Package 'RProbSup'

Title: Calculates Probability of Superiority
Description: The A() function calculates the A statistic, a nonparametric measure of effect size for two independent groups that’s also known as the probability of superiority (Ruscio, 2008), along with its standard error and a confidence interval constructed using bootstrap methods (Ruscio & Mullen, 2012). Optional arguments can be specified to calculate variants of the A statistic developed for other research designs (e.g., related samples, more than two independent groups or related samples; Ruscio & Gera, 2013). <DOI: 10.1037/1082-989X.13.1.19>. <DOI: 10.1080/00273171.2012.658329>. <DOI: 10.1080/00273171.2012.738184>.
Authors: John Ruscio
Maintainer: John Ruscio <[email protected]>
License: MIT + file LICENSE
Version: 3.0
Built: 2024-12-17 06:34:09 UTC
Source: CRAN

Help Index


A

Description

Calculates probability of superiority (A), its standard error, and a confidence interval.

Usage

A(data, design = 1, statistic = 1, weights = FALSE,
w = 0, w1 = 0, w2 = 0, increase = FALSE, ref = 1, r = 0,
n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1)

Arguments

data

For a between subjects design, a matrix of cases (rows) by scores (column 1) and group codes (column 2). For a within subjects design, a matrix of scores with each sample in its own column (matrix).

design

Design of experiment (scalar, default = 1 (for between subjects design), user can also call 2 (for within subjects design)).

statistic

Statistic to be calculated (scalar, default = 1 (A), user can also call 2 (A.AAD), 3 (A.AAPD), 4 (A.IK), or 5 (A.Ord)).

weights

Whether to assign weights to cases (default = FALSE); if set to TRUE, data contains case weights in final column.

w

Weights for cases (vector; default = 0).

w1

Weights for cases in group 1 (vector; default = 0).

w2

Weights for cases in group 2 (vector; default = 0).

increase

Set to TRUE if scores are predicted to increase with group codes (default = FALSE).

ref

Reference group (to compare to all others) (scalar, default = 1).

r

Vector of proportions (vector, default = 0, represents equal proportions).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile)).

seed

Random number seed (scalar, default = 1).

Value

Returns list object with the following elements: A : A statistic (scalar). SE : Standard error of A (scalar). ci.lower : Lower bound of confidence interval (scalar). ci.upper : Upper bound of confidence interval (scalar). conf.level : Confidence level (scalar). n.bootstrap : Number of bootstrap samples (scalar). boot.method : Bootstrap method ("BCA" or "percentile"). n : Sample size (after missing data removed; scalar). n.missing : Number of cases of missing data, removed listewise (scalar).

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
data <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
A(data, 1, 2)

A1

Description

Calculates the standard error and constructs a confidence interval for the A statistic using bootstrap methods.

Usage

A1(y1, y2, weights = FALSE, w1 = 0, w2 = 0, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y1

Scores for group 1 (vector).

y2

Scores for group 2 (vector).

weights

Whether to weight cases (default = FALSE).

w1

Weights for cases in group 1 (optional) (vector, default is 0).

w2

Weights for cases in group 2 (optional) (vector, default is 0).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

#Example used in Ruscio and Mullen (2012)
y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4)
y2 <- c(4, 3, 5, 3, 6, 2, 2, 1, 6, 7, 4, 3, 2, 4, 3)
A1(y1, y2)

A2

Description

Calculates the standard error and constructs a confidence interval for the A statistic for two correlated samples using bootstrap methods.

Usage

A2(y1, y2, weights = FALSE, w = 0, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y1

Scores for group 1 (vector).

y2

Scores for group 2 (vector).

weights

Whether to weight cases (default = FALSE).

w

Weights for cases in group 1 (optional) (vector, default is 0).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4)
y2 <- c(7, 5, 6, 7, 6, 4, 3, 5, 4, 5, 4, 5, 7, 4, 5)
A2(y1, y2)

AAD1

Description

Calculates the confidence interval for the A statistic for the average absolute deviation for two or more groups.

Usage

AAD1(y, r = 0, weights = FALSE, n.bootstrap = 1999, conf.level = .95,
ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

r

Vector of proportions (default = 0, represents equal proportions) (vector).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
AAD1(y)

AAD2

Description

Calculates the confidence interval for the A statistic for the average absolute deviation for two or more correlated samples.

Usage

AAD2(y, r = 0, weights = FALSE, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

r

Vector of proportions (default = 0, represents equal proportions) (vector).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
AAD2(y)

AAPD1

Description

Calculates the confidence interval for the A statistic for the average absolute paired deviation for two or more groups.

Usage

AAPD1(y, weights = FALSE, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
AAPD1(y)

AAPD2

Description

Calculates the confidence interval for the A statistic for the average absolute paired deviation for two or more correlated samples.

Usage

AAPD2(y, weights = FALSE, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
AAPD2(y)

CalcA1

Description

Calculates the A statistic for 2 groups.

Usage

CalcA1(y1, y2, weights = FALSE, w1 = 0, w2 = 0)

Arguments

y1

Scores for group 1 (vector).

y2

Scores for group 2 (vector).

weights

Whether to weight cases (default = FALSE).

w1

Weights for cases in group 1 (optional) (vector, default is 0).

w2

Weights for cases in group 2 (optional) (vector, default is 0).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

#Example used in Ruscio and Mullen (2012)
y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4)
y2 <- c(4, 3, 5, 3, 6, 2, 2, 1, 6, 7, 4, 3, 2, 4, 3)
CalcA1(y1, y2)

CalcA2

Description

Calculates the A statistic for 2 correlated samples.

Usage

CalcA2(y1, y2, weights = FALSE, w = 0)

Arguments

y1

Scores for variable 1 (vector).

y2

Scores for variable 2 (vector).

weights

Whether to weight cases (default = FALSE).

w

Weights (optional) (vector, default is 0).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4)
y2 <- c(7, 5, 6, 7, 6, 4, 3, 5, 4, 5, 4, 5, 7, 4, 5)
CalcA2(y1, y2)

CalcAAD1

Description

Calculates the A statistic for the average absolute deviation for two or more groups. Note: This function is not meant to be called by the user, but it is called by AAD1.

Usage

CalcAAD1(y, r = 0, weights = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

r

Vector of proportions (default = 0, represents equal proportions) (vector).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
CalcAAD1(y)

CalcAAD2

Description

Calculates the A statistic for the average absolute deviation for two or more correlated samples. Note: This function is not meant to be called by the user, but it is called by AAD2.

Usage

CalcAAD2(y, r = 0, weights = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

r

Vector of proportions (default = 0, represents equal proportions) (vector).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
CalcAAD2(y)

CalcAAPD1

Description

Calculates the A statistic for the average absolute paired deviation for two or more groups. Note: This function is not meant to be called by the user, but it is called by AAPD1.

Usage

CalcAAPD1(y, weights = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
AAPD1(y)

CalcAAPD2

Description

Calculates the A statistic for the average absolute paired deviation for two or more correlated samples. Note: This function is not meant to be called by the user, but it is called by AAPD2.

Usage

CalcAAPD2(y, weights = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
AAPD2(y)

CalcIK1

Description

Calculates the A statistic while singling out one group for two or more groups. Note: This function is not meant to be called by the user, but it is called by IK1.

Usage

CalcIK1(y, ref = 1, weights = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

ref

Reference group (to compare to all others) (scalar, default = 1).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
CalcIK1(y)

CalcIK2

Description

Calculates the A statistic while singling out one group for two or more correlated samples. Note: This function is not meant to be called by the user, but it is called by IK2.

Usage

CalcIK2(y, ref = 1, weights = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

ref

Reference group (to compare to all others) (scalar, default = 1).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
CalcIK2(y)

CalcOrd1

Description

Calculates the ordinal comparison of the A statistic for two or more groups. Note: This function is not meant to be called by the user, but it is called by AOrd1.

Usage

CalcOrd1(y, weights = FALSE, increase = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

increase

Set to TRUE if scores are predicted to increase with group codes (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
CalcOrd1(y)

CalcOrd2

Description

Calculates the ordinal comparison of the A statistic for two or more correlated samples. Note: This function is not meant to be called by the user, but it is called by AOrd2.

Usage

CalcOrd2(y, weights = FALSE, increase = FALSE)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

increase

Set to TRUE if scores are predicted to increase with group codes (default = FALSE).

Value

a

The A statistic.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
CalcOrd2(y)

IK1

Description

Calculates the confidence interval for the A statistic while singling out one group for two or more groups.

Usage

IK1(y, ref = 1, weights = FALSE, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

ref

Reference group (to compare to all others) (scalar, default = 1).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
IK1(y)

IK2

Description

Calculates the confidence interval for the A statistic while singling out one group for two or more correlated samples.

Usage

IK2(y, ref = 1, weights = FALSE, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

ref

Reference group (to compare to all others) (scalar, default = 1).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
IK2(y)

Ord1

Description

Calculates the confidence interval for the ordinal comparison of the A statistic for two or more groups.

Usage

Ord1(y, weights = FALSE, increase = FALSE, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

increase

Set to TRUE if scores are predicted to increase with group codes (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25)))
Ord1(y)

Ord2

Description

Calculates the confidence interval for the ordinal comparison of the A statistic for two or more correlated samples.

Usage

Ord2(y, weights = FALSE, increase = FALSE, n.bootstrap = 1999,
conf.level = .95, ci.method = 1, seed = 1)

Arguments

y

Matrix of cases (rows) by scores (column 1) and group codes (column 2) (matrix).

weights

Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE).

increase

Set to TRUE if scores are predicted to increase with group codes (default = FALSE).

n.bootstrap

Number of bootstrap samples (scalar, default = 1999).

conf.level

Confidence level (scalar, default = .95).

ci.method

Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile).

seed

Random number seed (scalar, default = 1).

Value

A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- rnorm(25)
x2 <- x1 - rnorm(25, mean = 1)
x3 <- x2 - rnorm(25, mean = 1)
y <- cbind(x1, x2, x3)
Ord2(y)

RemoveMissing

Description

Checks for missing data and performs listwise deletion if any is detected.

Usage

RemoveMissing(data)

Arguments

data

For a between subjects design, a matrix of cases (rows) by scores (column 1) and group codes (column 2). For a within subjects design, a matrix of scores with each sample in its own column (matrix).

Value

Data matrix with any missing data removed using listwise deletion of cases.

Author(s)

John Ruscio

References

Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013)

Examples

x1 <- c(rnorm(25), NA)
x2 <- x1 - rnorm(26, mean = 1)
x3 <- x2 - rnorm(26, mean = 1)
data <- cbind(c(x1, x2, x3), c(rep(1, 26), rep(2, 26), rep(3, 26)))
A(data, 1, 2)