Package 'betaNB' reference manual

Title:	Bootstrap for Regression Effect Sizes
Description:	Generates nonparametric bootstrap confidence intervals (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().
Authors:	Ivan Jacob Agaloos Pesigan [aut, cre, cph]
Maintainer:	Ivan Jacob Agaloos Pesigan <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.4.9000
Built:	2024-11-21 05:51:01 UTC
Source:	https://github.com/jeksterslab/betaNB

Estimate Standardized Regression Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Estimate Standardized Regression Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

BetaNB(object, alpha = c(0.05, 0.01, 0.001))
BetaNB(object, alpha = c(0.05, 0.01, 0.001))

Arguments

`object`	Object of class `nb`, that is, the output of the `NB()` function.
`alpha`	Numeric vector. Significance level $\alpha$ .

Details

The vector of standardized regression coefficients ( $\boldsymbol{\hat{\beta}}$ ) is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to $100(1 - \alpha)\%$ from the generated sampling distribution of $\boldsymbol{\hat{\beta}}$ , where $\alpha$ is the significance level.

Value

Returns an object of class betanb which is a list with the following elements:

call: Function call.
args: Function arguments.
thetahatstar: Sampling distribution of $\boldsymbol{\hat{\beta}}$ .
jackknife: Jackknife estimates.
est: Vector of estimated $\boldsymbol{\hat{\beta}}$ .
fun: Function used ("BetaNB").

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# BetaNB -------------------------------------------------------------------
out <- BetaNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# BetaNB -------------------------------------------------------------------
out <- BetaNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

Estimated Parameter Method for an Object of Class `betanb`

Description

Estimated Parameter Method for an Object of Class betanb

Usage

## S3 method for class 'betanb'
coef(object, ...)
## S3 method for class 'betanb'
coef(object, ...)

Arguments

`object`	Object of Class `betanb`, that is, the output of the `BetaNB()`, `RSqNB()`, `SCorNB()`, `DeltaRSqNB()`, `PCorNB()`, or `DiffBetaNB()` functions.
`...`	additional arguments.

Value

Returns a vector of estimated parameters.

Author(s)

Ivan Jacob Agaloos Pesigan

Confidence Intervals Method for an Object of Class `betanb`

Description

Confidence Intervals Method for an Object of Class betanb

Usage

## S3 method for class 'betanb'
confint(object, parm = NULL, level = 0.95, type = "pc", ...)
## S3 method for class 'betanb'
confint(object, parm = NULL, level = 0.95, type = "pc", ...)

Arguments

`object`	Object of Class `betanb`, that is, the output of the `BetaNB()`, `RSqNB()`, `SCorNB()`, `DeltaRSqNB()`, `PCorNB()`, or `DiffBetaNB()` functions.
`parm`	a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
`level`	the confidence level required.
`type`	Charater string. Confidence interval type, that is, `type = "pc"` for percentile; `type = "bc"` for bias corrected; `type = "bca"` for bias corrected and accelerated.
`...`	additional arguments.

Value

Returns a matrix of confidence intervals.

Author(s)

Ivan Jacob Agaloos Pesigan

Estimate Improvement in R-Squared and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Estimate Improvement in R-Squared and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

DeltaRSqNB(object, alpha = c(0.05, 0.01, 0.001))
DeltaRSqNB(object, alpha = c(0.05, 0.01, 0.001))

Arguments

`object`	Object of class `nb`, that is, the output of the `NB()` function.
`alpha`	Numeric vector. Significance level $\alpha$ .

Details

The vector of improvement in R-squared ( $\Delta R^{2}$ ) is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to $100(1 - \alpha)\%$ from the generated sampling distribution of $\Delta R^{2}$ , where $\alpha$ is the significance level.

Value

Returns an object of class betanb which is a list with the following elements:

call: Function call.
args: Function arguments.
thetahatstar: Sampling distribution of $\Delta R^{2}$ .
vcov: Sampling variance-covariance matrix of $\Delta R^{2}$ .
est: Vector of estimated $\Delta R^{2}$ .
fun: Function used ("DeltaRSqNB").

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# DeltaRSqNB ---------------------------------------------------------------
out <- DeltaRSqNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# DeltaRSqNB ---------------------------------------------------------------
out <- DeltaRSqNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

DiffBetaNB(object, alpha = c(0.05, 0.01, 0.001))
DiffBetaNB(object, alpha = c(0.05, 0.01, 0.001))

Arguments

`object`	Object of class `nb`, that is, the output of the `NB()` function.
`alpha`	Numeric vector. Significance level $\alpha$ .

Details

The vector of differences of standardized regression slopes is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to $100(1 - \alpha)\%$ from the generated sampling distribution of differences of standardized regression slopes, where $\alpha$ is the significance level.

Value

Returns an object of class betanb which is a list with the following elements:

call: Function call.
args: Function arguments.
thetahatstar: Sampling distribution of differences of standardized regression slopes.
vcov: Sampling variance-covariance matrix of differences of standardized regression slopes.
est: Vector of estimated differences of standardized regression slopes.
fun: Function used ("DiffBetaNB").

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# DiffBetaNB ---------------------------------------------------------------
out <- DiffBetaNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# DiffBetaNB ---------------------------------------------------------------
out <- DiffBetaNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

1982 National Academy of Sciences Doctoral Programs Data

Description

1982 National Academy of Sciences Doctoral Programs Data

Usage

nas1982
nas1982

Format

Ratings of 46 doctoral programs in psychology in the USA with the following variables:

QUALITY: Program quality ratings.
NFACUL: Number of faculty members in the program.
NGRADS: Number of program graduates.
PCTSUPP: Percentage of program graduates who received support.
PCTGRT: Percent of faculty members holding research grants.
NARTIC: Number of published articles attributed to program faculty member.
PCTPUB: Percent of faculty with one or more published article.

References

National Research Council. (1982). An assessment of research-doctorate programs in the United States: Social and behavioral sciences. doi:10.17226/9781. Reproduced with permission from the National Academy of Sciences, Courtesy of the National Academies Press, Washington, D.C.

Generate the Sampling Distribution of Sample Covariances Using Nonparametric Bootstrapping

Description

Generate the Sampling Distribution of Sample Covariances Using Nonparametric Bootstrapping

Usage

NB(object, R = 5000L, seed = NULL)
NB(object, R = 5000L, seed = NULL)

Arguments

`object`	Object of class `lm`.
`R`	Positive integer. Number of bootstrap replications.
`seed`	Integer. Seed number for reproducibility.

Value

Returns an object of class nb which is a list with the following elements:

call: Function call.
args: Function arguments.
lm_process: Processed lm object.
thetahatstar: Sampling distribution of sample covariances.
jackknife: Jackknife estimates.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Efron, B., & Tibshirani, R. J. (1993) An introduction to the bootstrap. Chapman & Hall.

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 20000L for actual research
  seed = 0508
)
nb
# The `nb` object can be passed as the first argument
# to the following functions
#   - BetaNB
#   - DeltaRSqNB
#   - DiffBetaNB
#   - PCorNB
#   - RSqNB
#   - SCorNB

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 20000L for actual research
  seed = 0508
)
nb
# The `nb` object can be passed as the first argument
# to the following functions
#   - BetaNB
#   - DeltaRSqNB
#   - DiffBetaNB
#   - PCorNB
#   - RSqNB
#   - SCorNB

Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

PCorNB(object, alpha = c(0.05, 0.01, 0.001))
PCorNB(object, alpha = c(0.05, 0.01, 0.001))

Arguments

`object`	Object of class `nb`, that is, the output of the `NB()` function.
`alpha`	Numeric vector. Significance level $\alpha$ .

Details

The vector of squared partial correlation coefficients ( $r^{2}_{p}$ ) is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to $100(1 - \alpha)\%$ from the generated sampling distribution of $r^{2}_{p}$ , where $\alpha$ is the significance level.

Value

Returns an object of class betanb which is a list with the following elements:

call: Function call.
args: Function arguments.
thetahatstar: Sampling distribution of $r^{2}_{p}$ .
vcov: Sampling variance-covariance matrix of $r^{2}_{p}$ .
est: Vector of estimated $r^{2}_{p}$ .
fun: Function used ("PCorNB").

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# PCorNB -------------------------------------------------------------------
out <- PCorNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# PCorNB -------------------------------------------------------------------
out <- PCorNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

Print Method for an Object of Class `betanb`

Description

Print Method for an Object of Class betanb

Usage

## S3 method for class 'betanb'
print(x, alpha = NULL, type = "pc", digits = 4, ...)
## S3 method for class 'betanb'
print(x, alpha = NULL, type = "pc", digits = 4, ...)

Arguments

`x`	Object of Class `betanb`, that is, the output of the `BetaNB()`, `RSqNB()`, `SCorNB()`, `DeltaRSqNB()`, `PCorNB()`, or `DiffBetaNB()` functions.
`alpha`	Numeric vector. Significance level $\alpha$ . If `alpha = NULL`, use the argument `alpha` used in `x`.
`type`	Charater string. Confidence interval type, that is, `type = "pc"` for percentile; `type = "bc"` for bias corrected; `type = "bca"` for bias corrected and accelerated.
`digits`	Digits to print.
`...`	additional arguments.

Value

Prints a matrix of estimates, standard errors, number of bootstrap replications, and confidence intervals.

Author(s)

Ivan Jacob Agaloos Pesigan

Print Method for an Object of Class `nb`

Description

Print Method for an Object of Class nb

Usage

## S3 method for class 'nb'
print(x, ...)
## S3 method for class 'nb'
print(x, ...)

Arguments

`x`	Object of Class `nb`.
`...`	additional arguments.

Value

Prints the first six bootstrap covariance matrices.

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
nb <- NB(object, R = 100)
print(nb)

object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
nb <- NB(object, R = 100)
print(nb)

Estimate Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared) and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Estimate Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared) and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

RSqNB(object, alpha = c(0.05, 0.01, 0.001))
RSqNB(object, alpha = c(0.05, 0.01, 0.001))

Arguments

`object`	Object of class `nb`, that is, the output of the `NB()` function.
`alpha`	Numeric vector. Significance level $\alpha$ .

Details

R-squared ( $R^{2}$ ) and adjusted R-squared ( $\bar{R}^{2}$ ) is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to $100(1 - \alpha)\%$ from the generated sampling distribution of $R^{2}$ and $\bar{R}^{2}$ , where $\alpha$ is the significance level.

Value

Returns an object of class betanb which is a list with the following elements:

call: Function call.
args: Function arguments.
thetahatstar: Sampling distribution of $R^{2}$ and $\bar{R}^{2}$ .
vcov: Sampling variance-covariance matrix of $R^{2}$ and $\bar{R}^{2}$ .
est: Vector of estimated $R^{2}$ and $\bar{R}^{2}$ .
fun: Function used ("RSqNB").

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# RSqNB --------------------------------------------------------------------
out <- RSqNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# RSqNB --------------------------------------------------------------------
out <- RSqNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

SCorNB(object, alpha = c(0.05, 0.01, 0.001))
SCorNB(object, alpha = c(0.05, 0.01, 0.001))

Arguments

`object`	Object of class `nb`, that is, the output of the `NB()` function.
`alpha`	Numeric vector. Significance level $\alpha$ .

Details

The vector of semipartial correlation coefficients ( $r_{s}$ ) is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to $100(1 - \alpha)\%$ from the generated sampling distribution of $r_{s}$ , where $\alpha$ is the significance level.

Value

Returns an object of class betanb which is a list with the following elements:

call: Function call.
args: Function arguments.
thetahatstar: Sampling distribution of $r_{s}$ .
vcov: Sampling variance-covariance matrix of $r_{s}$ .
est: Vector of estimated $r_{s}$ .
fun: Function used ("SCorNB").

Author(s)

Ivan Jacob Agaloos Pesigan

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# SCorNB -------------------------------------------------------------------
out <- SCorNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaNB")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# SCorNB -------------------------------------------------------------------
out <- SCorNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)

Summary Method for an Object of Class `betanb`

Description

Summary Method for an Object of Class betanb

Usage

## S3 method for class 'betanb'
summary(object, alpha = NULL, type = "pc", digits = 4, ...)
## S3 method for class 'betanb'
summary(object, alpha = NULL, type = "pc", digits = 4, ...)

Arguments

`object`	Object of Class `betanb`, that is, the output of the `BetaNB()`, `RSqNB()`, `SCorNB()`, `DeltaRSqNB()`, `PCorNB()`, or `DiffBetaNB()` functions.
`alpha`	Numeric vector. Significance level $\alpha$ . If `alpha = NULL`, use the argument `alpha` used in `object`.
`type`	Charater string. Confidence interval type, that is, `type = "pc"` for percentile; `type = "bc"` for bias corrected; `type = "bca"` for bias corrected and accelerated.
`digits`	Digits to print.
`...`	additional arguments.

Value

Returns a matrix of estimates, standard errors, number of bootstrap replications, and confidence intervals.

Author(s)

Ivan Jacob Agaloos Pesigan

Sampling Variance-Covariance Matrix Method for an Object of Class `betanb`

Description

Sampling Variance-Covariance Matrix Method for an Object of Class betanb

Usage

## S3 method for class 'betanb'
vcov(object, ...)
## S3 method for class 'betanb'
vcov(object, ...)

Arguments

`object`	Object of Class `betanb`, that is, the output of the `BetaNB()`, `RSqNB()`, `SCorNB()`, `DeltaRSqNB()`, `PCorNB()`, or `DiffBetaNB()` functions.
`...`	additional arguments.

Value

Returns the variance-covariance matrix of estimates.

Author(s)

Ivan Jacob Agaloos Pesigan

Package 'betaNB'

Help Index

Estimate Standardized Regression Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Estimated Parameter Method for an Object of Class betanb

Description

Usage

Arguments

Value

Author(s)

Confidence Intervals Method for an Object of Class betanb

Description

Usage

Arguments

Value

Author(s)

Estimate Improvement in R-Squared and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

1982 National Academy of Sciences Doctoral Programs Data

Description

Usage

Format

References

Generate the Sampling Distribution of Sample Covariances Using Nonparametric Bootstrapping

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Print Method for an Object of Class betanb

Description

Usage

Arguments

Value

Author(s)

Print Method for an Object of Class nb

Description

Usage

Arguments

Value

Author(s)

Examples

Estimate Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared) and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Usage

Estimated Parameter Method for an Object of Class `betanb`

Confidence Intervals Method for an Object of Class `betanb`

Print Method for an Object of Class `betanb`

Print Method for an Object of Class `nb`

Summary Method for an Object of Class `betanb`

Sampling Variance-Covariance Matrix Method for an Object of Class `betanb`