Title:	Broadly Useful Convenient and Efficient R Functions
Version:	2024.6
Date:	2024-06-12
Maintainer:	Han-Wu-Shuang Bao <baohws@foxmail.com>
Description:	Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	xz
URL:	https://psychbruce.github.io/bruceR/
BugReports:	https://github.com/psychbruce/bruceR/issues
Depends:	R (≥ 4.0.0)
Imports:	rstudioapi, data.table, rio, crayon, plyr, dplyr, tidyr, stringr, ggplot2, psych, afex, emmeans, effectsize, performance, mediation, interactions, lavaan, jtools, texreg, lmerTest
Suggests:	pacman, glue, tibble, forcats, haven, foreign, readxl, openxlsx, clipr, cowplot, ggtext, see, car, lmtest, lme4, nnet, vars, phia, MASS, MuMIn, BayesFactor, GGally, GPArotation
RoxygenNote:	7.3.1
NeedsCompilation:	no
Packaged:	2024-06-13 14:35:40 UTC; Bruce
Author:	Han-Wu-Shuang Bao [aut, cre]
Repository:	CRAN
Date/Publication:	2024-06-13 15:20:02 UTC

bruceR: BRoadly Useful Convenient and Efficient R functions

Description

BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.

Package homepage: https://psychbruce.github.io/bruceR/

Install the latest development version from GitHub: devtools::install_github("psychbruce/bruceR")

Report bugs at GitHub Issues.

Main Functions in bruceR

(1) Basic R Programming

set.wd (alias: set_wd)

import, export

cc

pkg_depend, pkg_install_suggested

formatF, formatN

print_table

Print, Glue, Run

%^%

%notin%

%allin%, %anyin%, %nonein%, %partin%

(2) Multivariate Computation

add, added

.sum, .mean

SUM, MEAN, STD, MODE, COUNT, CONSEC

RECODE, RESCALE

LOOKUP

(3) Reliability and Factor Analyses

Alpha

EFA / PCA

CFA

(4) Descriptive Statistics and Correlation Analyses

Describe

Freq

Corr

cor_diff

cor_multilevel

(5) T-Test, Multi-Factor ANOVA, Simple-Effect Analysis, and Post-Hoc Multiple Comparison

TTEST

MANOVA

EMMEANS

(6) Tidy Report of Regression Models

model_summary

lavaan_summary

GLM_summary

HLM_summary

HLM_ICC_rWG

regress

(7) Mediation and Moderation Analyses

PROCESS

med_summary

(8) Additional Toolbox for Statistics and Graphics

grand_mean_center

group_mean_center

ccf_plot

granger_test

granger_causality

theme_bruce

show_colors

Author(s)

Maintainer: Han-Wu-Shuang Bao baohws@foxmail.com (ORCID)

See Also

Useful links:

• https://psychbruce.github.io/bruceR/

• Report bugs at https://github.com/psychbruce/bruceR/issues

Multivariate computation.

Description

Easily compute multivariate sum, mean, and other scores. Reverse scoring can also be easily implemented without saving extra variables. Alpha function uses a similar method to deal with reverse scoring.

Three ways to specify variables:

1. var + items: common and unique parts of variable names (suggested).

2. vars: a character vector of variable names (suggested).

3. varrange: starting and stopping positions of variables (NOT suggested).

Usage

COUNT(data, var = NULL, items = NULL, vars = NULL, varrange = NULL, value = NA)

MODE(data, var = NULL, items = NULL, vars = NULL, varrange = NULL)

SUM(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

.sum(
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

MEAN(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

.mean(
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

STD(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

CONSEC(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  values = 0:9
)

Arguments

Value

A vector of computed values.

Functions

• COUNT(): Count a certain value across variables.

• MODE(): Compute mode across variables.

• SUM(): Compute sum across variables.

• .sum(): Tidy version of SUM, only can be used in add()/added()

• MEAN(): Compute mean across variables.

• .mean(): Tidy version of MEAN, only can be used in add()/added()

• STD(): Compute standard deviation across variables.

• CONSEC(): Compute consecutive identical digits across variables (especially useful in detecting careless responding).

Examples

d = data.table(
  x1 = 1:5,
  x4 = c(2,2,5,4,5),
  x3 = c(3,2,NA,NA,5),
  x2 = c(4,4,NA,2,5),
  x5 = c(5,4,1,4,5)
)
d
## I deliberately set this order to show you
## the difference between "vars" and "varrange".

## ====== Usage 1: data.table `:=` ====== ##
d[, `:=`(
  na = COUNT(d, "x", 1:5, value=NA),
  n.2 = COUNT(d, "x", 1:5, value=2),
  sum = SUM(d, "x", 1:5),
  m1 = MEAN(d, "x", 1:5),
  m2 = MEAN(d, vars=c("x1", "x4")),
  m3 = MEAN(d, varrange="x1:x2", rev="x2", range=1:5),
  cons1 = CONSEC(d, "x", 1:5),
  cons2 = CONSEC(d, varrange="x1:x5")
)]
d

## ====== Usage 2: `add()` & `added()` ====== ##
data = as.data.table(psych::bfi)
added(data, {
  gender = as.factor(gender)
  education = as.factor(education)
  E = .mean("E", 1:5, rev=c(1,2), range=1:6)
  A = .mean("A", 1:5, rev=1, range=1:6)
  C = .mean("C", 1:5, rev=c(4,5), range=1:6)
  N = .mean("N", 1:5, range=1:6)
  O = .mean("O", 1:5, rev=c(2,5), range=1:6)
}, drop=TRUE)
data

## ====== New Feature for `var` & `items` ====== ##
d = data.table(
  XX.1.pre = 1:5,
  XX.2.pre = 6:10,
  XX.3.pre = 11:15
)
add(d, { XX.mean = .mean("XX.{i}.pre", 1:3) })
add(d, { XX.mean = .mean("XX.{items}.pre", 1:3) })  # the same
add(d, { XX.mean = .mean("XX.{#$%^&}.pre", 1:3) })  # the same

Paste strings together.

Description

Paste strings together. A wrapper of paste0(). Why %^%? Because typing % and ^ is pretty easy by pressing Shift + 5 + 6 + 5.

Usage

x %^% y

Arguments

Value

A character string/vector of the pasted values.

Examples

"He" %^% "llo"
"X" %^% 1:10
"Q" %^% 1:5 %^% letters[1:5]

A simple extension of %in%.

Description

A simple extension of %in%.

Usage

x %allin% vector

Arguments

Value

TRUE or FALSE.

See Also

%in%, %anyin%, %nonein%, %partin%

Examples

1:2 %allin% 1:3  # TRUE
3:4 %allin% 1:3  # FALSE

A simple extension of %in%.

Description

A simple extension of %in%.

Usage

x %anyin% vector

Arguments

Value

TRUE or FALSE.

See Also

%in%, %allin%, %nonein%, %partin%

Examples

3:4 %anyin% 1:3  # TRUE
4:5 %anyin% 1:3  # FALSE

A simple extension of %in%.

Description

A simple extension of %in%.

Usage

x %nonein% vector

Arguments

Value

TRUE or FALSE.

See Also

%in%, %allin%, %anyin%, %partin%

Examples

3:4 %nonein% 1:3  # FALSE
4:5 %nonein% 1:3  # TRUE

The opposite of %in%.

Description

The opposite of %in%.

Usage

x %notin% vector

Arguments

Value

A vector of TRUE or FALSE.

See Also

%in%

Examples

data = data.table(ID=1:10, X=sample(1:10, 10))
data
data[ID %notin% c(1, 3, 5, 7, 9)]

A simple extension of %in%.

Description

A simple extension of %in%.

Usage

pattern %partin% vector

Arguments

Value

TRUE or FALSE.

See Also

%in%, %allin%, %anyin%, %nonein%

Examples

"Bei" %partin% c("Beijing", "Shanghai")  # TRUE
"bei" %partin% c("Beijing", "Shanghai")  # FALSE
"[aeiou]ng" %partin% c("Beijing", "Shanghai")  # TRUE

Reliability analysis (Cronbach's \alpha and McDonald's \omega).

Description

An extension of psych::alpha() and psych::omega(), reporting (1) scale statistics (Cronbach's \alpha and McDonald's \omega) and (2) item statistics (item-rest correlation [i.e., corrected item-total correlation] and Cronbach's \alpha if item deleted).

Three options to specify variables:

1. var + items: common and unique parts of variable names (suggested).

2. vars: a character vector of variable names (suggested).

3. varrange: starting and stopping positions of variables (NOT suggested).

Usage

Alpha(data, var, items, vars = NULL, varrange = NULL, rev = NULL, digits = 3)

Arguments

Value

A list of results obtained from psych::alpha() and psych::omega().

See Also

MEAN, EFA, CFA

Examples

# ?psych::bfi
data = psych::bfi
Alpha(data, "E", 1:5)   # "E1" & "E2" should be reversed
Alpha(data, "E", 1:5, rev=1:2)            # correct
Alpha(data, "E", 1:5, rev=cc("E1, E2"))   # also correct
Alpha(data, vars=cc("E1, E2, E3, E4, E5"), rev=cc("E1, E2"))
Alpha(data, varrange="E1:E5", rev=cc("E1, E2"))

# using dplyr::select()
data %>% select(E1, E2, E3, E4, E5) %>%
  Alpha(vars=names(.), rev=cc("E1, E2"))

Confirmatory Factor Analysis (CFA).

Description

An extension of lavaan::cfa().

Usage

CFA(
  data,
  model = "A =~ a[1:5]; B =~ b[c(1,3,5)]; C =~ c1 + c2 + c3",
  estimator = "ML",
  highorder = "",
  orthogonal = FALSE,
  missing = "listwise",
  digits = 3,
  file = NULL
)

Arguments

Value

A list of results returned by lavaan::cfa().

See Also

Alpha, EFA, lavaan_summary

Examples

data.cfa=lavaan::HolzingerSwineford1939
CFA(data.cfa, "Visual =~ x[1:3]; Textual =~ x[c(4,5,6)]; Speed =~ x7 + x8 + x9")
CFA(data.cfa, model="
    Visual =~ x[1:3]
    Textual =~ x[c(4,5,6)]
    Speed =~ x7 + x8 + x9
    ", highorder="Ability")

data.bfi = na.omit(psych::bfi)
CFA(data.bfi, "E =~ E[1:5]; A =~ A[1:5]; C =~ C[1:5]; N =~ N[1:5]; O =~ O[1:5]")

Correlation analysis.

Description

Correlation analysis.

Usage

Corr(
  data,
  method = "pearson",
  p.adjust = "none",
  all.as.numeric = TRUE,
  digits = 2,
  file = NULL,
  plot = TRUE,
  plot.r.size = 4,
  plot.colors = NULL,
  plot.file = NULL,
  plot.width = 8,
  plot.height = 6,
  plot.dpi = 500
)

Arguments

Value

Invisibly return a list with (1) correlation results from psych::corr.test() and (2) a ggplot2 object if plot=TRUE.

See Also

Describe

cor_multilevel

Examples

Corr(airquality)
Corr(airquality, p.adjust="bonferroni",
     plot.colors=c("#b2182b", "white", "#2166ac"))

d = as.data.table(psych::bfi)
added(d, {
  gender = as.factor(gender)
  education = as.factor(education)
  E = .mean("E", 1:5, rev=c(1,2), range=1:6)
  A = .mean("A", 1:5, rev=1, range=1:6)
  C = .mean("C", 1:5, rev=c(4,5), range=1:6)
  N = .mean("N", 1:5, range=1:6)
  O = .mean("O", 1:5, rev=c(2,5), range=1:6)
})
Corr(d[, .(age, gender, education, E, A, C, N, O)])

Descriptive statistics.

Description

Descriptive statistics.

Usage

Describe(
  data,
  all.as.numeric = TRUE,
  digits = 2,
  file = NULL,
  plot = FALSE,
  upper.triangle = FALSE,
  upper.smooth = "none",
  plot.file = NULL,
  plot.width = 8,
  plot.height = 6,
  plot.dpi = 500
)

Arguments

Value

Invisibly return a list with (1) a data frame of descriptive statistics and (2) a ggplot2 object if plot=TRUE.

See Also

Corr

Examples

set.seed(1)
Describe(rnorm(1000000), plot=TRUE)

Describe(airquality)
Describe(airquality, plot=TRUE, upper.triangle=TRUE, upper.smooth="lm")

# ?psych::bfi
Describe(psych::bfi[c("age", "gender", "education")])

d = as.data.table(psych::bfi)
added(d, {
  gender = as.factor(gender)
  education = as.factor(education)
  E = .mean("E", 1:5, rev=c(1,2), range=1:6)
  A = .mean("A", 1:5, rev=1, range=1:6)
  C = .mean("C", 1:5, rev=c(4,5), range=1:6)
  N = .mean("N", 1:5, range=1:6)
  O = .mean("O", 1:5, rev=c(2,5), range=1:6)
})
Describe(d[, .(age, gender, education)], plot=TRUE, all.as.numeric=FALSE)
Describe(d[, .(age, gender, education, E, A, C, N, O)], plot=TRUE)

Principal Component Analysis (PCA) and Exploratory Factor analysis (EFA).

Description

An extension of psych::principal() and psych::fa(), performing either Principal Component Analysis (PCA) or Exploratory Factor Analysis (EFA).

Three options to specify variables:

1. var + items: use the common and unique parts of variable names.

2. vars: directly define a character vector of variables.

3. varrange: use the starting and stopping positions of variables.

Usage

EFA(
  data,
  var,
  items,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  method = c("pca", "pa", "ml", "minres", "uls", "ols", "wls", "gls", "alpha"),
  rotation = c("none", "varimax", "oblimin", "promax", "quartimax", "equamax"),
  nfactors = c("eigen", "parallel", "(any number >= 1)"),
  sort.loadings = TRUE,
  hide.loadings = 0,
  plot.scree = TRUE,
  kaiser = TRUE,
  max.iter = 25,
  min.eigen = 1,
  digits = 3,
  file = NULL
)

PCA(..., method = "pca")

Arguments

Value

A list of results:

result

The R object returned from psych::principal() or psych::fa()

result.kaiser

The R object returned from psych::kaiser() (if any)

extraction.method

Extraction method

rotation.method

Rotation method

eigenvalues

A data.frame of eigenvalues and sum of squared (SS) loadings

loadings

A data.frame of factor/component loadings and communalities

scree.plot

A ggplot2 object of the scree plot

Functions

• EFA(): Exploratory Factor Analysis

• PCA(): Principal Component Analysis - a wrapper of EFA(..., method="pca")

Note

Results based on the varimax rotation method are identical to SPSS. The other rotation methods may produce results slightly different from SPSS.

See Also

MEAN, Alpha, CFA

Examples

data = psych::bfi
EFA(data, "E", 1:5)              # var + items
EFA(data, "E", 1:5, nfactors=2)  # var + items

EFA(data, varrange="A1:O5",
    nfactors="parallel",
    hide.loadings=0.45)

# the same as above:
# using dplyr::select() and dplyr::matches()
# to select variables whose names end with numbers
# (regexp: \d matches all numbers, $ matches the end of a string)
data %>% select(matches("\\d$")) %>%
  EFA(vars=names(.),       # all selected variables
      method="pca",        # default
      rotation="varimax",  # default
      nfactors="parallel", # parallel analysis
      hide.loadings=0.45)  # hide loadings < 0.45

Simple-effect analysis and post-hoc multiple comparison.

Description

Perform (1) simple-effect (and simple-simple-effect) analyses, including both simple main effects and simple interaction effects, and (2) post-hoc multiple comparisons (e.g., pairwise, sequential, polynomial), with p values adjusted for factors with >= 3 levels.

This function is based on and extends (1) emmeans::joint_tests(), (2) emmeans::emmeans(), and (3) emmeans::contrast(). You only need to specify the model object, to-be-tested effect(s), and moderator(s). Almost all results you need will be displayed together, including effect sizes (partial \eta^2 and Cohen's d) and their confidence intervals (CIs). 90% CIs for partial \eta^2 and 95% CIs for Cohen's d are reported.

By default, the root mean square error (RMSE) is used to compute the pooled SD for Cohen's d. Specifically, it uses:

1. the square root of mean square error (MSE) for between-subjects designs;

2. the square root of mean variance of all paired differences of the residuals of repeated measures for within-subjects and mixed designs.

Disclaimer: There is substantial disagreement on the appropriate pooled SD to use in computing the effect size. For alternative methods, see emmeans::eff_size() and effectsize::t_to_d(). Users should not take the default output as the only right results and are completely responsible for specifying sd.pooled.

Usage

EMMEANS(
  model,
  effect = NULL,
  by = NULL,
  contrast = "pairwise",
  reverse = TRUE,
  p.adjust = "bonferroni",
  sd.pooled = NULL,
  model.type = "multivariate",
  digits = 3,
  file = NULL
)

Arguments

Value

The same model object as returned by MANOVA (for recursive use), along with a list of tables: sim (simple effects), emm (estimated marginal means), con (contrasts).

Each EMMEANS(...) appends one list to the returned object.

Interaction Plot (See Examples Below)

You can save the returned object and use the emmeans::emmip() function to create an interaction plot (based on the fitted model and a formula). See examples below for the usage.

Note: emmeans::emmip() returns a ggplot object, which can be modified and saved with ggplot2 syntax.

Statistical Details

Some may confuse the statistical terms "simple effects", "post-hoc tests", and "multiple comparisons". Such a confusion is not uncommon. Here I explain what these terms actually refer to.

1. Simple Effect

When we speak of "simple effect", we are referring to ...

• simple main effect

• simple interaction effect (only for designs with 3 or more factors)

• simple simple effect (only for designs with 3 or more factors)

When the interaction effect in ANOVA is significant, we should then perform a "simple-effect analysis". In regression, we call this "simple-slope analysis". They are identical in statistical principles.

In a two-factors design, we only test "simple main effect". That is, at different levels of a factor "B", the main effects of "A" would be different. However, in a three-factors (or more) design, we may also test "simple interaction effect" and "simple simple effect". That is, at different combinations of levels of factors "B" and "C", the main effects of "A" would be different.

To note, simple effects per se never require p-value adjustment, because what we test in simple-effect analyses are still "omnibus F-tests".

2. Post-Hoc Test

The term "post-hoc" means that the tests are performed after ANOVA. Given this, some may (wrongly) regard simple-effect analyses also as a kind of post-hoc tests. However, these two terms should be distinguished. In many situations, "post-hoc tests" only refer to "post-hoc comparisons" using t-tests and some p-value adjustment techniques. We need post-hoc comparisons only when there are factors with 3 or more levels.

Post-hoc tests are totally independent of whether there is a significant interaction effect. It only deals with factors with multiple levels. In most cases, we use pairwise comparisons to do post-hoc tests. See the next part for details.

3. Multiple Comparison

As mentioned above, multiple comparisons are indeed post-hoc tests but have no relationship with simple-effect analyses. Post-hoc multiple comparisons are independent of interaction effects and simple effects. Furthermore, if a simple main effect contains 3 or more levels, we also need to do multiple comparisons within the simple-effect analysis. In this situation, we also need p-value adjustment with methods such as Bonferroni, Tukey's HSD (honest significant difference), FDR (false discovery rate), and so forth.

Options for multiple comparison:

• "pairwise" - Pairwise comparisons (default is "higher level - lower level")

• "seq" or "consec" - Consecutive (sequential) comparisons

• "poly" - Polynomial contrasts (linear, quadratic, cubic, quartic, ...)

• "eff" - Effect contrasts (vs. the grand mean)

See Also

TTEST, MANOVA, bruceR-demodata

Examples

#### Between-Subjects Design ####

between.1
MANOVA(between.1, dv="SCORE", between="A") %>%
  EMMEANS("A")
MANOVA(between.1, dv="SCORE", between="A") %>%
  EMMEANS("A", p.adjust="tukey")
MANOVA(between.1, dv="SCORE", between="A") %>%
  EMMEANS("A", contrast="seq")
MANOVA(between.1, dv="SCORE", between="A") %>%
  EMMEANS("A", contrast="poly")

between.2
MANOVA(between.2, dv="SCORE", between=c("A", "B")) %>%
  EMMEANS("A", by="B") %>%
  EMMEANS("B", by="A")
## How to create an interaction plot using `emmeans::emmip()`?
## See help page: ?emmeans::emmip()
m = MANOVA(between.2, dv="SCORE", between=c("A", "B"))
emmip(m, ~ A | B, CIs=TRUE)
emmip(m, ~ B | A, CIs=TRUE)
emmip(m, B ~ A, CIs=TRUE)
emmip(m, A ~ B, CIs=TRUE)

between.3
MANOVA(between.3, dv="SCORE", between=c("A", "B", "C")) %>%
  EMMEANS("A", by="B") %>%
  EMMEANS(c("A", "B"), by="C") %>%
  EMMEANS("A", by=c("B", "C"))
## Just to name a few...
## You may test other combinations...


#### Within-Subjects Design ####

within.1
MANOVA(within.1, dvs="A1:A4", dvs.pattern="A(.)",
       within="A") %>%
  EMMEANS("A")

within.2
MANOVA(within.2, dvs="A1B1:A2B3", dvs.pattern="A(.)B(.)",
       within=c("A", "B")) %>%
  EMMEANS("A", by="B") %>%
  EMMEANS("B", by="A")  # singular error matrix
# :::::::::::::::::::::::::::::::::::::::
# This would produce a WARNING because of
# the linear dependence of A2B2 and A2B3.
# See: Corr(within.2[c("A2B2", "A2B3")])

within.3
MANOVA(within.3, dvs="A1B1C1:A2B2C2", dvs.pattern="A(.)B(.)C(.)",
       within=c("A", "B", "C")) %>%
  EMMEANS("A", by="B") %>%
  EMMEANS(c("A", "B"), by="C") %>%
  EMMEANS("A", by=c("B", "C"))
## Just to name a few...
## You may test other combinations...


#### Mixed Design ####

mixed.2_1b1w
MANOVA(mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
       between="A", within="B", sph.correction="GG") %>%
  EMMEANS("A", by="B") %>%
  EMMEANS("B", by="A")

mixed.3_1b2w
MANOVA(mixed.3_1b2w, dvs="B1C1:B2C2", dvs.pattern="B(.)C(.)",
       between="A", within=c("B", "C")) %>%
  EMMEANS("A", by="B") %>%
  EMMEANS(c("A", "B"), by="C") %>%
  EMMEANS("A", by=c("B", "C"))
## Just to name a few...
## You may test other combinations...

mixed.3_2b1w
MANOVA(mixed.3_2b1w, dvs="B1:B2", dvs.pattern="B(.)",
       between=c("A", "C"), within="B") %>%
  EMMEANS("A", by="B") %>%
  EMMEANS("A", by="C") %>%
  EMMEANS(c("A", "B"), by="C") %>%
  EMMEANS("B", by=c("A", "C"))
## Just to name a few...
## You may test other combinations...


#### Other Examples ####

air = airquality
air$Day.1or2 = ifelse(air$Day %% 2 == 1, 1, 2) %>%
  factor(levels=1:2, labels=c("odd", "even"))
MANOVA(air, dv="Temp", between=c("Month", "Day.1or2"),
       covariate=c("Solar.R", "Wind")) %>%
  EMMEANS("Month", contrast="seq") %>%
  EMMEANS("Month", by="Day.1or2", contrast="poly")

Frequency statistics.

Description

Frequency statistics.

Usage

Freq(x, varname, labels, sort = "", digits = 1, file = NULL)

Arguments

Value

A data frame of frequency statistics.

Examples

data = psych::bfi

## Input `data$variable`
Freq(data$education)
Freq(data$gender, labels=c("Male", "Female"))
Freq(data$age)

## Input one data frame and one variable name
Freq(data, "education")
Freq(data, "gender", labels=c("Male", "Female"))
Freq(data, "age")

Tidy report of GLM (lm and glm models).

Description

NOTE: model_summary is preferred.

Usage

GLM_summary(model, robust = FALSE, cluster = NULL, digits = 3, ...)

Arguments

Value

No return value.

See Also

print_table (print simple table)

model_summary (highly suggested)

HLM_summary

regress

Examples

## Example 1: OLS regression
lm = lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
GLM_summary(lm)
GLM_summary(lm, robust="HC1")
# Stata's default is "HC1"
# R package <sandwich>'s default is "HC3"

## Example 2: Logistic regression
glm = glm(case ~ age + parity + education + spontaneous + induced,
          data=infert, family=binomial)
GLM_summary(glm)
GLM_summary(glm, robust="HC1", cluster="stratum")

Tidy report of HLM indices: ICC(1), ICC(2), and rWG/rWG(J).

Description

Compute ICC(1) (non-independence of data), ICC(2) (reliability of group means), and r_{WG}/r_{WG(J)} (within-group agreement for single-item/multi-item measures) in multilevel analysis (HLM).

Usage

HLM_ICC_rWG(
  data,
  group,
  icc.var,
  rwg.vars = icc.var,
  rwg.levels = 0,
  digits = 3
)

Arguments

Details

ICC(1) (intra-class correlation, or non-independence of data)

ICC(1) = var.u0 / (var.u0 + var.e) = \sigma_{u0}^2 / (\sigma_{u0}^2 + \sigma_{e}^2)

ICC(1) is the ICC we often compute and report in multilevel analysis (usually in the Null Model, where only the random intercept of group is included). It can be interpreted as either "the proportion of variance explained by groups" (i.e., heterogeneity between groups) or "the expectation of correlation coefficient between any two observations within any group" (i.e., homogeneity within groups).

ICC(2) (reliability of group means)

ICC(2) = mean(var.u0 / (var.u0 + var.e / n.k)) = \Sigma[\sigma_{u0}^2 / (\sigma_{u0}^2 + \sigma_{e}^2 / n_k)] / K

ICC(2) is a measure of "the representativeness of group-level aggregated means for within-group individual values" or "the degree to which an individual score can be considered a reliable assessment of a group-level construct".

r_{WG}/r_{WG(J)} (within-group agreement for single-item/multi-item measures)

r_{WG} = 1 - \sigma^2 / \sigma_{EU}^2

r_{WG(J)} = 1 - (\sigma_{MJ}^2 / \sigma_{EU}^2) / [J * (1 - \sigma_{MJ}^2 / \sigma_{EU}^2) + \sigma_{MJ}^2 / \sigma_{EU}^2]

r_{WG}/r_{WG(J)} is a measure of within-group agreement or consensus. Each group has an r_{WG}/r_{WG(J)}.

* Note for the above formulas

• \sigma_{u0}^2: between-group variance (i.e., tau00)

• \sigma_{e}^2: within-group variance (i.e., residual variance)

• n_k: group size of the k-th group

• K: number of groups

• \sigma^2: actual group variance of the k-th group

• \sigma_{MJ}^2: mean value of actual group variance of the k-th group across all J items

• \sigma_{EU}^2: expected random variance (i.e., the variance of uniform distribution)

• J: number of items

Value

Invisibly return a list of results.

References

Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and Analysis. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations (pp. 349–381). San Francisco, CA: Jossey-Bass, Inc.

James, L.R., Demaree, R.G., & Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85–98.

See Also

cor_multilevel

R package "multilevel"

Examples

data = lme4::sleepstudy  # continuous variable
HLM_ICC_rWG(data, group="Subject", icc.var="Reaction")

data = lmerTest::carrots  # 7-point scale
HLM_ICC_rWG(data, group="Consumer", icc.var="Preference",
            rwg.vars="Preference",
            rwg.levels=7)
HLM_ICC_rWG(data, group="Consumer", icc.var="Preference",
            rwg.vars=c("Sweetness", "Bitter", "Crisp"),
            rwg.levels=7)

Tidy report of HLM (lmer and glmer models).

Description

NOTE: model_summary is preferred.

Usage

HLM_summary(model = NULL, test.rand = FALSE, digits = 3, ...)

Arguments

Value

No return value.

References

Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R^2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4, 133–142.

Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine, 22, 3527–3541.

See Also

print_table (print simple table)

model_summary (highly suggested)

GLM_summary

regress

Examples

library(lmerTest)

## Example 1: data from lme4::sleepstudy
# (1) 'Subject' is a grouping/clustering variable
# (2) 'Days' is a level-1 predictor nested within 'Subject'
# (3) No level-2 predictors
m1 = lmer(Reaction ~ (1 | Subject), data=sleepstudy)
m2 = lmer(Reaction ~ Days + (1 | Subject), data=sleepstudy)
m3 = lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy)
HLM_summary(m1)
HLM_summary(m2)
HLM_summary(m3)

## Example 2: data from lmerTest::carrots
# (1) 'Consumer' is a grouping/clustering variable
# (2) 'Sweetness' is a level-1 predictor
# (3) 'Age' and 'Frequency' are level-2 predictors
hlm.1 = lmer(Preference ~ Sweetness + Age + Frequency +
               (1 | Consumer), data=carrots)
hlm.2 = lmer(Preference ~ Sweetness + Age + Frequency +
               (Sweetness | Consumer) + (1 | Product), data=carrots)
HLM_summary(hlm.1)
HLM_summary(hlm.2)

Search, match, and look up values (like Excel's functions INDEX + MATCH).

Description

In Excel, we can use VLOOKUP, HLOOKUP, XLOOKUP (a new function released in 2019), or the combination of INDEX and MATCH to search, match, and look up values. Here I provide a similar function.

Usage

LOOKUP(
  data,
  vars,
  data.ref,
  vars.ref,
  vars.lookup,
  return = c("new.data", "new.var", "new.value")
)

Arguments

Details

If multiple values were simultaneously matched, a warning message would be printed.

Value

New data object, new variable, or new value (see the argument return).

See Also

dplyr::left_join()

XLOOKUP: Excel University

Examples

ref = data.table(City=rep(c("A", "B", "C"), each=5),
                 Year=rep(2013:2017, times=3),
                 GDP=sample(1000:2000, 15),
                 PM2.5=sample(10:300, 15))
ref

data = data.table(sub=1:5,
                  city=c("A", "A", "B", "C", "C"),
                  year=c(2013, 2014, 2015, 2016, 2017))
data

LOOKUP(data, c("city", "year"), ref, c("City", "Year"), "GDP")
LOOKUP(data, c("city", "year"), ref, c("City", "Year"), c("GDP", "PM2.5"))

Multi-factor ANOVA.

Description

Multi-factor ANOVA (between-subjects, within-subjects, and mixed designs), with and without covariates (ANCOVA).

This function is based on and extends afex::aov_ez(). You only need to specify the data, dependent variable(s), and factors (between-subjects and/or within-subjects). Almost all results you need will be displayed together, including effect sizes (partial \eta^2) and their confidence intervals (CIs). 90% CIs for partial \eta^2 (two-sided) are reported, following Steiger (2004). In addition to partial \eta^2, it also reports generalized \eta^2, following Olejnik & Algina (2003).

How to prepare your data and specify the arguments of MANOVA?

• Wide-format data (one person in one row, and repeated measures in multiple columns):

  Betweem-subjects design

  MANOVA(data=, dv=, between=, ...)

  Within-subjects design

  MANOVA(data=, dvs=, dvs.pattern=, within=, ...)

  Mixed design

  MANOVA(data=, dvs=, dvs.pattern=, between=, within=, ...)

• Long-format data (one person in multiple rows, and repeated measures in one column):

  Betweem-subjects design

  (not applicable)

  Within-subjects design

  MANOVA(data=, subID=, dv=, within=, ...)

  Mixed design

  MANOVA(data=, subID=, dv=, between=, within=, ...)

Usage

MANOVA(
  data,
  subID = NULL,
  dv = NULL,
  dvs = NULL,
  dvs.pattern = NULL,
  between = NULL,
  within = NULL,
  covariate = NULL,
  ss.type = "III",
  sph.correction = "none",
  aov.include = FALSE,
  digits = 3,
  file = NULL
)

Arguments

Details

If observations are not uniquely identified in user-defined long-format data, the function takes averages across those multiple observations for each case. In technical details, it specifies fun_aggregate=mean in afex::aov_ez() and values_fn=mean in tidyr::pivot_wider().

Value

A result object (list) returned by afex::aov_ez(), along with several other elements: between, within, data.wide, data.long.

Interaction Plot

You can save the returned object and use the emmeans::emmip() function to create an interaction plot (based on the fitted model and a formula specification). For usage, please see the help page of emmeans::emmip(). It returns an object of class ggplot, which can be easily modified and saved using ggplot2 syntax.

References

Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8(4), 434–447.

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), 164–182.

See Also

TTEST, EMMEANS, bruceR-demodata

Examples

#### Between-Subjects Design ####

between.1
MANOVA(between.1, dv="SCORE", between="A")

between.2
MANOVA(between.2, dv="SCORE", between=c("A", "B"))

between.3
MANOVA(between.3, dv="SCORE", between=c("A", "B", "C"))

## How to create an interaction plot using `emmeans::emmip()`?
## See help page for its usage: ?emmeans::emmip()
m = MANOVA(between.2, dv="SCORE", between=c("A", "B"))
emmip(m, ~ A | B, CIs=TRUE)
emmip(m, ~ B | A, CIs=TRUE)
emmip(m, B ~ A, CIs=TRUE)
emmip(m, A ~ B, CIs=TRUE)


#### Within-Subjects Design ####

within.1
MANOVA(within.1, dvs="A1:A4", dvs.pattern="A(.)",
       within="A")
## the same:
MANOVA(within.1, dvs=c("A1", "A2", "A3", "A4"), dvs.pattern="A(.)",
       within="MyFactor")  # renamed the within-subjects factor

within.2
MANOVA(within.2, dvs="A1B1:A2B3", dvs.pattern="A(.)B(.)",
       within=c("A", "B"))

within.3
MANOVA(within.3, dvs="A1B1C1:A2B2C2", dvs.pattern="A(.)B(.)C(.)",
       within=c("A", "B", "C"))


#### Mixed Design ####

mixed.2_1b1w
MANOVA(mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
       between="A", within="B")
MANOVA(mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
       between="A", within="B", sph.correction="GG")

mixed.3_1b2w
MANOVA(mixed.3_1b2w, dvs="B1C1:B2C2", dvs.pattern="B(.)C(.)",
       between="A", within=c("B", "C"))

mixed.3_2b1w
MANOVA(mixed.3_2b1w, dvs="B1:B2", dvs.pattern="B(.)",
       between=c("A", "C"), within="B")


#### Other Examples ####

data.new = mixed.3_1b2w
names(data.new) = c("Group", "Cond_01", "Cond_02", "Cond_03", "Cond_04")
MANOVA(data.new,
       dvs="Cond_01:Cond_04",
       dvs.pattern="Cond_(..)",
       between="Group",
       within="Condition")  # rename the factor

# ?afex::obk.long
MANOVA(afex::obk.long,
       subID="id",
       dv="value",
       between=c("treatment", "gender"),
       within=c("phase", "hour"),
       cov="age",
       sph.correction="GG")

PROCESS for mediation and/or moderation analyses.

Description

To perform mediation, moderation, and conditional process (moderated mediation) analyses, people may use software like Mplus, SPSS "PROCESS" macro, and SPSS "MLmed" macro. Some R packages can also perform such analyses separately and in a complex way, including R package "mediation", R package "interactions", and R package "lavaan". Some other R packages or scripts/modules have been further developed to improve the convenience, including jamovi module "jAMM" (by Marcello Gallucci, based on the lavaan package), R package "processR" (by Keon-Woong Moon, not official, also based on the lavaan package), and R script file "process.R" (the official PROCESS R code by Andrew F. Hayes, but it is not yet an R package and has some bugs and limitations).

Here, the bruceR::PROCESS() function provides an alternative to performing mediation/moderation analyses in R. This function supports a total of 24 kinds of SPSS PROCESS models (Hayes, 2018) and also supports multilevel mediation/moderation analyses. Overall, it supports the most frequently used types of mediation, moderation, moderated moderation (3-way interaction), and moderated mediation (conditional indirect effect) analyses for (generalized) linear or linear mixed models.

Specifically, the bruceR::PROCESS() function fits regression models based on the data, variable names, and a few other arguments that users input (with no need to specify the PROCESS model number and no need to manually mean-center the variables). The function can automatically judge the model number/type and also conduct grand-mean centering before model building (using the bruceR::grand_mean_center() function).

This automatic grand-mean centering can be turned off by setting center=FALSE.

Note that this automatic grand-mean centering (1) makes the results of main effects accurate for interpretation; (2) does not change any results of model fit (it only affects the interpretation of main effects); (3) is only conducted in "PART 1" (for an accurate estimate of main effects) but not in "PART 2" because it is more intuitive and interpretable to use the raw values of variables for the simple-slope tests in "PART 2"; (4) is not optional to users because mean-centering should always be done when there is an interaction; (5) is not conflicted with group-mean centering because after group-mean centering the grand mean of a variable will also be 0, such that the automatic grand-mean centering (with mean = 0) will not change any values of the variable.

If you need to do group-mean centering, please do this before using PROCESS. bruceR::group_mean_center() is a useful function of group-mean centering. Remember that the automatic grand-mean centering in PROCESS never affects the values of a group-mean centered variable, which already has a grand mean of 0.

The bruceR::PROCESS() function uses:

1. the interactions::sim_slopes() function to estimate simple slopes (and conditional direct effects) in moderation, moderated moderation, and moderated mediation models (PROCESS Models 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 58, 59, 72, 73, 75, 76).

2. the mediation::mediate() function to estimate (conditional) indirect effects in (moderated) mediation models (PROCESS Models 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 58, 59, 72, 73, 75, 76).

3. the lavaan::sem() function to perform serial multiple mediation analysis (PROCESS Model 6).

If you use this function in your research and report its results in your paper, please cite not only bruceR but also the other R packages it uses internally (mediation, interactions, and/or lavaan).

Two parts of results are printed:

PART 1. Regression model summary (using bruceR::model_summary() to summarize the models)

PART 2. Mediation/moderation effect estimates (using one or a combination of the above packages and functions to estimate the effects)

To organize the PART 2 output, the results of Simple Slopes are titled in green, whereas the results of Indirect Path are titled in blue.

Disclaimer: Although this function is named after PROCESS, Andrew F. Hayes has no role in its design, and its development is independent from the official SPSS PROCESS macro and "process.R" script. Any error or limitation should be attributed to the three R packages/functions that bruceR::PROCESS() uses internally. Moreover, as mediation analyses include random processes (i.e., bootstrap resampling or Monte Carlo simulation), the results of mediation analyses are unlikely to be exactly the same across different software (even if you set the same random seed in different software).

Usage

PROCESS(
  data,
  y = "",
  x = "",
  meds = c(),
  mods = c(),
  covs = c(),
  clusters = c(),
  hlm.re.m = "",
  hlm.re.y = "",
  hlm.type = c("1-1-1", "2-1-1", "2-2-1"),
  med.type = c("parallel", "serial"),
  mod.type = c("2-way", "3-way"),
  mod.path = c("x-y", "x-m", "m-y", "all"),
  cov.path = c("y", "m", "both"),
  mod1.val = NULL,
  mod2.val = NULL,
  ci = c("boot", "bc.boot", "bca.boot", "mcmc"),
  nsim = 100,
  seed = NULL,
  center = TRUE,
  std = FALSE,
  digits = 3,
  file = NULL
)

Arguments

Details

For more details and illustrations, see PROCESS-bruceR-SPSS (PDF and Markdown files).

Value

Invisibly return a list of results:

process.id

PROCESS model number.

process.type

PROCESS model type.

model.m

"Mediator" (M) models (a list of multiple models).

model.y

"Outcome" (Y) model.

results

Effect estimates and other results (unnamed list object).

References

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis (second edition): A regression-based approach. Guilford Press.

Yzerbyt, V., Muller, D., Batailler, C., & Judd, C. M. (2018). New recommendations for testing indirect effects in mediational models: The need to report and test component paths. Journal of Personality and Social Psychology, 115(6), 929–943.

See Also

lavaan_summary

model_summary

med_summary

Examples

#### NOTE ####
## In the following examples, I set nsim=100 to save time.
## In formal analyses, nsim=1000 (or larger) is suggested!

#### Demo Data ####
# ?mediation::student
data = mediation::student %>%
  dplyr::select(SCH_ID, free, smorale, pared, income,
                gender, work, attachment, fight, late, score)
names(data)[2:3] = c("SCH_free", "SCH_morale")
names(data)[4:7] = c("parent_edu", "family_inc", "gender", "partjob")
data$gender01 = 1 - data$gender  # 0 = female, 1 = male
# dichotomous X: as.factor()
data$gender = factor(data$gender01, levels=0:1, labels=c("Female", "Male"))
# dichotomous Y: as.factor()
data$pass = as.factor(ifelse(data$score>=50, 1, 0))

#### Descriptive Statistics and Correlation Analyses ####
Freq(data$gender)
Freq(data$pass)
Describe(data)     # file="xxx.doc"
Corr(data[,4:11])  # file="xxx.doc"

#### PROCESS Analyses ####

## Model 1 ##
PROCESS(data, y="score", x="late", mods="gender")  # continuous Y
PROCESS(data, y="pass", x="late", mods="gender")   # dichotomous Y

# (multilevel moderation)
PROCESS(data, y="score", x="late", mods="gender",  # continuous Y (LMM)
        clusters="SCH_ID")
PROCESS(data, y="pass", x="late", mods="gender",   # dichotomous Y (GLMM)
        clusters="SCH_ID")

# (Johnson-Neyman (J-N) interval and plot)
PROCESS(data, y="score", x="gender", mods="late") -> P
P$results[[1]]$jn[[1]]       # Johnson-Neyman interval
P$results[[1]]$jn[[1]]$plot  # Johnson-Neyman plot (ggplot object)
GLM_summary(P$model.y)       # detailed results of regression

# (allows multicategorical moderator)
d = airquality
d$Month = as.factor(d$Month)  # moderator: factor with levels "5"~"9"
PROCESS(d, y="Temp", x="Solar.R", mods="Month")

## Model 2 ##
PROCESS(data, y="score", x="late",
        mods=c("gender", "family_inc"),
        mod.type="2-way")  # or omit "mod.type", default is "2-way"

## Model 3 ##
PROCESS(data, y="score", x="late",
        mods=c("gender", "family_inc"),
        mod.type="3-way")
PROCESS(data, y="pass", x="gender",
        mods=c("late", "family_inc"),
        mod1.val=c(1, 3, 5),     # moderator 1: late
        mod2.val=seq(1, 15, 2),  # moderator 2: family_inc
        mod.type="3-way")

## Model 4 ##
PROCESS(data, y="score", x="parent_edu",
        meds="family_inc", covs="gender",
        ci="boot", nsim=100, seed=1)

# (allows an infinite number of multiple mediators in parallel)
PROCESS(data, y="score", x="parent_edu",
        meds=c("family_inc", "late"),
        covs=c("gender", "partjob"),
        ci="boot", nsim=100, seed=1)

# (multilevel mediation)
PROCESS(data, y="score", x="SCH_free",
        meds="late", clusters="SCH_ID",
        ci="mcmc", nsim=100, seed=1)

## Model 6 ##
PROCESS(data, y="score", x="parent_edu",
        meds=c("family_inc", "late"),
        covs=c("gender", "partjob"),
        med.type="serial",
        ci="boot", nsim=100, seed=1)

## Model 8 ##
PROCESS(data, y="score", x="fight",
        meds="late",
        mods="gender",
        mod.path=c("x-m", "x-y"),
        ci="boot", nsim=100, seed=1)

## For more examples and details, see the "note" subfolder at:
## https://github.com/psychbruce/bruceR/tree/main/note

Print strings with rich formats and colors.

Description

Be frustrated with print() and cat()? Try Print()! Run examples to see what it can do.

Usage

Print(...)

Glue(...)

Arguments

Details

Possible formats/colors that can be used in "<< >>" include:

(1) bold, italic, underline, reset, blurred, inverse, hidden, strikethrough;

(2) black, white, silver, red, green, blue, yellow, cyan, magenta;

(3) bgBlack, bgWhite, bgRed, bgGreen, bgBlue, bgYellow, bgCyan, bgMagenta.

See more details in glue::glue() and glue::glue_col().

Value

Formatted text.

Functions

• Print(): Paste and print strings.

• Glue(): Paste strings.

Examples

name = "Bruce"
Print("My name is <<underline <<bold {name}>>>>.
       <<bold <<blue Pi = {pi:.15}.>>>>
       <<italic <<green 1 + 1 = {1 + 1}.>>>>
       sqrt({x}) = <<red {sqrt(x):.3}>>", x=10)

Recode a variable.

Description

A wrapper of car::recode().

Usage

RECODE(var, recodes)

Arguments

Value

A vector of recoded variable.

Examples

d = data.table(var=c(NA, 0, 1, 2, 3, 4, 5, 6))
added(d, {
  var.new = RECODE(var, "lo:1=0; c(2,3)=1; 4=2; 5:hi=3; else=999")
})
d

Rescale a variable (e.g., from 5-point to 7-point).

Description

Rescale a variable (e.g., from 5-point to 7-point).

Usage

RESCALE(var, from = range(var, na.rm = T), to)

Arguments

Value

A vector of rescaled variable.

Examples

d = data.table(var=rep(1:5, 2))
added(d, {
  var1 = RESCALE(var, to=1:7)
  var2 = RESCALE(var, from=1:5, to=1:7)
})
d  # var1 is equal to var2

A simple extension of rgb().

Description

A simple extension of rgb().

Usage

RGB(r, g, b, alpha)

Arguments

Value

"#rrggbb" or "#rrggbbaa".

Examples

RGB(255, 0, 0)  # red: "#FF0000"
RGB(255, 0, 0, 0.8)  # red with 80\% opacity: "#FF0000CC"

Run code parsed from text.

Description

Run code parsed from text.

Usage

Run(..., silent = FALSE)

Arguments

Value

Invisibly return the running expression(s).

Examples

Run("a=1", "b=2")
Run("print({a+b})")

One-sample, independent-samples, and paired-samples t-test.

Description

One-sample, independent-samples, and paired-samples t-test, with both Frequentist and Bayesian approaches. The output includes descriptives, t statistics, mean difference with 95% CI, Cohen's d with 95% CI, and Bayes factor (BF10; BayesFactor package needs to be installed). It also tests the assumption of homogeneity of variance and allows users to determine whether variances are equal or not.

Users can simultaneously test multiple dependent and/or independent variables. The results of one pair of Y-X would be summarized in one row in the output. Key results can be saved in APA format to MS Word.

Usage

TTEST(
  data,
  y,
  x = NULL,
  paired = FALSE,
  paired.d.type = "dz",
  var.equal = TRUE,
  mean.diff = TRUE,
  test.value = 0,
  test.sided = c("=", "<", ">"),
  factor.rev = TRUE,
  bayes.prior = "medium",
  digits = 2,
  file = NULL
)

Arguments

Details

Note that the point estimate of Cohen's d is computed using the common method "Cohen's d = mean difference / (pooled) standard deviation", which is consistent with results from other R packages (e.g., effectsize) and software (e.g., jamovi). The 95% CI of Cohen's d is estimated based on the 95% CI of mean difference (i.e., also divided by the pooled standard deviation).

However, different packages and software diverge greatly on the estimate of the 95% CI of Cohen's d. R packages such as psych and effectsize, R software jamovi, and several online statistical tools for estimating effect sizes indeed produce surprisingly inconsistent results on the 95% CI of Cohen's d.

See an illustration of this issue in the section "Examples".

References

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, Article 863.

See Also

MANOVA, EMMEANS

Examples

## Demo data ##
d1 = between.3
d1$Y1 = d1$SCORE  # shorter name for convenience
d1$Y2 = rnorm(32)  # random variable
d1$B = factor(d1$B, levels=1:2, labels=c("Low", "High"))
d1$C = factor(d1$C, levels=1:2, labels=c("M", "F"))
d2 = within.1

## One-sample t-test ##
TTEST(d1, "SCORE")
TTEST(d1, "SCORE", test.value=5)

## Independent-samples t-test ##
TTEST(d1, "SCORE", x="A")
TTEST(d1, "SCORE", x="A", var.equal=FALSE)
TTEST(d1, y="Y1", x=c("A", "B", "C"))
TTEST(d1, y=c("Y1", "Y2"), x=c("A", "B", "C"),
      mean.diff=FALSE,  # remove to save space
      file="t-result.doc")
unlink("t-result.doc")  # delete file for code check

## Paired-samples t-test ##
TTEST(d2, y=c("A1", "A2"), paired=TRUE)
TTEST(d2, y=c("A1", "A2", "A3", "A4"), paired=TRUE)


## Not run: 

  ## Illustration for the issue stated in "Details"

  # Inconsistency in the 95% CI of Cohen's d between R packages:
  # In this example, the true point estimate of Cohen's d = 3.00
  # and its 95% CI should be equal to 95% CI of mean difference.

  data = data.frame(X=rep(1:2, each=3), Y=1:6)
  data  # simple demo data

  TTEST(data, y="Y", x="X")
  # d = 3.00 [0.73, 5.27] (estimated based on 95% CI of mean difference)

  MANOVA(data, dv="Y", between="X") %>%
    EMMEANS("X")
  # d = 3.00 [0.73, 5.27] (the same as TTEST)

  psych::cohen.d(x=data, group="X")
  # d = 3.67 [0.04, 7.35] (strange)

  psych::d.ci(d=3.00, n1=3, n2=3)
  # d = 3.00 [-0.15, 6.12] (significance inconsistent with t-test)

  # jamovi uses psych::d.ci() to compute 95% CI
  # so its results are also: 3.00 [-0.15, 6.12]

  effectsize::cohens_d(Y ~ rev(X), data=data)
  # d = 3.00 [0.38, 5.50] (using the noncentrality parameter method)

  effectsize::t_to_d(t=t.test(Y ~ rev(X), data=data, var.equal=TRUE)$statistic,
                     df_error=4)
  # d = 3.67 [0.47, 6.74] (merely an approximate estimate, often overestimated)
  # see ?effectsize::t_to_d

  # https://www.psychometrica.de/effect_size.html
  # d = 3.00 [0.67, 5.33] (slightly different from TTEST)

  # https://www.campbellcollaboration.org/escalc/
  # d = 3.00 [0.67, 5.33] (slightly different from TTEST)

  # Conclusion:
  # TTEST() provides a reasonable estimate of Cohen's d and its 95% CI,
  # and effectsize::cohens_d() offers another method to compute the CI.

## End(Not run)

Create, modify, and delete variables.

Description

Enhanced functions to create, modify, and/or delete variables. The functions combine the advantages of within (base), mutate (dplyr), transmute (dplyr), and := (data.table). See examples below for the usage and convenience.

Usage

add(data, expr, when, by, drop = FALSE)

added(data, expr, when, by, drop = FALSE)

Arguments

Value

add() returns a new data.table, with the raw data unchanged.

added() returns nothing and has already changed the raw data.

Functions

• add(): Return the new data.

  You need to assign the new data to an object:

  data = add(data, {...})

• added(): Return nothing and change the raw data immediately.

  NO need to assign the new data:

  added(data, {...})

Examples

## ====== Usage 1: add() ====== ##

d = as.data.table(within.1)
d$XYZ = 1:8
d

# add() does not change the raw data:
add(d, {B = 1; C = 2})
d

# new data should be assigned to an object:
d = d %>% add({
  ID = str_extract(ID, "\\d")  # modify a variable
  XYZ = NULL                   # delete a variable
  A = .mean("A", 1:4)          # create a new variable
  B = A * 4    # new variable is immediately available
  C = 1        # never need ,/; at the end of any line
})
d


## ====== Usage 2: added() ====== ##

d = as.data.table(within.1)
d$XYZ = 1:8
d

# added() has already changed the raw data:
added(d, {B = 1; C = 2})
d

# raw data has already become the new data:
added(d, {
  ID = str_extract(ID, "\\d")
  XYZ = NULL
  A = .mean("A", 1:4)
  B = A * 4
  C = 1
})
d


## ====== Using `when` and `by` ====== ##

d = as.data.table(between.2)
d

added(d, {SCORE2 = SCORE - mean(SCORE)},
      A == 1 & B %in% 1:2,  # `when`: for what conditions
      by=B)                 # `by`: by what groups
d
na.omit(d)


## ====== Return Only New Variables ====== ##

newvars = add(within.1, {
  ID = str_extract(ID, "\\d")
  A = .mean("A", 1:4)
}, drop=TRUE)
newvars


## ====== Better Than `base::within()` ====== ##

d = as.data.table(within.1)

# wrong order: C B A
within(d, {
  A = 4
  B = A + 1
  C = 6
})

# correct order: A B C
add(d, {
  A = 4
  B = A + 1
  C = 6
})

Demo data.

Description

Demo datasets of multi-factor ANOVA as examples to show how the functions MANOVA and EMMEANS work.

Format

1. Between-Subjects Design

• between.1 - A(4)

• between.2 - A(2) * B(3)

• between.3 - A(2) * B(2) * C(2)

2. Within-Subjects Design

• within.1 - A(4)

• within.2 - A(2) * B(3)

• within.3 - A(2) * B(2) * C(2)

3. Mixed Design

• mixed.2_1b1w - A(2, between) * B(3, within)

• mixed.3_1b2w - A(2, between) * B(2, within) * C(2, within)

• mixed.3_2b1w - A(2, between) * B(2, within) * C(2, between)

Source

Multi-Factor Experimental Design in Psychology and Education

Split up a string (with separators) into a character vector.

Description

Split up a string (with separators) into a character vector (whitespace around separator is trimmed).

Usage

cc(..., sep = "auto", trim = TRUE)

Arguments

Value

Character vector.

Examples

cc("a,b,c,d,e")

cc(" a , b , c , d , e ")

cc(" a , b , c , d , e ", trim=FALSE)

cc("1, 2, 3, 4, 5")

cc("A 1 , B 2 ; C 3 | D 4 \t E 5")

cc("A, B, C",
   " D | E ",
   c("F", "G"))

cc("
American
British
Chinese
")

Cross-correlation analysis.

Description

Plot the results of cross-correlation analysis using ggplot2 (rather than R base plot) for more flexible modification of the plot.

Usage

ccf_plot(
  formula,
  data,
  lag.max = 30,
  sig.level = 0.05,
  xbreaks = seq(-100, 100, 10),
  ybreaks = seq(-1, 1, 0.2),
  ylim = NULL,
  alpha.ns = 1,
  pos.color = "black",
  neg.color = "black",
  ci.color = "blue",
  title = NULL,
  subtitle = NULL,
  xlab = "Lag",
  ylab = "Cross-Correlation"
)

Arguments

Details

Significant correlations with negative time lags suggest shifts in a predictor precede shifts in an outcome.

Value

A gg object, which you can further modify using ggplot2 syntax and save using ggsave().

See Also

granger_test

Examples

# resemble the default plot output by `ccf()`
p1 = ccf_plot(chicken ~ egg, data=lmtest::ChickEgg)
p1

# a more colorful plot
p2 = ccf_plot(chicken ~ egg, data=lmtest::ChickEgg, alpha.ns=0.3,
              pos.color="#CD201F",
              neg.color="#21759B",
              ci.color="black")
p2

Test the difference between two correlations.

Description

Test the difference between two correlations.

Usage

cor_diff(r1, n1, r2, n2, n = NULL, rcov = NULL)

Arguments

Value

Invisibly return the p value.

Examples

# two independent rs (X~Y vs. Z~W)
cor_diff(r1=0.20, n1=100, r2=0.45, n2=100)

# two nonindependent rs (X~Y vs. X~Z, with Y and Z also correlated [rcov])
cor_diff(r1=0.20, r2=0.45, n=100, rcov=0.80)

Multilevel correlations (within-level and between-level).

Description

Multilevel correlations (within-level and between-level). For details, see description in HLM_ICC_rWG.

Usage

cor_multilevel(data, group, digits = 3)

Arguments

Value

Invisibly return a list of results.

See Also

Corr

HLM_ICC_rWG

Examples

# see https://psychbruce.github.io/supp/CEM

Timer (compute time difference).

Description

Timer (compute time difference).

Usage

dtime(t0, unit = "secs", digits = 0)

Arguments

Value

A character string of time difference.

Examples

## Not run: 

t0 = Sys.time()
dtime(t0)

## End(Not run)

Export data to a file (TXT, CSV, Excel, SPSS, Stata, ...) or clipboard.

Description

Export data to a file, with format automatically judged from file extension. This function is inspired by rio::export() and has several modifications. Its purpose is to avoid using lots of write_xxx() functions in your code and to provide one tidy function for data export.

It supports many file formats and uses corresponding R functions:

• Plain text (.txt, .csv, .csv2, .tsv, .psv), using data.table::fwrite(); if the encoding argument is specified, using utils::write.table() instead

• Excel (.xls, .xlsx), using openxlsx::write.xlsx()

• SPSS (.sav), using haven::write_sav()

• Stata (.dta), using haven::write_dta()

• R objects (.rda, .rdata, .RData), using base::save()

• R serialized objects (.rds), using base::saveRDS()

• Clipboard (on Windows and Mac OS), using clipr::write_clip()

• Other formats, using rio::export()

Usage

export(
  x,
  file,
  encoding = NULL,
  header = "auto",
  sheet = NULL,
  overwrite = TRUE,
  verbose = FALSE
)

Arguments

Value

No return value.

See Also

import, print_table

Examples

## Not run: 

  export(airquality)  # paste to clipboard
  export(airquality, file="mydata.csv")
  export(airquality, file="mydata.sav")

  export(list(airquality, npk), file="mydata.xlsx")  # Sheet1, Sheet2
  export(list(air=airquality, npk=npk), file="mydata.xlsx")  # a named list
  export(list(airquality, npk), sheet=c("air", "npk"), file="mydata.xlsx")

  export(list(a=1, b=npk, c="character"), file="abc.Rdata")  # .rda, .rdata
  d = import("abc.Rdata")  # load only the first object and rename it to `d`
  load("abc.Rdata")  # load all objects with original names to environment

  export(lm(yield ~ N*P*K, data=npk), file="lm_npk.Rdata")
  model = import("lm_npk.Rdata")
  load("lm_npk.Rdata")  # because x is unnamed, the object has a name "List1"

  export(list(m1=lm(yield ~ N*P*K, data=npk)), file="lm_npk.Rdata")
  model = import("lm_npk.Rdata")
  load("lm_npk.Rdata")  # because x is named, the object has a name "m1"

## End(Not run)

Format numeric values.

Description

Format numeric values.

Usage

formatF(x, digits = 3)

Arguments

Value

Formatted character string.

See Also

format, formatN

Examples

formatF(pi, 20)

Format "1234" to "1,234".

Description

Format "1234" to "1,234".

Usage

formatN(x, mark = ",")

Arguments

Value

Formatted character string.

See Also

format, formatF

Examples

formatN(1234)

Expand all interaction terms in a formula.

Description

Expand all interaction terms in a formula.

Usage

formula_expand(formula, as.char = FALSE)

Arguments

Value

A formula/character object including all expanded terms.

Examples

formula_expand(y ~ a*b*c)
formula_expand("y ~ a*b*c")

Paste a formula into a string.

Description

Paste a formula into a string.

Usage

formula_paste(formula)

Arguments

Value

A character string indicating the formula.

Examples

formula_paste(y ~ x)
formula_paste(y ~ x + (1 | g))

Grand-mean centering.

Description

Compute grand-mean centered variables. Usually used for GLM interaction-term predictors and HLM level-2 predictors.

Usage

grand_mean_center(data, vars = names(data), std = FALSE, add.suffix = "")

Arguments

Value

A new data object containing the centered variable(s).

See Also

group_mean_center

Examples

d = data.table(a=1:5, b=6:10)

d.c = grand_mean_center(d, "a")
d.c

d.c = grand_mean_center(d, c("a", "b"), add.suffix="_center")
d.c

Granger causality test (multivariate).

Description

Granger test of predictive causality (between multivariate time series) based on vector autoregression (VAR) model. Its output resembles the output of the vargranger command in Stata (but here using an F test).

Usage

granger_causality(
  varmodel,
  var.y = NULL,
  var.x = NULL,
  test = c("F", "Chisq"),
  file = NULL,
  check.dropped = FALSE
)

Arguments

Details

Granger causality test (based on VAR model) examines whether the lagged values of a predictor (or predictors) help to predict an outcome when controlling for the lagged values of the outcome itself.

Granger causality does not necessarily constitute a true causal effect.

Value

A data frame of results.

See Also

ccf_plot, granger_test

Examples

# R package "vars" should be installed
library(vars)
data(Canada)
VARselect(Canada)
vm = VAR(Canada, p=3)
model_summary(vm)
granger_causality(vm)

Granger causality test (bivariate).

Description

Granger test of predictive causality (between two time series) using the lmtest::grangertest() function.

Usage

granger_test(formula, data, lags = 1:5, test.reverse = TRUE, file = NULL, ...)

Arguments

Details

Granger causality test examines whether the lagged values of a predictor have an incremental role in predicting (i.e., help to predict) an outcome when controlling for the lagged values of the outcome.

Granger causality does not necessarily constitute a true causal effect.

Value

A data frame of results.

See Also

ccf_plot, granger_causality

Examples

granger_test(chicken ~ egg, data=lmtest::ChickEgg)
granger_test(chicken ~ egg, data=lmtest::ChickEgg, lags=1:10, file="Granger.doc")
unlink("Granger.doc")  # delete file for code check

Group-mean centering.

Description

Compute group-mean centered variables. Usually used for HLM level-1 predictors.

Usage

group_mean_center(
  data,
  vars = setdiff(names(data), by),
  by,
  std = FALSE,
  add.suffix = "",
  add.group.mean = "_mean"
)

Arguments

Value

A new data object containing the centered variable(s).

See Also

grand_mean_center

Examples

d = data.table(x=1:9, g=rep(1:3, each=3))

d.c = group_mean_center(d, "x", by="g")
d.c

d.c = group_mean_center(d, "x", by="g", add.suffix="_c")
d.c

Import data from a file (TXT, CSV, Excel, SPSS, Stata, ...) or clipboard.

Description

Import data from a file, with format automatically judged from file extension. This function is inspired by rio::import() and has several modifications. Its purpose is to avoid using lots of read_xxx() functions in your code and to provide one tidy function for data import.

It supports many file formats (local or URL) and uses the corresponding R functions:

• Plain text (.txt, .csv, .csv2, .tsv, .psv), using data.table::fread()

• Excel (.xls, .xlsx), using readxl::read_excel()

• SPSS (.sav), using haven::read_sav() or foreign::read.spss()

• Stata (.dta), using haven::read_dta() or foreign::read.dta()

• R objects (.rda, .rdata, .RData), using base::load()

• R serialized objects (.rds), using base::readRDS()

• Clipboard (on Windows and Mac OS), using clipr::read_clip_tbl()

• Other formats, using rio::import()

Usage

import(
  file,
  encoding = NULL,
  header = "auto",
  sheet = NULL,
  range = NULL,
  pkg = c("haven", "foreign"),
  value.labels = FALSE,
  as = "data.frame",
  verbose = FALSE
)

Arguments

Value

A data object (default class is data.frame).

See Also

export

Examples

## Not run: 

  # Import data from system clipboard
  data = import()  # read from clipboard (on Windows and Mac OS)

  # If you have an Excel file named "mydata.xlsx"
  export(airquality, file="mydata.xlsx")

  # Import data from a file
  data = import("mydata.xlsx")  # default: data.frame
  data = import("mydata.xlsx", as="data.table")

## End(Not run)

Tidy report of lavaan model.

Description

Tidy report of lavaan model.

Usage

lavaan_summary(
  lavaan,
  ci = c("raw", "boot", "bc.boot", "bca.boot"),
  nsim = 100,
  seed = NULL,
  digits = 3,
  print = TRUE,
  covariance = FALSE,
  file = NULL
)

Arguments

Value

Invisibly return a list of results:

fit

Model fit indices.

measure

Latent variable measures.

regression

Regression paths.

covariance

Variances and/or covariances.

effect

Defined effect estimates.

See Also

PROCESS, CFA

Examples

## Simple Mediation:
## Solar.R (X) => Ozone (M) => Temp (Y)

# PROCESS(airquality, y="Temp", x="Solar.R",
#         meds="Ozone", ci="boot", nsim=1000, seed=1)

model = "
Ozone ~ a*Solar.R
Temp ~ c.*Solar.R + b*Ozone
Indirect := a*b
Direct := c.
Total := c. + a*b
"
lv = lavaan::sem(model=model, data=airquality)
lavaan::summary(lv, fit.measure=TRUE, ci=TRUE, nd=3)  # raw output
lavaan_summary(lv)
# lavaan_summary(lv, ci="boot", nsim=1000, seed=1)


## Serial Multiple Mediation:
## Solar.R (X) => Ozone (M1) => Wind(M2) => Temp (Y)

# PROCESS(airquality, y="Temp", x="Solar.R",
#         meds=c("Ozone", "Wind"),
#         med.type="serial", ci="boot", nsim=1000, seed=1)

model0 = "
Ozone ~ a1*Solar.R
Wind ~ a2*Solar.R + d12*Ozone
Temp ~ c.*Solar.R + b1*Ozone + b2*Wind
Indirect_All := a1*b1 + a2*b2 + a1*d12*b2
Ind_X_M1_Y := a1*b1
Ind_X_M2_Y := a2*b2
Ind_X_M1_M2_Y := a1*d12*b2
Direct := c.
Total := c. + a1*b1 + a2*b2 + a1*d12*b2
"
lv0 = lavaan::sem(model=model0, data=airquality)
lavaan::summary(lv0, fit.measure=TRUE, ci=TRUE, nd=3)  # raw output
lavaan_summary(lv0)
# lavaan_summary(lv0, ci="boot", nsim=1000, seed=1)

model1 = "
Ozone ~ a1*Solar.R
Wind ~ d12*Ozone
Temp ~ c.*Solar.R + b1*Ozone + b2*Wind
Indirect_All := a1*b1 + a1*d12*b2
Ind_X_M1_Y := a1*b1
Ind_X_M1_M2_Y := a1*d12*b2
Direct := c.
Total := c. + a1*b1 + a1*d12*b2
"
lv1 = lavaan::sem(model=model1, data=airquality)
lavaan::summary(lv1, fit.measure=TRUE, ci=TRUE, nd=3)  # raw output
lavaan_summary(lv1)
# lavaan_summary(lv1, ci="boot", nsim=1000, seed=1)

Tidy report of mediation analysis.

Description

Tidy report of mediation analysis, which is performed using the mediation package.

Usage

med_summary(model, digits = 3, file = NULL)

Arguments

Value

Invisibly return a data frame containing the results.

See Also

PROCESS

Examples

## Not run: 

library(mediation)
# ?mediation::mediate

## Example 1: OLS Regression
## Bias-corrected and accelerated (BCa) bootstrap confidence intervals

## Hypothesis: Solar radiation -> Ozone -> Daily temperature
lm.m = lm(Ozone ~ Solar.R + Month + Wind, data=airquality)
lm.y = lm(Temp ~ Ozone + Solar.R + Month + Wind, data=airquality)
set.seed(123)  # set a random seed for reproduction
med = mediate(lm.m, lm.y,
            treat="Solar.R", mediator="Ozone",
            sims=1000, boot=TRUE, boot.ci.type="bca")
med_summary(med)

## Example 2: Multilevel Linear Model (Linear Mixed Model)
## (models must be fit using "lme4::lmer" rather than "lmerTest::lmer")
## Monte Carlo simulation (quasi-Bayesian approximation)
## (bootstrap method is not applicable to "lmer" models)

## Hypothesis: Crips -> Sweetness -> Preference (for carrots)
data = lmerTest::carrots  # long-format data
data = na.omit(data)  # omit missing values
lmm.m = lme4::lmer(Sweetness ~ Crisp + Gender + Age + (1 | Consumer), data=data)
lmm.y = lme4::lmer(Preference ~ Sweetness + Crisp + Gender + Age + (1 | Consumer), data=data)
set.seed(123)  # set a random seed for reproduction
med.lmm = mediate(lmm.m, lmm.y,
                  treat="Crisp", mediator="Sweetness",
                  sims=1000)
med_summary(med.lmm)

## End(Not run)

Tidy report of regression models.

Description

Tidy report of regression models (most model types are supported). This function uses:

• texreg::screenreg()

• texreg::htmlreg()

• MuMIn::std.coef()

• MuMIn::r.squaredGLMM()

• performance::r2_mcfadden()

• performance::r2_nagelkerke()

Usage

model_summary(
  model.list,
  std = FALSE,
  digits = 3,
  file = NULL,
  check = TRUE,
  zero = ifelse(std, FALSE, TRUE),
  modify.se = NULL,
  modify.head = NULL,
  line = TRUE,
  bold = 0,
  ...
)

Arguments

Value

Invisibly return the output (character string).

See Also

print_table (print simple table)

GLM_summary

HLM_summary

med_summary

lavaan_summary

PROCESS

Examples

#### Example 1: Linear Model ####
lm1 = lm(Temp ~ Month + Day, data=airquality)
lm2 = lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
model_summary(lm1)
model_summary(lm2)
model_summary(list(lm1, lm2))
model_summary(list(lm1, lm2), std=TRUE, digits=2)
model_summary(list(lm1, lm2), file="OLS Models.doc")
unlink("OLS Models.doc")  # delete file for code check

#### Example 2: Generalized Linear Model ####
glm1 = glm(case ~ age + parity,
           data=infert, family=binomial)
glm2 = glm(case ~ age + parity + education + spontaneous + induced,
           data=infert, family=binomial)
model_summary(list(glm1, glm2))  # "std" is not applicable to glm
model_summary(list(glm1, glm2), file="GLM Models.doc")
unlink("GLM Models.doc")  # delete file for code check

#### Example 3: Linear Mixed Model ####
library(lmerTest)
hlm1 = lmer(Reaction ~ (1 | Subject), data=sleepstudy)
hlm2 = lmer(Reaction ~ Days + (1 | Subject), data=sleepstudy)
hlm3 = lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy)
model_summary(list(hlm1, hlm2, hlm3))
model_summary(list(hlm1, hlm2, hlm3), std=TRUE)
model_summary(list(hlm1, hlm2, hlm3), file="HLM Models.doc")
unlink("HLM Models.doc")  # delete file for code check

#### Example 4: Generalized Linear Mixed Model ####
library(lmerTest)
data.glmm = MASS::bacteria
glmm1 = glmer(y ~ trt + week + (1 | ID), data=data.glmm, family=binomial)
glmm2 = glmer(y ~ trt + week + hilo + (1 | ID), data=data.glmm, family=binomial)
model_summary(list(glmm1, glmm2))  # "std" is not applicable to glmm
model_summary(list(glmm1, glmm2), file="GLMM Models.doc")
unlink("GLMM Models.doc")  # delete file for code check

#### Example 5: Multinomial Logistic Model ####
library(nnet)
d = airquality
d$Month = as.factor(d$Month)  # Factor levels: 5, 6, 7, 8, 9
mn1 = multinom(Month ~ Temp, data=d, Hess=TRUE)
mn2 = multinom(Month ~ Temp + Wind + Ozone, data=d, Hess=TRUE)
model_summary(mn1)
model_summary(mn2)
model_summary(mn2, file="Multinomial Logistic Model.doc")
unlink("Multinomial Logistic Model.doc")  # delete file for code check

Compute p value.

Description

Compute p value.

Usage

p(
  z = NULL,
  t = NULL,
  f = NULL,
  r = NULL,
  chi2 = NULL,
  n = NULL,
  df = NULL,
  df1 = NULL,
  df2 = NULL,
  digits = 2
)

p.z(z)

p.t(t, df)

p.f(f, df1, df2)

p.r(r, n)

p.chi2(chi2, df)

Arguments

Value

p value statistics.

Functions

• p.z(): Two-tailed p value of z.

• p.t(): Two-tailed p value of t.

• p.f(): One-tailed p value of F. (Note: F test is one-tailed only.)

• p.r(): Two-tailed p value of r.

• p.chi2(): One-tailed p value of \chi^2. (Note: \chi^2 test is one-tailed only.)

Examples

p.z(1.96)
p.t(2, 100)
p.f(4, 1, 100)
p.r(0.2, 100)
p.chi2(3.84, 1)

p(z=1.96)
p(t=2, df=100)
p(f=4, df1=1, df2=100)
p(r=0.2, n=100)
p(chi2=3.84, df=1)

Check dependencies of R packages.

Description

Check dependencies of R packages.

Usage

pkg_depend(pkgs, excludes = NULL)

Arguments

Value

A character vector of package names.

See Also

pkg_install_suggested

Install suggested R packages.

Description

Install suggested R packages.

Usage

pkg_install_suggested(by)

Arguments

Value

No return value.

See Also

pkg_depend

Examples

## Not run: 

pkg_install_suggested()  # install all packages suggested by me

## End(Not run)

Print a three-line table (to R Console and Microsoft Word).

Description

This basic function prints any data frame as a three-line table to either R Console or Microsoft Word (.doc). It has been used in many other functions of bruceR (see below).

Usage

print_table(
  x,
  digits = 3,
  nspaces = 1,
  row.names = TRUE,
  col.names = TRUE,
  title = "",
  note = "",
  append = "",
  line = TRUE,
  file = NULL,
  file.align.head = "auto",
  file.align.text = "auto"
)

Arguments

Value

Invisibly return a list of data frame and HTML code.

See Also

These functions have implemented MS Word file output using this function:

• Describe

• Freq

• Corr

• EFA / PCA

• CFA

• TTEST

• MANOVA

• model_summary

• med_summary

• lavaan_summary

• PROCESS

• granger_test

• granger_causality

Examples

print_table(data.frame(x=1))

print_table(airquality, file="airquality.doc")
unlink("airquality.doc")  # delete file for code check

model = lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
print_table(model)
print_table(model, file="model.doc")
unlink("model.doc")  # delete file for code check

Regression analysis.

Description

NOTE: model_summary is preferred.

Usage

regress(
  formula,
  data,
  family = NULL,
  digits = 3,
  robust = FALSE,
  cluster = NULL,
  test.rand = FALSE
)

Arguments

Value

No return value.

See Also

print_table (print simple table)

model_summary (highly suggested)

GLM_summary

HLM_summary

Examples

## Not run: 

  ## lm
  regress(Temp ~ Month + Day + Wind + Solar.R, data=airquality, robust=TRUE)

  ## glm
  regress(case ~ age + parity + education + spontaneous + induced,
          data=infert, family=binomial, robust="HC1", cluster="stratum")

  ## lmer
  library(lmerTest)
  regress(Reaction ~ Days + (Days | Subject), data=sleepstudy)
  regress(Preference ~ Sweetness + Gender + Age + Frequency +
            (1 | Consumer), data=carrots)

  ## glmer
  library(lmerTest)
  data.glmm = MASS::bacteria
  regress(y ~ trt + week + (1 | ID), data=data.glmm, family=binomial)
  regress(y ~ trt + week + hilo + (1 | ID), data=data.glmm, family=binomial)

## End(Not run)

Repeat a character string for many times and paste them up.

Description

Repeat a character string for many times and paste them up.

Usage

rep_char(char, rep.times)

Arguments

Value

Character string.

Examples

rep_char("a", 5)

Min-max scaling (min-max normalization).

Description

This function resembles RESCALE() and it is just equivalent to RESCALE(var, to=0:1).

Usage

scaler(v, min = 0, max = 1)

Arguments

Value

A vector of rescaled variable.

Examples

scaler(1:5)
# the same: RESCALE(1:5, to=0:1)

Set working directory to the path of currently opened file.

Description

Set working directory to the path of currently opened file (usually an R script). You can use this function in both .R/.Rmd files and R Console. RStudio (version >= 1.2) is required for running this function.

Usage

set.wd(path = NULL, ask = FALSE)

set_wd(path = NULL, ask = FALSE)

Arguments

Value

Invisibly return the path.

Functions

• set.wd(): Main function

• set_wd(): The alias of set.wd (the same)

See Also

setwd

Examples

## Not run: 

  # RStudio (version >= 1.2) is required for running this function.
  set.wd()  # set working directory to the path of the currently opened file
  set.wd("~/")  # set working directory to the home path
  set.wd("../")  # set working directory to the parent path
  set.wd(ask=TRUE)  # select a folder with the prompt of a dialog

## End(Not run)

Show colors.

Description

Show colors.

Usage

show_colors(colors)

Arguments

Value

A gg object.

Examples

show_colors("blue")
show_colors("#0000FF")  # blue (hex name)
show_colors(RGB(0, 0, 255))  # blue (RGB)
show_colors(see::social_colors())
show_colors(see::pizza_colors())

A nice ggplot2 theme that enables Markdown/HTML rich text.

Description

A nice ggplot2 theme for scientific publication. It uses ggtext::element_markdown() to render Markdown/HTML formatted rich text. You can use a combination of Markdown and/or HTML syntax (e.g., "*y* = *x*<sup>2</sup>") in plot text or title, and this function draws text elements with rich text format.

For more usage, see:

• ggtext::geom_richtext()

• ggtext::geom_textbox()

• ggtext::element_markdown()

• ggtext::element_textbox()

Usage

theme_bruce(
  markdown = FALSE,
  base.size = 12,
  line.size = 0.5,
  border = "black",
  bg = "white",
  panel.bg = "white",
  tag = "bold",
  plot.title = "bold",
  axis.title = "plain",
  title.pos = 0.5,
  subtitle.pos = 0.5,
  caption.pos = 1,
  font = NULL,
  grid.x = "",
  grid.y = "",
  line.x = TRUE,
  line.y = TRUE,
  tick.x = TRUE,
  tick.y = TRUE
)

Arguments

Value

A theme object that should be used for ggplot2.

Examples

## Example 1 (bivariate correlation)
d = as.data.table(psych::bfi)
added(d, {
  E = .mean("E", 1:5, rev=c(1,2), range=1:6)
  O = .mean("O", 1:5, rev=c(2,5), range=1:6)
})
ggplot(data=d, aes(x=E, y=O)) +
  geom_point(alpha=0.1) +
  geom_smooth(method="loess") +
  labs(x="Extraversion<sub>Big 5</sub>",
       y="Openness<sub>Big 5</sub>") +
  theme_bruce(markdown=TRUE)

## Example 2 (2x2 ANOVA)
d = data.frame(X1 = factor(rep(1:3, each=2)),
               X2 = factor(rep(1:2, 3)),
               Y.mean = c(5, 3, 2, 7, 3, 6),
               Y.se = rep(c(0.1, 0.2, 0.1), each=2))
ggplot(data=d, aes(x=X1, y=Y.mean, fill=X2)) +
  geom_bar(position="dodge", stat="identity", width=0.6, show.legend=FALSE) +
  geom_errorbar(aes(x=X1, ymin=Y.mean-Y.se, ymax=Y.mean+Y.se),
                width=0.1, color="black", position=position_dodge(0.6)) +
  scale_y_continuous(expand=expansion(add=0),
                     limits=c(0,8), breaks=0:8) +
  scale_fill_brewer(palette="Set1") +
  labs(x="Independent Variable (*X*)",  # italic X
       y="Dependent Variable (*Y*)",  # italic Y
       title="Demo Plot<sup>bruceR</sup>") +
  theme_bruce(markdown=TRUE, border="")