This package simply provides wrappers around well-established and validated packages (e.g. stats, lawstat, psychometric, MBESS, lme4, psych, epitools) to make their use and output more user friendly.
Because using R for statistics should not be hard. This package:
- Provides a standard operating approach for running statistics in R with results that generally align with those that would be obtained through SPSS, SAS, and/or Systat.
- Provides automatic posthoc decomposition of significant effects.
- Provides statistical results, effect sizes, effect size confidence intervals, and interpretations in APA format.
- Provides a graphical user interface for point and click analysis.
This R package mimics the functionalities of popular commercial statistics software packages in that it will compute multiple-related tests. However, rather than outputting all results blindly, the package uses the specified/ automatically determined test correction/adjustments and outputs the results in APA format. Additionally, when appropriate, functions will automatically break down the data to consider all potential comparisons (A vs B, A vs C, B vs C).
This package deviates from the typical R ethos such that "super functions" will output results to the console window in a style that roughly mimics the output of popular statistics software packages. Test results are also available as environmental variables and console outputs can be suppressed using verbose calls to the function.
Several functions are also available through the R Studio Addins dropdown menu to provide a graphic user interface similar to popular commercial statistics packages. You may have to restart R Studio after installing the package for the functions to show up. Functions accessed through the dropdown menu will pop up a graphical user interface for selecting variables and parameters, when the results of the function printed to the console window along with the syntax.
To use this package, from R run the following commands:
tryCatch(library(devtools), error=function(e){install.packages("devtools"); library(devtools)})
devtools::install_github("mattpontifex/Rmimic", force=TRUE); library(Rmimic) Common Issues
- You may be prompted to update packages, you can select 'None' (3) to just install Rmimic.
- You may get a backports error installing devtools, this may require you to restart R and then the code below. After completing that step, you can repeat the installation instructions above.
install.packages("backports") - You may get an error indicating that R cannot remove prior installation of a package. You can try installing the package again to see if that fixes it. Most times you will need to run the code below, then manually delete the folder and then run the install packages command.
find.package('farver') These functions mimic the overarching outputs of popular commercial statistics software packages that lump together several related or inherently sequential tests.
- RmimicAnova: Function that computes a SPSS style univariate ANOVA with effect size and confidence intervals using the ezANOVA function. Main effects and interactions are automatically decomposed using the specified post-hoc corrections.
anovaresult <- Rmimic::RmimicAnova(data = PlantGrowth, dependentvariable='weight',
subjectid=NULL, between='group', within=NULL, sphericity='Greenhouse-Geisser',
posthoc='False Discovery Rate Control', verbose=TRUE) 
- RmimicLMcontrast: Compute SPSS style results for regression analysis with effect size and confidence intervals. This function takes stats::lm fits for a base model and the model of interest and calculates statistics for the model of interest relative to the base model. This function is also able to take stats::glm binomial family model fits for logistic regression.
basefit <- lm(mpg ~ am + wt, data = mtcars)
fit <- lm(mpg ~ am + wt + qsec, data = mtcars)
regresult <- Rmimic::RmimicLMcontrast(basefit, fit,
confidenceinterval=0.95, studywiseAlpha=0.05, verbose=TRUE)
- RmimicTtest: Function that computes SPSS style t-tests with effect size and confidence intervals. Optional parameters are also provided to compute non-parametric t-tests with appropriate non-parametric effect size estimates. For parametric test the function automatically determines if the variances are equal using levene's test and outputs the correct statistcs. The function can handle factors with more than 2 levels and will perform t-tests for each comparison with post-hoc comparison corrections.
ttestresult <- Rmimic::RmimicTtest(PlantGrowth, dependentvariable='weight',
subjectid=NULL, between='group', within=NULL,
nonparametric=FALSE, posthoc='Holm-Bonferroni', verbose=TRUE) 
- RmimicChisquare: Function that computes SPSS style results for Chi-square analysis with odds ratios and confidence intervals. For samples less than 1000, Fishers exact test statistic is used if possible. The function can handle outcomes with more than 2 levels and will perform comparisons for each pair of outcomes.
tempdata <- data.frame("Age"="8","Pet"="Dog","Freq"=282)
tempdata <- rbind(tempdata, data.frame("Age"="30","Pet"="Dog","Freq"=199))
tempdata <- rbind(tempdata, data.frame("Age"="8","Pet"="Cat","Freq"=170))
tempdata <- rbind(tempdata, data.frame("Age"="30","Pet"="Cat","Freq"=240))
chisquareresult <- Rmimic::RmimicChisquare(x='Age', y='Pet', data=tempdata,
posthoc='False Discovery Rate Control',
confidenceinterval=0.95, studywiseAlpha=0.05,
planned=FALSE, verbose=TRUE)
chisquareresult <- Rmimic::RmimicChisquare(x='Sex', y='Survived', data=Titanic,
posthoc='False Discovery Rate Control', planned=FALSE,
confidenceinterval=0.95, studywiseAlpha=0.05, verbose=TRUE) 
- correlation: Function that computes SPSS style correlations or partial correlations, with optional parameters for the approach (pearson (default), spearman, or kendall).
tempdata <- data.frame("X" = runif(100), "Y" = runif(100), "Z" = runif(100))
corresult <- Rmimic::correlation(variables=c('X', 'Y', 'Z'), partial=FALSE,
data=tempdata, method='pearson', listwise=TRUE, studywiseAlpha=0.05,
confidenceinterval=0.95, verbose=TRUE) 
- descriptives: Function that computes SPSS style descriptive statistics and frequencies.
tempdata <- data.frame("Group" = sample(1:2,100, replace=TRUE), "X" = runif(100), "Y" = runif(100))
desc <- Rmimic::descriptives(variables=c('X','Y'), groupvariable=c("Group"), data=tempdata, verbose=TRUE) 
- mediate2text: Function that outputs mediation results in a more intelligible format.
workingdatabase <- Rmimic::gradwakefulness
fitM <- stats::lm(CaffeineConsumption ~ HoursAwake, data=workingdatabase)
fitY <- stats::lm(Wakefulness ~ HoursAwake + CaffeineConsumption, data=workingdatabase)
fitMed <- mediation::mediate(fitM, fitY, treat='HoursAwake', mediator='CaffeineConsumption',
boot=FALSE, sims=1000, conf.level=0.95)
res <- Rmimic::mediate2text(fitMed, studywiseAlpha=0.05)
- RmimicMLAnova: Function that computes a SPSS style univariate ANOVA with effect size and confidence intervals using a multi-level model from the lme4 function. Main effects and interactions are automatically decomposed using the specified post-hoc corrections.
workingdatabase <- Rmimic::alertness
workingdatabase <- workingdatabase[which(workingdatabase$Condition == 'Condition2'),]
anovaresult <- Rmimic::RmimicMLAnova(data = workingdatabase,dependentvariable = "Alertness",
subjectid = "PartID", between = "Group", within = c("Time"),
randomintercept = c("PartID"))
- clusterthreshold1d: Function that calculates contiguous clusters of locations in a 1D array that are above or below some threshold and of some minimum cluster size (i.e., a cluster of 30 points all below 0.05).
tempdata <- c(0.2, 0.3, 0.05, 0.04, 0.06, 0.08, 0.009, 0.05, 0.02, 0.03, 0.08, 0.1, 0.4)
Rmimic::clusterthreshold1d(tempdata, crit = 0.05, clustersize = 3, direction = 'LessThan')
# returns: c(0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0)- computechange: Function to compute the change or difference between within subjects conditions. A new variable is returned to the data containing the change calculation. Optional parameters are included for computing PercentChange, specifying the factor level to use as the baseline, and for other factors to control for.
mockdatabase <- data.frame("ID" = rep_len(1:20,60),
"Time" = c(rep_len("Time1",20),rep_len("Time2",20),rep_len("Time3",20)),
"X" = runif(60))
mockdatabase <- Rmimic::computechange(mockdatabase, dependentvariable='X', subjectid='ID', within='Time')- identifyoutliers: Function to identify outliers based upon the interquartile range (as SPSS does for boxplots) and replace those values with NA.
tempdata <- runif(100,1,10); tempdata[6] <- 1000; tempdata[10] <- 1000
tempdata <- Rmimic::identifyoutliers(tempdata, iqrlimit = 3, verbose=TRUE) - multipleimputation: Function that uses the mice package to replace missing data points.
tempdata <- data.frame("X" = runif(100), "Y" = runif(100))
tempdata[6,'X'] <- NA; tempdata[10,'Y'] <- NA;
tempdata <- Rmimic::multipleimputation(tempdata, imputations=10) - ezANOVA2text: Function to output ezANOVA results in APA style format with effect sizes and confidence intervals.
result <- ez::ezANOVA(data=elashoff,dv=Alertness,wid=PartID,
between=Group,within=.(Drug,Dose),type=3,detailed=TRUE,return_aov=TRUE)
result <- Rmimic::ezANOVA2text(result, numparticipants=16, feffect="Generalized Eta Squared",
sphericity="Greenhouse-Geisser", confidenceinterval=0.95, studywiseAlpha=0.05)- lmer2text: Function to output lmerTest::lmer results in APA style format with effect sizes and confidence intervals. The function will also work with lme4:lmer models, however these models do not report probability values.
fit <- lmerTest::lmer(Alertness ~ Group*Drug*Dose + (1 | PartID), data=Rmimic::elashoff)
result <- Rmimic::lmer2text(fit, df="Kenward-Roger", numparticipants=16, numfactors=4)- ttest2text: Function that takes a t-test result from the stats package and outputs the t-test result for use in an APA style manuscript (i.e., t(18) = 2.3, p = 0.031) with proper rounding. Supports independent and paired t-tests for both parametric and nonparametric data.
comparison1 <- c(2, 3, 4, 6, 8, 9, 10, 11, 13, 15); comparison2 <- c( 5, 7, 9, 10, 13, 15, 16, 17, 18, 20)
ttestresult <- stats::t.test(x= comparison1, y=comparison2, paired=FALSE, var.equal=TRUE)
ttestresult$effectsize <- ttestresult$statistic * sqrt((1/length(comparison1)) + (1/length(comparison2)))
tempout <- Rmimic::ttest2text(ttestresult, verbose=TRUE)
#returns: t(18) = 2.3, p = 0.031, ds = -1.04.- pseudobootstrapsamples: Data exploration function that randomly samples a dataset to increase the sample size.
- cell2span: Function that takes a vector of data table cells and creates a character string of a particular length. The text empty will result in an open span in the output.
- decimalplaces: Function to obtain the number of decimal places of precision in a vector.
- determineallpossiblecombinations: Function to determine all possible combinations of an input array. For instance, an array containing A, B, and C could be assessed looking at A, B, C, A:B, A:C, B:C, or A:B:C.
- fuzzyP: Function to round P values for reporting. Because reporting p = 0.912 to three digits of precision is a bit silly.
- posthoc2text: Function to output ANOVA posthoc results in APA style format with effect sizes and confidence intervals.
- table2console: Function that takes a data table and prints a nicely formatted table to the console.
- typewriter: Function to control the text outputted to the console to create a consistent indent.