Package 'rosetta'

Description

Builds model for moderated mediation anaysis using SEM

Usage

buildModMedSemModel(
  xvar,
  mvars,
  yvar,
  xmmod = NULL,
  mymod = NULL,
  cmvars = NULL,
  cyvars = NULL
)

Arguments

Value

lavaan model to be used in moderatedMediationSem

Examples

model <-  buildModMedSemModel(xvar="procJustice", mvars= c("cynicism"),
          yvar = "CPB", xmmod = "insecure",mymod = "gender" ,cmvars =c("age"))

Description

The cat0 function is to cat what paste0 is to paste; it simply makes concatenating many strings without a separator easier.

Usage

cat0(..., sep = "")

Arguments

Value

Nothing (invisible NULL, like cat).

Examples

cat0("The first variable is '", names(mtcars)[1], "'.");

Description

This function is vectorized.

Usage

confIntSD(x, n = NULL, conf.level = 0.95)

Arguments

Value

A vector or matrix.

Examples

rosetta::confIntSD(mtcars$mpg);
rosetta::confIntSD(c(6, 7), c(32, 32));

Description

The data are about the attitudes of employees of an organisation that is in the middle of a reorganization. The model predicts that feelings of procedural injustice may lead to cynicism and less trust in the management. This relation may be stronger among employees who are insecure about their job continuation. Cynisicm may lead to contra-productive behaviour (CPB). However, strong personal norms may prevent CPB. Cynicism is expected to increase with age, and men may be more inclined towards CPB than women.

Usage

cpbExample

Format

A data frame with 320 rows and 8 variables:

gender

gender participant

age

age participant

procJustice

prodedural justice

trust

trust in management

cynicism

cynicism about the management

CPB

contr-productive behaviour

insecure

insecure about job continuation

norms

personal norms about CPB

Description

This function produces a cross table, computes Chi Square, and computes the point estimate and confidence interval for Cramer's V.

Usage

crossTab(x, y = NULL, conf.level = 0.95, digits = 2, pValueDigits = 3, ...)

## S3 method for class 'crossTab'
print(x, digits = x$input$digits, pValueDigits = x$input$pValueDigits, ...)

## S3 method for class 'crossTab'
pander(x, digits = x$input$digits, pValueDigits = x$input$pValueDigits, ...)

Arguments

Value

The results of ufs::confIntV(), but also prints the cross table and the chi square test results.

Examples

crossTab(infert$education, infert$induced, samples=50);

Description

This function provides a number of descriptives about your data, similar to what SPSS's DESCRIPTIVES (often called with DESCR) does.

Usage

descr(
  x,
  items = names(x),
  varLabels = NULL,
  mean = TRUE,
  meanCI = TRUE,
  median = TRUE,
  mode = TRUE,
  var = TRUE,
  sd = TRUE,
  se = FALSE,
  min = TRUE,
  max = TRUE,
  q1 = FALSE,
  q3 = FALSE,
  IQR = FALSE,
  skewness = TRUE,
  kurtosis = TRUE,
  dip = TRUE,
  totalN = TRUE,
  missingN = TRUE,
  validN = TRUE,
  histogram = FALSE,
  boxplot = FALSE,
  digits = 2,
  errorOnFactor = FALSE,
  convertFactor = FALSE,
  maxModes = 1,
  maxPlotCols = 4,
  t = FALSE,
  headingLevel = 3,
  conf.level = 0.95,
  quantileType = 2
)

rosettaDescr_partial(
  x,
  digits = attr(x, "digits"),
  show = attr(x, "show"),
  headingLevel = attr(x, "headingLevel"),
  maxPlotCols = attr(x, "maxPlotCols"),
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaDescr'
knit_print(
  x,
  digits = attr(x, "digits"),
  show = attr(x, "show"),
  headingLevel = attr(x, "headingLevel"),
  maxPlotCols = attr(x, "maxPlotCols"),
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaDescr'
print(
  x,
  digits = attr(x, "digits"),
  show = attr(x, "show"),
  maxPlotCols = attr(x, "maxPlotCols"),
  headingLevel = attr(x, "headingLevel"),
  forceKnitrOutput = FALSE,
  ...
)

Arguments

Details

Note that R (of course) has many similar functions, such as summary, psych::describe() in the excellent psych::psych package.

The Hartigans' Dip Test may be unfamiliar to users; it is a measure of uni- vs. multimodality, computed by the dip.test() function from the {diptest} package from the. Depending on the sample size, values over .025 can be seen as mildly indicative of multimodality, while values over .05 probably warrant closer inspection (the p-value can be obtained using that dip.test() function from {diptest}; also see Table 1 of Hartigan & Hartigan (1985) for an indication as to critical values).

Value

A list of dataframes with the requested values.

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters [email protected]

References

Hartigan, J. A.; Hartigan, P. M. The Dip Test of Unimodality. Ann. Statist. 13 (1985), no. 1, 70–84. doi:10.1214/aos/1176346577. https://projecteuclid.org/euclid.aos/1176346577.

See Also

summary, [psych::describe()

Examples

### Simplest example with default settings
descr(mtcars$mpg);

### Also requesting a histogram and boxplot
descr(mtcars$mpg, histogram=TRUE, boxplot=TRUE);

### To show the output as Rmd Partial in the viewer
rosetta::rosettaDescr_partial(
  rosetta::descr(
    mtcars$mpg
  )
);

### Multiple variables, including one factor
rosetta::rosettaDescr_partial(
  rosetta::descr(
    iris
  )
);

Description

Descriptives with confidence intervals

Usage

descriptiveCIs(
  data,
  items = NULL,
  itemLabels = NULL,
  conf.level = 0.95,
  digits = 2
)

## S3 method for class 'rosettaDescriptiveCIs'
print(x, digits = attr(x, "digits"), forceKnitrOutput = FALSE, ...)

Arguments

Value

A data frame with class rosettaDescriptiveCIs prepended to allow printing neatly while knitting to Markdown.

Examples

descriptiveCIs(mtcars);

Description

The dlvPlot function produces a dot-violin-line plot, and dlvTheme is the default theme.

Usage

dlvTheme(base_size = 11, base_family = "", ...)

dlvPlot(
  dat,
  x = NULL,
  y,
  z = NULL,
  conf.level = 0.95,
  jitter = "FALSE",
  binnedDots = TRUE,
  binwidth = NULL,
  error = "lines",
  dotsize = "density",
  singleColor = "black",
  comparisonColors = rosetta::opts$get("dlvPlotCompCols"),
  densityDotBaseSize = 3,
  normalDotBaseSize = 1,
  violinAlpha = 0.2,
  dotAlpha = 0.4,
  lineAlpha = 1,
  connectingLineAlpha = 1,
  meanDotSize = 5,
  posDodge = 0.2,
  errorType = "both",
  outputFile = NULL,
  outputWidth = 10,
  outputHeight = 10,
  ggsaveParams = list(units = "cm", dpi = 300, type = "cairo")
)

## S3 method for class 'dlvPlot'
print(x, ...)

Arguments

Details

This function creates Dot Violin Line plots. One image says more than a thousand words; I suggest you run the example :-)

Value

The behavior of this function depends on the arguments.

If no x and z are provided and y is a character value, dlvPlot produces a univariate plot for the numerical y variable.

If no x and z are provided, and y is c character vector, dlvPlot produces multiple Univariate plots, with variable names determining categories on x-axis and with numerical y variables on y-axis

If both x and y are a character value, and no z is provided, dlvPlot produces a bivariate plot where factor x determines categories on x-axis with numerical variable y on the y-axis (roughly a line plot with a single line)

Finally, if x, y and z are each a character value, dlvPlot produces multivariate plot where factor x determines categories on x-axis, factor z determines the different lines, and with the numerical y variable on the y-axis

An object is returned with the following elements:

Examples

### Note: the 'not run' is simply because running takes a lot of time,
###       but these examples are all safe to run!
## Not run: 
### Create simple dataset
dat <- data.frame(x1 = factor(rep(c(0,1), 20)),
                  x2 = factor(c(rep(0, 20), rep(1, 20))),
                  y=rep(c(4,5), 20) + rnorm(40));
### Generate a simple dlvPlot of y
dlvPlot(dat, y='y');
### Now add a predictor
dlvPlot(dat, x='x1', y='y');
### And finally also a moderator:
dlvPlot(dat, x='x1', y='y', z='x2');
### The number of datapoints might be a bit clearer if we jitter
dlvPlot(dat, x='x1', y='y', z='x2', jitter=TRUE);
### Although just dodging the density-sized dots might work better
dlvPlot(dat, x='x1', y='y', z='x2', posDodge=.3);

## End(Not run)

Description

These functions are one of many R functions enabling users to assess variable descriptives. They have been developed to mimic SPSS' 'EXAMINE' syntax command ('Explore' in the menu) as closely as possible to ease the transition for new R users and facilitate teaching courses where both programs are taught alongside each other.

Usage

examine(
  ...,
  stem = TRUE,
  plots = TRUE,
  extremeValues = 5,
  qqCI = TRUE,
  conf.level = 0.95
)

## S3 method for class 'examine'
print(x, ...)

## S3 method for class 'examine'
pander(
  x,
  headerPrefix = "",
  headerStyle = "**",
  secondaryHeaderPrefix = "",
  secondaryHeaderStyle = "*",
  ...
)

examineBy(
  ...,
  by = NULL,
  stem = TRUE,
  plots = TRUE,
  extremeValues = 5,
  qqCI = TRUE,
  conf.level = 0.95
)

## S3 method for class 'examineBy'
print(x, ...)

## S3 method for class 'examineBy'
pander(
  x,
  headerPrefix = "",
  headerStyle = "**",
  secondaryHeaderPrefix = "",
  secondaryHeaderStyle = "*",
  tertairyHeaderPrefix = "--> ",
  tertairyHeaderStyle = "",
  separator = paste0("\n\n", repStr("-", 10), "\n\n"),
  ...
)

Arguments

Details

This function basically just calls the descr function, optionally supplemented with calls to stem, ufs::dataShape().

Value

A list that is displayed when printed.

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters [email protected]

Examples

### Look at the miles per gallon descriptives:
rosetta::examine(mtcars$mpg, stem=FALSE, plots=FALSE);

### Separate for the different number of cylinders:
rosetta::examineBy(
  mtcars$mpg, by=mtcars$cyl,
  stem=FALSE, plots=FALSE,
  extremeValues=FALSE
);

Description

Basic functons to make working with R easier for SPSS users: getData and getDat provide an easy way to load SPSS datafiles, and exportToSPSS to write to a datafile and syntax file that SPSS can import; filterBy and useAll allow easy temporary filtering of rows from the dataframe; mediaan and modus compute the median and mode of ordinal or numeric data.

Usage

exportToSPSS(
  dat,
  savfile = NULL,
  datafile = NULL,
  codefile = NULL,
  fileEncoding = "UTF-8",
  newLinesInString = " |n| "
)

filterBy(
  dat,
  expression,
  replaceOriginalDataframe = TRUE,
  envir = parent.frame()
)

getData(
  filename = NULL,
  file = NULL,
  errorMessage = "[defaultErrorMessage]",
  applyRioLabels = TRUE,
  use.value.labels = FALSE,
  to.data.frame = TRUE,
  stringsAsFactors = FALSE,
  silent = FALSE,
  ...
)

getDat(..., dfName = "dat", backup = TRUE)

mediaan(vector)

modus(vector)

useAll(dat, replaceFilteredDataframe = TRUE)

Arguments

Value

getData returns the imported dataframe, with the filename from which it was read stored in the 'filename' attribute.

getDat is a simple wrapper for getData() which creates a dataframe in the parent environment, by default with the name 'dat'. Therefore, calling getDat() in the console will allow the user to select a file, and the data from the file will then be read and be available as 'dat'. If an object with dfName (i.e. 'dat' by default) already exists, it will be backed up with a warning. getDat() therefore returns nothing.

mediaan returns the median, or, in the case of a factor where the median is in between two categories, both categories.

modus returns the mode.

Note

getData() currently can't read from LibreOffice or OpenOffice files. There doesn't seem to be a platform-independent package that allows this. Non-CRAN package ROpenOffice from OmegaHat should be able to do the trick, but fails to install (manual download and installation using http://www.omegahat.org produces "ERROR: dependency 'Rcompression' is not available for package 'ROpenOffice'" - and manual download and installation of RCompression produces "Please define LIB_ZLIB; ERROR: configuration failed for package 'Rcompression'"). If you have any suggestions, please let me know!

Examples

## Not run: 
### Open a dialogue to read an SPSS file
getData();

## End(Not run)

### Get a median and a mode
mediaan(c(1,2,2,3,4,4,5,6,6,6,7));
modus(c(1,2,2,3,4,4,5,6,6,6,7));

### Create an example dataframe
(exampleDat <- data.frame(x=rep(8, 8), y=rep(c(0,1), each=4)));
### Filter it, replacing the original dataframe
(filterBy(exampleDat, "y=0"));
### Restore the old dataframe
(useAll(exampleDat));

Description

This is a wrapper for the psych functions psych::pca() and psych::fa() to produce output that it similar to the output produced by jamovi.

Usage

factorAnalysis(
  data,
  nfactors,
  items = names(data),
  rotate = "oblimin",
  covar = FALSE,
  na.rm = TRUE,
  kaiser = 1,
  loadings = TRUE,
  summary = FALSE,
  correlations = FALSE,
  modelFit = FALSE,
  eigenValues = FALSE,
  screePlot = FALSE,
  residuals = FALSE,
  itemLabels = items,
  colorLoadings = FALSE,
  fm = "minres",
  digits = 2,
  headingLevel = 3,
  ...
)

principalComponentAnalysis(
  data,
  items,
  nfactors,
  rotate = "oblimin",
  covar = FALSE,
  na.rm = TRUE,
  kaiser = 1,
  loadings = TRUE,
  summary = FALSE,
  correlations = FALSE,
  eigenValues = FALSE,
  screePlot = FALSE,
  residuals = FALSE,
  itemLabels = items,
  colorLoadings = FALSE,
  digits = 2,
  headingLevel = 3,
  ...
)

rosettaDataReduction_partial(
  x,
  digits = x$input$digits,
  headingLevel = x$input$headingLevel,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaDataReduction'
knit_print(
  x,
  digits = x$input$digits,
  headingLevel = x$input$headingLevel,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaDataReduction'
print(
  x,
  digits = x$input$digits,
  headingLevel = x$input$headingLevel,
  forceKnitrOutput = FALSE,
  ...
)

Arguments

Details

The code in these functions uses parts of the code in jamovi, written by Jonathon Love and Ravi Selker.

Value

An object with the object resulting from the call to the psych functions and some extracted information that will be printed.

Examples

### Load example dataset
data("pp15", package="rosetta");

### Get variable names with expected
### effects of a high dose of MDMA
items <-
  grep(
    "highDose_AttBeliefs_",
    names(pp15),
    value=TRUE
  );

### Do a factor analysis
rosetta::factorAnalysis(
  data = pp15,
  items = items,
  nfactors = "eigen",
  scree = TRUE
);

if (FALSE) {
  ### To get more output, show the
  ### output as Rmd Partial in the viewer,
  ### and color/size the factor loadings
  rosetta::rosettaDataReduction_partial(
    rosetta::factorAnalysis(
      data = pp15,
      items = items,
      nfactors = "eigen",
      summary = TRUE,
      correlations = TRUE,
      colorLoadings = TRUE
    )
  );
}

Description

Factor Analysis

Usage

factorAnalysisjmv(
  data,
  items,
  nFactorMethod = "eigen",
  nFactors = 1,
  minEigen = 1,
  extraction = "minres",
  rotation = "oblimin",
  colorLoadings = TRUE,
  screePlot = FALSE,
  eigen = FALSE,
  factorCor = FALSE,
  factorSummary = FALSE,
  modelFit = FALSE
)

Arguments

Value

A results object containing:

Description

This function is meant as a userfriendly wrapper to approximate the way analysis of variance is done in SPSS.

Usage

fanova(
  data,
  y,
  between = NULL,
  covar = NULL,
  withinReference = 1,
  betweenReference = NULL,
  withinNames = NULL,
  plot = FALSE,
  levene = FALSE,
  digits = 2,
  contrast = NULL
)

## S3 method for class 'fanova'
print(x, digits = x$input$digits, ...)

Arguments

Details

This wrapper uses oneway and lm and lmer in combination with car's Anova function to conduct the analysis of variance.

Value

Mainly, this function prints its results, but it also returns them in an object containing three lists:

Author(s)

Gjalt-Jorn Peters and Peter Verboon

Maintainer: Gjalt-Jorn Peters [email protected]

See Also

regr and logRegr for similar functions for linear and logistic regression and oneway, lm, lmer and Anova for the functions used behind the scenes.

Examples

### Oneway anova with a plot
fanova(dat=mtcars, y='mpg', between='cyl', plot=TRUE);

### Factorial anova
fanova(dat=mtcars, y='mpg', between=c('vs', 'am'), plot=TRUE);

### Ancova
fanova(dat=mtcars, y='mpg', between=c('vs', 'am'), covar='hp');

### Don't run these examples to not take too much time during testing
### for CRAN
## Not run: 
### Repeated measures anova; first generate datafile
dat <- mtcars[, c('am', 'drat', 'wt')];
names(dat) <- c('factor', 't0_dependentVar' ,'t1_dependentVar');
dat$factor <- factor(dat$factor);

### Then do the repeated measures anova
fanova(dat, y=c('t0_dependentVar' ,'t1_dependentVar'),
       between='factor', plot=TRUE);

## End(Not run)

Description

Pretty formatting of p values

Usage

formatPvalue(values, digits = 3, spaces = TRUE, includeP = TRUE)

Arguments

Value

A formatted P value, roughly according to APA style guidelines. This means that the noZero function is used to remove the zero preceding the decimal point, and p values that would round to zero given the requested number of digits are shown as e.g. p<.001.

See Also

formatCI(), formatR(), noZero()

Examples

formatPvalue(cor.test(mtcars$mpg,
                      mtcars$disp)$p.value);
formatPvalue(cor.test(mtcars$drat,
                      mtcars$qsec)$p.value);

Description

Pretty formatting of correlation coefficients

Usage

formatR(r, digits = 2)

Arguments

Value

The formatted correlation.

See Also

noZero(), formatCI(), formatPvalue()

Examples

formatR(cor(mtcars$mpg, mtcars$disp));

Description

Function to show frequencies in a manner similar to what SPSS' "FREQUENCIES" command does. Note that frequency is an alias for freq.

Usage

freq(
  vector,
  digits = 1,
  nsmall = 1,
  transposed = FALSE,
  round = 1,
  plot = FALSE,
  plotTheme = ggplot2::theme_bw()
)

## S3 method for class 'freq'
print(
  x,
  digits = x$input$digits,
  nsmall = x$input$nsmall,
  transposed = x$input$transposed,
  ...
)

## S3 method for class 'freq'
pander(x, ...)

frequencies(
  ...,
  digits = 1,
  nsmall = 1,
  transposed = FALSE,
  round = 1,
  plot = FALSE,
  plotTheme = ggplot2::theme_bw()
)

## S3 method for class 'frequencies'
print(x, ...)

## S3 method for class 'frequencies'
pander(x, prefix = "###", ...)

Arguments

Value

An object with several elements, the most notable of which is:

For frequencies, these objects are in a list of their own.

Examples

### Create factor vector
ourFactor <- factor(mtcars$gear, levels=c(3,4,5),
                    labels=c("three", "four", "five"));
### Add some missing values
factorWithMissings <- ourFactor;
factorWithMissings[10] <- factorWithMissings[20] <- NA;

### Show frequencies
freq(ourFactor);
freq(factorWithMissings);

### ... Or for all of them at one
frequencies(ourFactor, factorWithMissings);

Description

Frequencies

Usage

freqjmv(data, vector)

Arguments

Value

A results object containing:

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

results$table$asDF

as.data.frame(results$table)

Description

Analyze moderated mediation model using SEM

Usage

gemm(
  data = NULL,
  xvar,
  mvars,
  yvar,
  xmmod = NULL,
  mymod = NULL,
  cmvars = NULL,
  cyvars = NULL,
  estMethod = "bootstrap",
  nboot = 1000
)

Arguments

Value

gemm object

Examples

## Not run: 
data("cpbExample")
       res <- gemm(dat = cpbExample, xvar="procJustice", mvars= c("cynicism","trust"),
       yvar = "CPB", nboot=500)
print(res)

## End(Not run)

Description

This function provides a simple interface to create a ggplot2::ggplot() bar chart.

Usage

ggBarChart(vector, plotTheme = ggplot2::theme_bw(), ...)

Arguments

Value

A ggplot2::ggplot() plot is returned.

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters [email protected]

See Also

ggplot2::geom_bar()

Examples

rosetta::ggBarChart(mtcars$cyl);

Description

This function provides a simple interface to create a ggplot box plot, organising different boxplots by levels of a factor is desired, and showing row numbers of outliers.

Usage

ggBoxplot(
  dat,
  y = NULL,
  x = NULL,
  labelOutliers = TRUE,
  outlierColor = "red",
  theme = ggplot2::theme_bw(),
  ...
)

Arguments

Details

This function is based on JasonAizkalns' answer to a question on Stack Exchange (Cross Validated; see https://stackoverflow.com/questions/33524669/labeling-outliers-of-boxplots-in-r).

Value

A ggplot plot is returned.

Author(s)

Jason Aizkalns; implemented in this package (and tweaked a bit) by Gjalt-Jorn Peters.

Maintainer: Gjalt-Jorn Peters [email protected]

See Also

geom_boxplot

Examples

### A box plot for miles per gallon in the mtcars dataset:
ggBoxplot(mtcars$mpg);

### And separate for each level of 'cyl' (number of cylinder):
ggBoxplot(mtcars, y='mpg', x='cyl');

Description

This function creates a qq-plot with a confidence interval.

Usage

ggqq(
  x,
  distribution = "norm",
  ...,
  ci = TRUE,
  line.estimate = NULL,
  conf.level = 0.95,
  sampleSizeOverride = NULL,
  observedOnX = TRUE,
  scaleExpected = TRUE,
  theoryLab = "Theoretical quantiles",
  observeLab = "Observed quantiles",
  theme = ggplot2::theme_bw()
)

Arguments

Details

This is strongly based on the answer by user Floo0 to a Stack Overflow question at Stack Exchange (see https://stackoverflow.com/questions/4357031/qqnorm-and-qqline-in-ggplot2/27191036#27191036), also posted at GitHub (see https://gist.github.com/rentrop/d39a8406ad8af2a1066c). That code is in turn based on the qqPlot() function from the car package.

Value

A ggplot plot is returned.

Author(s)

John Fox and Floo0; implemented in this package (and tweaked a bit) by Gjalt-Jorn Peters.

Maintainer: Gjalt-Jorn Peters [email protected]

Examples

ggqq(mtcars$mpg);

Description

This function provides a simple interface to create a ggplot2::ggplot() bar chart.

Usage

ggScatterPlot(
  x,
  y,
  jitter = TRUE,
  size = 3,
  alpha = 0.66,
  shape = 16,
  color = "black",
  fill = "black",
  stroke = 1,
  plotTheme = ggplot2::theme_bw(),
  ...
)

Arguments

Value

A ggplot2::ggplot() plot is returned.

See Also

ggplot2::geom_point()

Examples

rosetta::ggScatterPlot(mtcars$hp, mtcars$mpg);

Description

Simple function to create a histogram

Usage

histogram(
  vector,
  bins = NULL,
  theme = ggplot2::theme_bw(),
  xLabel = NULL,
  yLabel = "Count"
)

Arguments

Value

A ggplot2 plot.

Examples

rosetta::histogram(mtcars$mpg);

Description

This function is meant as a userfriendly wrapper to approximate the way logistic regression is done in SPSS.

Usage

logRegr(
  formula,
  data = NULL,
  conf.level = 0.95,
  digits = 2,
  predictGroupValue = NULL,
  comparisonGroupValue = NULL,
  pvalueDigits = 3,
  crossTabs = TRUE,
  oddsRatios = TRUE,
  plot = FALSE,
  collinearity = FALSE,
  env = parent.frame(),
  predictionColor = rosetta::opts$get("viridis3")[3],
  predictionAlpha = 0.5,
  predictionSize = 2,
  dataColor = rosetta::opts$get("viridis3")[1],
  dataAlpha = 0.33,
  dataSize = 2,
  observedMeansColor = rosetta::opts$get("viridis3")[2],
  binObservedMeans = 7,
  observedMeansSize = 2,
  observedMeansWidth = NULL,
  observedMeansAlpha = 0.5,
  theme = ggplot2::theme_bw(),
  headingLevel = 3
)

rosettaLogRegr_partial(
  x,
  digits = x$input$digits,
  pvalueDigits = x$input$pvalueDigits,
  headingLevel = x$input$headingLevel,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaLogRegr'
knit_print(
  x,
  digits = x$input$digits,
  headingLevel = x$input$headingLevel,
  pvalueDigits = x$input$pvalueDigits,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaLogRegr'
print(
  x,
  digits = x$input$digits,
  pvalueDigits = x$input$pvalueDigits,
  headingLevel = x$input$headingLevel,
  forceKnitrOutput = FALSE,
  ...
)

Arguments

Value

Mainly, this function prints its results, but it also returns them in an object containing three lists:

Author(s)

Ron Pat-El & Gjalt-Jorn Peters (both while at the Open University of the Netherlands)

Maintainer: Gjalt-Jorn Peters [email protected]

See Also

regr and fanova for similar functions for linear regression and analysis of variance and stats::glm() for the regular interface for logistic regression.

Examples

### Simplest way to call logRegr
rosetta::logRegr(data=mtcars, formula = vs ~ mpg);

### Also ordering a plot
rosetta::logRegr(
  data=mtcars,
  formula = vs ~ mpg,
  plot=TRUE
);

### Only use five bins
rosetta::logRegr(
  data=mtcars,
  formula = vs ~ mpg,
  plot=TRUE,
  binObservedMeans=5
);

## Not run: 
### Mimic output that would be obtained
### when calling from an R Markdown file
rosetta::rosettaLogRegr_partial(
  rosetta::logRegr(
    data=mtcars,
    formula = vs ~ mpg,
    plot=TRUE
  )
);

## End(Not run)

Description

The meanDiff function compares the means between two groups. It computes Cohen's d, the unbiased estimate of Cohen's d (Hedges' g), and performs a t-test. It also shows the achieved power, and, more usefully, the power to detect small, medium, and large effects.

Usage

meanDiff(
  x,
  y = NULL,
  paired = FALSE,
  r.prepost = NULL,
  var.equal = "test",
  conf.level = 0.95,
  plot = FALSE,
  digits = 2,
  envir = parent.frame()
)

## S3 method for class 'meanDiff'
print(x, digits = x$digits, powerDigits = x$digits + 2, ...)

## S3 method for class 'meanDiff'
pander(x, digits = x$digits, powerDigits = x$digits + 2, ...)

Arguments

Details

This function uses the formulae from Borenstein, Hedges, Higgins & Rothstein (2009) (pages 25-32).

Value

An object is returned with the following elements:

Warning

Note that when different variances are assumed for the t-test (i.e. the null-hypothesis test), the values of Cohen's d are still based on the assumption that the variance is equal. In this case, the confidence interval might, for example, not contain zero even though the NHST has a non-significant p-value (the reverse can probably happen, too).

References

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to meta-analysis. John Wiley & Sons.

Examples

### Create simple dataset
dat <- PlantGrowth[1:20,];
### Remove third level from group factor
dat$group <- factor(dat$group);
### Compute mean difference and show it
meanDiff(dat$weight ~ dat$group);

### Look at second treatment
dat <- rbind(PlantGrowth[1:10,], PlantGrowth[21:30,]);
### Remove third level from group factor
dat$group <- factor(dat$group);
### Compute mean difference and show it
meanDiff(x=dat$group, y=dat$weight);

Description

The meanDiff.multi function compares many means for many groups. It presents the results in a dataframe summarizing all relevant information, and produces plot showing the confidence intervals for the effect sizes for each predictor (i.e. dichotomous variable). Like meanDiff, it computes Cohen's d, the unbiased estimate of Cohen's d (Hedges' g), and performs a t-test. It also shows the achieved power, and, more usefully, the power to detect small, medium, and large effects.

Usage

meanDiff.multi(
  dat,
  y,
  x = NULL,
  var.equal = "yes",
  conf.level = 0.95,
  digits = 2,
  orientation = "vertical",
  zeroLineColor = "grey",
  zeroLineSize = 1.2,
  envir = parent.frame()
)

## S3 method for class 'meanDiff.multi'
print(x, digits = x$digits, powerDigits = x$digits + 2, ...)

Arguments

Details

This function uses the meanDiff function, which uses the formulae from Borenstein, Hedges, Higgins & Rothstein (2009) (pages 25-32).

Value

An object is returned with the following elements:

Warning

Note that when different variances are assumed for the t-test (i.e. the null-hypothesis test), the values of Cohen's d are still based on the assumption that the variance is equal. In this case, the confidence interval might, for example, not contain zero even though the NHST has a non-significant p-value (the reverse can probably happen, too).

References

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to meta-analysis. John Wiley & Sons.

Examples

### Create simple dataset
dat <- data.frame(x1 = factor(rep(c(0,1), 20)),
                  x2 = factor(c(rep(0, 20), rep(1, 20))),
                  y=rep(c(4,5), 20) + rnorm(40));
### Compute mean difference and show it
meanDiff.multi(dat, x=c('x1', 'x2'), y='y', var.equal="yes");

Description

These functions allow easily computing means and sums. Note that if you attach rosetta to the search path,

Usage

means(
  ...,
  data = NULL,
  requiredValidValues = 0,
  returnIfInvalid = NA,
  silent = FALSE
)

sums(
  ...,
  data = NULL,
  requiredValidValues = 0,
  returnIfInvalid = NA,
  silent = FALSE
)

Arguments

Value

The means or sums.

Examples

rosetta::means(mtcars$mpg, mtcars$disp, mtcars$wt);
rosetta::means(data=mtcars, 'mpg', 'disp', 'wt');
rosetta::sums(mtcars$mpg, mtcars$disp, mtcars$wt);
rosetta::sums(data=mtcars, 'mpg', 'disp', 'wt');

Description

The oneway function wraps a number of analysis of variance functions into one convenient interface that is similar to the oneway anova command in SPSS.

Usage

oneway(
  y,
  x,
  posthoc = NULL,
  means = FALSE,
  fullDescribe = FALSE,
  levene = FALSE,
  plot = FALSE,
  digits = 2,
  omegasq = TRUE,
  etasq = TRUE,
  corrections = FALSE,
  pvalueDigits = 3,
  t = FALSE,
  conf.level = 0.95,
  posthocLetters = FALSE,
  posthocLetterAlpha = 0.05,
  overrideVarNames = NULL,
  silent = FALSE
)

## S3 method for class 'oneway'
print(
  x,
  digits = x$input$digits,
  pvalueDigits = x$input$pvalueDigits,
  na.print = "",
  ...
)

## S3 method for class 'oneway'
pander(
  x,
  digits = x$input$digits,
  pvalueDigits = x$input$pvalueDigits,
  headerStyle = "**",
  na.print = "",
  ...
)

Arguments

Value

A list of three elements:

Note

By my knowledge the Brown-Forsythe correction was not yet available in R. I took this from the original paper (directed there by Field, 2014). Note that this is the corrected F value, not the Brown-Forsythe test for normality!

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters [email protected]

References

Brown, M., & Forsythe, A. (1974). The small sample behavior of some statistics which test the equality of several means. Technometrics, 16(1), 129-132. https://doi.org/10.2307/1267501

Field, A. (2014) Discovering statistics using SPSS (4th ed.). London: Sage.

Steiger, J. H. (2004). Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychological methods, 9(2), 164-82. doi:10.1037/1082-989X.9.2.164

Examples

### Do a oneway Anova
oneway(y=ChickWeight$weight, x=ChickWeight$Diet);

### Also order means and transpose the results
oneway(y=ChickWeight$weight, x=ChickWeight$Diet, means=TRUE, t=TRUE);

Description

The rosetta::opts object contains three functions to set, get, and reset options used by the rosetta package. Use rosetta::opts$set to set options, rosetta::opts$get to get options, or rosetta::opts$reset to reset specific or all options to their default values.

Usage

opts

Format

An object of class list of length 4.

Details

It is normally not necessary to get or set rosetta options.

The following arguments can be passed:

...

For rosetta::opts$set, the dots can be used to specify the options to set, in the format option = value, for example, varViewCols = c("values", "level"). For rosetta::opts$reset, a list of options to be reset can be passed.

option

For rosetta::opts$set, the name of the option to set.

default

For rosetta::opts$get, the default value to return if the option has not been manually specified.

The following options can be set:

varViewCols

The order and names of the columns to include in the variable view.

showLabellerWarning

Whether to show a warning if labeller labels are encountered.

Examples

### Get the default columns in the variable view
rosetta::opts$get(varViewCols);

### Set it to a custom version
rosetta::opts$set(varViewCols = c("values", "level"));

### Check that it worked
rosetta::opts$get(varViewCols);

### Reset this option to its default value
rosetta::opts$reset(varViewCols);

### Check that the reset worked, too
rosetta::opts$get(varViewCols);

Description

This is a subsets of the Party Panel 2015 dataset. Party Panel is an annual semi-panel determinant study among Dutch nightlife patrons, where every year, the determinants of another nightlife-related risk behavior are mapped. In 2015, determinants were measured of behaviors related to using highly dosed ecstasy pills.

Usage

data(pp15)

Format

A data.frame with 128 columns and 829 rows. Note that many rows contain missing values; the columns and rows were taken directly from the original Party Panel dataset, and represent all participants that made it past a given behavior.

Details

The full dataset is publicly available through the Open Science Framework (https://osf.io/s4fmu/). Also see the GitLab repository (https://gitlab.com/partypanel) and the website at https://partypanel.eu.

Examples

data('pp15', package='rosetta');
rosetta::freq(pp15$gender);

Description

Makes plot of Index of Moderated Mediation of gemm object

Usage

plotIMM(x, ...)

Arguments

Value

simple slope plots for each mediator and simple slopes parameter estimates

Description

Makes 3D plots of Index of Moderated Mediation of gemm object

Usage

plotIMM3d(x, ...)

Arguments

Value

empty, directly plots all indices of mediation

Description

Makes simple slope plots of gemm object

Usage

plotSS(x, ...)

Arguments

Value

simple slope plots for each mediator and simple slopes parameter estimates

Description

This function is used by the 'oneway' function for oneway analysis of variance in case a user requests post-hoc tests using the Tukey or Games-Howell methods.

Usage

posthocTGH(
  y,
  x,
  method = c("games-howell", "tukey"),
  conf.level = 0.95,
  digits = 2,
  p.adjust = "none",
  formatPvalue = TRUE
)

## S3 method for class 'posthocTGH'
print(x, digits = x$input$digits, ...)

Arguments

Value

A list of three elements:

Note

This function is based on a file that was once hosted at http://www.psych.yorku.ca/cribbie/6130/games_howell.R, but has been removed since. It was then adjusted for implementation in the userfriendlyscience package. Jeffrey Baggett needed the confidence intervals, and so emailed them, after which his updated function was used. In the meantime, it appears Aaron Schlegel (https://rpubs.com/aaronsc32) independently developed a version with confidence intervals and posted it on RPubs at https://rpubs.com/aaronsc32/games-howell-test.

Also, for some reason, p.adjust can be used to specify additional correction of p values. I'm not sure why I implemented this, but I'm not entirely sure it was a mistake either. Therefore, in userfriendlyscience version 0.6-2, the default of this setting changed from "holm" to "none" (also see https://stats.stackexchange.com/questions/83941/games-howell-post-hoc-test-in-r).

Author(s)

Gjalt-Jorn Peters (Open University of the Netherlands) & Jeff Bagget (University of Wisconsin - La Crosse)

Maintainer: Gjalt-Jorn Peters [email protected]

Examples

### Compute post-hoc statistics using the tukey method
posthocTGH(y=ChickWeight$weight, x=ChickWeight$Diet, method="tukey");
### Compute post-hoc statistics using the games-howell method
posthocTGH(y=ChickWeight$weight, x=ChickWeight$Diet);

Description

Computes Index of moderated mediation of gemm object

Usage

prepIMM3d(M1, M2, parEst = parEst, i = 1)

Arguments

Value

vector of index of moderated mediation with CI limits for a given mediator

Description

Makes Index of Mediated Moderated plots

Usage

prepPlotIMM(
  data,
  xvar,
  yvar,
  mod,
  mvars,
  parEst,
  vdichotomous,
  modLevels,
  path = NULL
)

Arguments

Value

empty, directly plots all simple slopes and all indices of mediation

Description

Makes simple slope plots

Usage

prepPlotSS(
  data,
  xvar,
  yvar,
  mod,
  mvars,
  parEst,
  vdichotomous,
  modLevels,
  predLevels = NULL,
  xquant,
  yquant,
  path = NULL
)

Arguments

Value

empty, directly plots all simple slopes and all indices of mediation

Description

print method of object of class gemm

Usage

## S3 method for class 'gemm'
print(x, ..., digits = 2, silence = FALSE)

Arguments

Description

idSlug is a convenience function with swapped argument order.

Usage

randomSlug(x = 10, id = NULL, chars = c(letters, LETTERS, 0:9))

idSlug(id = NULL, x = 10, chars = c(letters, LETTERS, 0:9))

Arguments

Value

A character value.

Examples

randomSlug();
idSlug("identifier");

Description

This function is from the car package. Please see that help page for details: car::recode().

Usage

recode(
  var,
  recodes,
  as.factor,
  as.numeric = TRUE,
  levels,
  to.value = "=",
  interval = ":",
  separator = ";"
)

Arguments

Author(s)

John Fox [email protected]

References

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Examples

x<-rep(1:3,3)
x
rosetta::recode(
  x,
  "c(1,2)='A'; else='B'"
);
rosetta::recode(
  x,
  "1:2='A'; 3='B'"
);

Description

The regr function wraps a number of linear regression functions into one convenient interface that provides similar output to the regression function in SPSS. It automatically provides confidence intervals and standardized coefficients. Note that this function is meant for teaching purposes, and therefore it's only for very basic regression analyses; for more functionality, use the base R function lm or e.g. the lme4 package.

Usage

regr(
  formula,
  data = NULL,
  conf.level = 0.95,
  digits = 2,
  pvalueDigits = 3,
  coefficients = c("raw", "scaled"),
  plot = FALSE,
  pointAlpha = 0.5,
  collinearity = FALSE,
  influential = FALSE,
  ci.method = c("widest", "r.con", "olkinfinn"),
  ci.method.note = FALSE,
  headingLevel = 3,
  env = parent.frame()
)

rosettaRegr_partial(
  x,
  digits = x$input$digits,
  pvalueDigits = x$input$pvalueDigits,
  headingLevel = x$input$headingLevel,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaRegr'
knit_print(
  x,
  digits = x$input$digits,
  headingLevel = x$input$headingLevel,
  pvalueDigits = x$input$pvalueDigits,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaRegr'
print(
  x,
  digits = x$input$digits,
  pvalueDigits = x$input$pvalueDigits,
  headingLevel = x$input$headingLevel,
  forceKnitrOutput = FALSE,
  ...
)

## S3 method for class 'rosettaRegr'
pander(x, digits = x$input$digits, pvalueDigits = x$input$pvalueDigits, ...)

Arguments

Value

A list of three elements:

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters [email protected]

Examples

### Do a simple regression analysis
rosetta::regr(age ~ circumference, dat=Orange);

### Show more digits for the p-value
rosetta::regr(Orange$age ~ Orange$circumference, pvalueDigits=18);

## Not run: 
### An example with an interaction term, showing in the
### viewer
rosetta::rosettaRegr_partial(
  rosetta::regr(
    mpg ~ wt + hp + wt:hp,
    dat=mtcars,
    coefficients = "raw",
    plot=TRUE,
    collinearity=TRUE
  )
);

## End(Not run)

Description

The reliability() analysis is the only one most users will need. It tries to apply best practices by, as much as possible, complementing point estimates with confidence intervals.

Usage

reliability(
  data,
  items = NULL,
  scaleStructure = TRUE,
  descriptives = FALSE,
  itemLevel = FALSE,
  scatterMatrix = FALSE,
  scatterMatrixArgs = list(progress = FALSE),
  digits = 2,
  conf.level = 0.95,
  itemLabels = NULL,
  itemOmittedCorsWithRest = FALSE,
  itemOmittedCorsWithTotal = FALSE,
  alphaOmittedCIs = FALSE,
  omegaFromMBESS = FALSE,
  omegaFromPsych = TRUE,
  ordinal = FALSE,
  headingLevel = 3,
  ...
)

rosettaReliability_partial(
  x,
  digits = x$digits,
  headingLevel = x$headingLevel,
  printPlots = TRUE,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaReliability'
knit_print(
  x,
  digits = x$digits,
  headingLevel = x$headingLevel,
  printPlots = TRUE,
  echoPartial = FALSE,
  partialFile = NULL,
  quiet = TRUE,
  ...
)

## S3 method for class 'rosettaReliability'
print(
  x,
  digits = x$digits,
  headingLevel = x$headingLevel,
  forceKnitrOutput = FALSE,
  printPlots = TRUE,
  ...
)

Arguments

Details

The rosettaReliability object that is returned has its own print() method, that, when using knitr, will use the rmdpartials package to insert an RMarkdown partial. That partial is created using rosettaReliability_partial(), which is also called by a specific knit_print() method.

Value

An object with all results

Examples

### These examples aren't run during tests
### because they can take quite long
## Not run: 
### Simple example with only main reliability results
data(pp15, package="rosetta");
rosetta::reliability(
  pp15,
  c(
    "highDose_AttGeneral_good",
    "highDose_AttGeneral_prettig",
    "highDose_AttGeneral_slim",
    "highDose_AttGeneral_gezond",
    "highDose_AttGeneral_spannend"
  )
);

### More extensive example with an RMarkdown partial that
### displays in the viewer
rosetta::rosettaReliability_partial(
  rosetta::reliability(
    attitude,
    descriptives = TRUE,
    itemLevel = TRUE,
    scatterMatrix = TRUE
  )
);

## End(Not run)

Description

Repeat a string a number of times

Usage

repeatStr(n = 1, str = " ")

Arguments

Value

A character vector of length 1.

Examples

### 10 spaces:
repStr(10);

### Three euro symbols:
repStr("\u20ac", 3);

Description

rMatrix provides a correlation matrix with confidence intervals and a p-value adjusted for multiple testing.

Usage

rMatrix(
  dat,
  x,
  y = NULL,
  conf.level = 0.95,
  correction = "fdr",
  digits = 2,
  pValueDigits = 3,
  colspace = 2,
  rowspace = 0,
  colNames = "numbers"
)

## S3 method for class 'rMatrix'
print(
  x,
  digits = x$digits,
  pValueDigits = x$pValueDigits,
  colNames = x$colNames,
  ...
)

Arguments

Details

rMatrix provides a symmetric or asymmetric matrix of correlations, their confidence intervals, and p-values. The p-values can be corrected for multiple testing.

Value

An rMatrix object that when printed shows the correlation matrix

An object with the input and several output variables. Most notably a number of matrices:

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters [email protected]

Examples

rMatrix(mtcars, x=c('disp', 'hp', 'drat'))

Description

scatterMatrix produces a matrix with jittered scatterplots, histograms, and correlation coefficients.

Usage

scatterMatrix(
  dat,
  items = NULL,
  itemLabels = NULL,
  plotSize = 180,
  sizeMultiplier = 1,
  pointSize = 1,
  axisLabels = "none",
  normalHist = TRUE,
  progress = NULL,
  theme = ggplot2::theme_minimal(),
  hideGrid = TRUE,
  conf.level = 0.95,
  ...
)

Arguments

Value

An object with the input and several output variables. Most notably:

Examples

### Note: the 'not run' is simply because running takes a lot of time,
###       but these examples are all safe to run!
## Not run: 

### Generate a datafile to use
exampleData <- data.frame(item1=rnorm(100));
exampleData$item2 <- exampleData$item1+rnorm(100);
exampleData$item3 <- exampleData$item1+rnorm(100);
exampleData$item4 <- exampleData$item2+rnorm(100);
exampleData$item5 <- exampleData$item2+rnorm(100);

### Use all items
scatterMatrix(dat=exampleData);

## End(Not run)

Description

This function is intended to provide a very easy interface to generating pretty (and pretty versatile) ggplot2::ggplot() scatter plots.

Usage

scatterPlot(
  x,
  y,
  pointsize = 3,
  theme = theme_bw(),
  regrLine = FALSE,
  regrCI = FALSE,
  regrLineCol = "blue",
  regrCIcol = regrLineCol,
  regrCIalpha = 0.25,
  width = 0,
  height = 0,
  position = "identity",
  xVarName = NULL,
  yVarName = NULL,
  ...
)

Arguments

Details

Note that if position is set to 'jitter', unless width and/or height is set to a non-zero value, there will still not be any jittering.

Value

A ggplot2::ggplot() plot is returned.

Examples

### A simple scatter plot
rosetta::scatterPlot(
  mtcars$mpg, mtcars$hp
);

### The same scatter plot, now with a regression line
### and its confidence interval added.
rosetta::scatterPlot(
  mtcars$mpg, mtcars$hp,
  regrLine=TRUE,
  regrCI=TRUE
);

Description

This function provides an overview of the variables in a dataframe, allowing efficient inspection of the factor levels, ranges for numeric variables, and numbers of missing values.

Usage

varView(
  data,
  columns = names(data),
  varViewCols = rosetta::opts$get(varViewCols),
  varViewRownames = TRUE,
  maxLevels = 10,
  truncLevelsAt = 50,
  showLabellerWarning = rosetta::opts$get(showLabellerWarning),
  output = rosetta::opts$get("tableOutput")
)

## S3 method for class 'rosettaVarView'
print(x, output = attr(x, "output"), ...)

Arguments

Value

A dataframe with the variable view.

Author(s)

Gjalt-Jorn Peters & Melissa Gordon Wolf

Examples

### The default variable view
rosetta::varView(iris);

### Only for a few variables in the dataset
rosetta::varView(iris, columns=c("Sepal.Length", "Species"));

### Set some variable and value labels using the `labelled`
### standard, which is also used by `haven`
dat <- iris;
attr(dat$Sepal.Length, "label") <- "Sepal length";
attr(dat$Sepal.Length, "labels") <-
  c('one' = 1,
    'two' = 2,
    'three' = 3);

### varView automatically recognizes and shows these, adding
### a 'label' column
rosetta::varView(dat);

### You can also specify that you only want to see some columns
### in the variable view
rosetta::varView(dat,
                 varViewCols = c('label', 'values', 'level'));

Description

vecTxtQ, vecTxtB, and vecTxtM and are convenience functions with default quotes that can be useful when working in R Markdown documents.

Usage

vecTxt(
  vector,
  delimiter = ", ",
  useQuote = "",
  firstDelimiter = NULL,
  lastDelimiter = " & ",
  firstElements = 0,
  lastElements = 1,
  lastHasPrecedence = TRUE
)

vecTxtQ(vector, useQuote = "'", ...)

vecTxtB(vector, useQuote = "`", ...)

vecTxtM(vector, useQuote = "$", ...)

Arguments

Value

A character vector of length 1.

Examples

vecTxtQ(names(mtcars));