A vector of beta coefficients

Examples

Data<-TestData()
BarebonesBetas(Data)
Data<-TestData()
BarebonesBetas(Data)

BCa

Description

BCa

Usage

BCa(
  Boot,
  data,
  alphalower = 0.025,
  alphaupper = 0.975,
  accelleration = "jack",
  FUN,
  ...
)
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()
boots<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boots<-bootAnalysis(boots, cbind, DFanalysis, 1,2,3, robust=FALSE)
BCa(boots, data, .025,.975, accelleration="bootstrap", DFanalysis, 1,2,3, robust=FALSE)

data<-DFSimulated()
boots<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boots<-bootAnalysis(boots, cbind, DFanalysis, 1,2,3, robust=FALSE)
BCa(boots, data, .025,.975, accelleration="bootstrap", DFanalysis, 1,2,3, robust=FALSE)

Title

Description

Title

Usage

bias(boot, theta)
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)
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, FUN, ...)
bootAnalysis(boot, collapse, 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)

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)
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)
X<-TestData()
Y<-bootsample(X)

cent

Description

cent

Usage

cent(X)
cent(X)

Arguments

X

vector to be centered

Value

Returns a centered vector

Examples

X<-c(1:10)
cent(X)
X<-c(1:10)
cent(X)

centerData

Description

centerData

Usage

centerData(data)
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)
X<-data.frame(X=c(1:4),Y=c(6:9))
centerData(X)

cholcors

Description

cholcors

Usage

cholcors(X)
cholcors(X)

Arguments

X

A matrix of data.

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)
X<-stats::rnorm(100)
Y<-stats::rnorm(100)+X
Z<-cbind(X,Y)
cholcors(Z)

DFanalysis

Description

DFanalysis

Usage

DFanalysis(
  data = NULL,
  proband,
  sibling,
  Rs,
  RK = TRUE,
  robust = TRUE,
  DE = TRUE,
  betasonly = FALSE,
  typicalSE = FALSE
)
DFanalysis(
  data = NULL,
  proband,
  sibling,
  Rs,
  RK = TRUE,
  robust = TRUE,
  DE = TRUE,
  betasonly = FALSE,
  typicalSE = FALSE
)

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

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)
TwinData<-DFSimulated(2000,2000,.3,.3)
p<-TwinData[,1]
s<-TwinData[,2]
r<-TwinData[,3]
DFanalysis(data=NULL, p,s,r)

DFSimulated

Description

DFSimulated

Usage

DFSimulated(MZ = 250, DZ = 250, a2 = 0.3, c2 = 0.3)
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)
TwinData<-DFSimulated(200,200,.3,.3)

DFSimulatedChisq

Description

DFSimulatedChisq

Usage

DFSimulatedChisq(MZ = 250, DZ = 250, a2 = 0.3, c2 = 0.3, df = 10)
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)
TwinData<-DFSimulatedChisq(200,200,.3,.3, 10)

DoubleEnter

Description

DoubleEnter

Usage

doubleEnter(proband, sibling, Rs)
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"])
X<-DFSimulated(10,10,.2,.2)
Y<-doubleEnter(X[,"proband"], X[,"sibling"], X[,"Rs"])

endparallel

Description

endparallel

Usage

endparallel(clust)
endparallel(clust)

Arguments

clust

dummy variable so that the function executes

Value

Examples

print("NA")
print("NA")

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)
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)
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)

HoffPseudoStandard

Description

HoffPseudoStandard

Usage

HoffPseudoStandard(betas, SDX, interceptvar)
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")
print("none")

jackknife

Description

jackknife

Usage

jackknife(data)
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)

data<-cbind(1:10,1:10)
result<-jackknife(data)
lapply(result,mean)

justBetas

Description

justBetas

Usage

justBetas(data, Y, X)
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))
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))

leave1out

Description

leave1out

Usage

leave1out(x, data)
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)

data<-cbind(1:10,1:10)
leave1out(5,data)

MyLM

Description

MyLM

Usage

MyLM(Y, X, robust = FALSE, betasonly = FALSE, typicalSE = TRUE)
MyLM(Y, X, robust = FALSE, betasonly = FALSE, typicalSE = TRUE)

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)
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 = FALSE, size = 1)
NaiveBoot(data, B = 1000, groups = NULL, keepgroups = FALSE, 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)
X<-TestData()
Y<-NaiveBoot(X)

Parboot

Description

Parboot

Usage

Parboot(X, data, groups = NULL, keepgroups = FALSE, size = 1, HIcor = NULL)
Parboot(X, data, groups = NULL, keepgroups = FALSE, size = 1, HIcor = NULL)

Arguments

X

A dummy variable to make parLapply happy

data

The data frame to be resampled

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 hypothesis imposed correlations are to be used, this is where the HI correlation matrix goes.

Value

A list of bootstrap samples

Examples

#A single univariate bootsrap sample
X<-TestData()
Y<-Parboot(data=X)
#A single univariate bootsrap sample
X<-TestData()
Y<-Parboot(data=X)

parUniboot

Description

parUniboot

Usage

parUniboot(
  data,
  B,
  clust,
  groups = NULL,
  keepgroups = FALSE,
  size = 1,
  HIcor = NULL,
  ...
)
parUniboot(
  data,
  B,
  clust,
  groups = NULL,
  keepgroups = FALSE,
  size = 1,
  HIcor = NULL,
  ...
)

Arguments

data

data to be bootstrapped

B

the number of bootstrap replications

clust

The list of clusters to use. Should be initialized using startparallel()

groups

Groups to be independently bootstrapped

keepgroups

Should the grouping variable be kept in the final dataset?

size

Size of the bootstrap sample relative to the original sample

HIcor

A hypothesis imposed correlation matrix to be used. Default is NULL

...

additional arguments to be passed. Currently does nothing

Value

A list of dataframes of size (size*nrow(data))

Examples

#data<-TestData()
#clust<-startparallel("data")
#results<-parUniboot(data,1000,clust)
#endparallel(clust)
#data<-TestData()
#clust<-startparallel("data")
#results<-parUniboot(data,1000,clust)
#endparallel(clust)

resample

Description

resample

Usage

resample(X, size)
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)
X<-c(1:10)
resample(X,.5)

RK

Description

Usage

RK(proband, sibling, Rs, DE = TRUE)
RK(proband, sibling, Rs, DE = TRUE)

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])
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)
Sfunc(X, e)

Arguments

X

A matrix of X variables

e

A matrix of error terms

Value

A matrix

Examples

print("Nah")
print("Nah")

Title

Description

Title

Usage

standardBootIntervals(boot, lower = 0.025, upper = 0.975)
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)
boots<-bootAnalysis(boots, cbind, DFanalysis,1,2,3,TRUE,FALSE,TRUE,TRUE,FALSE)
apply(boots,1, standardBootIntervals)
DFanalysis(data,1,2,3)
data<-DFSimulated()
boots<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boots<-bootAnalysis(boots, cbind, DFanalysis,1,2,3,TRUE,FALSE,TRUE,TRUE,FALSE)
apply(boots,1, standardBootIntervals)
DFanalysis(data,1,2,3)

startparallel

Description

startparallel

Usage

startparallel(data)
startparallel(data)

Arguments

data

data to pass to the clusters. Must be the name of the data in quotes

Value

Examples

#data<-TestData()
#clust<-startparallel("data")
#endparallel(clust)
#data<-TestData()
#clust<-startparallel("data")
#endparallel(clust)

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)
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()
X<-TestData()

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 = FALSE,
  size = 1,
  HIcor = NULL,
  sampleframe = "group"
)
uniboot(
  data,
  B = 1000,
  groups = NULL,
  keepgroups = FALSE,
  size = 1,
  HIcor = NULL,
  sampleframe = "group"
)

Arguments

data

The data frame to be resampled

B

The number of bootstrap samples. Alternatively "sampleframe" which will return the univariate sampling frame. "sampleframe" 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).

sampleframe

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).

Value

A list of bootstrap samples

Examples

data<-TestData()
X<-uniboot(data,1000)
data<-TestData()
X<-uniboot(data,1000)

unibootsample

Description

unibootsample

Usage

unibootsample(data, size)
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)
X<-c(0:9)
Y<-c(20:29)
Z<-cbind(X,Y)
unibootsample(Z,1)

unibootVar

Description

unibootVar

Usage

unibootVar(X, times)
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)
X<-c(1,2)
times<-100
unibootVar(X,times)
var(X)

zScore

Description

zScore

Usage

zScore(X, times)
zScore(X, times)

Arguments

X

vector to be converted to z scores

times

exponent controlling the denominator scaling

Value

Returns a vector of z scores

Examples

X<-c(1:10)
zScore(X, times=1)
X<-c(1:10)
zScore(X, times=1)

centerData

Description

centerData

Usage

zScoreData(data)
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)
X<-data.frame(X=c(1:4),Y=c(6:9))
zScoreData(X)

Package 'Omisc'

Help Index

Title

Description

Usage

Arguments

Value

Examples

aCalc

Description

Usage

Arguments

Value

Examples

add

Description

Usage

Arguments

Value

Examples

ajack

Description

Usage

Arguments

Value

Examples

AllBootResults

Description

Usage

Arguments

Value

Examples

BarebonesBetas

Description

Usage

Arguments

Value

Examples

BCa

Description

Usage

Arguments

Value

Examples

Title

Description

Usage

Arguments

Value

Examples

bootAnalysis

Description

Usage

Arguments

Value

Examples

bootsample

Description

Usage

Arguments

Value

Examples

cent

Description

Usage

Arguments

Value

Examples

centerData

Description

Usage

Arguments

Value

Examples

cholcors

Description

Usage

Arguments

Value

Examples