| Type: | Package |
| Title: | Reproducible Reports in Psychology |
| Version: | 4.0.1 |
| 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. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Imports: | broom, cli, dplyr (≥ 1.0), xtable |
| Suggests: | testthat |
| Depends: | R (≥ 4.0.0) |
| NeedsCompilation: | no |
| Packaged: | 2026-04-09 06:01:28 UTC; ian |
| Author: | Ian G Mackenzie [cre, aut], Carolin Dudschig [aut] |
| Maintainer: | Ian G Mackenzie <ian.mackenzie@uni-tuebingen.de> |
| Repository: | CRAN |
| Date/Publication: | 2026-04-10 08:00:02 UTC |
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 ian.mackenzie@uni-tuebingen.de
Authors:
Carolin Dudschig carolin.dudschig@uni-tuebingen.de
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 partial eta squared (pes) effect size to ANOVA table.
Usage
aovEffectSize(aovObj)
Arguments
aovObj |
Output from aov |
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 (only "pes", partial eta squared, is supported) |
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 |
Only "pes" (partial eta squared) is supported |
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, "")
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))
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
requiredPackages
Description
Installs (default if required) and loads specified packages.
Usage
requiredPackages(
packages,
installPackages = FALSE,
lib = .libPaths()[1],
repos = "https://cloud.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 = 0.45
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)