Package 'psychReport'

Title: Reproducible Reports in Psychology
Description: Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.
Authors: Ian G Mackenzie [cre, aut], Carolin Dudschig [aut]
Maintainer: Ian G Mackenzie <[email protected]>
License: MIT + file LICENSE
Version: 4.0.1
Built: 2026-05-11 07:13:09 UTC
Source: https://github.com/igmmgi/psychreport

Help Index

psychReport

Description

Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.

Author(s)

Maintainer: Ian G Mackenzie [email protected]

Authors:

addDataDF

Description

Add simulated ex-gaussian reaction-time (RT) data and binary error (Error = 1, Correct = 0) data to an R DataFrame. This function can be used to create simulated data sets.

Usage

addDataDF(dat, RT = NULL, Error = NULL)

Arguments

dat

DataFrame (see createDF)
RT

RT parameters (see rtDist)
Error

Error parameters (see errDist)

Value

DataFrame with RT (ms) and Error (bool) columns

Examples

# Example 1: default dataframe
dat <- createDF()
dat <- addDataDF(dat)
hist(dat$RT, 100)
table(dat$Error)

# Example 2: defined overall RT parameters
dat <- createDF(nVP = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp")))
dat <- addDataDF(dat, RT = c(500, 150, 100))
boxplot(dat$RT ~ dat$Comp)
table(dat$Comp, dat$Error)

# Example 3: defined RT + Error parameters across conditions
dat <- createDF(nVP = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp")))
dat <- addDataDF(dat,
  RT = list(
    "Comp comp" = c(500, 80, 100),
    "Comp incomp" = c(550, 80, 140)
  ),
  Error = list(
    "Comp comp" = 5,
    "Comp incomp" = 10
  )
)
boxplot(dat$RT ~ dat$Comp)
table(dat$Comp, dat$Error)

# Example 4:
# create dataframe with defined RT + Error parameters across different conditions
dat <- createDF(nVP = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp", "neutral")))
dat <- addDataDF(dat,
  RT = list(
    "Comp comp" = c(500, 150, 100),
    "Comp neutral" = c(550, 150, 100),
    "Comp incomp" = c(600, 150, 100)
  ),
  Error = list(
    "Comp comp" = 5,
    "Comp neutral" = 10,
    "Comp incomp" = 15
  )
)
boxplot(dat$RT ~ dat$Comp)
table(dat$Comp, dat$Error)

# Example 5:
# create dataframe with defined RT + Error parameters across different conditions
dat <- createDF(
  nVP = 50, nTrl = 50,
  design = list(
    "Hand" = c("left_a", "right_a"),
    "Side" = c("left_a", "right_a")
  )
)
dat <- addDataDF(dat,
  RT = list(
    "Hand:Side left_a:left_a" = c(400, 150, 100),
    "Hand:Side left_a:right_a" = c(500, 150, 100),
    "Hand:Side right_a:left_a" = c(500, 150, 100),
    "Hand:Side right_a:right_a" = c(400, 150, 100)
  ),
  Error = list(
    "Hand:Side left_a:left_a" = c(5, 4, 2, 2, 1),
    "Hand:Side left_a:right_a" = c(15, 4, 2, 2, 1),
    "Hand:Side right_a:left_a" = c(15, 7, 4, 2, 1),
    "Hand:Side right_a:right_a" = c(5, 8, 5, 3, 1)
  )
)

boxplot(dat$RT ~ dat$Hand + dat$Side)
table(dat$Error, dat$Hand, dat$Side)

aovDispMeans

Description

Displays marginal means from model.tables in the command window.

Usage

aovDispMeans(aovObj, value = "value", caption = sys.call())

Arguments

aovObj

Output from aov
value

String for column name
caption

Required for heading

Value

NULL (prints means to console)

Examples

# Example 1:
# create dataframe
dat <- createDF(nVP = 50, nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 100, 100),
                                "Comp incomp" = c(520, 100, 100)))

aovRT <- aov(RT ~ Comp + Error(VP/(Comp)), dat)
aovDispMeans(aovRT)

aovDispTable

Description

Display formatted ANOVA table in command window.

Usage

aovDispTable(aovObj, caption = sys.call())

Arguments

aovObj

Output from aov
caption

Required for heading

Value

NULL (prints table to console)

Examples

# Example 1:
# create dataframe
dat <- createDF(nVP = 6, nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 150, 100),
                                "Comp incomp" = c(520, 150, 100)))

aovObj <- aov(RT ~ Comp + Error(VP/(Comp)), dat)
aovDispTable(aovObj)

aovEffectSize

Description

Add effect size to ANOVA table. Effect sizes: partial eta squared (pes) vs. ges (generalized eta squared).

Usage

aovEffectSize(aovObj, effectSize = "pes")

Arguments

aovObj

Output from aov
effectSize

Effect size (pes vs. ges)

Value

list

Examples

# Example 1:
# create dataframe with 2(Comp: comp vs. incomp) and 2(Side: left vs. right) factors/levels
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp", "neutral"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat,
                 RT = list("Comp:Side comp:left"     = c(500, 150, 150),
                           "Comp:Side comp:right"    = c(500, 150, 150),
                           "Comp:Side incomp:left"   = c(550, 150, 150),
                           "Comp:Side incomp:right"  = c(550, 150, 150),
                           "Comp:Side neutral:left"  = c(525, 150, 150),
                           "Comp:Side neutral:right" = c(525, 150, 150)))

aovRT <- aov(RT ~ Comp * Side + Error(VP/(Comp*Side)), dat)
aovDispMeans(aovRT)
aovRT <- aovEffectSize(aovRT)
aovRT <- aovDispTable(aovRT)

aovJackknifeAdjustment

Description

Adjust ANOVA table with corrected F (Fc = F/(n-1)^2) and p values for jackkniffed data (see Ulrich and Miller, 2001. Using the jackknife-based scoring method for measuring LRP onset effects in factorial designs. Psychophysiology, 38, 816-827.)

Usage

aovJackknifeAdjustment(aovObj, numVPs)

Arguments

aovObj

Output from aov
numVPs

The number of participants

Value

list

Examples

# Example 1:
# create dataframe with 2(Comp: comp vs. incomp) and 2(Side: left vs. right) factors/levels
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat,
                 RT = list("Comp:Side comp:left"    = c(500, 150, 150),
                           "Comp:Side comp:right"   = c(500, 150, 150),
                           "Comp:Side incomp:left"  = c(500, 150, 150),
                           "Comp:Side incomp:right" = c(500, 150, 150)))

aovRT <- aov(RT ~ Comp*Side + Error(VP/(Comp*Side)), dat)
aovRT <- aovJackknifeAdjustment(aovRT, length(unique(dat$VP)))
aovDispTable(aovRT)

aovRoundDigits

Description

Round digits to n decimal places in ANOVA table

Usage

aovRoundDigits(aovObj)

Arguments

aovObj

Output from aov

Value

dataframe

Examples

# Example 1:
# create dataframe with 2(Comp: comp vs. incomp) and 2(Side: left vs. right) factors/levels
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat,
                 RT = list("Comp:Side comp:left"    = c(500, 150, 150),
                           "Comp:Side comp:right"   = c(500, 150, 150),
                           "Comp:Side incomp:left"  = c(500, 150, 150),
                           "Comp:Side incomp:right" = c(500, 150, 150)))

aovRT <- aov(RT ~ Comp*Side + Error(VP/(Comp*Side)), dat)
aovRT <- aovRoundDigits(aovRT)
aovDispTable(aovRT)

aovSphericityAdjustment

Description

Adjust ANOVA table with corrections for sphericity (Greenhouse-Geisser or Huynh-Feldt). Called by default within aovTable. Note: sphericity corrections require an object with a "Sphericity Corrections" component.

Usage

aovSphericityAdjustment(aovObj, type = "GG", adjDF = TRUE)

Arguments

aovObj

The returned ANOVA object
type

"GG" (Greenhouse-Geisser) or "HF" (Huynh-Feldt)
adjDF

TRUE/FALSE Should DFs be adjusted?

Value

list

Examples

# Example 1:
# create dataframe with 3(Comp: neutral vs. comp vs. incomp) factors/levels
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("neutral", "comp", "incomp")))

dat <- addDataDF(dat,
                 RT = list("Comp neutral" = c(510, 150, 100),
                           "Comp comp"    = c(500, 150, 100),
                           "Comp incomp"  = c(520, 150, 100)))
aovRT <- aov(RT ~ Comp + Error(VP/(Comp)), dat)
aovRT <- aovTable(aovRT)

aovTable

Description

Adjust ANOVA table output. Options include calculation of alternative effect sizes (eta squared, partial eta squared), the calculation of marginal means and formatting options for the ANOVA table (e.g., rounding).

Usage

aovTable(
  aovObj,
  effectSize = "pes",
  sphericityCorrections = TRUE,
  sphericityCorrectionType = "GG",
  sphericityCorrectionAdjDF = FALSE,
  removeSumSquares = TRUE
)

Arguments

aovObj

Output from aov
effectSize

Effect size (pes vs. ges)
sphericityCorrections

TRUE/FALSE
sphericityCorrectionType

"GG" (default) vs. "HF"
sphericityCorrectionAdjDF

TRUE/FALSE Should DFs be corrected?
removeSumSquares

TRUE/FALSE Remove SSn/SSd columns from the ANOVA table

Value

list

Examples

# Example 1:
# create dataframe with 2(Comp: comp vs. incomp) and 2(Side: left vs. right) factors/levels
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat,
                 RT = list("Comp:Side comp:left"    = c(500, 150, 150),
                           "Comp:Side comp:right"   = c(500, 150, 150),
                           "Comp:Side incomp:left"  = c(500, 150, 150),
                           "Comp:Side incomp:right" = c(500, 150, 150)))

aovRT <- aov(RT ~ Comp*Side + Error(VP/(Comp*Side)), dat)
aovRT <- aovTable(aovRT)

aovTidyTable

Description

Take output from base aov function and produce a "tidy" ANOVA table. The output also contains the marginal means.

Usage

aovTidyTable(aovObj, data = NULL)

Arguments

aovObj

Output from aov function
data

Optional. The original data frame used in the aov call. When provided, sphericity corrections (Mauchly's test, GG/HF epsilon) are computed.

Value

list

Examples

# create dataframe
dat <- createDF(nVP = 6, nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 150, 100),
                                "Comp incomp" = c(520, 150, 100)))

aovObj <- aov(RT ~ Comp + Error(VP/(Comp)), dat)
aovObj <- aovTable(aovObj)
aovObj$ANOVA
printTable(aovObj$ANOVA)

ciStrT

Description

Returns a string with the 95% CI from a t.test in Latex format.

Usage

ciStrT(tObj, numDigits = 0, unit = "")

Arguments

tObj

The returned object from a call to t.test
numDigits

The number of digits to round to
unit

"" vs. "ms" vs. "mv" vs. "%"

Value

character

Examples

# Example 1:
# create dataframe and add data with 2(Comp: comp vs. incomp) levels
dat <- createDF(nVP = 50,
                nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 100, 100),
                                "Comp incomp" = c(600, 100, 100)))

tObj <- t.test(dat$RT[dat$Comp == "incomp"],
               dat$RT[dat$Comp == "comp"],
               paired = TRUE)

ciString <- ciStrT(tObj, unit = "ms")

createDF

Description

Create dataframe (see also addDataDF)

Usage

createDF(
  nVP = 20,
  nTrl = 50,
  design = list(A = c("A1", "A2"), B = c("B1", "B2"))
)

Arguments

nVP

Number of participants
nTrl

Number of trials per factor/level for each participant
design

Factors and levels

Value

dataframe

Examples

# Example 1
dat <- createDF()

# Example 2
dat <- createDF(nVP = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp")))

# Example 3
dat <- createDF(nVP = 50, nTrl = 50, design = list(
  "Comp" = c("comp", "incomp"),
  "Side" = c("left", "right", "middle")
))

effectsizeValueString

Description

Returns required Latex formatted string for effect size (partial eta squared) = XXX for R/knitr integration. Returns values to 2 sig decimal places.

Usage

effectsizeValueString(aovObj, effect, effectSize = "pes")

Arguments

aovObj

Output from aov
effect

The effect within the ANOVA table to return
effectSize

pes (partial eta squared) vs. ges (generalised eta squared)

Value

character

Examples

# Example 1:
# create dataframe and add data with 2(Comp: comp vs. incomp) and 2(Side: left vs. right)
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat, RT = list("Comp:Side comp:left"    = c(500, 150, 100),
                                "Comp:Side comp:right"   = c(500, 150, 100),
                                "Comp:Side incomp:left"  = c(520, 150, 100),
                                "Comp:Side incomp:right" = c(520, 150, 100)))

aovRT <- aov(RT ~ Comp*Side + Error(VP/(Comp*Side)), dat)
aovRT <- aovTable(aovRT)

pesString <- effectsizeValueString(aovRT, "Comp")  # partial eta squared
pesString <- effectsizeValueString(aovRT, "Comp:Side")

errDist

Description

Returns a random vector of 0's (correct) and 1's (incorrect) with defined proportions (default = 10% errors).

Usage

errDist(n = 10000, proportion = 10)

Arguments

n

Number
proportion

Approximate proportion of errors in percentage

Value

double

Examples

# Example 1: approx 10% errors
x <- errDist(1000)
table(x)

# Example 2: approx 20% errors
x <- errDist(1000, 20)
table(x)

fValueString

Description

Returns required Latex formatted string for F(df1, df2) = XXX for R/knitr integration. For example, F(1, 23) = 3.45. Returns values to 2 sig decimal places.

Usage

fValueString(aovObj, effect)

Arguments

aovObj

Output from aov
effect

The effect within the ANOVA table to return

Value

character

Examples

# Example 1:
# create dataframe and add data with 2(Comp: comp vs. incomp) and 2(Side: left vs. right)
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat, RT = list("Comp:Side comp:left"    = c(500, 150, 100),
                                "Comp:Side comp:right"   = c(500, 150, 100),
                                "Comp:Side incomp:left"  = c(520, 150, 100),
                                "Comp:Side incomp:right" = c(520, 150, 100)))

aovRT <- aov(RT ~ Comp*Side + Error(VP/(Comp*Side)), dat)
aovRT <- aovTable(aovRT)

fString <- fValueString(aovRT, "Comp")
fString <- fValueString(aovRT, "Comp:Side")

mathString

Description

Returns formatted string following addition/subtraction.

Usage

mathString(str1, str2, operation = "-", numDigits = 0, unit = "ms")

Arguments

str1

string
str2

string
operation

"+", "-", "*", "/"
numDigits

number 0 (default)
unit

"ms" , "mV" , "mv", or "%"

Value

character

Examples

# Example 1:
string <- mathString("550 ms", "480 ms", "-")

# Example 2:
string <- mathString("2.34", "1.65", "+", numDigits = 2, unit = "mV")

meanStrAov

Description

Returns marginal means from ANOVA object for requested effect in Latex format.

Usage

meanStrAov(aovObj, effect, level, unit = "ms", numDigits = 0)

Arguments

aovObj

Output from aov
effect

Effect to return
level

Level of effect
unit

"ms" vs. "mv" vs. "%"
numDigits

Number of decimal places (default 0)

Value

character

Examples

# Example 1:
# create dataframe and add data with 2(Comp: comp vs. incomp) and 2(Side: left vs. right)
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat, RT = list("Comp:Side comp:left"    = c(500, 150, 100),
                                "Comp:Side comp:right"   = c(500, 150, 100),
                                "Comp:Side incomp:left"  = c(520, 150, 100),
                                "Comp:Side incomp:right" = c(520, 150, 100)))

aovRT <- aov(RT ~ Comp*Side + Error(VP/(Comp*Side)), dat)
aovRT <- aovTable(aovRT)

meanString <- meanStrAov(aovRT, "Comp", "comp")
meanString <- meanStrAov(aovRT, "Comp:Side", "incomp:left")

meanStrT

Description

Returns a string with the mean value from a t.test in Latex format.

Usage

meanStrT(tObj, numDigits = 0, unit = "")

Arguments

tObj

The returned object from a call to t.test
numDigits

The number of digits to round to
unit

"" vs. "ms" vs. "mv" vs. "%"

Value

character

Examples

# Example 1:
# create dataframe and add data
dat <- createDF(nVP = 50,
                nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 100, 100),
                                "Comp incomp" = c(600, 100, 100)))

tObj <- t.test(dat$RT[dat$Comp == "incomp"],
               dat$RT[dat$Comp == "comp"],
               paired = TRUE)

tString <- meanStrT(tObj, numDigits = 0, unit = "ms")

normData

Description

Normalise within-subjects data by removing between-subjects variability. Each participant's scores are centered on the grand mean.

Usage

normData(data, idvar, dvs)

Arguments

data

A dataframe
idvar

Column indicating the individual participants
dvs

List of numeric data columns to normalise

Value

dataframe

Examples

# Example 1:
library(dplyr)
dat <- createDF(nVP = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp")))
dat <- addDataDF(dat,
  RT = list(
    "Comp comp" = c(500, 80, 100),
    "Comp incomp" = c(550, 80, 140)
  ),
  Error = list(
    "Comp comp" = 5,
    "Comp incomp" = 10
  )
)
datAggVP <- dat %>%
  group_by(VP, Comp) %>%
  summarize(
    N = n(),
    RT = mean(RT[Error == 0]),
    ER = (sum(Error) / N) * 100
  )
datAggVP <- normData(datAggVP, "VP", c("RT", "ER"))

numValueString

Description

Returns numerical value with requested unit in Latex format with numDigits number of decimal places and unit symbol.

Usage

numValueString(value, numDigits = 2, unit = "")

Arguments

value

number
numDigits

number 2 (default)
unit

"ms", "mv", "mV", or "%" or "" (default)

Value

character

Examples

# Example 1:
string <- numValueString(100.341, 0, "ms")

# Example 2:
string <- numValueString(2.3412, 2, "mv")

# Example 3:
string <- numValueString(63.9812, 2, "")

printAovMeans

Description

Returns Latex formatted table of marginal means from model.tables. Uses printTable (xtable) latex package with some basic defaults. For more examples, see R package xtable

Usage

printAovMeans(..., caption = "Mean", digits = 3, dv = "ms")

Arguments

...

Output from aov
caption

Title for the table
digits

Number of digits to round to
dv

Name of the dependent variable (e.g., "ms", "%")

Value

character

Examples

# Example 1:
# create dataframe
dat <- createDF(nVP = 6, nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 150, 100),
                                "Comp incomp" = c(520, 150, 100)))

aovRT <- aov(RT ~ Comp + Error(VP/(Comp)), dat)
aovRT <- aovTable(aovRT)
printAovMeans(aovRT, digits = 3, dv = "ms")  # latex formatted

printTable

Description

Returns Latex formatted table from dataframe or ANOVA table. Uses xtable latex package with some basic defaults. For more examples, see R package xtable

Usage

printTable(obj, caption = "DF", digits = 3, onlyContents = FALSE)

Arguments

obj

Dataframe or ANOVA table to print
caption

Title of the dataframe
digits

Number of digits to round to NB. length can be 1, or vector with length equal to the number of numeric columns
onlyContents

TRUE/FALSE

Value

character

Examples

# Example 1:
# create dataframe
dat <- createDF(nVP = 6, nTrl = 1,
                design = list("Comp" = c("comp", "incomp", "neutral")))

dat <- addDataDF(dat, RT = list("Comp comp"    = c(500, 150, 100),
                                "Comp incomp"  = c(520, 150, 100),
                                "Comp neutral" = c(510, 150, 100)))
printTable(dat, digits = c(0, 2)) # latex formatted
printTable(dat, digits = 0)       # latex formatted

aovRT <- aov(RT ~ Comp + Error(VP/(Comp)), dat)
aovRT <- aovTable(aovRT)
printTable(aovRT$ANOVA) # latex formatted
printTable(aovRT$ANOVA, digits = c(0,2,2,2)) # latex formatted

pValueString

Description

Returns Latex formatted string from a p-value required for R/knitr integration. For example, p = 0.11 or p < 0.01 Returns values to 3 sig decimal places or < .001

Usage

pValueString(pVal)

Arguments

pVal

p-value between 0 and 1

Value

character

Examples

# Example 1:
pString <- pValueString(0.670)

# Example 2:
pString <- pValueString(0.1234)

# Example 3:
pString <- pValueString("0.03")

pValueSummary

Description

Returns p-values summarized using ***, **, *, or exact value when p > .05 (default 2 significant decimal places).

Usage

pValueSummary(pVal)

Arguments

pVal

vector with p-value between 0 and 1

Value

character

Examples

# Examples:
psum <- pValueSummary(0.0067)
psum <- pValueSummary(c(0.0001, 0.002, 0.02, 0.1))

requiredPackages

Description

Installs (default if required) and loads specified packages.

Usage

requiredPackages(
  packages,
  installPackages = FALSE,
  lib = .libPaths()[1],
  repos = "http://cran.us.r-project.org"
)

Arguments

packages

A list of packages
installPackages

TRUE/FALSE Install package if not installed
lib

character vector giving the library directories where to install the packages. Recycled as needed. If missing, defaults to the first element of .libPaths()
repos

character vector, the base URL(s) of the repositories to use, e.g., the URL of a CRAN mirror such as "https://cloud.r-project.org". For more details on supported URL schemes see url. Can be NULL to install from local files, directories or URLs: this will be inferred by extension from pkgs if of length one.

rtDist

Description

Returns value(s) from a distribution appropriate to simulate reaction times. The distribution is a combined exponential and gaussian distribution called an exponentially modified Gaussian (EMG) distribution or ex-gaussian distribution.

Usage

rtDist(n = 10000, gaussMean = 600, gaussSD = 50, expRate = 200)

Arguments

n

Number of observations
gaussMean

Mean of the gaussian distribution
gaussSD

SD of the gaussian distribution
expRate

Rate of the exponential function

Value

double

Examples

# Example 1:
x <- rtDist()
hist(x, 100)

# Example 2:
x <- rtDist(n = 20000, gaussMean = 800, gaussSD = 50, expRate = 100)
hist(x, 100)

sphericityValueString

Description

Returns required Latex formatted string for sphericity epsilon values (HF, GG) = XXX for R/knitr integration. Returns values to 2 sig decimal places.

Usage

sphericityValueString(aovObj, effect)

Arguments

aovObj

The returned ANOVA object
effect

The effect within the ANOVA table to return

Value

character

Examples

# Example 1
# create dataframe and add data with 3(Comp: neutral vs. comp vs. incomp) levels
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("neutral", "comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp neutral" = c(510, 150, 100),
                                "Comp comp"    = c(500, 150, 100),
                                "Comp incomp"  = c(520, 150, 100)))
aovRT <- aov(RT ~ Comp + Error(VP/(Comp)), dat)
aovRT <- aovTable(aovRT)
sphericityValueString(aovRT, "Comp")

statStrAov

Description

Returns Latex formatted string from ANOVA required for R/knitr integration. For example,

F(1,20)=8.45,p<0.01,pes=0.45F(1, 20) = 8.45, p < 0.01, pes = 0.45

Returns values to 2 sig decimal places and < 0.01, < 0.001 for p values.

Usage

statStrAov(aovObj, effect)

Arguments

aovObj

Output from aov
effect

The effect required from the anova table

Examples

# Example 1:
# create dataframe and add data with 2(Comp: comp vs. incomp) and 2(Side: left vs. right)
dat <- createDF(nVP = 20, nTrl = 1,
                design = list("Comp" = c("comp", "incomp"),
                              "Side" = c("left", "right")))

dat <- addDataDF(dat, RT = list("Comp:Side comp:left"    = c(500, 150, 100),
                                "Comp:Side comp:right"   = c(500, 150, 100),
                                "Comp:Side incomp:left"  = c(520, 150, 100),
                                "Comp:Side incomp:right" = c(520, 150, 100)))

aovRT <- aov(RT ~ Comp*Side + Error(VP/(Comp*Side)), dat)
aovRT <- aovTable(aovRT)

aovString <- statStrAov(aovRT, "Comp")
aovString <- statStrAov(aovRT, "Comp:Side")

statStrT

Description

Returns required Latex formatted string T-test required for R/Knitr integration. For example, t(11) = 3.45, p < 0.05. Returns values to 2 sig decimal places and < 0.01, < 0.001 for p values.

Usage

statStrT(tObj)

Arguments

tObj

The returned object from a call to t.test

Value

character

Examples

# Example 1:
# create dataframe and add data with 2(Comp: comp vs. incomp) levels
dat <- createDF(nVP = 50,
                nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 100, 100),
                                "Comp incomp" = c(600, 100, 100)))

tObj <- t.test(dat$RT[dat$Comp == "incomp"],
               dat$RT[dat$Comp == "comp"],
               paired = TRUE)

statStrT <- statStrT(tObj)

summaryMSDSE

Description

Aggregate data returning the mean, standard deviation, and standard error

Usage

summaryMSDSE(data, factors, dvs, withinCorrection = NULL)

Arguments

data

A dataframe
factors

List of factors over which to aggregate
dvs

List of numeric data columns to aggregate
withinCorrection

List of dvs which to apply within-subjects correction to the calculation of the standard deviation and standard error. Within-subject correction calculated according to Morey (2008). NB Data should be normed first (see normData).

Value

dataframe

Examples

# Example 1:
library(dplyr)
dat <- createDF(nVP = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp")))
dat <- addDataDF(dat,
  RT = list(
    "Comp comp" = c(500, 80, 100),
    "Comp incomp" = c(550, 80, 140)
  ),
  Error = list(
    "Comp comp" = 5,
    "Comp incomp" = 10
  )
)
datAggVP <- dat %>%
  group_by(VP, Comp) %>%
  summarize(
    N  = n(),
    RT = mean(RT[Error == 0]),
    ER = (sum(Error) / N) * 100
  )
datAgg <- summaryMSDSE(datAggVP, "Comp", c("RT", "ER"))

# Example 2:
dat <- createDF(nVP = 50, nTrl = 50, design = list("Comp" = c("comp", "incomp")))
dat <- addDataDF(dat,
  RT = list(
    "Comp comp" = c(500, 80, 100),
    "Comp incomp" = c(550, 80, 140)
  ),
  Error = list(
    "Comp comp" = 5,
    "Comp incomp" = 10
  )
)
datAggVP <- dat %>%
  group_by(VP, Comp) %>%
  summarize(
    N = n(),
    RT = mean(RT[Error == 0]),
    ER = (sum(Error) / N) * 100
  )
datAggVP <- normData(datAggVP, "VP", c("RT", "ER"))
datAgg <- summaryMSDSE(
  datAggVP, "Comp", c("RT", "ER", "RT_norm", "ER_norm"),
  c("RT_norm", "ER_norm")
)

tValueString

Description

Returns required Latex formatted string for t(df) = XXX for R/knitr integration. Returns values to 2 sig decimal places.

Usage

tValueString(tObj)

Arguments

tObj

The returned object from a call to t.test

Value

character

Examples

# Example 1:
# create dataframe and add data with 2(Comp: comp vs. incomp) levels
dat <- createDF(nVP = 50,
                nTrl = 1,
                design = list("Comp" = c("comp", "incomp")))

dat <- addDataDF(dat, RT = list("Comp comp"   = c(500, 100, 100),
                                "Comp incomp" = c(600, 100, 100)))

tObj <- t.test(dat$RT[dat$Comp == "incomp"],
               dat$RT[dat$Comp == "comp"],
               paired = TRUE)

tString <- tValueString(tObj)