Package 'WRS2'

Title: A Collection of Robust Statistical Methods
Description: A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Authors: Patrick Mair [cre, aut], Rand Wilcox [aut], Indrajeet Patil [ctb]
Maintainer: Patrick Mair <[email protected]>
License: GPL-3
Version: 1.1-6
Built: 2024-10-16 05:16:16 UTC
Source: https://github.com/r-forge/psychor

Help Index


Robust ANCOVA

Description

This function computes robust ANCOVA for 2 independent groups and one covariate. It compares trimmed means. No parametric assumption (e.g. homogeneity) is made about the form of the regression lines. A running interval smoother is used. A bootstrap version which computes confidence intervals using a percentile t-bootstrap is provided as well.

Usage

ancova(formula, data, tr = 0.2, fr1 = 1, fr2 = 1, pts = NA, ...)

ancboot(formula, data, tr = 0.2, nboot = 599, fr1 = 1, fr2 = 1, pts = NA, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

tr

trim level for the mean.

fr1

values of the span for the first group (1 means unspecified)

fr2

values of the span for the second group (1 means unspecified)

pts

can be used to specify the design points where the regression lines are to be compared; if NA design points are chosen.

nboot

number of bootstrap samples

...

currently ignored.

Value

Returns an object of class ancova containing:

evalpts

covariate values (including points close to these values) where the test statistic is evaluated

n1

number of subjects at evaluation point (first group)

n2

number of subjects at evaluation point (first group)

trDiff

trimmed mean differences

se

standard errors for trimmed mean differences

ci.low

lower confidence limit for trimmed mean differences

ci.hi

upper confidence limit for trimmed mean differences

test

values of the test statistic

crit.vals

critical values

p.vals

p-values

fitted.values

fitted values from interval smoothing

call

function call

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

t2way

Examples

head(invisibility)
ancova(mischief2 ~ cloak + mischief1, data = invisibility)

## specifying covariate evaluation points
ancova(mischief2 ~ cloak + mischief1, data = invisibility, pts = c(3, 4, 8, 1))

## bootstrap version
ancboot(mischief2 ~ cloak + mischief1, data = invisibility)

Comparison of discrete distributions

Description

binband compares two independent variables in terms of their probability function. discANOVA Tests the global hypothesis that for two or more independent groups, the corresponding discrete distributions are identical. That is, test the hypothesis that independent groups have identical multinomial distributions. discmcp provides multiple comparisons for J independent groups having discrete distributions. discstep implements the step-down multiple comparison procedure for comparing J independent discrete random variables.

Usage

binband(x, y, KMS = FALSE, alpha = 0.05, ADJ.P = FALSE, ...)
discANOVA(formula, data, nboot = 500, ...)
discmcp(formula, data, alpha = 0.05, nboot = 500, ...)
discstep(formula, data, nboot = 500, alpha = 0.05, ...)

Arguments

x

an numeric vector of data values for group 1.

y

an numeric vector of data values for group 2.

formula

an object of class formula.

data

an optional data frame for the input data.

nboot

number of bootstrap samples.

alpha

alpha level.

KMS

whether the Kulinskaya-Morgenthaler-Staudte method for comparing binomials should be used.

ADJ.P

whether the critical p-value should be adjusted to control FWE when the sample size is small (<50)

...

currently ignored.

Value

discANOVA returns an object of class "med1way" containing:

test

value of the test statistic

crit.val

critical value

p.value

p-value

call

function call

The remaining functions return an object of class "robtab" containing:

partable

parameter table

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

Kulinskaya, E., Morgenthaler, S. and Staudte, R. (2010). Variance stabilizing the difference of two binomial proportions. American Statistician, 64, p. 350-356.

See Also

t1way, Qanova

Examples

## Consider a study aimed at comparing two methods for reducing shoulder pain after surgery.
## For the first method, the shoulder pain measures are:
x1 <- c(2, 4, 4, 2, 2, 2, 4, 3, 2, 4, 2, 3, 2, 4, 3, 2, 2, 3, 5, 5, 2, 2)
## and for the second method they are:
x2 <- c(5, 1, 4, 4, 2, 3, 3, 1, 1, 1, 1, 2, 2, 1, 1, 5, 3, 5)

fit1 <- binband(x1, x2)
fit1

fit2 <- binband(x1, x2, KMS = TRUE, alpha = 0.01)
fit2

## More than two groups:
discANOVA(libido ~ dose, viagra, nboot = 200)

## Multiple comparisons:
discmcp(libido ~ dose, viagra)

discstep(libido ~ dose, viagra)

Bushtucker Foods

Description

In the TV show "I'm a celebrity, get me out of here" the celebrities had to eat things like stick insects, fish eyes, etc. This dataset records the time taken to retch when eating these things.

Usage

bush

Format

A data frame with 5 variables and 8 observations:

participant

participant ID

stick_insect

time taken to retch when eating a stick insect

kangaroo_testicle

time taken to retch when eating a kangaroo testicle

fish_eye

time taken to retch when eating a fish eye

witchetty_grub

time taken to retch when eating a witchetty grub

Details

Dataset from Field et al. book (p. 557).

References

Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage.

Examples

bush
summary(bush)

A robust two-way mixed ANOVA using trimmed means.

Description

The bwtrim function computes a two-way between-within subjects ANOVA on the trimmed means. It is designed for one between-subjects variable and one within-subjects variable. The functions sppba, sppbb, and sppbi compute the main fixed effect, the main within-subjects effect, and the interaction effect only, respectively, using bootstrap. For these 3 functions the user can choose an M-estimator for group comparisons.

Usage

bwtrim(formula, id, data, tr = 0.2, ...)
tsplit(formula, id, data, tr = 0.2, ...)
sppba(formula, id, data, est = "mom", avg = TRUE, nboot = 500, MDIS = FALSE, ...)
sppbb(formula, id, data, est = "mom", nboot = 500, ...)
sppbi(formula, id, data, est = "mom", nboot = 500, ...)

Arguments

formula

an object of class formula.

id

subject ID.

data

an optional data frame for the input data.

tr

trim level for the mean.

est

Estimate to be used for the group comparisons: either "onestep" for one-step M-estimator of location using Huber's Psi, "mom" for the modified one-step (MOM) estimator of location based on Huber's Psi, or "median".

avg

If TRUE, the analysis is done by averaging K measures of location for each level of the fixed effect, and then comparing averages by testing the hypothesis that all pairwise differences are equal to zero. If FALSE the analysis is done by testing whether K equalities are simultaneously true.

nboot

number of bootstrap samples.

MDIS

if TRUE the depths of the points in the bootstrap cloud are based on Mahalanobis distance, if FALSE a projection distance is used.

...

currently ignored.

Details

The tsplit function is doing exactly the same thing as bwtrim. It is kept in the package in order to be consistent with older versions of the Wilcox (2012) book. For sppba, sppbb, and sppbi the analysis is carried out on the basis of all pairs of difference scores. The null hypothesis is that all such differences have a robust location value of zero. In the formula interface it is required to specify full model.

Value

bwtrim returns an object of class "bwtrim" containing:

Qa

first main effect

A.p.value

p-value first main effect

A.df

df F-distribution first main effect

Qb

second main effect

B.p.value

p-value second main effect

B.df

df F-distribution second main effect

Qab

interaction effect

AB.p.value

p-value interaction effect

AB.df

df F-distribution interaction

call

function call

varnames

variable names

sppba, sppbb, and sppbi returns an object of class "spp" containing:

test

value of the test statistic

p.value

p-value

contrasts

contrasts matrix

References

Wilcox, R. (2017). Introduction to Robust Estimation and Hypothesis Testing (4th ed.). Elsevier.

See Also

t2way

Examples

## data need to be on long format
pictureLong <- reshape(picture, direction = "long", varying = list(3:4), idvar = "case",
timevar = c("pictype"), times = c("couple", "alone"))
pictureLong$pictype <- as.factor(pictureLong$pictype)
colnames(pictureLong)[4] <- "friend_requests"

## 2-way within-between subjects ANOVA
bwtrim(friend_requests ~ relationship_status*pictype, id = case, data = pictureLong)

## between groups effect only (MOM estimator)
sppba(friend_requests ~ relationship_status*pictype, case, data = pictureLong)

## within groups effect only (MOM estimator)
sppbb(friend_requests ~ relationship_status*pictype, case, data = pictureLong)

## interaction effect only (MOM estimator)
sppbi(friend_requests ~ relationship_status*pictype, case, data = pictureLong)

Chile Heat and Length

Description

Originally from pepperjoe.com, this dataset contains the name, length, and heat of chiles. Heat is measured on a scale from 0-11. (0-2 ... for sissys, 3-4 ... sort of hot, 5-6 ... fairly hot, 7-8 ... real hot, 9.5-9 ... torrid, 9.5-11 ... nuclear).

Usage

chile

Format

A data frame with 3 variables and 85 observations:

name

name of the chile

length

length in cm

heat

heat of the chile

References

Wright, D. B., & London, K. (2009). Modern Regression Techniques Using R. Sage.

Examples

summary(chile)

Diet and Weight Loss

Description

Weight loss is studied for three different types of diets.

Usage

bush

Format

A data frame with 7 variables and 78 observations:

gender

gender

age

age

height

body height

diet.type

three types of diet

initial.weight

initial weight before diet

final.weight

final weight after diet

weight.loss

weight loss

References

Couturier, D. L., Nicholls, R., and Fernandes, M. (2018). ANOVA with R: analysis of the diet dataset. Retrieved online.

Examples

str(diet)

The Electric Company

Description

These data are based on an educational TV show for children called "The Electric Company". In each of four grades, the classes were randomized into treated (TV show) and control groups (no TV show). At the beginning and at the end of the school year, students in all the classes were given a reading test. The average test scores per class were recorded.

Usage

electric

Format

A data frame with 5 variables and 192 observations:

City

Fresno and Youngstown

Grade

grade

Pretest

reading scores at the beginning of the semester

Posttest

reading scores at the end of the semester

Group

treatment vs. control

References

Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press: New York, NY.

Examples

summary(electric)

Academic Writing Data

Description

This study looked at the effects of two forms of written corrective feedback on lexico-grammatical accuracy in the academic writing of English as a foreign language university students. It had a 3x4 within-by-between design with three groups (two treatment and one control) measured over four occasions (pretest/treatment, treatment, posttest, delayed posttest).

Usage

bush

Format

A data frame with 4 variables and 120 observations:

id

participant ID

group

control, direct, indirect

essay

four measurement occasions

errorRatio

error ratio

References

McGrath, D. (2016). The Effects of Comprehensive Direct and Indirect Written Corrective Feedback on Accuracy in English as a Foreign Language Students' Writing (Unpublished master's thesis). Macquarie University, Sydney, Australia.

Examples

head(essays)
summary(essays)

European Soccer Leagues

Description

Contains various team stats from five European soccer leagues (2008/09 season).

Usage

eurosoccer

Format

A data frame with 11 variables and 96 teams:

League

Country

Team

Team

Games

Number of games

Won

Games won

Tied

Games tied

Lost

Games lost

GoalsScored

Goals scored

GoalsConceded

Goals conceded

GoalDifference

Goal difference

Points

Final amount of points

GoalsGame

Goal scored per game

Examples

head(eurosoccer)

Beer Goggles Effect

Description

This dataset is about the effects of alcohol on mate selection in night-clubs. The hypothesis is that after alcohol had been consumed, subjective perceptions of physical attractiveness would become more inaccurate (beer-goggles effect). There are 48 participants: 24 males, 24 females. The reseacher took 3 groups of 8 participants to a night club. One group got no alcohol, one group 2 pints, and one group 4 pints. At the end of the evening the researcher took a photograph of the person the participant was chatting up. The attractiveness of the person on the photo was then evaluated by independent judges.

Usage

goggles

Format

A data frame with 3 variables and 48 observations:

gender

24 male, 24 female students

alcohol

amount of alcohol consumed

attractiveness

attractiveness rating (0-100)

Details

Dataset from Field et al. book (p. 501).

References

Field, A., Miles, J, & Field, Z. (2012). Discovering Statistics Using R. Sage.

Examples

goggles
summary(goggles)

Hangover Symptoms

Description

In a study on the effect of consuming alcohol, hangover symptoms were measured for two independent groups, with each subject consuming alcohol and being measured on three different occasions. One group consisted of sons of alcoholics and the other was a control group.

Usage

hangover

Format

A data frame with 4 variables and 120 observations:

symptoms

number of hangover symptoms

group

son of alcoholic vs. control

time

measurement occasion

id

subject ID

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

Examples

summary(hangover)

Cloaks of Invisibility

Description

We are interested in the effect that wearing a cloak of invisibility has on people's tendency to mischief. 80 participants were placed in an enclosed community. Hidden cameras recorded mischievous acts. It was recorded how many mischievous acts were conducted in the first 3 weeks (mischief1). After 3 weeks 34 participants were told that the cameras were switched off so that no one would be able to see what they're getting up to. The remaining 46 subjects were given a cloak of invisibility. These people were told not to tell anyone else about their cloak and they could wear it whenever they liked. The number of mischievous acts were recorded over the next 3 weeks (mischief2).

Usage

invisibility

Format

A data frame with 3 variables and 80 observations:

cloak

factor with 34 subjects in the no cloak condition, 46 in the cloak condition

mischief1

number of mischievous acts during the first 3 weeks

mischief2

number of mischievous acts during the second 3 weeks

Details

Fictional dataset from Field et al. book (p. 485).

References

Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage.

Examples

invisibility
summary(invisibility)

Maternal Self-Efficacy

Description

TIn this dataset (n = 92) the relationship between how girls were raised by there own mother and their later feelings of maternal self-efficacy, i.e. our belief in our ability to succeed in specific situations. The other variable is self-esteem (can act as mediator). All variables are scored on a continuous scale from 1 to 4.

Usage

Leerkes

Format

A data frame with 3 variables and 92 observations:

MatCare

maternal care

Efficacy

maternal self-efficacy

Esteem

self-esteen

References

Leerkes, E.M. & Crockenberg, S.C. (2002). The development of maternal self-efficacy and its impact on maternal behavior. Infancy, 3, 227-247.

Howell, D.C. (2012). Statistical Methods for Psychology (8th edition). Wadsworth, Belmont, CA.

Examples

summary(Leerkes)

Heteroscedastic one-way ANOVA for medians.

Description

This function computes a one-way ANOVA for the medians. Homoscedasticity assumption not required. There shouldn't be too many ties.

Usage

med1way(formula, data, iter = 1000, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

iter

number of iterations to determine critical value.

...

currently ignored.

Details

Evaluating the test statistic using the df proposed in the literature can result in the actual level being less than the nominal level, (i.e., around 0.02-0.025 when testing at the 0.05 level and the sample size is small). A better strategy is to simulate the critical value and computing the p-value accordingly, as implemented in this function.

Value

Returns an object of class med1way containing:

test

F-value of the test statistic

crit.val

critical value

p.value

p-value

call

function call

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

t1way,t1waybt, Qanova

Examples

set.seed(123)
med1way(libido ~ dose, data = viagra, iter = 3000)

A two-way ANOVA for medians.

Description

This function computes a two-way ANOVA medians with interactions effects.

Usage

med2way(formula, data, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

...

currently ignored.

Details

There should not be too many ties in the data. The test statistics for the main effects in med2way are F-distributed, the (heteroscedastic) test for the interaction is chi-square distributed. Post hoc tests can be performed using mcp2a.

Value

Returns an object of class t2way containing:

Qa

first main effect

A.p.value

p-value first main effect

Qb

second main effect

B.p.value

p-value second main effect

Qab

interaction effect

AB.p.value

p-value interaction effect

call

function call

varnames

variable names

dim

design dimensions

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

t2way, med1way

Examples

med2way(attractiveness ~ gender*alcohol, data = goggles)
mcp2a(attractiveness ~ gender*alcohol, data = goggles, est = "median")

Movies and Aggressive Affect

Description

Participants are randomly assigned to one of two groups. The first group watches a violent film, and the other watches a nonviolent film. Afterwards, the aggressive affect is measured, and it is desired to compare three groups, taking gender and degress into account as well.

Usage

movie

Format

A data frame with 4 variables and 68 observations:

degree

no degree vs. degree

gender

36 males, 32 females

type

violend vs. nonviolent

aggressive

aggressive affect

Details

Artificial dataset from Wilcox book (p. 316).

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

Examples

movie
summary(movie)

One-sample percentile bootstrap

Description

Tests whether a robust location measure (median, Huber Psi) differs from a null value and reports a 95% confidence interval based on percentile bootstrap.

Usage

onesampb(x, est = "onestep", nboot = 2000, nv = 0, alpha = 0.05, ...)

Arguments

x

a numeric vector.

est

robust estimator to be used ("onestep", "mom", or "median").

nboot

number of bootstrap samples.

nv

value under H0.

alpha

alpha level.

...

currently ignored.

Value

Returns an object of class "onesampb" containing:

ci

95% confidence interval

estimate

robust location sample estimate

p.value

p-value

n

number of effective observations

call

function call

References

Wilcox, R. (2017). Introduction to Robust Estimation and Hypothesis Testing (4th ed.). Elsevier.

See Also

t1way

Examples

set.seed(123)
x <- rnorm(30)
onesampb(x, nboot = 100)    ## H0: Psi = 0

set.seed(123)
x <- rlnorm(30)
onesampb(x, est = "median", nv = 1)   ## H0: median = 1

Robust correlation coefficients.

Description

The pbcor function computes the percentage bend correlation coefficient, wincor the Winsorized correlation, pball the percentage bend correlation matrix, winall the Winsorized correlation matrix.

Usage

pbcor(x, y = NULL, beta = 0.2, ci = FALSE, nboot = 500, alpha = 0.05, ...)
pball(x, beta = 0.2, ...)
wincor(x, y = NULL, tr = 0.2, ci = FALSE, nboot = 500, alpha = 0.05, ...)
winall(x, tr = 0.2, ...)

Arguments

x

a numeric vector, a matrix or a data frame.

y

a second numeric vector (for correlation functions).

beta

bending constant.

tr

amount of Winsorization.

ci

whether boostrap CI should be computed or not.

nboot

number of bootstrap samples for CI computation.

alpha

alpha level for CI computation.

...

currently ignored.

Details

It tested is whether the correlation coefficient equals 0 (null hypothesis) or not. Missing values are deleted pairwise. The tests are sensitive to heteroscedasticity. The test statistic H in pball tests the hypothesis that all correlations are equal to zero.

Value

pbcor and wincor return an object of class "pbcor" containing:

cor

robust correlation coefficient

test

value of the test statistic

p.value

p-value

n

number of effective observations

cor_ci

bootstrap confidence interval

call

function call

pball and winall return an object of class "pball" containing:

pbcorm

robust correlation matrix

p.values

p-values

H

H-statistic

H.p.value

p-value H-statistic

cov

variance-covariance matrix

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

twocor

Examples

x1 <- subset(hangover, subset = (group == "control" & time == 1))$symptoms
x2 <- subset(hangover, subset = (group == "control" & time == 2))$symptoms

pbcor(x1, x2)
pbcor(x1, x2, beta = 0.1, ci = TRUE)

wincor(x1, x2)
wincor(x1, x2, tr = 0.1, ci = TRUE)

require(reshape)
hanglong <- subset(hangover, subset = group == "control")
hangwide <- cast(hanglong, id ~ time, value = "symptoms")[,-1]

pball(hangwide)
winall(hangwide)

Profile Pictures

Description

This dataset examines how the profile pictures on social network platforms affect the number of friend requests when females are in a relationship. The relationship status is a between-subject variable (part of the participants did set their status to relationship). For the first 3 weeks the subjects had a picture of their own in their profiles. For the following 3 weeks they posted a picture with a man.

Usage

picture

Format

A data frame with 4 variables and 40 observations:

case

subject id

relationship_status

Relationship status on social network platform

couple

amount of friend requests when profile picture as couple

alone

amount of friend requests when profile picture as single

Details

Dataset from Field et al. book (p. 644).

References

Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage.

Examples

picture
summary(picture)

Pygmalion Data

Description

The Pygmalion effect is the phenomenon where higher expectations lead to an increase in performance. For instance, when teachers expect students to do well and show intellectual growth, they do; when teachers do not have such expectations, performance and growth are not so encouraged and may in fact be discouraged in a variety of ways. This dataset contains reasoning IQ scores of children. For the experimental group, positive expectancies had been suggested to teachers after the pretest. For the experimental group, no expectancies had been suggested after the pretest. For both groups we have reasoning IQ posttest scores. The dataset is taken from Elashoff and Snow (1970).

Usage

Pygmalion

Format

A data frame with 3 variables and 114 observations:

Pretest

pretest score

Posttest

posttest score

Group

treatment vs. control

References

Elashoff, J. D., & Snow, R. E. (1970). A case study in statistical inference: Reconsideration of the Rosenthal-Jacobson data on teacher expectancy. Technical Report No. 15, School of Education, Stanford University.

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

Examples

summary(Pygmalion)

Quantile ANOVA

Description

One-way ANOVA based on quantiles. Only known method to work well when tied values are likely to occur.

Usage

Qanova(formula, data, q = 0.5, nboot = 600, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

q

quantile (or vector of quantiles) to be used.

nboot

number of bootstrap samples

...

currently ignored.

Details

Test global hypothesis that J independent groups have equal quantiles (default: median) using the Harrell-Davis estimator. Performs well when there are tied values.

Value

Qanova an object of class "qanova" containing:

psihat

value of the test statistics

contrasts

contrasts

p.value

p-value

call

function call

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

qcomhd, t1way, discANOVA

Examples

## median comparison
set.seed(123)
fitmed <- Qanova(libido ~ dose, viagra, nboot = 200)
fitmed

## 1st, 3rd quartile comparison
set.seed(123)
fitquart <- Qanova(libido ~ dose, viagra, q = c(0.25, 0.75), nboot = 200)
fitquart

A heteroscedastic one-way repeated measures ANOVA for trimmed means.

Description

The rmanova function computes a one-way repeated measures ANOVA for the trimmed means. Homoscedasticity assumption not required. Corresponding post hoc tests can be performed using rmmcp.

Usage

rmanova(y, groups, blocks, tr = 0.2, ...)
rmmcp(y, groups, blocks, tr = 0.2, alpha = 0.05, ...)

Arguments

y

a numeric vector of data values (response).

groups

a vector giving the group of the corresponding elements of y.

blocks

a vector giving the block of the corresponding elements of y.

tr

trim level for the mean.

alpha

alpha level for post hoc comparisons.

...

currently ignored.

Value

rmanova an object of class "t1way" containing:

test

value of the test statistic

df1

degrees of freedom

df2

degrees of freedom

p.value

p-value

call

function call

rmmcp returns an object of class "mcp1" containing:

comp

inference for all pairwise comparisons

fnames

names of the factor levels

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

rmanovab, med1way, t1way

Examples

head(WineTasting)
rmanova(WineTasting$Taste, WineTasting$Wine, WineTasting$Taster)

## post hoc
rmmcp(WineTasting$Taste, WineTasting$Wine, WineTasting$Taster)

head(bush)
require(reshape)
bushLong <- melt(bush, id.var = "participant", variable_name = "food")
rmanova(bushLong$value, bushLong$food, bushLong$participant)

## post hoc
rmmcp(bushLong$value, bushLong$food, bushLong$participant)

A heteroscedastic one-way repeated measures bootstrap ANOVA for trimmed means.

Description

The rmanovab function computes a bootstrap version of the one-way repeated measures ANOVA for the trimmed means. Homoscedasticity assumption not required. Corresponding post hoc tests can be performed using pairdepb.

Usage

rmanovab(y, groups, blocks, tr = 0.2, nboot = 599, ...)
pairdepb(y, groups, blocks, tr = 0.2, nboot = 599, ...)

Arguments

y

a numeric vector of data values (response).

groups

a vector giving the group of the corresponding elements of y.

blocks

a vector giving the block of the corresponding elements of y.

tr

trim level for the mean.

nboot

number of bootstrap samples.

...

currently ignored.

Value

rmanovab an object of class "rmanovab" containing:

test

value of the test statistic

crit

critical value

call

function call

pairdepb returns an object of class "mcp2" containing:

comp

inference for all pairwise comparisons

fnames

names of the factor levels

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

rmanova

Examples

set.seed(123)
rmanovab(WineTasting$Taste, WineTasting$Wine, WineTasting$Taster, nboot = 300)

## post hoc
set.seed(123)
pairdepb(WineTasting$Taste, WineTasting$Wine, WineTasting$Taster, nboot = 300)

Running Interval Smoother

Description

The runmean implements a running interval smoother on the trimmed mean, rungen uses general M-estimators, runmbo performs interval smoothing on M-estimators with bagging.

Usage

runmean(x, y, fr = 1, tr = 0.2, ...)
rungen(x, y, fr = 1, est = "mom", ...)
runmbo(x, y, fr = 1, est = "mom", nboot = 40, ...)

Arguments

x

a numeric vector of data values (predictor)

y

a numeric vector of data values (response)

fr

smoothing factor (see details)

tr

trim level for the mean

est

type of M-estimator ("mom", "onestep", or "median")

nboot

number of bootstrap samples

...

currently ignored.

Details

The larger the smoothing factor, the stronger the smoothing. Often the choice fr = 1 gives good results; the general strategy is to find the smallest constant so that the plot looks reasonably smooth.

Value

Returns the fitted values.

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

ancova

Examples

## trimmed mean smoother
fitmean <- runmean(Pygmalion$Pretest, Pygmalion$Posttest)
## MOM smoother
fitmest <- rungen(Pygmalion$Pretest, Pygmalion$Posttest)
## median smoother
fitmed <- rungen(Pygmalion$Pretest, Pygmalion$Posttest, est = "median")
## bagged onestep smoother
fitbag <- runmbo(Pygmalion$Pretest, Pygmalion$Posttest, est = "onestep")

## plot smoothers
plot(Pygmalion$Pretest, Pygmalion$Posttest, col = "gray", xlab = "Pretest", ylab = "Posttest",
     main = "Pygmalion Smoothing")
orderx <- order(Pygmalion$Pretest)
lines(Pygmalion$Pretest[orderx], fitmean[orderx], lwd = 2)
lines(Pygmalion$Pretest[orderx], fitmest[orderx], lwd = 2, col = 2)
lines(Pygmalion$Pretest[orderx], fitmed[orderx], lwd = 2, col = 3)
lines(Pygmalion$Pretest[orderx], fitbag[orderx], lwd = 2, col = 4)
legend("topleft", legend = c("Trimmed Mean", "MOM", "Median", "Bagged Onestep"), col = 1:4, lty = 1)

Arachnophobes

Description

24 arachnophobes were used in all. 12 were asked to play with a big hairy tarantula spider with big fangs and an evil look. Their subsequent anxiety was measured. The remaining 12 were shown only picture of the same hairy tarantula. Again, the anxiety was measured.

Usage

spider

Format

A data frame with 2 variables and 24 observations:

Group

picture vs. real spider

Anxiety

anxiety measure

Details

Dataset from Field et al. book (p. 362).

References

Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage.

Examples

spider

Optimistic and Pessimistic Swimmers

Description

At a swimming team practice, all participants were asked to swim their best event as far as possible, but in each case the time that was reported was falsified to indicate poorer than expected performance (i.e., each swimmer was disappointed). 30 min later, they did the same performance. The authors predicted that on the second trial more pessimistic swimmers would do worse than on their first trial, whereas optimistic swimmers would do better. The response is ratio = Time1/Time2 (> 1 means that a swimmer did better in trial 2).

Usage

swimming

Format

A data frame with 4 variables and 58 observations:

Optim

Optimists and pessimists

Sex

Gender of the swimmer

Event

Swimming event: freestyle, breaststroke, backstroke

Ratio

Ratio between the swimming times

References

Seligman, M. E. P., Nolen-Hoeksema, S., Thornton, N., & Thornton, C. M. (1990). Explanatory style as a mechanism of disappointing athletic performance. Psychological Science, 1, 143-146.

Examples

summary(swimming)

A heteroscedastic one-way ANOVA for trimmed means.

Description

The t1way function computes a one-way ANOVA on trimmed means. Homoscedasticity assumption not required. It uses a generalization of Welch's method. Corresponding post hoc tests can be performed using lincon.

Usage

t1way(formula, data, tr = 0.2, alpha = 0.05, nboot = 100, ...)
lincon(formula, data, tr = 0.2, alpha = 0.05, method = "hochberg", ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

tr

trim level for the mean.

alpha

alpha level for CI computation.

nboot

number of bootstrap samples for effect size CI computation.

method

method to correct the p-value (see p.adjust)

...

currently ignored.

Details

In the post hoc computations, confidence intervals and p-values are adjusted to control FWE. The default for the p-values is to use Hochberg's 1988 sharper Bonferroni procedure.

Value

t1way returns an object of class "t1way" containing:

test

value of the test statistic (F-statistic)

df1

degrees of freedom

df2

degrees of freedom

p.value

p-value

effsize

explanatory measure of effect size

effsize_ci

boostrap effect size CI

call

function call

lincon returns an object of class "mcp1" containing:

comp

inference for all pairwise comparisons

fnames

names of the factor levels

linconv2 returns an object of class "linconv2" containing:

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

med1way,t1waybt

Examples

set.seed(123)
t1way(libido ~ dose, data = viagra)

## post hoc tests
lincon(libido ~ dose, data = viagra)

Bootstrap version of the heteroscedastic one-way ANOVA for trimmed means.

Description

Test the hypothesis of equal trimmed means using a percentile t bootstrap method. Corresponding post hoc tests are provided in mcppb20.

Usage

t1waybt(formula, data, tr = 0.2, nboot = 599, ...)
mcppb20(formula, data, tr = 0.2, nboot = 599, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

tr

trim level for the mean.

nboot

number of bootstrap samples.

...

currently ignored.

Value

Returns an object of class t1waybt containing:

test

value of the test statistic

p.value

p-value

Var.Explained

explained amount of variance

Effect.Size

effect size

nboot.eff

effective number of bootstrap samples

call

function call

mcppb20 returns an object of class "mcp1" containing:

comp

inference for all pairwise comparisons

fnames

names of the factor levels

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

t1way,med1way

Examples

t1waybt(libido ~ dose, data = viagra)

## post hoc
mcppb20(libido ~ dose, data = viagra)

A two-way ANOVA for trimmed means, M-estimators, and medians.

Description

The t2way function computes a two-way ANOVA for trimmed means with interactions effects. Corresponding post hoc tests are in mcp2atm. pbad2way performs a two-way ANOVA using M-estimators for location with mcp2a for post hoc tests.

Usage

t2way(formula, data, tr = 0.2, ...)
pbad2way(formula, data, est = "mom", nboot = 599, pro.dis = FALSE, ...)
mcp2atm(formula, data, tr = 0.2, ...)
mcp2a(formula, data, est = "mom", nboot = 599, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

tr

trim level for the mean.

est

Estimate to be used for the group comparisons: either "onestep" for one-step M-estimator of location using Huber's Psi, "mom" for the modified one-step (MOM) estimator of location based on Huber's Psi, or "median".

nboot

number of bootstrap samples.

pro.dis

If FALSE, Mahalanobis distances are used; if TRUE projection distances are computed.

...

currently ignored.

Details

t2way does not report any degrees of freedom since an adjusted critical value is used. pbad2way returns p-values only; if it happens that the variance-covariance matrix in the Mahalanobis distance computation is singular, it is suggested to use the projection distances by setting pro.dis = TRUE.

Value

The functions t2way and pbad2way return an object of class t2way containing:

Qa

first main effect

A.p.value

p-value first main effect

Qb

second main effect

B.p.value

p-value second main effect

Qab

interaction effect

AB.p.value

p-value interaction effect

call

function call

varnames

variable names

dim

design dimensions

The functions mcp2atm and mcp2a return an object of class mcp containing:

effects

list with post hoc comparisons for all effects

contrasts

design matrix

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

t1way, med1way, t2way

Examples

## 2-way ANOVA on trimmed means
t2way(attractiveness ~ gender*alcohol, data = goggles)

## post hoc tests
mcp2atm(attractiveness ~ gender*alcohol, data = goggles)

## 2-way ANOVA on MOM estimator
pbad2way(attractiveness ~ gender*alcohol, data = goggles)

## post hoc tests
mcp2a(attractiveness ~ gender*alcohol, data = goggles)

## 2-way ANOVA on medians
pbad2way(attractiveness ~ gender*alcohol, data = goggles, est = "median")

## post hoc tests
mcp2a(attractiveness ~ gender*alcohol, data = goggles, est = "median")

## extract design matrix
model.matrix(mcp2a(attractiveness ~ gender*alcohol, data = goggles, est = "median"))

A three-way ANOVA for trimmed means.

Description

This function computes a three-way ANOVA for trimmed means with all interactions effects.

Usage

t3way(formula, data, tr = 0.2, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

tr

trim level for the mean.

...

currently ignored.

Value

Returns an object of class t3way containing:

Qa

first main effect

A.p.value

p-value first main effect

Qb

second main effect

B.p.value

p-value second main effect

Qc

third main effect

C.p.value

p-value third main effect

Qab

first two-way interaction effect

AB.p.value

p-value first two-way interaction effect

Qac

second two-way interaction effect

AC.p.value

p-value second two-way interaction effect

Qbc

third two-way interaction effect

BC.p.value

p-value third two-way interaction effect

Qabc

three-way interaction effect

ABC.p.value

p-value three-way interaction effect

call

function call

varnames

variable names

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

t1way, t2way

Examples

t3way(aggressive ~ degree*gender*type, data = movie)

Bootstrap-t method for one-sample test

Description

Compute a 1-alpha confidence interval for the trimmed mean using a bootstrap percentile t method.

Usage

trimcibt(x, nv = 0, tr = 0.2, alpha = 0.05, nboot = 200, ...)

Arguments

x

a numeric vector.

nv

value under H0.

tr

trim level for the mean.

alpha

alpha level.

nboot

number of bootstrap samples.

...

currently ignored.

Value

Returns an object of class "trimcibt" containing:

ci

95% confidence interval

estimate

trimmed mean

p.value

p-value

test.stat

t-statistic

tr

trimming level

n

number of effective observations

References

Wilcox, R. (2017). Introduction to Robust Estimation and Hypothesis Testing (4th ed.). Elsevier.

See Also

onesampb

Examples

set.seed(123)
  x <- rnorm(30)
  trimcibt(x, nboot = 100)    ## H0: Psi = 0

Robust location measures and their standard errors (se).

Description

The following functions for estimating robust location measures and their standard errors are provided: winmean for the Winsorized mean, winse for its se, trimse for the trimmend mean se, msmedse for the median se, mest for the M-estimator with se in mestse. The functions onestep and mom compute the one-step and modified one-step (MOM) M-estimator. The Winsorized variance is implemented in winvar.

Usage

winmean(x, tr = 0.2, na.rm = FALSE, ...)
winvar(x, tr = 0.2, na.rm = FALSE, STAND = NULL, ...)
winse(x, tr = 0.2, ...)
trimse(x, tr = 0.2, na.rm = FALSE, ...)
msmedse(x, sewarn = TRUE, ...)
mest(x, bend = 1.28, na.rm = FALSE, ...)
mestse(x, bend = 1.28, ...)
onestep(x, bend = 1.28, na.rm = FALSE, MED = TRUE, ...)
mom(x, bend = 2.24, na.rm = TRUE, ...)

Arguments

x

a numeric vector containing the values whose measure is to be computed.

tr

trim lor Winsorizing level.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

sewarn

a logical value indicating whether warnings for ties should be printed.

bend

bending constant for M-estimator.

MED

if TRUE, median is used as initial estimate.

STAND

no functionality, kept for WRS compatibility purposes.

...

currently ignored.

Details

The standard error for the median is computed according to McKean and Shrader (1984).

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

McKean, J. W., & Schrader, R. M. (1984). A comparison of methods for studentizing the sample median. Communications in Statistics - Simulation and Computation, 13, 751-773.

Dana, E. (1990). Salience of the self and salience of standards: Attempts to match self to standard. Unpublished PhD thesis, Department of Psychology, University of Southern California.

Examples

## Self-awareness data (Dana, 1990): Time persons could keep a portion of an
## apparatus in contact with a specified range.
self <- c(77, 87, 88, 114, 151, 210, 219, 246, 253, 262, 296, 299, 306, 376,
          428, 515, 666, 1310, 2611)
mean(self, 0.1)     ## .10 trimmed mean
trimse(self, 0.1)   ## se trimmed mean
winmean(self, 0.1)  ## Winsorized mean (.10 Winsorizing amount)
winse(self, 0.1)    ## se Winsorized mean
winvar(self, 0.1)   ## Winsorized variance
median(self)        ## median
msmedse(self)       ## se median
mest(self)          ## Huber M-estimator
mestse(self)
onestep(self)       ## one-step M-estimator
mom(self)           ## modified one-step M-estimator

Confidence intervals for two-sided tests on correlation coefficients.

Description

The twopcor function tests whether the difference between two Pearson correlations is 0. The twocor function performs the same test on a robust correlation coefficient (percentage bend correlation or Winsorized correlation).

Usage

twopcor(x1, y1, x2, y2, nboot = 599, ...)
twocor(x1, y1, x2, y2, corfun = "pbcor", nboot = 599, tr = 0.2, beta = 0.2, ...)

Arguments

x1

a numeric vector.

y1

a numeric vector.

x2

a numeric vector.

y2

a numeric vector.

nboot

number of bootstrap samples.

corfun

Either "pbcor" for percentage based correlation or "wincor" for Winsorized correlation.

tr

amount of Winsorization.

beta

bending constant.

...

currently ignored.

Details

It is tested whether the first correlation coefficient (based on x1 and y1) equals to the second correlation coefficient (based on x2 and y2). Both approaches return percentile bootstrap CIs.

Value

twopcor and twocor return an object of class "twocor" containing:

r1

robust correlation coefficient

r2

value of the test statistic

ci

confidence interval

p.value

p-value

call

function call

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

pbcor, wincor

Examples

ct1 <- subset(hangover, subset = (group == "control" & time == 1))$symptoms
ct2 <- subset(hangover, subset = (group == "control" & time == 2))$symptoms
at1 <- subset(hangover, subset = (group == "alcoholic" & time == 1))$symptoms
at2 <- subset(hangover, subset = (group == "alcoholic" & time == 2))$symptoms

set.seed(111)
twopcor(ct1, ct2, at1, at2)
set.seed(123)
twocor(ct1, ct2, at1, at2, corfun = "pbcor", beta = 0.15)
set.seed(224)
twocor(ct1, ct2, at1, at2, corfun = "wincor", tr = 0.15, nboot = 50)

Effects of Viagra

Description

Participants were assigned randomly to three viagra dosages (placebo, low dosage, high dosage). The dependent variable was an objective measure of libido.

Usage

viagra

Format

A data frame with 2 variables and 15 observations:

dose

viagra dosage

libido

objective measure of libido

Details

Artificial dataset from Field et al. book (p. 401).

References

Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage.

Examples

viagra

Wine Tasting

Description

In this hypothetical dataset we have three types of wine (A, B and C). We asked 22 friends to taste each of the three wines (in a blind fold fashion), and then to give a grade of 1 to 7. We asked them to rate the wines 5 times each, and then averaged their results to give a number for a persons preference for each wine.

Usage

WineTasting

Format

A data frame with 3 variables and 66 observations:

Taste

Taste Rating

Wine

Wine (A, B, C)

Taster

Taster (index)

Examples

WineTasting
summary(WineTasting)

Effect size for dependent samples ANOVA

Description

Compute an AKP-type effect size for dependent sample ANOVA

Usage

wmcpAKP(x, tr = 0.2, nboot = 200, ...)

Arguments

x

data frame in wide format (no missing values allowed)

tr

trim level for the means.

nboot

number of bootstrap samples.

...

currently ignored.

Details

The computation is based on a modification of the Algina-Keselman-Penfield effect size for J dependent samples.

References

Algina, J., Keselman, H.J., & Penfield, R.D. (2005). An alternative to Cohen's standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10, 317-328.

See Also

rmanova, yuend

Examples

## Not run: 
require(reshape)
WineLong <- cast(WineTasting, Taster ~ Wine, value = "Taste")[,-1]
set.seed(123)
effsize <- wmcpAKP(WineLong, nboot = 20)
effsize

## End(Not run)

Independent samples t-tests on robust location measures including effect sizes.

Description

The function yuen performs Yuen's test for trimmed means, yuenbt is a bootstrap version of it. akp.effect and yuen.effect.ci can be used for effect size computation. The pb2gen function performs a t-test based on various robust estimators, medpb2 compares two independent groups using medians, and qcomhd compares arbitrary quantiles.

Usage

yuen(formula, data, tr = 0.2, ...)
yuenbt(formula, data, tr = 0.2, nboot = 599, side = TRUE, ...)
akp.effect(formula, data, EQVAR = TRUE, tr = 0.2, nboot = 200, alpha = 0.05, ...)
yuen.effect.ci(formula, data, tr = 0.2, nboot = 400, alpha = 0.05, ...)
pb2gen(formula, data, est = "mom", nboot = 599, ...)
medpb2(formula, data, nboot = 2000, ...)
qcomhd(formula, data, q = c(0.1, 0.25, 0.5, 0.75, 0.9),
       nboot = 2000, alpha = 0.05, ADJ.CI = TRUE, ...)

Arguments

formula

an object of class formula.

data

an optional data frame for the input data.

tr

trim level for the mean.

nboot

number of bootstrap samples.

side

side = TRUE indicates two-sided method using absolute value of the test statistics within the bootstrap; otherwise the equal-tailed method is used.

est

estimate to be used for the group comparisons: either "onestep" for one-step M-estimator of location using Huber's Psi, "mom" for the modified one-step (MOM) estimator of location based on Huber's Psi, or "median", "mean".

q

quantiles to be used for comparison.

alpha

alpha level.

ADJ.CI

whether CIs should be adjusted.

EQVAR

whether variances are assumed to be equal across groups.

...

currently ignored.

Details

If yuenbt is used, p-value computed only when side = TRUE. medpb2 is just a wrapper function for pb2gen with the median as M-estimator. It is the only known method to work well in simulations when tied values are likely to occur.qcomhd returns p-values and critical p-values based on Hochberg's method.

Value

Returns objects of classes "yuen" or "pb2" containing:

test

value of the test statistic (t-statistic)

p.value

p-value

conf.int

confidence interval

df

degress of freedom

diff

trimmed mean difference

effsize

explanatory measure of effect size

call

function call

qcomhd returns an object of class "robtab" containing:

partable

parameter table

References

Algina, J., Keselman, H.J., & Penfield, R.D. (2005). An alternative to Cohen's standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10, 317-328.

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

Wilcox, R., & Tian, T. (2011). Measuring effect size: A robust heteroscedastic approach for two or more groups. Journal of Applied Statistics, 38, 1359-1368.

Yuen, K. K. (1974). The two sample trimmed t for unequal population variances. Biometrika, 61, 165-170.

See Also

t1way,t1waybt

Examples

set.seed(123)
## Yuen's test
yuen(Anxiety ~ Group, data = spider)

## Bootstrap version of Yuen's test (symmetric CIs)
yuenbt(Anxiety ~ Group, data = spider)

## Robust Cohen's delta
akp.effect(Anxiety ~ Group, data = spider)

## Using an M-estimator
pb2gen(Anxiety ~ Group, data = spider, est = "mom")
pb2gen(Anxiety ~ Group, data = spider, est = "mean")
pb2gen(Anxiety ~ Group, data = spider, est = "median")

## Using the median
medpb2(Anxiety ~ Group, data = spider)

## Quantiles
set.seed(123)
qcomhd(Anxiety ~ Group, data = spider, q = c(0.8, 0.85, 0.9), nboot = 500)

Paired samples robust t-tests.

Description

The function yuend performs Yuen's test on trimmed means for dependent samples. Dqcomhd compares the quantiles of the marginal distributions associated with two dependent groups via hd estimator. Tied values are allowed. dep.effect computes various effect sizes and confidence intervals for two dependent samples (see Details).

Usage

yuend(x, y, tr = 0.2, ...)
Dqcomhd(x, y, q = c(1:9)/10, nboot = 1000, na.rm = TRUE, ...)
dep.effect(x, y, tr = 0.2, nboot = 1000, ...)

Arguments

x

an numeric vector of data values (e.g. for time 1).

y

an numeric vector of data values (e.g. for time 2).

tr

trim level for the means.

q

quantiles to be compared.

nboot

number of bootstrap samples.

na.rm

whether missing values should be removed.

...

currently ignored.

Details

The test statistic is a paired samples generalization of Yuen's independent samples t-test on trimmed means.

dep.effect computes the following effect sizes:

AKP: robust standardized difference similar to Cohen's d

QS: Quantile shift based on the median of the distribution of difference scores,

QStr: Quantile shift based on the trimmed mean of the distribution of X-Y

SIGN: P(X<Y), probability that for a random pair, the first is less than the second.

Value

yuend returns an object of class "yuen" containing:

test

value of the test statistic (t-statistic)

p.value

p-value

conf.int

confidence interval

df

degress of freedom

diff

trimmed mean difference

call

function call

Dqcomhd returns an object of class "robtab" containing:

partable

parameter table

dep.effect returns a matrix with the null value of the effect size, the estimated effect size, small/medium/large conventions, and lower/upper CI bounds.

References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

See Also

yuen, qcomhd

Examples

## Cholesterol data from Wilcox (2012, p. 197)
before <- c(190, 210, 300,240, 280, 170, 280, 250, 240, 220)
after <- c(210, 210, 340, 190, 260, 180, 200, 220, 230, 200)
yuend(before, after)

set.seed(123)
Dqcomhd(before, after, nboot = 200, q = c(0.25, 0.5, 0.75))

set.seed(123)
dep.effect(before, after)

Robust mediation test

Description

Performs a robust mediation test according to the method proposed by Zu & Yuan (2010).

Usage

ZYmediate(x, y, med, nboot = 2000, alpha = 0.05, kappa = 0.05, ...)

Arguments

x

vector with predictor values.

y

vector with response values.

med

vector with mediator values.

nboot

number of bootstrap samples.

alpha

alpha level.

kappa

the percent of cases to be controlled when robust method is used.

...

currently ignored.

Value

ZYmediate returns an object of class "robmed" containing:

a.est

effect of predictor on mediator)

b.est

effect of mediator on response (in multiple regression model which includes the predictor as well)

ab.est

indirect effect (mediation effect)

CI.ab

confidence interval mediation effect

p.value

p-value mediation test

call

function call

References

Zu J., Yuan, K.-H. (2010). Local influence and robust procedures for mediation analysis. Multivariate Behavioral Research, 45, 1-44.

Examples

## Leerkes data:
## Y: Efficacy
## X: MatCare
## M: Esteem

## fitting robust mediator regressions
require(MASS)
summary(rlm(Efficacy ~ MatCare, data = Leerkes))
summary(rlm(Esteem ~ MatCare, data = Leerkes))
summary(rlm(Efficacy ~ MatCare + Esteem, data = Leerkes))

## robust testing of mediating effect (indirect effect)
with(Leerkes, ZYmediate(MatCare, Efficacy, Esteem))