SIMPLE.REGRESSION

Description

Provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo (MCMC) analyses. There are numerous options for model plots.

The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models (lme models are from the nlme package). There is also a function for partial and semipartial correlations and a function for conducting Cohen's set correlation analyses.

References

Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. Guilford Press.

Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer.

Hayes, A. F. (2018a). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.

Huitema, B. (2011). The analysis of covariance and alternatives: Statistical methods for experiments, quasi-experiments, and single-case studies. John Wiley & Sons.

Johnson, P. O., & Fey, L. C. (1950). The Johnson-Neyman technique, its theory, and application. Psychometrika, 15, 349-367.

Lorah, J. A. & Wong, Y. J. (2018). Contemporary applications of moderation analysis in counseling psychology. Counseling Psychology, 65(5), 629-640.

Orme, J. G., & Combs-Orme, T. (2009). Multiple regression with discrete dependent variables. Oxford University Press.

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. (3rd ed.). Wadsworth Thomson Learning.

Count data regression

Description

Provides SPSS- and SAS-like output for count data regression, including Poisson, quasi-Poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, hurdle poisson, and hurdle negative binomial models. The output includes model summaries, classification tables, omnibus tests of the model coefficients, overdispersion tests, model effect sizes, the model coefficients, correlation matrix for the model coefficients, collinearity statistics, and casewise regression diagnostics.

Usage

COUNT_REGRESSION(data, DV, forced = NULL, hierarchical = NULL,
                 model_type = 'poisson',
                 offset = NULL,
                 plot_type = 'residuals',
                 CI_level = 95,
                 MCMC = FALSE,
                 Nsamples = 4000,
                 GoF_model_types = TRUE,
                 verbose = TRUE )

Arguments

Details

This function uses the glm function from the stats package, the negative.binomial function from the MASS package, and the zeroinfl and hurdle functions from the pscl package (Zeileis, Kleiber, & Jackman, 2008). It supplements the output from these packages with additional statistics and in formats that resemble SPSS and SAS output. The predictor variables can be numeric or factors.

The following descriptions of zero-inflated and hurdle models were provided by Atkins and Baldwin (2013), by Friendly and Meyer (2016), and at https://stats.oarc.ucla.edu/r/dae/zinb/:

Zero-inflated and hurdle models are used when there is an overabundance of zero counts (excessive, or over-dispersed zero counts). Both have two submodels, one related to the zeroes and a second related to the counts. The key difference between hurdle and zero-inflated models is how they handle zeroes: Hurdle models cleanly divide the models, with all zeroes accounted for in the logistic regression, whereas zero-inflated models treat the observed zeroes as a mixture from two latent classes that produce zeroes.

Zero-inflated models assume that the observed counts arise from a mixture of two latent classes of observations: some structural zeros for whom the DV will always be zero, and a second class for whom the observed count may be zero or above zero. The excess zeros are assumed to have been generated by a separate process from the count values and it is assumed that the excess zeros can be modeled independently.

For example, imagine that wildlife biologists want to model how many fish are being caught by visitors to a park. Some visitors do not fish (structural zeros), but there is no data on whether a person fished or not. Some visitors who did fish did not catch any fish so there are excess zeros in the data because of the people that did not fish. The variables that predict whether or not visitors fished may or may not be the same variables that predict how many fish visitors caught. Separate models for the zeroes and for the counts can be examined. Zero-inflated models assume that zero values are due to two dierent processes, e.g., that a visitor has gone fishing vs. not gone fishing. If not gone fishing, the only outcome possible is zero. If gone fishing, it is then a count process. The two parts of the a zero-inflated model are a binary (logistic) model and a count model (Poisson or negative binomial). The expected counts are expressed as a combination of the two processes.

For the zero (logistic) portion of zero-inflated models, the predicted outcomes are the zero values (excess zeros) for the DV. A positive coefficient (B) for a predictor thus means that as values on a predictor increase, the probability of observing a zero value for the DV increases.

Hurdle models also deal with an excess of zero DV values, but without assuming that zero values arise from a mixture of two latent classes of observations. Imagine that it is (somehow) known that every visitor to a park did in fact fish. There could be an excess of zeroes because many of the visitors did not know how to fish. A separate logistic regression submodel is used to distinguish zero counts from the larger counts. The submodel for the positive counts is a truncated Poisson or negative-binomial model, excluding the zero counts. In other words, there is one process and submodel accounting for the zero counts and a separate process accounting for the positive counts, once the zero hurdle has been crossed. In zero-inflation models, the first process generates only extra zeros beyond those of the regular Poisson distribution. For hurdle models, the first process generates all of the zeros. In hurdle models, the zero values are considered fully observed, rather than latent.

For the zero (logistic) portion of hurdle models, the predicted outcomes are for going from zero to greater than zero values for the DV. A positive coefficient (B) for a predictor thus means that as values on a predictor increase, the probability of crossing the hurdle (obtaining a value higher than zero) for the DV increases.

Predicted values, for selected levels of the predictor variables, can be produced and plotted using the PLOT_MODEL funtion in this package.

The Bayesian MCMC analyses can be time-consuming for larger datasets. The MCMC analyses are conducted using functions, and their default settings, from the rstanarm package (Goodrich, Gabry, Ali, & Brilleman, 2024). Family = 'quasibinomial' analyses are currently not possible for the MCMC analyses. model_type = 'binomial' is therefore used instead. The Bayesian MCMC analyses are also currently not available for zero-inflated poisson and zero-inflated negative binomial models.

The MCMC results can be verified using the model checking functions in the rstanarm package (e.g., Muth, Oravecz, & Gabry, 2018).

Good sources for interpreting count data regression residuals and diagnostics plots:

• rpubs.com/benhorvath

• library.virginia.edu

Value

An object of class "COUNT_REGRESSION". The object is a list containing the following possible components:

Author(s)

Brian P. O'Connor

References

Atkins, D. C., Baldwin, S. A., Zheng, C., Gallop, R. J., & Neighbors, C. (2013). A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors, 27(1), 166177. https://doi.org/10.1037/a0029508

Atkins, D. C., & Gallop, R. J. (2007). Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models. Journal of Family Psychology, 21(4), 726-735.

Beaujean, A. A., & Grant, M. B. (2019). Tutorial on using regression models with count outcomes using R. Practical Assessment, Research, and Evaluation: Vol. 21, Article 2.

Coxe, S., West, S.G., & Aiken, L.S. (2009). The analysis of count data: A gentle introduction to Poisson regression and its alternatives. Journal of Personality Assessment, 91, 121-136.

Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer.

Friendly, M., & Meyer, D. (2016). Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. Chapman and Hall/CRC.

Hardin, J. W., & Hilbe, J. M. (2007). Generalized linear models and extensions. Stata Press.

Muth, C., Oravecz, Z., & Gabry, J. (2018). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology, 14(2), 99119.
https://doi.org/10.20982/tqmp.14.2.p099

Orme, J. G., & Combs-Orme, T. (2009). Multiple regression with discrete dependent variables. Oxford University Press.

Rindskopf, D. (2023). Generalized linear models. In H. Cooper, M. N. Coutanche, L. M. McMullen, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology: Data analysis and research publication, (2nd ed., pp. 201-218). American Psychological Association.

Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression Models for Count Data in R. Journal of Statistical Software, 27(8). https://www.jstatsoft.org/v27/i08/.

Examples

COUNT_REGRESSION(data=data_Kremelburg_2011, DV='OVRJOYED', 
                 forced=c('AGE','EDUC','REALRINC','SEX_factor'))


# negative binomial regression
COUNT_REGRESSION(data=data_Kremelburg_2011, DV='HURTATWK', 
                 forced=c('AGE','EDUC','REALRINC','SEX_factor'),
                 model_type = 'negbin',
                 plot_type = 'diagnostics')

# with an offset variable
COUNT_REGRESSION(data=data_Orme_2009_5, DV='NumberAdopted', forced=c('Married'), 
                 offset='lnYearsFostered')

omod <- COUNT_REGRESSION(data=data_Orme_2009_5, DV='NumberAdopted', forced=c('Married'), 
                model_type = 'zinfl_negbin',
                 offset='lnYearsFostered')



# zero-inflated poisson regression
COUNT_REGRESSION(data=data_Kremelburg_2011, DV='HURTATWK', 
                 forced=c('AGE','EDUC','REALRINC','SEX_factor'),
                 model_type = 'zinfl_poisson',
                 plot_type = 'diagnostics')

# hurdle negative binomial regression
COUNT_REGRESSION(data=data_Kremelburg_2011, DV='HURTATWK', 
                 forced=c('AGE','EDUC','REALRINC','SEX_factor'),
                 model_type = 'hurdle_negbin',
                 plot_type = 'diagnostics')

Logistic regression

Description

Logistic regression analyses with SPSS- and SAS-like output. The output includes model summaries, classification tables, omnibus tests of model coefficients, the model coefficients, likelihood ratio tests for the predictors, overdispersion tests, model effect sizes, the correlation matrix for the model coefficients, collinearity statistics, and casewise regression diagnostics.

Usage

LOGISTIC_REGRESSION(data, DV, forced = NULL, hierarchical = NULL,
                    ref_category = NULL,
                    family = 'binomial',
                    plot_type = 'residuals',
                    CI_level = 95,
                    MCMC = FALSE,
                    Nsamples = 4000,
                    verbose = TRUE)

Arguments

Details

This function uses the glm function from the stats package and supplements the output with additional statistics and in formats that resembles SPSS and SAS output. The predictor variables can be numeric or factors.

Predicted values for this model, for selected levels of the predictor variables, can be produced and plotted using the PLOT_MODEL funtion in this package.

The Bayesian MCMC analyses can be time-consuming for larger datasets. The MCMC analyses are conducted using functions, and their default settings, from the rstanarm package (Goodrich, Gabry, Ali, & Brilleman, 2024). The MCMC results can be verified using the model checking functions in the rstanarm package (e.g., Muth, Oravecz, & Gabry, 201).

Good sources for interpreting logistic regression residuals and diagnostics plots:

• rpubs.com/benhorvath

• library.virginia.edu

Value

An object of class "LOGISTIC_REGRESSION". The object is a list containing the following possible components:

Author(s)

Brian P. O'Connor

References

Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer.

Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Los Angeles, CA: Sage.

Goodrich, B., Gabry, J., Ali, I., & Brilleman, S. (2024). rstanarm: Bayesian applied regression modeling via Stan. R package version 2.32.1, https://mc-stan.org/rstanarm/.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis, (8th ed.). Lawrence Erlbaum Associates.

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) Applied logistic regression. (3rd ed.). John Wiley & Sons.

Muth, C., Oravecz, Z., & Gabry, J. (2018). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology, 14(2), 99119.
https://doi.org/10.20982/tqmp.14.2.p099

Orme, J. G., & Combs-Orme, T. (2009). Multiple regression with discrete dependent variables. Oxford University Press.

Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM's SPSS, (6th ed.). Routledge.

Rindskopf, D. (2023). Generalized linear models. In H. Cooper, M. N. Coutanche, L. M. McMullen, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology: Data analysis and research publication, (2nd ed., pp. 201-218). American Psychological Association.

Examples

# forced (simultaneous) entry
LOGISTIC_REGRESSION(data = data_Meyers_2013, DV='graduated', 
                    forced=c('sex','family_encouragement'),
                    plot_type = 'diagnostics')
	
# hierarchical entry, and using family = "quasibinomial"
LOGISTIC_REGRESSION(data = data_Kremelburg_2011, DV='OCCTRAIN',
                    hierarchical=list( step1=c('AGE'), step2=c('EDUC','REALRINC')),
                    family = "quasibinomial") 

Moderated multiple regression

Description

Conducts moderated regression analyses for two-way interactions with extensive options for interaction plots, including Johnson-Neyman regions of significance. The output includes the Anova Table (Type III tests), standardized coefficients, partial and semi-partial correlations, collinearity statistics, casewise regression diagnostics, plots of residuals and regression diagnostics, and detailed information about simple slopes. The output includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo (MCMC) analyses.

Usage

MODERATED_REGRESSION(data, DV, IV, MOD,
                     IV_type = 'numeric', IV_range = 'tumble',
                     MOD_type='numeric', MOD_levels='quantiles', MOD_range=NULL,
                     quantiles_IV = c(.1, .9), quantiles_MOD = c(.25, .5, .75),
                     COVARS = NULL,
                     center = TRUE, 
                     CI_level = 95,
                     MCMC = FALSE,
                     Nsamples = 10000,
                     plot_type = 'residuals', plot_title=NULL, DV_range = NULL,
                     Xaxis_label = NULL, Yaxis_label=NULL, legend_label=NULL,
                     JN_type = 'Huitema', 
                     verbose = TRUE )

Arguments

Details

The Bayesian MCMC analyses can be time-consuming for larger datasets. The MCMC analyses are conducted using functions, and their default settings, from the BayesFactor package (Morey & Rouder, 2024). The MCMC results can be verified using the model checking functions in the rstanarm package (e.g., Muth, Oravecz, & Gabry, 201).

Value

An object of class "MODERATED_REGRESSION". The object is a list containing the following possible components:

Author(s)

Brian P. O'Connor

References

Bodner, T. E. (2016). Tumble graphs: Avoiding misleading end point extrapolation when graphing interactions from a moderated multiple regression analysis. Journal of Educational and Behavioral Statistics, 41, 593-604.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. Guilford Press.

Hayes, A. F. (2018a). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.

Hayes, A. F., & Montoya, A. K. (2016). A tutorial on testing, visualizing, and probing an interaction involving a multicategorical variable in linear regression analysis. Communication Methods and Measures, 11, 1-30.

Lee M. D., & Wagenmakers, E. J. (2014) Bayesian cognitive modeling: A practical course. Cambridge University Press.

Morey, R. & Rouder, J. (2024). BayesFactor: Computation of Bayes Factors for Common Designs. R package version 0.9.12-4.7, https://github.com/richarddmorey/bayesfactor.

Muth, C., Oravecz, Z., & Gabry, J. (2018). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology, 14(2), 99119.
https://doi.org/10.20982/tqmp.14.2.p099

O'Connor, B. P. (1998). All-in-one programs for exploring interactions in moderated multiple regression. Educational and Psychological Measurement, 58, 833-837.

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. (3rd ed.). Wadsworth Thomson Learning.

Examples

# moderated regression	-- with IV_range = 'AikenWest'
MODERATED_REGRESSION(data=data_Lorah_Wong_2018, DV='suicidal', IV='burden',  MOD='belong_thwarted', 
                     IV_range='AikenWest',
                     MOD_levels='quantiles',
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     center = TRUE, COVARS='depression', 
                     plot_type = 'interaction', plot_title=NULL, DV_range = c(1,1.25))

# moderated regression	-- with  IV_range = 'tumble'
MODERATED_REGRESSION(data=data_Lorah_Wong_2018, DV='suicidal', IV='burden', MOD='belong_thwarted', 
                     IV_range='tumble',
                     MOD_levels='quantiles',
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     center = TRUE, COVARS='depression', 
                     plot_type = 'interaction', plot_title=NULL, DV_range = c(1,1.25)) 

# moderated regression	-- with numeric values for IV_range & MOD_levels='AikenWest'       
MODERATED_REGRESSION(data=data_OConnor_Dvorak_2001, DV='Aggressive_Behavior', 
                     IV='Maternal_Harshness', MOD='Resiliency', 
                     IV_range=c(1,7.7), 
                     MOD_levels='AikenWest', MOD_range=NULL,
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     center = FALSE, 
                     plot_type = 'interaction', 
                     DV_range = c(1,6), 
                     Xaxis_label='Maternal Harshness', 
                     Yaxis_label='Adolescent Aggressive Behavior', 
                     legend_label='Resiliency')

Ordinary least squares regression

Description

Provides SPSS- and SAS-like output for ordinary least squares simultaneous entry regression and hierarchical entry regression. The output includes the Anova Table (Type III tests), standardized coefficients, partial and semi-partial correlations, collinearity statistics, casewise regression diagnostics, plots of residuals and regression diagnostics. The output includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo (MCMC) analyses.

Usage

OLS_REGRESSION(data, DV, forced=NULL, hierarchical=NULL, 
               COVARS=NULL,
               plot_type = 'residuals', 
               CI_level = 95,
               MCMC = FALSE,
               Nsamples = 10000,
               verbose=TRUE, ...)

Arguments

Details

This function uses the lm function from the stats package, supplements the output with additional statistics, and it formats the output so that it resembles SPSS and SAS regression output. The predictor variables can be numeric or factors.

The Bayesian MCMC analyses can be time-consuming for larger datasets. The MCMC analyses are conducted using functions, and their default settings, from the BayesFactor package (Morey & Rouder, 2024). The MCMC results can be verified using the model checking functions in the rstanarm package (e.g., Muth, Oravecz, & Gabry, 2018).

Good sources for interpreting residuals and diagnostics plots:

• library.virginia.edu

• sthda.com

Value

An object of class "OLS_REGRESSION". The object is a list containing the following possible components:

Author(s)

Brian P. O'Connor

References

Bodner, T. E. (2016). Tumble graphs: Avoiding misleading end point extrapolation when graphing interactions from a moderated multiple regression analysis. Journal of Educational and Behavioral Statistics, 41, 593-604.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. Guilford Press.

Hayes, A. F. (2018a). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.

Hayes, A. F., & Montoya, A. K. (2016). A tutorial on testing, visualizing, and probing an interaction involving a multicategorical variable in linear regression analysis. Communication Methods and Measures, 11, 1-30.

Lee M. D., & Wagenmakers, E. J. (2014) Bayesian cognitive modeling: A practical course. Cambridge University Press.

Morey, R. & Rouder, J. (2024). BayesFactor: Computation of Bayes Factors for Common Designs. R package version 0.9.12-4.7, https://github.com/richarddmorey/bayesfactor.

Muth, C., Oravecz, Z., & Gabry, J. (2018). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology, 14(2), 99119.
https://doi.org/10.20982/tqmp.14.2.p099

O'Connor, B. P. (1998). All-in-one programs for exploring interactions in moderated multiple regression. Educational and Psychological Measurement, 58, 833-837.

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. (3rd ed.). Wadsworth Thomson Learning.

Examples

# forced (simultaneous) entry
head(data_Green_Salkind_2014)
OLS_REGRESSION(data=data_Green_Salkind_2014, DV='injury', 
               forced = c('quads','gluts','abdoms','arms','grip'))

# hierarchical entry
OLS_REGRESSION(data=data_Green_Salkind_2014, DV='injury', 
               hierarchical = list( step1=c('quads','gluts','abdoms'), 
                                    step2=c('arms','grip')) )

Partial and semipartial correlations

Description

Produces partial correlations between two or more variables (in set Y) while statistically controlling for one or more covariates (set C). It also produces partial correlations, semipartial correlations, and standardized regression coefficients for predicting variables (in set Y) from one or more set X variables.

Usage

PARTIAL_COR(data, Y, X=NULL, C=NULL, Ncases=NULL, verbose=TRUE)

Arguments

Details

Y must be provided along with either one, or both, of C and X. Y, C, and X can be the names of single variables or of multiple variables.

Value

A list containing the correlations, standardized regression coefficients (betas), partial correlations, semi-partial correlations, t-test values, and p values.

Author(s)

Brian P. O'Connor

References

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

Examples

PARTIAL_COR(data = data_DeLeo_2013, 
            Y = c('Problematic_Internet_Use','Tobacco_Use','Alcohol_Use','Illicit_Drug_Use'), 
            C = c('Age','Parents_Income'), 
            X = NULL)

PARTIAL_COR(data = data_DeLeo_2013, 
            Y = c('Problematic_Internet_Use','Tobacco_Use','Alcohol_Use','Illicit_Drug_Use'), 
            C = NULL, 
            X = c('Impulsivity','Social_Interaction_Anxiety',
                  'Social_Support','Intolerance_of_Deviance','Family_Morals',
                  'Grade_Point_Average','Depression','Family_Conflict'))

Plots predicted values for a regression model

Description

Plots predicted values of the outcome variable for specified levels of predictor variables for OLS_REGRESSION, MODERATED_REGRESSION, LOGISTIC_REGRESSION, and COUNT_REGRESSION models from this package.

Usage

PLOT_MODEL(model, 
           IV_focal_1, IV_focal_1_values=NULL, 
           IV_focal_2=NULL, IV_focal_2_values=NULL, 
           IVs_nonfocal_values = NULL,
           bootstrap=FALSE, N_sims=100, CI_level=95, 
           xlim=NULL, xlab=NULL,
           ylim=NULL, ylab=NULL,
           title = NULL,
           plot_save = FALSE, plot_save_type = 'png',
           cols_user = NULL,
           verbose=TRUE)

Arguments

Details

Plots predicted values of the outcome variable for specified levels of predictor variables for OLS_REGRESSION, MODERATED_REGRESSION, LOGISTIC_REGRESSION, and COUNT_REGRESSION models from this package.

A plot with both IV_focal_1 and IV_focal_2 predictor variables will look like an interaction plot. But it is only a true interaction plot if the required product term(s) was entered as a predictor when the model was created.

Value

A matrix with the levels of the variables that were used for the plot along with the predicted values, confidence intervals, and se.fit values.

Author(s)

Brian P. O'Connor

Examples

ols_GS <- 
OLS_REGRESSION(data=data_Green_Salkind_2014, DV='injury', 
               hierarchical = list( step1=c('age','quads','gluts','abdoms'), 
                                    step2=c('arms','grip')) )

PLOT_MODEL(model = ols_GS, 
           IV_focal_1 = 'gluts', IV_focal_1_values=NULL,
           IV_focal_2 = 'age', IV_focal_2_values=NULL, 
           IVs_nonfocal_values = NULL,
           bootstrap=TRUE, N_sims=100, CI_level=95, 
           ylim=NULL, ylab=NULL, title=NULL,
           verbose=TRUE) 
	
ols_LW <- 
MODERATED_REGRESSION(data=data_Lorah_Wong_2018, DV='suicidal', IV='burden', MOD='belong_thwarted', 
                     IV_range='tumble',
                     MOD_levels='quantiles',
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     COVARS='depression', 
                     plot_type = 'interaction', DV_range = c(1,1.25)) 
                     
PLOT_MODEL(model = ols_LW, 
           IV_focal_1 = 'burden', IV_focal_1_values=NULL,
           IV_focal_2 = 'belong_thwarted', IV_focal_2_values=NULL, 
           bootstrap=TRUE, N_sims=100, CI_level=95) 
                     
logmod_Meyers <- 
  LOGISTIC_REGRESSION(data = data_Meyers_2013, DV='graduated', 
                      forced = c('sex','family_encouragement')) 

PLOT_MODEL(model = logmod_Meyers, 
           IV_focal_1 = 'family_encouragement', IV_focal_1_values=NULL,
           IV_focal_2=NULL, IV_focal_2_values=NULL, 
           bootstrap=FALSE, N_sims=100, CI_level=95) 
           
pois_Krem <-
  COUNT_REGRESSION(data=data_Kremelburg_2011, DV='OVRJOYED', forced=NULL, 
                   hierarchical= list( step1=c('AGE', 'SEX_factor'), 
                                       step2=c('EDUC','REALRINC','DEGREE')) )

PLOT_MODEL(model = pois_Krem, 
           IV_focal_1 = 'AGE', 
           IV_focal_2 = 'DEGREE',
           IVs_nonfocal_values = list( EDUC = 5, SEX_factor = '2'),
           bootstrap=FALSE, N_sims=100, CI_level=95) 
           

Plots of Johnson-Neyman regions of significance for interactions

Description

Plots of Johnson-Neyman regions of significance for interactions in moderated multiple regression, for both MODERATED_REGRESSION models (which are produced by this package) and for lme models (from the nlme package).

Usage

REGIONS_OF_SIGNIFICANCE(model,
                        IV_range=NULL, MOD_range=NULL,
                        plot_title=NULL, Xaxis_label=NULL,
                        Yaxis_label=NULL, legend_label=NULL,
                        names_IV_MOD=NULL)

Arguments

Value

A list with the following possible components:

Author(s)

Brian P. O'Connor

References

Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400.

Huitema, B. (2011). The analysis of covariance and alternatives: Statistical methods for experiments, quasi-experiments, and single-case studies. John Wiley & Sons.

Johnson, P. O., & Neyman, J. (1936). Tests of certain linear hypotheses and their application to some educational problems. Statistical Research Memoirs, 1, 57-93.

Johnson, P. O., & Fey, L. C. (1950). The Johnson-Neyman technique, its theory, and application. Psychometrika, 15, 349-367.

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. (3rd ed.). Wadsworth Thomson Learning

Rast, P., Rush, J., Piccinin, A. M., & Hofer, S. M. (2014). The identification of regions of significance in the effect of multimorbidity on depressive symptoms using longitudinal data: An application of the Johnson-Neyman technique. Gerontology, 60, 274-281.

Examples

head(data_Cohen_Aiken_West_2003_7)

CAW_7 <- 
MODERATED_REGRESSION(data=data_Cohen_Aiken_West_2003_7, DV='yendu',
                     IV='xage',IV_range='tumble',
                     MOD='zexer', MOD_levels='quantiles', 
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     plot_type = 'interaction') 

REGIONS_OF_SIGNIFICANCE(model=CAW_7) 

head(data_Bauer_Curran_2005)

HSBmod <-nlme::lme(MathAch ~ Sector + CSES + CSES:Sector,
                   data = data_Bauer_Curran_2005, 
                   random = ~1 + CSES|School, method = "ML") 
summary(HSBmod)

REGIONS_OF_SIGNIFICANCE(model=HSBmod,  
                        plot_title='Johnson-Neyman Regions of Significance', 
                        Xaxis_label='Child SES',
                        Yaxis_label='Slopes of School Sector on Math achievement')  
                        

# moderated regression	-- with numeric values for IV_range & MOD_levels='AikenWest'       
mharsh_agg <- 
  MODERATED_REGRESSION(data=data_OConnor_Dvorak_2001, DV='Aggressive_Behavior',
                       IV='Maternal_Harshness', IV_range=c(1,7.7), 
                       MOD='Resiliency', MOD_levels='AikenWest', 
                       quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                       center = FALSE, 
                       plot_type = 'interaction', 
                       DV_range = c(1,6), 
                       Xaxis_label='Maternal Harshness', 
                       Yaxis_label='Adolescent Aggressive Behavior', 
                       legend_label='Resiliency') 

REGIONS_OF_SIGNIFICANCE(model=mharsh_agg,  
                        plot_title='Johnson-Neyman Regions of Significance', 
                        Xaxis_label='Resiliency', 
                        Yaxis_label='Slopes of Maternal Harshness on Aggressive Behavior') 

Cohen's Set Correlation Analysis

Description

Performs Cohen's set correlation analysis of associations between two sets of variables while statistically controlling for one or more other variables. Estimates of overall, multivariate association between the two sets of variables are provided, along with partial correlations and output from OLS regression analyses for each dependent variable.

Usage

SET_CORRELATION(data, IVs, DVs, IV_covars=NULL, DV_covars=NULL,
                Ncases=NULL, verbose=TRUE, display_cormats=FALSE)

Arguments

Details

Set correlation analysis and canonical correlation analysis are both fully multivariate methods for examining associations between two sets of variables. However, in CCA the focus is on linear combinations of predictor and criterion variables, which are often difficult to interpret. In contrast, in set correlation analysis the focus is typically on the associations between two sets of variables while statistically controlling for other variables (rather than on linear combinations). The outcome variables of interest in set correlation analysis are the (possibly partialled) dependent variables themselves and not composites of variables.

A key feature of set correlation analysis is the option of examining the overlap between two sets of variables while statistically controlling for one or more other variables. The covariates that are removed from one set of variables (e.g., the DVs) may or may not be the same covariates that are removed from the other set of variables (e.g., the IVs).

In the present function, when there is a wish to statistically remove the same covariates from both sets (i.e., from both the IVs and DVs), then simply enter the same covariate names on both the IV_covars and DV_covars arguments.

The options together result in five different types of data scenarios that can be examined:

Whole, in which the associations between two sets (IVs and DVs) are assessed without any partialling out whatsoever;

Partial, in which the associations between two sets (IVs and DVs) are assessed while partialling the same covariates (one or more) out of both the IVs and DVs;

X Semipartial, in which the associations between two sets (IVs and DVs) are assessed while partialling one or more covariates out of the IV set while leaving the variables in the DV set untouched (unpartialled);

Y Semipartial, in which the associations between two sets (IVs and DVs) are assessed while partialling one or more covariates out of the DV set while leaving the variables in the IV set untouched (unpartialled); and

Bipartial, in which the associations between two sets (IVs and DVs) are assessed while partialling one or more covariates out of the DV set and while partialling one or more other (different) covariates out of the IV set.

The set correlation analyses in this function are conducted using only the correlations between the variables. When raw data are entered into the function, the variable correlation matrix is computed and becomes the sole basis of all further set correlation analyses.

Value

An object of class "SET_CORRELATION". The object is a list containing the following components:

Author(s)

Brian P. O'Connor

References

Cohen, J. (1982). Set correlation as a general multivariate data-analytic method. Multivariate Behavioral Research, 17(3), 301-341.

Cohen, J. (1988). Set correlation and multivariate Methods. In J. Cohen, Statistical power analysis for the behavioral sciences (2nd ed., pp. 467-530). Mahwah, NJ: Erlbaum.

Cohen, J. (1993). Set correlation. In G. Keren & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Statistical issues (pp. 165-198). Mahwah, NJ: Erlbaum.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Multiple dependent variables: Set correlation. In, Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed., pp. 608-628). Lawrence Erlbaum Associates.

Examples

# data from Cohen et al. (2003)
Cohen_2003_p621 <- '
 1.0
 .53  1.0  
 .62  .61  1.0
 .19  .23  .03  1.0
-.09  .10  .10 -.02  1.0
 .08  .18  .12  .02  .05  1.0
 .02  .02  .03  .00  .06  .22  1.0
-.12 -.10 -.06 -.02  .18 -.07 -.01  1.0
 .08  .15  .12 -.02  .02  .36 -.05 -.03  1.0'

Cohen_2003_p621_noms <- c('ADHD', 'CD', 'ODD', 'Sex', 'Age', 'MONLY', 
                          'MWORK', 'MAGE', 'Poverty')

Cohen_2003_p621 <- data.matrix( read.table(text=Cohen_2003_p621, fill=TRUE, 
                                           col.names=Cohen_2003_p621_noms,
                                           row.names=Cohen_2003_p621_noms ))
Cohen_2003_p621[upper.tri(Cohen_2003_p621)] <- 
  t(Cohen_2003_p621)[upper.tri(Cohen_2003_p621)]

# whole
SET_CORRELATION(data=Cohen_2003_p621, 
                IVs = c('Sex', 'Age', 'MONLY', 'MWORK', 'MAGE', 'Poverty'), 
                DVs = c('ADHD', 'CD', 'ODD'), 
                IV_covars = NULL, 
                DV_covars = NULL,
                Ncases = 701) 

# bipartial
SET_CORRELATION(data=data_DeLeo_2013, 
                IVs = c('Grade_Point_Average','Family_Morals','Social_Support',
                        'Intolerance_of_Deviance','Impulsivity','Social_Interaction_Anxiety'), 
                DVs = c('Problematic_Internet_Use','Tobacco_Use','Alcohol_Use','Illicit_Drug_Use'), 
                IV_covars = c('Age','Parents_Income'), 
                DV_covars = c('Gambling_Behavior','Unprotected_Sex'),
                display_cormats=TRUE) 

# X semipartial
SET_CORRELATION(data=data_DeLeo_2013, 
                IVs = c('Grade_Point_Average','Family_Morals','Social_Support',
                        'Intolerance_of_Deviance','Impulsivity','Social_Interaction_Anxiety'), 
                DVs = c('Problematic_Internet_Use','Tobacco_Use','Alcohol_Use','Illicit_Drug_Use'), 
                IV_covars = c('Age','Parents_Income'), 
                DV_covars = NULL) 

# partial
SET_CORRELATION(data=data_DeLeo_2013, 
                IVs = c('Grade_Point_Average','Family_Morals','Social_Support',
                        'Intolerance_of_Deviance','Impulsivity','Social_Interaction_Anxiety'), 
                DVs = c('Problematic_Internet_Use','Tobacco_Use','Alcohol_Use','Illicit_Drug_Use'), 
                IV_covars = c('Age','Parents_Income'), 
                DV_covars = c('Age','Parents_Income')) 

data_Bauer_Curran_2005

Description

Multilevel moderated regression data from Bauer and Curran (2005).

Usage

data(data_Bauer_Curran_2005)

Source

Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400.

Examples

head(data_Bauer_Curran_2005)

HSBmod <-nlme::lme(MathAch ~ Sector + CSES + CSES:Sector,
                   data = data_Bauer_Curran_2005, 
                   random = ~1 + CSES|School, method = "ML") 
summary(HSBmod)

REGIONS_OF_SIGNIFICANCE(model=HSBmod,  
                        plot_title='Johnson-Neyman Regions of Significance', 
                        Xaxis_label='Child SES',
                        Yaxis_label='Slopes of School Sector on Math achievement')  

data_Bodner_2016

Description

Moderated regression data used by Bodner (2016) to illustrate the tumble graphs method of plotting interactions. The data were also used by Bauer and Curran (2005).

Usage

data(data_Bodner_2016)

Source

Bodner, T. E. (2016). Tumble Graphs: Avoiding misleading end point extrapolation when graphing interactions from a moderated multiple regression analysis. Journal of Educational and Behavioral Statistics, 41(6), 593-604.

Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400.

Examples

head(data_Bodner_2016)

# replicates p 599 of Bodner (2016)
MODERATED_REGRESSION(data=data_Bodner_2016, DV='math90',
                     IV='Anti90', IV_range='tumble',
                     MOD='Hyper90', MOD_levels='quantiles', 
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     COVARS=c('age90month','female','grade90','minority'),
                     center = FALSE, 
                     plot_type = 'interaction')	

data_Chapman_Little_2016

Description

Moderated regression data from Chapman and Little (2016).

Usage

data(data_Chapman_Little_2016)

Source

Chapman, D. A., & Little, B. (2016). Climate change and disasters: How framing affects justifications for giving or withholding aid to disaster victims. Social Psychological and Personality Science, 7, 13-20.

Examples

head(data_Chapman_Little_2016)
 
# the data used by Hayes (2018, Introduction to Mediation, Moderation, and 
# Conditional Process Analysis: A Regression-Based Approach), replicating p. 239
MODERATED_REGRESSION(data=data_Chapman_Little_2016, DV='justify',
                     IV='frame', IV_range='tumble',
                     MOD='skeptic', MOD_levels='AikenWest', 
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     center = FALSE, 
                     plot_type = 'regions') 

data_Cohen_Aiken_West_2003_7

Description

Moderated regression data for a continuous predictor and a continuous moderator from Cohen, Cohen, West, & Aiken (2003, Chapter 7).

Usage

data(data_Cohen_Aiken_West_2003_7)

Source

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

Examples

head(data_Cohen_Aiken_West_2003_7)

# replicates p 276 of Chapter 7 of Cohen, Cohen, West, & Aiken (2003)
MODERATED_REGRESSION(data=data_Cohen_Aiken_West_2003_7, DV='yendu',
                     IV='xage', IV_range='tumble',
                     MOD='zexer', MOD_levels='AikenWest', 
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     center = TRUE, 
                     plot_type = 'regions') 

data_Cohen_Aiken_West_2003_9

Description

Moderated regression data for a continuous predictor and a categorical moderator from Cohen, Cohen, West, & Aiken (2003, Chapter 9).

Usage

data(data_Cohen_Aiken_West_2003_9)

Source

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

Examples

head(data_Cohen_Aiken_West_2003_9)

# replicates p 376 of Chapter 9 of Cohen, Cohen, West, & Aiken (2003)
MODERATED_REGRESSION(data=data_Cohen_Aiken_West_2003_9, DV='SALARY',
                     IV='PUB', IV_range='tumble',
                     MOD='DEPART_f', MOD_type = 'factor', MOD_levels='AikenWest', 
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     center = TRUE,  
                     plot_type = 'regions') 

data_DeLeo_2013

Description

A dataset with multiple continuous variables that simulate the data from De Leo and Wulfert (2013). The dataset is used in the examples for the present PARTIAL_COR and SET_CORRELATION functions.

Usage

data(data_DeLeo_2013)

Source

De Leo, J. A., & Wulfert, E. (2013). Problematic internet use and other risky behaviors in college students: An application of problem-behavior theory. Psychology of Addictive Behaviors, 27(1), 133-141.

Examples

head(data_DeLeo_2013)

# bipartial
SET_CORRELATION(data=data_DeLeo_2013, 
                IVs = c('Grade_Point_Average','Family_Morals','Social_Support',
                        'Intolerance_of_Deviance','Impulsivity','Social_Interaction_Anxiety'), 
                DVs = c('Problematic_Internet_Use','Tobacco_Use','Alcohol_Use','Illicit_Drug_Use'), 
                IV_covars = c('Age','Parents_Income'), 
                DV_covars = c('Gambling_Behavior','Unprotected_Sex'),
                display_cormats=TRUE) 

data_Green_Salkind_2014

Description

Mutiple regression data from Green and Salkind (2018).

Usage

data(data_Green_Salkind_2014)

Source

Green, S. B., & Salkind, N. J. (2014). Lesson 34: Multiple linear regression (pp. 257-269). In, Using SPSS for Windows and Macintosh: Analyzing and understanding data. New York, NY: Pearson.

Examples

head(data_Green_Salkind_2014)

# forced (simultaneous) entry; replicating the output on p. 263	
OLS_REGRESSION(data=data_Green_Salkind_2014, DV='injury', 
               forced=c('quads','gluts','abdoms','arms','grip')) 

# hierarchical entry; replicating the output on p. 265-266	
OLS_REGRESSION(data=data_Green_Salkind_2014, DV='injury', 
               hierarchical = list( step1=c('quads','gluts','abdoms'), 
                                    step2=c('arms','grip')) )

data_Halvorson_2022_log

Description

Logistic regression data from Halvorson et al. (2022, p. 291).

Usage

data(data_Halvorson_2022_log)

Source

Halvorson, M. A., McCabe, C. J., Kim, D. S., Cao, X., & King, K. M. (2022). Making sense of some odd ratios: A tutorial and improvements to present practices in reporting and visualizing quantities of interest for binary and count outcome models. Psychology of Addictive Behaviors, 36(3), 284-295.

Examples

head(data_Halvorson_2022_log)

log_Halvorson <-
  LOGISTIC_REGRESSION(data=data_Halvorson_2022_log, DV='Y', forced=c('x1','x2'), 
                      plot_type = 'diagnostics')

# high & low values for x2
x2_high <- mean(data_Halvorson_2022_log$x1) + sd(data_Halvorson_2022_log$x1)
x2_low  <- mean(data_Halvorson_2022_log$x1) - sd(data_Halvorson_2022_log$x1)

PLOT_MODEL(model = log_Halvorson, 
           IV_focal_1 = 'x1',   
           IV_focal_2 = 'x2',  IV_focal_2_values = c(x2_low, x2_high),
           bootstrap=FALSE, N_sims=1000, CI_level=95, 
           ylim = c(0, 1), 
           xlab = 'x1',
           ylab = 'Expected Probability', 
           title = 'Probability of Y by x1 and x2 for Simulated Data Example') 
 

data_Halvorson_2022_pois

Description

Poisson regression data from Halvorson et al. (2022, p. 293).

Usage

data(data_Halvorson_2022_pois)

Source

Halvorson, M. A., McCabe, C. J., Kim, D. S., Cao, X., & King, K. M. (2022). Making sense of some odd ratios: A tutorial and improvements to present practices in reporting and visualizing quantities of interest for binary and count outcome models. Psychology of Addictive Behaviors, 36(3), 284-295.

Examples

head(data_Halvorson_2022_pois)

# replicating Table 3, p 293
pois_Halvorson <-
  COUNT_REGRESSION(data=data_Halvorson_2022_pois, DV='Neg_OH_conseqs', 
                   forced=c('Gender_factor','Positive_urgency','Planning',
                            'Sensation_seeking'), 
                   plot_type = 'diagnostics')

# replicating Figure 4, p 294
PLOT_MODEL(model = pois_Halvorson, 
           IV_focal_1 = 'Positive_urgency',   
           IV_focal_2 = 'Gender_factor',
           bootstrap=FALSE, N_sims=1000, CI_level=95, 
           ylim = c(0, 20), 
           xlab = 'Positive Urgency',
           ylab = 'Expected Count of Alcohol Consequences', 
           title = 'Expected Count of Alcohol Consequences 
                    by Positive Urgency and Gender') 

data_Huitema_2011

Description

Moderated regression data for a continuous predictor and a dichotomous moderator from Huitema (2011, p. 253).

Usage

data(data_Huitema_2011)

Source

Huitema, B. (2011). The analysis of covariance and alternatives: Statistical methods for experiments, quasi-experiments, and single-case studies. Hoboken, NJ: Wiley.

Examples

head(data_Huitema_2011)

# replicating results on p. 255 for the Johnson-Neyman technique for a categorical moderator
MODERATED_REGRESSION(data=data_Huitema_2011, DV='Y', 
                     IV='X', IV_range='tumble',
                     MOD='D', MOD_type = 'factor',  
                     center = FALSE,  
                     plot_type = 'interaction',
                     JN_type = 'Huitema') 

data_Kremelburg_2011

Description

Logistic and Poisson regression data from Kremelburg (2011).

Usage

data(data_Kremelburg_2011)

Source

Kremelburg, D. (2011). Chapter 6: Logistic, ordered, multinomial, negative binomial, and Poisson regression. Practical statistics: A quick and easy guide to IBM SPSS Statistics, STATA, and other statistical software. Sage.

Examples

head(data_Kremelburg_2011)

LOGISTIC_REGRESSION(data = data_Kremelburg_2011, DV='OCCTRAIN',
                    hierarchical=list( step1=c('AGE'), step2=c('EDUC','REALRINC')) )
         
COUNT_REGRESSION(data=data_Kremelburg_2011, DV='OVRJOYED', 
                 forced=c('AGE','EDUC','REALRINC','SEX_factor'))

data_Lorah_Wong_2018

Description

Moderated regression data from Lorah and Wong (2018).

Usage

data(data_Lorah_Wong_2018)

Source

Lorah, J. A. & Wong, Y. J. (2018). Contemporary applications of moderation analysis in counseling psychology. Journal of Counseling Psychology, 65(5), 629-640.

Examples

head(data_Lorah_Wong_2018)

model_Lorah <- 
MODERATED_REGRESSION(data=data_Lorah_Wong_2018, DV='suicidal',
                     IV='burden', IV_range='tumble',
                     MOD='belong_thwarted', MOD_levels='quantiles',
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     COVARS='depression', center = TRUE, 
                     plot_type = 'regions') 
       
REGIONS_OF_SIGNIFICANCE(model=model_Lorah,  
                        plot_title='Johnson-Neyman Regions of Significance', 
                        Xaxis_label='Thwarted Belongingness', 
                        Yaxis_label='Slopes of Burdensomeness on Suicical Ideation', 
                        legend_label=NULL)        

data_Meyers_2013

Description

Logistic regression data from Myers et al. (2013).

Usage

data(data_Meyers_2013)

Source

Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Chapter 30: Binary logistic regression. Performing data analysis using IBM SPSS. Hoboken, NJ: Wiley.

Examples

head(data_Meyers_2013)

LOGISTIC_REGRESSION(data= data_Meyers_2013, DV='graduated', forced= c('sex','family_encouragement'))

data_OConnor_Dvorak_2001

Description

Moderated regression data from O'Connor and Dvorak (2001)

Details

A data frame with scores for 131 male adolescents on resiliency, maternal harshness, and aggressive behavior. The data are from O'Connor and Dvorak (2001, p. 17) and are provided as trial moderated regression data for the MODERATED_REGRESSION and REGIONS_OF_SIGNIFICANCE functions.

References

O'Connor, B. P., & Dvorak, T. (2001). Conditional associations between parental behavior and adolescent problems: A search for personality-environment interactions. Journal of Research in Personality, 35, 1-26.

Examples

head(data_OConnor_Dvorak_2001)

mharsh_agg <- 
  MODERATED_REGRESSION(data=data_OConnor_Dvorak_2001, DV='Aggressive_Behavior',
                       IV='Maternal_Harshness', IV_range=c(1,7.7), 
                       MOD='Resiliency',MOD_levels='AikenWest', 
                       quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                       center = FALSE,  
                       plot_type = 'interaction', 
                       DV_range = c(1,6), 
                       Xaxis_label='Maternal Harshness', 
                       Yaxis_label='Adolescent Aggressive Behavior', 
                       legend_label='Resiliency') 

REGIONS_OF_SIGNIFICANCE(model=mharsh_agg,  
           plot_title='Slopes of Maternal Harshness on Aggression by Resiliency', 
           Xaxis_label='Resiliency', 
           Yaxis_label='Slopes of Maternal Harshness on Aggressive Behavior ') 

data_Orme_2009_2

Description

Logistic regression data from Orme and Combs-Orme (2009), Chapter 2.

Usage

data(data_Orme_2009_2)

Source

Orme, J. G., & Combs-Orme, T. (2009). Multiple Regression With Discrete Dependent Variables. Oxford University Press.

Examples

LOGISTIC_REGRESSION(data = data_Orme_2009_2, DV='ContinueFostering', 
                    forced= c('zResources', 'Married'))

data_Orme_2009_5

Description

Data for count regression from Orme and Combs-Orme (2009), Chapter 5.

Usage

data(data_Orme_2009_5)

Source

Orme, J. G., & Combs-Orme, T. (2009). Multiple Regression With Discrete Dependent Variables. Oxford University Press.

Examples

COUNT_REGRESSION(data=data_Orme_2009_5, DV='NumberAdopted', forced=c('Married','zParentRole'))

data_Pedhazur_1997

Description

Moderated regression data for a continuous predictor and a dichotomous moderator from Pedhazur (1997, p. 588).

Usage

data(data_Pedhazur_1997)

Source

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. (3rd ed.). Fort Worth, Texas: Wadsworth Thomson Learning.

Examples

head(data_Pedhazur_1997)

# replicating results on p. 594 for the Johnson-Neyman technique for a categorical moderator	
MODERATED_REGRESSION(data=data_Pedhazur_1997, DV='Y', 
                     IV='X', IV_range='tumble',
                     MOD='Directive', MOD_type = 'factor', MOD_levels='quantiles', 
                     quantiles_IV=c(.1, .9), quantiles_MOD=c(.25, .5, .75),
                     center = FALSE, 
                     plot_type = 'interaction', 
                     JN_type = 'Pedhazur') 

data_Pituch_Stevens_2016

Description

Logistic regression data from Pituch and Stevens (2016), Chapter 11.

Usage

data(data_Pituch_Stevens_2016)

Source

Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences : Analyses with SAS and IBMs SPSS, (6th ed.). Routledge.

Examples

LOGISTIC_REGRESSION(data = data_Pituch_Stevens_2016, DV='Health', 
                    forced= c('Treatment','Motivation'))