Package 'Omisc' reference manual

Title:	Univariate Bootstrapping and Other Things
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>.
Authors:	Patrick O'Keefe
Maintainer:	Patrick O'Keefe <[email protected]>
License:	GPL-3
Version:	0.1.5
Built:	2024-11-09 06:22:48 UTC
Source:	CRAN

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, Y = NULL, RHS = NULL)
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)
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()
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)

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)

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 = cbind, FUN, ...)
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)

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, use = "everything")
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)
X<-stats::rnorm(100)
Y<-stats::rnorm(100)+X
Z<-cbind(X,Y)
cholcors(Z)

cholcovs

Description

cholcovs

Usage

cholcovs(X, use = "everything")
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)
X<-stats::rnorm(100)
Y<-stats::rnorm(100)+X
Z<-cbind(X,Y)
cholcovs(Z)

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
)
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)
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)

DFSimulatedChisqNew

Description

DFSimulatedChisqNew

Usage

DFSimulatedChisqNew(MZ = 250, DZ = 250, a2 = 0.3, c2 = 0.3, df = 5)
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)
TwinData<-DFSimulatedChisqNew(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"])

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)

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"
)
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)
#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)
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))

lbind

Description

lbind is meant to be used in conjuction with lapply to combine elements of lists using rbind.

Usage

lbind(index, alist, n)
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)
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)
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 = F, betasonly = F, typicalSE = T)
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)
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)
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)
X<-TestData()
Y<-NaiveBoot(X)

The Naive Bootstrap

Description

The Naive Bootstrap

Usage

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

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 = T)
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])
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")

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)
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)
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)

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 = F,
  size = 1,
  HIcor = NULL,
  samplefrom = "group",
  use = "everything",
  standardized = T
)
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)
data<-TestData()
X<-uniboot(data,1000)

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"
)
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)
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)

Title

Description

Title

Usage

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

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