Help for package walrus

Type:

Package

Title:

Robust Statistical Methods

Version:

1.0.5

Date:

2022-09-07

Author:

Jonathon Love, Patrick Mair

Maintainer:

Jonathon Love <jon@thon.cc>

Description:

A toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for 'jamovi' (see https://www.jamovi.org for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book 'Introduction to Robust Estimation & Hypothesis Testing'.

License:

GPL-3

Encoding:

UTF-8

Imports:

WRS2, ggplot2, jmvcore (≥ 2.2), R6

Suggests:

MASS

URL:

https://github.com/jamovi/walrus

BugReports:

https://github.com/jamovi/walrus/issues

RoxygenNote:

6.1.1

NeedsCompilation:

Packaged:

2022-09-08 02:54:49 UTC; c3113592

Repository:

CRAN

Date/Publication:

2022-09-08 03:50:02 UTC

Walrus

Description

A toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for 'jamovi' (see www.jamovi.org for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book Introduction to Robust Estimation & Hypothesis Testing.

Details

Box & Violin Plots				`rplots()`
Robust Descriptives				`rdesc()`
Robust Independent Samples T-Test				`rttestIS`()
Robust Paired Samples T-Test				`rttestIS()`
Robust ANOVA				`ranova()`

Ravi:
    "Should we create a logo for walrus?"

Jonathon:
    "Yeah, I guess. Maybe a walrus, or a
    skewed distribution?
    Bonus points if it somehow contains both."

Ravi gets bonus points

Robust ANOVA

Description

Robust Analysis of Variance

Usage

ranova(
  data,
  dep,
  factors = NULL,
  method = "trim",
  ph = FALSE,
  tr = 0.2,
  est = "mom",
  nboot = 599,
  dist = "proj"
)

Arguments

data

the data as a data frame

dep

a string naming the dependent variable from data; the variable must be numeric

factors

a vector of strings naming the fixed factors from data

method

'median', 'trim' (default) or 'boot'; the method to use, median, trimmed means, or bootstrapped

ph

TRUE or FALSE (default), provide post hoc tests

tr

a number between 0 and 0.5, (default: 0.2), the proportion of measurements to trim from each end, when using the trim and bootstrap methods

est

'onestep', 'mom' (default) or 'median', the M-estimator to use; One-step, Modified one-step or Median respectively

nboot

a number (default: 599) specifying the number of bootstrap samples to use when using the bootstrap method

dist

'maha' or 'proj' (default), whether to use Mahalanobis or Projection distances respectively

Value

A results object containing:

`results$main`					the table of ANOVA results
`results$phs`					the table of posthoc tests

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$main$asDF

as.data.frame(results$main)

Examples

data('goggles', package='WRS2')

ranova(goggles,
       dep = 'attractiveness',
       factors = c('gender', 'alcohol'),
       ph = TRUE)

#
#  ROBUST ANOVA
#
#  Robust ANOVA
#  ------------------------------------
#                      Q        p
#  ------------------------------------
#    gender             1.67    0.209
#    alcohol           48.28    0.001
#    gender:alcohol    26.26    0.001
#  ------------------------------------
#   Note. Method of trimmed means,
#   trim level 0.2
#
#
#  POST HOC TESTS
#
#  Post Hoc Tests - gender
#  --------------------------------------------------------
#                       psi-hat   p        Lower    Upper
#  --------------------------------------------------------
#    Female    Male     10.0      0.209    -6.00    26.0
#  --------------------------------------------------------
#
#
#  Post Hoc Tests - alcohol
#  -------------------------------------------------------------
#                           psi-hat   p         Lower    Upper
#  -------------------------------------------------------------
#    None       2 Pints     -3.33      0.611    -20.5     13.8
#    None       4 Pints     35.83     < .001     19.3     52.3
#    2 Pints    4 Pints     39.17     < .001     22.5     55.9
#  -------------------------------------------------------------
#

Robust Descriptives

Description

Robust Descriptives

Usage

rdesc(
  data,
  vars,
  splitBy = NULL,
  mean = TRUE,
  trim = TRUE,
  tr = 0.2,
  win = FALSE,
  wl = 0.2,
  mest = FALSE,
  bend = 1.28,
  med = FALSE
)

Arguments

data

the data as a data frame

vars

a vector of strings naming the variables in data of interest

splitBy

a string naming the variable in data to split the data by

mean

TRUE (default) or FALSE, provide a 'normal' arithmetic mean

trim

TRUE (default) or FALSE, provide a trimmed mean

tr

a number between 0 and 0.5 (default: 0.2); the proportion of measurements to trim from each end when producing trimmed means

win

TRUE or FALSE (default), provide a 'Winsorized' mean

wl

a number between 0 and 0.5 (default: 0.2); the level of 'winsorizing' when producing winsorized means

mest

TRUE or FALSE (default), provide an 'M-estimated' value

bend

a number (default: 1.28), the bending constant to use when using M-estimators

med

TRUE or FALSE (default), provide medians

Value

A results object containing:

`results$table`					the table of descriptives

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$table$asDF

as.data.frame(results$table)

Examples


data('eurosoccer', package='WRS2')

SpainGermany <- subset(eurosoccer, eurosoccer$League == 'Spain' | eurosoccer$League == 'Germany')
SpainGermany <- droplevels(SpainGermany)

walrus::rdesc(
    data = SpainGermany,
    vars = "GoalsGame",
    splitBy = "League",
    med = TRUE)

#
#  ROBUST DESCRIPTIVES
#
#  Robust Descriptives
#  ----------------------------------------------------------
#                                                    SE
#  ----------------------------------------------------------
#    GoalsGame    Germany    Mean            1.46     0.105
#                            Trimmed mean    1.45    0.1341
#                            Median          1.43    0.1599
#
#                 Spain      Mean            1.45     0.101
#                            Trimmed mean    1.33    0.0601
#                            Median          1.30    0.0766
#  ----------------------------------------------------------
#

Box & Violin Plots

Description

Box & Violin Plots

Usage

rplots(
  data,
  vars,
  splitBy = NULL,
  violin = TRUE,
  boxplot = FALSE,
  dot = TRUE,
  dotType = "stack"
)

Arguments

data

the data as a data frame

vars

a vector of strings naming the variables in data of interest

splitBy

a string naming the variable in data to split the data by

violin

TRUE (default) or FALSE, provide violin plots

boxplot

TRUE or FALSE (default), provide box plots

dot

TRUE (default) or FALSE, plot each measurement as a dot

dotType

'jitter' or 'stack' (default); whether data dots are jittered or stacked

Value

A results object containing:

`results$plots`					an array of images

Examples

data('eurosoccer', package='WRS2')

# violin plots

walrus::rplots(
    data = eurosoccer,
    vars = "GoalsGame",
    splitBy = "League")


# box plots

walrus::rplots(
    data = eurosoccer,
    vars = "GoalsGame",
    splitBy = "League",
    violin = FALSE,
    boxplot = TRUE,
    dot = FALSE)

Robust Independent Samples T-Test

Description

Robust Independent Samples T-Test

Usage

rttestIS(
  data,
  deps,
  group,
  yuen = TRUE,
  tr = 0.2,
  mest = FALSE,
  method = "mom",
  yuenbt = FALSE,
  nboot = 599,
  md = FALSE,
  ci = FALSE,
  es = FALSE,
  esci = FALSE
)

Arguments

data

the data as a data frame

deps

a vector of strings naming the dependent variables in data

group

a string naming the grouping variable in data; must have 2 levels

yuen

TRUE (default) or FALSE, use the Yuen's trim method

tr

a number between 0 and 0.5, (default: 0.2), the proportion of measurements to trim from each end, when using the trim and bootstrap methods

mest

TRUE or FALSE (default), use an M-estimator

method

'onestep', 'mom' (default) or 'median', the M-estimator to use; One-step, Modified one-step or Median respectively

yuenbt

TRUE or FALSE (default), use the Yuen's bootstrap method

nboot

a number (default: 599) specifying the number of bootstrap samples to use when using the bootstrap method

md

TRUE or FALSE (default), provide the mean difference

ci

TRUE or FALSE (default), provide a 95% confidence interval on the mean difference

es

TRUE or FALSE (default), provide the effect-size

esci

TRUE or FALSE (default), provide a 95% confidence interval on the effect-size

Value

A results object containing:

`results$ttest`					the table of t-test results

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$ttest$asDF

as.data.frame(results$ttest)

Examples

data('eurosoccer', package='WRS2')

SpainGermany <- subset(eurosoccer, eurosoccer$League == 'Spain' | eurosoccer$League == 'Germany')
SpainGermany <- droplevels(SpainGermany)

rttestIS(SpainGermany,
         dep = 'GoalsScored',
         group = 'League',
         yuen = TRUE,
         mest = TRUE)

#
#  ROBUST INDEPENDENT SAMPLES T-TEST
#
#  Robust Independent Samples T-Test
#  ---------------------------------------------------------
#                                  t         df      p
#  ---------------------------------------------------------
#    GoalsScored    Yuen's test     0.297    17.3    0.770
#                   M-estimator    -0.933            0.993
#  ---------------------------------------------------------
#

Robust Paired Samples T-Test

Description

Robust Paired Samples T-Test

Usage

rttestPS(data, pairs, tr = 0.2, md = FALSE, es = FALSE, ci = FALSE)

Arguments

data

the data as a data frame

pairs

a list of lists specifying the pairs of measurement in data

tr

a number between 0 and 0.5, (default: 0.2), the proportion of measurements to trim from each end, when using the trim and bootstrap methods

md

TRUE or FALSE (default), provide means and standard errors

es

TRUE or FALSE (default), provide effect sizes

ci

TRUE or FALSE (default), provide confidence intervals

Value

A results object containing:

`results$ttest`					the table of t-test results

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$ttest$asDF

as.data.frame(results$ttest)

Examples

data(anorexia, package='MASS')
anorexiaFT <- subset(anorexia, subset = Treat == "FT")

rttestPS(anorexiaFT,
         pairs = list(
             list(i1 = 'Prewt', i2 = 'Postwt')))

#
#  ROBUST PAIRED SAMPLES T-TEST
#
#  Robust Paired Samples T-Test
#  ---------------------------------------------
#                       t        df      p
#  ---------------------------------------------
#    Prewt    Postwt    -3.83    10.0    0.003
#  ---------------------------------------------
#

Walrus

Description

Details

See Also

Robust ANOVA

Description

Usage

Arguments

Value

Examples

Robust Descriptives

Description

Usage

Arguments

Value

Examples

Box & Violin Plots

Description

Usage

Arguments

Value

Examples

Robust Independent Samples T-Test

Description

Usage

Arguments

Value

Examples

Robust Paired Samples T-Test

Description

Usage

Arguments

Value

Examples