Type:	Package
Title:	Integrated Toolkit for Psychological Analysis and Modeling in R
Version:	0.5.0
Description:	A beginner-friendly R package for modeling in psychology or related field. It allows fitting models, plotting, checking goodness of fit, and model assumption violations all in one place. It also produces beautiful and easy-to-read output.
License:	GPL (≥ 3)
URL:	https://github.com/jasonmoy28/psycModel
Depends:	R (≥ 3.2)
Imports:	dplyr, ggplot2, glue, insight, lavaan, lifecycle, lme4, lmerTest, parameters, patchwork, performance, psych, rlang (≥ 0.1.2), stringr, tibble, tidyr, utils, tidyselect
Suggests:	correlation, covr, cowplot, fansi, ggrepel, GPArotation, gridExtra, interactions, knitr, nFactors, nlme, pagedown, qqplotr, rmarkdown, roxygen2, sandwich, see, semPlot, spelling, testthat (≥ 3.0.0), lavaSearch2
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
Language:	en-US
NeedsCompilation:	no
Packaged:	2023-11-01 05:41:16 UTC; Jasonmoy
Author:	Jason Moy [aut, cre]
Maintainer:	Jason Moy <jasonmoy28@gmail.com>
Repository:	CRAN
Date/Publication:	2023-11-01 06:00:02 UTC

Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Value

no return value

ANOVA Plot

Description

Plot categorical variable with barplot. Continuous moderator are plotted at ± 1 SD from the mean.

Usage

anova_plot(model, predictor = NULL, graph_label_name = NULL)

Arguments

Value

a ggplot object

Examples

# Main effect plot with 1 categorical variable
fit_1 = lavaan::HolzingerSwineford1939 %>% 
  dplyr::mutate(school = as.factor(school)) %>% 
  lm(data = ., grade ~ school)
anova_plot(fit_1,predictor = school)

# Interaction effect plot with 2 categorical variables 
fit_2 = lavaan::HolzingerSwineford1939 %>% 
  dplyr::mutate(dplyr::across(c(school,sex),as.factor)) %>% 
  lm(data = ., grade ~ school*sex)
anova_plot(fit_2)

# Interaction effect plot with 1 categorical variable and 1 continuous variable
fit_3 = lavaan::HolzingerSwineford1939 %>% 
  dplyr::mutate(school = as.factor(school)) %>% 
  dplyr::mutate(ageyr = as.numeric(ageyr)) %>% 
  lm(data = ., grade ~ ageyr*school)
anova_plot(fit_3)

Confirmatory Factor Analysis (groupwise)

Description

This function will run N number of CFA where N = length(group), and report the fit measures of CFA in each group. The function is intended to help you get a better understanding of which group has abnormal fit indicator

Usage

cfa_groupwise(data, ..., group, model = NULL, ordered = FALSE)

Arguments

Details

All argument must be explicitly specified. If not, all arguments will be treated as CFA items

Value

a data.frame with group-wise CFA result

Examples

# The example is used as the illustration of the function output only.
# It does not imply the data is appropriate for the analysis.
cfa_groupwise(
  data = lavaan::HolzingerSwineford1939,
  group = "school",
  x1:x3,
  x4:x6,
  x7:x9
)

Confirmatory Factor Analysis

Description

The function fits a CFA model using the lavaan::cfa(). Users can fit single and multiple factors CFA, and it also supports multilevel CFA (by specifying the group). Users can fit the model by passing the items using dplyr::select() syntax or an explicit lavaan model for more versatile usage. All arguments (except the CFA items) must be explicitly named (e.g., model = your-model; see example for inappropriate behavior).

Usage

cfa_summary(
  data,
  ...,
  model = NULL,
  group = NULL,
  ordered = FALSE,
  digits = 3,
  estimator = "ML",
  model_covariance = TRUE,
  model_variance = TRUE,
  plot = TRUE,
  group_partial = NULL,
  streamline = FALSE,
  quite = FALSE,
  return_result = FALSE
)

Arguments

Details

First, just like researchers have argued against p value of 0.05 is not a good cut-of, researchers have also argue against that fit indicies (more importantly, the cut-off criteria) are not completely representative of the goodness of fit. Nonetheless, you are required to report them if you are publishing an article anyway. I will summarize the general recommended cut-off criteria for CFA model below. Researchers consider models with CFI (Bentler, 1990) that is > 0.95 to be excellent fit (Hu & Bentler, 1999), and > 0.9 to be acceptable fit. Researchers considered a model is excellent fit if CFI > 0.95 (Hu & Bentler, 1999), RMSEA < 0.06 (Hu & Bentler, 1999), TLI > 0.95, SRMR < 0.08. The model is considered an acceptable fit if CFI > 0.9 and RMSEA < 0.08. I need some time to find all the relevant references, but this should be the general consensus.

Value

a lavaan object if return_result is TRUE

References

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. https://doi.org/10.1080/10705519909540118

Examples

# REMEMBER, YOU MUST NAMED ALL ARGUMENT EXCEPT THE CFA ITEMS ARGUMENT
# Fitting a multilevel single factor CFA model
fit <- cfa_summary(
  data = lavaan::HolzingerSwineford1939,
  x1:x3,
  x4:x6,
  x7:x9,
  group = "sex",
  model_variance = FALSE, # do not print the model_variance
  model_covariance = FALSE # do not print the model_covariance
)


# Fitting a CFA model by passing explicit lavaan model (equivalent to the above model)
# Note in the below function how I added `model = ` in front of the lavaan model.
# Similarly, the same rule apply for all arguments (e.g., `ordered = FALSE` instead of just `FALSE`)

fit <- cfa_summary(
  model = "visual  =~ x1 + x2 + x3",
  data = lavaan::HolzingerSwineford1939,
  quite = TRUE # silence all output
)

## Not run: 
# This will fail because I did not add `model = ` in front of the lavaan model.
# Therefore,you must add the tag in front of all arguments
# For example, `return_result = 'model'` instaed of `model`
cfa_summary("visual  =~ x1 + x2 + x3
             textual =~ x4 + x5 + x6
             speed   =~ x7 + x8 + x9 ",
  data = lavaan::HolzingerSwineford1939
)

## End(Not run)

change coefficient to p value for model_table

Description

change coefficient to p value for model_table

Usage

coefficent_to_p(data_frame, marginal_alpha = 0.1, show_p = FALSE)

Arguments

Comparison of Model Fit

Description

Compare the fit indices of models (see below for model support)

Usage

compare_fit(
  ...,
  digits = 3,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

Value

a dataframe with fit indices and change in fit indices

Examples

# lme model

fit1 <- lm_model(
  data = popular,
  response_variable = popular,
  predictor_var = c(sex, extrav)
)

fit2 <- lm_model(
  data = popular,
  response_variable = popular,
  predictor_var = c(sex, extrav),
  two_way_interaction_factor = c(sex, extrav)
)

compare_fit(fit1, fit2)

# see ?measurement_invariance for measurement invariance example

Correlation table

Description

This function uses the correlation::correlation() to generate the correlation table.

Usage

cor_test(
  data,
  cols,
  ...,
  digits = 3,
  method = "pearson",
  p_adjust = "none",
  streamline = FALSE,
  quite = FALSE,
  return_result = FALSE
)

Arguments

Value

a data.frame of the correlation table

Examples

cor_test(iris, where(is.numeric))

Cronbach alpha

Description

Computing the Cronbach alphas for multiple factors.

Usage

cronbach_alpha(
  ...,
  data,
  var_name,
  group = NULL,
  quite = FALSE,
  return_result = FALSE
)

Arguments

Value

a data.frame object if return_result is TRUE

Examples

cronbach_alpha(
  data = lavaan::HolzingerSwineford1939,
  var_name = c('Visual','Textual','Speed'),
  c(x1,x2,x3), # one way to pass the items of a factor is by wrapping it with c()
  x4:x6, # another way to pass the items is use tidyselect syntax 
  x7:x9)

Data Check (internal use only)

Description

Data Check (internal use only)

Usage

data_check(data)

Arguments

Value

a data frame with all columns converted to numeric

Descriptive Statistics Table

Description

This function generates a table of descriptive statistics (mainly using psych::describe()) and or a correlation table. User can export this to a csv file (optionally, using the file_path argument). Users can open the csv file with MS Excel then copy and paste the table into MS Word table.

Usage

descriptive_table(
  data,
  cols,
  ...,
  digits = 3,
  descriptive_indicator = c("mean", "sd", "cor"),
  file_path = NULL,
  streamline = FALSE,
  quite = FALSE,
  return_result = FALSE
)

Arguments

Value

a data.frame of the descriptive table

Examples

descriptive_table(iris, cols = where(is.numeric)) # all numeric columns

descriptive_table(iris,
  cols = where(is.numeric),
  # get missing count, non-missing count, and mean & sd & correlation table
  descriptive_indicator = c("missing", "non_missing", "mean", "sd", "cor")
)

Exploratory Factor Analysis

Description

The function is used to fit a exploratory factor analysis model. It will first find the optimal number of factors using parameters::n_factors. Once the optimal number of factor is determined, the function will fit the model using psych::fa(). Optionally, you can request a post-hoc CFA model based on the EFA model which gives you more fit indexes (e.g., CFI, RMSEA, TLI)

Usage

efa_summary(
  data,
  cols,
  rotation = "varimax",
  optimal_factor_method = FALSE,
  efa_plot = TRUE,
  digits = 3,
  n_factor = NULL,
  post_hoc_cfa = FALSE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

Value

a fa object from psych

Examples

efa_summary(lavaan::HolzingerSwineford1939, starts_with("x"), post_hoc_cfa = TRUE)

Format digits (internal use only)

Description

Format digits (internal use only)

Usage

format_round(x, digits = 3)

Arguments

Value

a rounded string

get interaction term

Description

get interaction term

Usage

get_interaction_term(model)

Arguments

Value

a list with predict vars names

get factor df to combine with mean_df

Description

get factor df to combine with mean_df

Usage

get_predict_df(data)

Arguments

Value

factor_df

Generalized Linear Regression

Description

Fit a generalized linear regression using glm(). This function is still in early development stage.

Usage

glm_model(
  data,
  response_variable,
  predictor_variable,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  family,
  quite = FALSE
)

Arguments

Value

an object class of glm representing the linear regression fit

Examples

fit <- glm_model(
  response_variable = incidence,
  predictor_variable = period,
  family = "poisson", # or you can enter as poisson(link = 'log'),
  data = lme4::cbpp
)

Convert HTML to PDF

Description

This is a helper function for knitting Rmd. Due to technological limitation, the output cannot knit to PDF in Rmd directly (the problem is with the latex engine printing unicode character). Therefore, to bypass this problem, you will first need to knit to html file first, then use this function to convert it to a PDF file.

Usage

html_to_pdf(file_path = NULL, dir = NULL, scale = 1, render_exist = FALSE)

Arguments

Value

no return value

Examples

## Not run: 
html_to_pdf(file_path = "html_name.html")
# all HTML files in the my_html_folder will be converted
html_to_pdf(dir = "Users/Desktop/my_html_folder")

## End(Not run)

interaction_check

Description

interaction_check

Usage

interaction_check(interaction_term)

Arguments

Value

nothing

Interaction plot

Description

The function creates a two-way or three-way interaction plot. It will creates a plot with ± 1 SD from the mean of the independent variable. See below for supported model. I recommend using concurrently with lm_model(), lme_model().

Usage

interaction_plot(
  model,
  data = NULL,
  graph_label_name = NULL,
  cateogrical_var = NULL,
  y_lim = NULL,
  plot_color = FALSE
)

Arguments

Value

a ggplot object

Examples

lm_fit_2 <- lm(Sepal.Length ~ Sepal.Width + Petal.Length +
  Sepal.Width*Petal.Length, data = iris)
  
interaction_plot(lm_fit_2)

lm_fit_3 <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + 
  Sepal.Width*Petal.Length:Petal.Width, data = iris)
  
interaction_plot(lm_fit_3)

Knit Rmd Files Instruction

Description

This is a helper function that instruct users of the package how to knit a R Markdown (Rmd) files

Usage

knit_to_Rmd()

Value

no return value

Examples

knit_to_Rmd()

get label name

Description

get label name

Usage

label_name(
  graph_label_name,
  response_var_name,
  predict_var1_name,
  predict_var2_name,
  predict_var3_name
)

Arguments

Value

vector of var name

Linear Regressions / ANOVA / ANCOVA

Description

Fit a linear regression using lm(). Linear regression is used to explore the effect of continuous variables / categorical variables in predicting a normally-distributed continuous variables.

Usage

lm_model(
  data,
  response_variable,
  predictor_variable,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  quite = FALSE
)

Arguments

Value

an object class of lm representing the linear regression fit

Examples

fit <- lm_model(
  data = iris,
  response_variable = Sepal.Length,
  predictor_variable = dplyr::everything(),
  two_way_interaction_factor = c(Sepal.Width, Species)
)

Model Summary for Linear Regression

Description

An integrated function for fitting a linear regression model.

Usage

lm_model_summary(
  data,
  response_variable = NULL,
  predictor_variable = NULL,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  family = NULL,
  cateogrical_var = NULL,
  graph_label_name = NULL,
  model_summary = TRUE,
  interaction_plot = TRUE,
  y_lim = NULL,
  plot_color = FALSE,
  digits = 3,
  simple_slope = FALSE,
  assumption_plot = FALSE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

Value

a list of all requested items in the order of model, model_summary, interaction_plot, simple_slope

Examples

fit <- lm_model_summary(
  data = iris,
  response_variable = "Sepal.Length",
  predictor_variable = dplyr::everything(),
  two_way_interaction_factor = c(Sepal.Width, Species),
  interaction_plot = FALSE, # you can also request the interaction plot
  simple_slope = FALSE, # you can also request simple slope estimate 
  assumption_plot = FALSE, # you can also request assumption plot
  streamline = FALSE #you can change this to get the least amount of info
)

Linear Regression Model Table Generate tables with multiple response and predictor variable (only lm models are supported)

Description

Linear Regression Model Table Generate tables with multiple response and predictor variable (only lm models are supported)

Usage

lm_model_table(
  data,
  response_variable,
  predictor_variable,
  control_variable = NULL,
  marginal_alpha = 0.1,
  return_result = FALSE,
  verbose = TRUE,
  show_p = FALSE
)

Arguments

Value

data.frame

Examples

lm_model_table(data = iris, 
            response_variable = c(Sepal.Length,Sepal.Width),
            predictor_variable = Petal.Width)

Linear Mixed Effect Model

Description

Fit a linear mixed effect model (i.e., hierarchical linear model, multilevel linear model) using the nlme::lme() or the lmerTest::lmer() function. Linear mixed effect model is used to explore the effect of continuous / categorical variables in predicting a normally distributed continuous variable.

Usage

lme_model(
  data,
  model = NULL,
  response_variable,
  random_effect_factors = NULL,
  non_random_effect_factors = NULL,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  id,
  estimation_method = "REML",
  opt_control = "bobyqa",
  na.action = stats::na.omit,
  use_package = "lmerTest",
  quite = FALSE
)

Arguments

Details

Here is a little tip. If you are using generic selecting syntax (e.g., contains() or start_with()), you don't need to remove the response variable and the id from the factors. It will be automatically remove. For example, if you have x1:x9 as your factors. You want to regress x2:x8 on x1. Your probably pass something like response_variable = x1, random_effect_factors = c(contains('x'),-x1) to the function. However, you don't need to do that, you can just pass random_effect_factors = c(contains('x')) to the function since it will automatically remove the response variable from selection.

Value

an object representing the linear mixed-effects model fit (it maybe an object from lme or lmer depending of the package you use)

Examples

# two-level model with level-1 and level-2 variable with random intercept and random slope
fit1 <- lme_model(
  data = popular,
  response_variable = popular,
  random_effect_factors = c(extrav, sex),
  non_random_effect_factors = texp,
  id = class
)


# added two-way interaction factor
fit2 <- lme_model(
  data = popular,
  response_variable = popular,
  random_effect_factors = c(extrav, sex),
  non_random_effect_factors = texp,
  two_way_interaction_factor = c(extrav, texp),
  id = class
)

# pass a explicit lme model (I don't why you want to do that, but you can)
lme_fit <- lme_model(
  model = "popular ~ extrav*texp + (1 + extrav | class)",
  data = popular
)

Model Summary for Mixed Effect Model

Description

An integrated function for fitting a multilevel linear regression (also known as hierarchical linear regression).

Usage

lme_multilevel_model_summary(
  data,
  model = NULL,
  response_variable = NULL,
  random_effect_factors = NULL,
  non_random_effect_factors = NULL,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  family = NULL,
  cateogrical_var = NULL,
  id = NULL,
  graph_label_name = NULL,
  estimation_method = "REML",
  opt_control = "bobyqa",
  na.action = stats::na.omit,
  model_summary = TRUE,
  interaction_plot = TRUE,
  y_lim = NULL,
  plot_color = FALSE,
  digits = 3,
  use_package = "lmerTest",
  standardize = NULL,
  ci_method = "satterthwaite",
  simple_slope = FALSE,
  assumption_plot = FALSE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

Value

a list of all requested items in the order of model, model_summary, interaction_plot, simple_slope

Examples

fit <- lme_multilevel_model_summary(
  data = popular,
  response_variable = popular,
  random_effect_factors = NULL, # you can add random effect predictors here 
  non_random_effect_factors = c(extrav,texp),
  two_way_interaction_factor = NULL, # you can add two-way interaction plot here 
  graph_label_name = NULL, #you can also change graph lable name here
  id = class,
  simple_slope = FALSE, # you can also request simple slope estimate 
  assumption_plot = FALSE, # you can also request assumption plot
  plot_color = FALSE, # you can also request the plot in color
  streamline = FALSE # you can change this to get the least amount of info
)

Measurement Invariance

Description

Compute the measurement invariance model (i.e., measurement equivalence model) using multi-group confirmatory factor analysis (MGCFA; Jöreskog, 1971). This function uses the lavaan::cfa() in the backend. Users can run the configural-metric or the configural-metric-scalar comparisons (see below for detail instruction). All arguments (except the CFA items) must be explicitly named (like model = your-model; see example for inappropriate behavior).

Usage

measurement_invariance(
  data,
  ...,
  model = NULL,
  group,
  ordered = FALSE,
  group_partial = NULL,
  invariance_level = "scalar",
  estimator = "ML",
  digits = 3,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

Details

Chen (2007) suggested that change in CFI <= |-0.010| supplemented by RMSEA <= 0.015 indicate non-invariance when sample sizes were equal across groups and larger than 300 in each group (Chen, 2007). And, Chen (2007) suggested that change in CFI <= |-0.005| and change in RMSEA <= 0.010 for unequal sample size with each group smaller than 300. For SRMR, Chen (2007) recommend change in SRMR < 0.030 for metric-invariance and change in SRMR < 0.015 for scalar-invariance. For large group size, Rutowski & Svetina (2014) recommended a more liberal cut-off for metric non-invariance for CFI (change in CFI <= |-0.020|) and RMSEA (RMSEA <= 0.030). However, this more liberal cut-off DOES NOT apply to testing scalar non-invariance. If measurement-invariance is not achieved, some researchers suggesting partial invariance is acceptable (by releasing the constraints on some factors). For example, Steenkamp and Baumgartner (1998) suggested that ideally more than half of items on a factor should be invariant. However, it is important to note that no empirical studies were cited to support the partial invariance guideline (Putnick & Bornstein, 2016).

Value

a data.frame of the fit measure summary

References

Chen, F. F. (2007). Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409-426.

Putnick, D. L., & Bornstein, M. H. (2016). Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research. Developmental Review: DR, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004

Rutkowski, L., & Svetina, D. (2014). Assessing the Hypothesis of Measurement Invariance in the Context of Large-Scale International Surveys. Educational and Psychological Measurement, 74(1), 31–57. https://doi.org/10.1177/0013164413498257

Steenkamp, J.-B. E. M., & Baumgartner, H. (n.d.). Assessing Measurement Invariance in Cross-National Consumer Research. JOURNAL OF CONSUMER RESEARCH, 13.

Examples

# REMEMBER, YOU MUST NAMED ALL ARGUMENT EXCEPT THE CFA ITEMS ARGUMENT
# Fitting a multiple-factor measurement invariance model by passing items.
measurement_invariance(
  x1:x3,
  x4:x6,
  x7:x9,
  data = lavaan::HolzingerSwineford1939,
  group = "school",
  invariance_level = "scalar" # you can change this to metric
)

# Fitting measurement invariance model by passing explicit lavaan model
# I am also going to only test for metric invariance instead of the default scalar invariance

measurement_invariance(
  model = "visual  =~ x1 + x2 + x3;
           textual =~ x4 + x5 + x6;
           speed   =~ x7 + x8 + x9",
  data = lavaan::HolzingerSwineford1939,
  group = "school",
  invariance_level = "metric"
)


## Not run: 
# This will fail because I did not add `model = ` in front of the lavaan model.
# Therefore,you must add the tag in front of all arguments
# For example, `return_result = 'model'` instaed of `model`
measurement_invariance(
  "visual  =~ x1 + x2 + x3;
             textual =~ x4 + x5 + x6;
             speed   =~ x7 + x8 + x9",
  data = lavaan::HolzingerSwineford1939
)

## End(Not run)

Mediation Analysis

Description

It currently only support simple mediation analysis using the path analysis approach with the lavaan package. I am trying to implement multilevel mediation in lavaan. In the future, I will try supporting moderated mediation (through lavaan or mediation) and mediation with latent variable (through lavaan).

Usage

mediation_summary(
  data,
  response_variable,
  mediator,
  predictor_variable,
  control_variable = NULL,
  group = NULL,
  standardize = TRUE,
  digits = 3,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

Value

an object from lavaan

Examples

mediation_summary(
  data = lmerTest::carrots,
  response_variable = Preference,
  mediator = Sweetness,
  predictor_variable = Crisp
)

Model Summary for Regression Models

Description

The function will extract the relevant coefficients from the regression models (see below for supported model).

Usage

model_summary(
  model,
  digits = 3,
  assumption_plot = FALSE,
  quite = FALSE,
  streamline = TRUE,
  return_result = FALSE,
  standardize = NULL,
  ci_method = "satterthwaite"
)

Arguments

Value

a list of model estimate data frame, model performance data frame, and the assumption plot (an ggplot object)

References

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x

Examples

# I am going to show the more generic usage of this function
# You can also use this package's built in function to fit the models
# I recommend using the integrated_multilevel_model_summary to get everything

# lme example
lme_fit <- lme4::lmer("popular ~ texp  + (1 | class)",
  data = popular
)

model_summary(lme_fit)

# lm example

lm_fit <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width,
  data = iris
)

model_summary(lm_fit)

Polynomial Regression Plot

Description

The function create a simple regression plot (no interaction). Can be used to visualize polynomial regression.

Usage

polynomial_regression_plot(
  model,
  model_data = NULL,
  predictor,
  graph_label_name = NULL,
  x_lim = NULL,
  y_lim = NULL,
  plot_color = FALSE
)

Arguments

Details

It appears that predict cannot handle categorical factors. All variables are converted to numeric before plotting.

Value

an object of class ggplot

Examples

fit = lm(data = iris, Sepal.Length ~ poly(Petal.Length,2))
polynomial_regression_plot(model = fit,predictor = 'Petal.Length')

Popular dataset

Description

Classic data-set from Chapter 2 of Joop Hox’s Multilevel Analysis (2010). The popular dataset included student from different class (i.e., class is the nesting variable). The outcome variable is a self-rated popularity scale. Individual-level (i.e., level 1) predictors are sex, extroversion. Class level (i.e., level 2) predictor is teacher experience.

Usage

popular

Format

A data frame with 2000 rows and 6 variables:

pupil

Subject ID

popular

Self-rated popularity scale ranging from 1 to 10

class

the class that students belong to (nesting variable)

extrav

extraversion scale (individual-level)

sex

gender of the student (individual-level)

texp

teacher experience (class-level)

Source

http://joophox.net/mlbook2/DataExchange.zip

print_table (internal use only)

Description

Rich-text fitted table printing in console

Usage

print_table(data_frame, marginal_alpha = 0.1, digits = 3)

Arguments

Value

no return value

Reliability Analysis

Description

First, it will determine whether the data is uni-dimensional or multi-dimensional using parameters::n_factors(). If the data is uni-dimensional, then it will print a summary consists of alpha, G6, single-factor CFA, and descriptive statistics result. If it is multi-dimensional, it will print a summary consist of alpha, G6, omega result. You can bypass this by specifying the dimensionality argument.

Usage

reliability_summary(
  data,
  cols,
  dimensionality = NULL,
  digits = 3,
  descriptive_table = TRUE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

Value

a psych::alpha object for unidimensional scale, and a psych::omega object for multidimensional scale.

Examples

fit <- reliability_summary(data = lavaan::HolzingerSwineford1939, cols = x1:x3)
fit <- reliability_summary(data = lavaan::HolzingerSwineford1939, cols = x1:x9)

Slope Estimate at Varying Level of Moderators

Description

The function uses the interaction::sim_slopes() to calculate the slope estimate at varying level of moderators (+/- 1 SD and mean). Additionally, it will produce a Johnson-Newman plot that shows when the slope estimate is not significant

Usage

simple_slope(model, data = NULL)

Arguments

Value

a list with the slope estimate data frame and a Johnson-Newman plot.

Examples

fit <- lm_model(
  data = iris,
  response_variable = Sepal.Length,
  predictor_variable = dplyr::everything(),
  three_way_interaction_factor = c(Sepal.Width, Petal.Width, Petal.Length)
)

simple_slope_fit <- simple_slope(
  model = fit,
)

super_print (internal use only)

Description

print color, greek-letter, font-style formatted text. Inputted text need to be 'command|$greek letter$'. Command options are bold, italic, underline, red, green, yellow, blue, silver. Greek letter options are alpha, beta, gamma, delta, epsilon, eta, theta, lamda, pi, rho, sigma, chi, omega. If capital letter greek letter, use all capital letter command (e.g., ALPHA) If you wish to print normal text, then the inputted text should '|text'

Usage

super_print(text, env = parent.frame())

Arguments

Value

formatted text (greek letter, color, font style formatted)

text_convert for super_print (internal use only)

Description

Command options are bold, italic, underline, red, green, yellow, blue, silver. Greek letter options are alpha, beta, gamma, delta, epsilon, eta, theta, lamda, pi, rho, sigma, chi, omega. If capital letter greek letter, use all capital letter command (e.g., ALPHA)

Usage

text_convert(text, type)

Arguments

Value

converted unicode-based character for greek letter, color, font style

Three-way Interaction Plot

Description

The function creates a two-way interaction plot. It will creates a plot with ± 1 SD from the mean of the independent variable. See below for supported model. I recommend using concurrently with lm_model(), lme_model().

Usage

three_way_interaction_plot(
  model,
  data = NULL,
  cateogrical_var = NULL,
  graph_label_name = NULL,
  y_lim = NULL,
  plot_color = FALSE
)

Arguments

Details

It appears that “predict' cannot handle categorical factors. All variables are converted to numeric before plotting.

Value

a ggplot object

Examples

lm_fit <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width +
  Sepal.Width:Petal.Length:Petal.Width, data = iris)

three_way_interaction_plot(lm_fit, data = iris)

Tidy eval helpers

Description

• sym() creates a symbol from a string and syms() creates a list of symbols from a character vector.

• enquo() and enquos() delay the execution of one or several function arguments. enquo() returns a single quoted expression, which is like a blueprint for the delayed computation. enquos() returns a list of such quoted expressions.

• expr() quotes a new expression locally. It is mostly useful to build new expressions around arguments captured with enquo() or enquos(): expr(mean(!!enquo(arg), na.rm = TRUE)).

• as_name() transforms a quoted variable name into a string. Supplying something else than a quoted variable name is an error.

  That's unlike as_label() which also returns a single string but supports any kind of R object as input, including quoted function calls and vectors. Its purpose is to summarise that object into a single label. That label is often suitable as a default name.

  If you don't know what a quoted expression contains (for instance expressions captured with enquo() could be a variable name, a call to a function, or an unquoted constant), then use as_label(). If you know you have quoted a simple variable name, or would like to enforce this, use as_name().

To learn more about tidy eval and how to use these tools, visit https://www.tidyverse.org and the Metaprogramming section of Advanced R.

Value

no return value

Two-way Interaction Plot

Description

The function creates a two-way interaction plot. It will creates a plot with ± 1 SD from the mean of the independent variable. See supported model below. I recommend using concurrently with lm_model or lme_model.

Usage

two_way_interaction_plot(
  model,
  data = NULL,
  graph_label_name = NULL,
  cateogrical_var = NULL,
  y_lim = NULL,
  plot_color = FALSE
)

Arguments

Details

It appears that “predict' cannot handle categorical factors. All variables are converted to numeric before plotting.

Value

an object of class ggplot

Examples

lm_fit <- lm(Sepal.Length ~ Sepal.Width * Petal.Width,
  data = iris
)
two_way_interaction_plot(lm_fit, data = iris)

Interaction term for Mixed Effect Model (internal use only) Create interaction terms for regression models

Description

Interaction term for Mixed Effect Model (internal use only) Create interaction terms for regression models

Usage

two_way_interaction_terms(interaction_terms)

Arguments

Value

a vector of all two-way interaction terms