predict_anger_experience_expression.Rmd

---
title: "Predicting Anger Experience and Expression"
author: "Pooya Razavi"
date: "last knitted: `r Sys.time()`"
output: 
  html_document:
    theme: cosmo
    highlight: textmate
    toc: TRUE
    toc_float: TRUE
editor_options: 
  chunk_output_type: console
---

This script represents the test of the research questions about the relation between different predictors and two outcomes: (a) the intensity of anger experience; (b) the intensity of anger expression. 


```{r setup, include=FALSE, warning=FALSE}
#load libraries
package_list <- c("dplyr", "tidyr", "ggplot2", "MetBrewer", "lmerTest")
lapply(package_list, require, character.only = TRUE)

#read in the data
df <- readxl::read_xlsx("C:/Users/pooya/Dropbox (University of Oregon)/Anger Dissertation/Prototype study analysis/ProcessedData_F21_W22_S22_F22.xlsx")

#Function to report correlation
cor_report <- function(cor_output) {
          r <- cor_output[["estimate"]] %>% round(2)
          df <- cor_output[["parameter"]] %>% round(1)
          ci_lb <- (cor_output[["conf.int"]])[1] %>% round(2)
          ci_ub <- (cor_output[["conf.int"]])[2] %>% round(2)
          original_p <- cor_output[["p.value"]] %>% round(3)
          p <- if_else(original_p >= .001, paste0("= ", as.character(original_p)), "< .001")
          
          print(paste0("r(", df, ") = ", r, " [", ci_lb, ", ", ci_ub, "], p ", p))
    }

#Function to report independent-samples t-test
  ind_ttest_report <- function(iv, dv) {
      ttest <- t.test(dv ~ iv)
      effect_size <- effectsize::cohens_d(dv ~ iv, pooled_sd = FALSE)
      t <- ttest[["statistic"]] %>% round(2)
      df <- ttest[["parameter"]] %>% round(1)
      original_p <- ttest[["p.value"]] %>% round(3)
      p <- if_else(original_p >= .001, paste0("= ", as.character(original_p)), "< .001")
      d <- effect_size[1,1] %>% round(2)    
      
      print(paste0("t(", df, ") = ", t, ", p ", p, ", d = ", d))
      
  }
  
#Function to report paired-samples t-test
    paired_ttest_report <- function(t1, t2) {
      ttest <- t.test(Pair(t1, t2) ~ 1)
      effect_size <- effectsize::cohens_d(Pair(t1, t2) ~ 1, pooled_sd = FALSE)
      t <- ttest[["statistic"]] %>% round(2)
      df <- ttest[["parameter"]] %>% round(1)
      original_p <- ttest[["p.value"]] %>% round(3)
      p <- if_else(original_p >= .001, paste0("= ", as.character(original_p)), "< .001")
      d <- effect_size[1,1] %>% round(2)    
      
      print(paste0("t(", df, ") = ", t, ", p ", p, ", d = ", d))
      
  }
  
#Function to calculate percentages for each category of a Factor variable
    percentage <- function(var, includeNA = TRUE) {
          tabb <- table(var) %>% as.data.frame()
              if (includeNA == TRUE) {
                   tabb$percentage <- (tabb$Freq * 100 / length(var))
              } else {
                    tabb$percentage <- (tabb$Freq * 100 / sum(tabb$Freq))
              }
          colnames(tabb)[1] <- c("category")
          print(tabb)
    }

#Function to report multiple correlation
    reg_report <- function(reg_output) {
              (summary(reg_output)$coefficients) %>% 
                cbind(confint(reg_output)) %>% 
                as.data.frame() %>% 
                    tibble::rownames_to_column("Predictor") %>% 
                    mutate(b = round(Estimate, 2),
                           t = round(`t value`, 2),
                           p = if_else(`Pr(>|t|)` >= .001, as.character(round(`Pr(>|t|)`, 3)), "< .001"),
                           CI = paste0("[", round(`2.5 %`, 2), ",", round(`97.5 %`, 2), "]")) %>% 
          select(Predictor, b, CI, t, p) %>% 
        print()
    }

#turn off scientific notation
    options(scipen=999)
        
knitr::opts_chunk$set(echo = TRUE)
```


```{r, data-exclusion}
#assigning values to factor levels
  df$NarrativeWritten <- as.factor(df$NarrativeWritten)
  df$NarrativeRelevant <- as.factor(df$NarrativeRelevant)
  df$Condition <- as.factor(df$Condition)
  
  levels(df$NarrativeWritten) <- c("No", "Yes")
  levels(df$NarrativeRelevant) <- c("No", "Yes", NA, NA) 
  levels(df$Condition) <- c("justified", "nonjustified", NA)

#drop cases following preregistration
  df1 <- df %>% 
    filter(NarrativeWritten != "No") %>% 
    filter(NarrativeRelevant != "No") %>% 
    filter(!is.na(Condition))

```

# Predicting Anger Experience

## Incremental Association Beyond Harm/Threat

**Research Question 1a:** What is the association between various characteristics of the anger eliciting event/person (i.e., perception of fairness/justification, perception of target’s regret, perception of target's tendency to apologize, causal attributions, norm violations, relationship closeness prior to the event) and participants’ anger experience over and above perception of harm/threat (to self and to others)?

```{r, increment-feel}
#before creating composite scores, test the correlation between items. If r > .5, proceed with creating a composite score; otherwise, each item will be entered as a predictor in the following models.

  #Fairness/justification
    cor.test(df1$beh_fair, df1$beh_justified) %>% cor_report()
  #Harm/threat to self
    cor.test(df1$harm_you, df1$threat_you) %>% cor_report()
  #Harm/threat to other
    cor.test(df1$harm_others, df1$threat_others) %>% cor_report()
  #Causal attribution
    cor.test(df1$cause_circumst, df1$behave_same) %>% cor_report() #this one does not meet the r > .5 assumption.


#Create composite scores for the three constructs to which the corresponding items have high correlations (i.e., above r > .5): 
df1 <- df1 %>% 
          mutate(fair_just = ((beh_fair + beh_justified) / 2),
                 harm.threat_self = ((harm_you + threat_you) / 2),
                  harm.threat_other = ((harm_others + threat_others) / 2)) 

  #The two items related to causal attribution will be entered into the models individually (instead of being entered as a composite score).

  #reverse-scoring the norm items
  df1 <- df1 %>% 
              mutate(injunctive = 6 - how_acceptable,
                      descriptive = 6 - how_common)

#A base model predicting anger experience from perceptions of harm/threat
      experience_base_model <- lm(anger_feel ~ harm.threat_self + harm.threat_other,
                     data = df1)
      summary(experience_base_model)
      confint(experience_base_model) %>% round(2)
      
#Testing the incremental associations (above and beyond harm/threat) for each of the other predictors separately:
      #fairness/justification
      experience_inc_model_fairness <- lm(anger_feel ~ harm.threat_self + harm.threat_other + fair_just,
                     data = df1)
      summary(experience_inc_model_fairness)
      
      #perception of target's regret
      experience_inc_model_regret <- lm(anger_feel ~ harm.threat_self + harm.threat_other + person_regret,
                     data = df1)
      summary(experience_inc_model_regret)
      
      #perception of target's tendency to apologize
      experience_inc_model_apology <- lm(anger_feel ~ harm.threat_self + harm.threat_other + person_apologize,
                     data = df1)
      summary(experience_inc_model_apology)
      
      #perception that target's behavior had an internal cause
      experience_inc_model_circumst <- lm(anger_feel ~ harm.threat_self + harm.threat_other + cause_circumst,
                     data = df1)
      summary(experience_inc_model_circumst)
      
      #perception that target would behave the same (i.e., stable characteristic)
      experience_inc_model_stable <- lm(anger_feel ~ harm.threat_self + harm.threat_other + behave_same,
                     data = df1)
      summary(experience_inc_model_stable)

      #behavior violated injunctive norms
      experience_inc_model_injunctive <- lm(anger_feel ~ harm.threat_self + harm.threat_other + injunctive,
                     data = df1)
      summary(experience_inc_model_injunctive)      

      #behavior violated descriptive norms
      experience_inc_model_descriptive <- lm(anger_feel ~ harm.threat_self + harm.threat_other + descriptive,
                     data = df1)
      summary(experience_inc_model_descriptive) 

      #relationship closeness prior to the event
      experience_inc_model_closeness <- lm(anger_feel ~ harm.threat_self + harm.threat_other + prior_closeness,
                     data = df1)
      summary(experience_inc_model_closeness) 


```

Reporting the models in APA style (for the manuscript)

```{r, eval=FALSE}
#save the results for each model in a word doc
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_fairness,
                               filename = "predict_experience_fairness.doc")
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_regret,
                               filename = "predict_experience_regret") #does not run because the models have different sample sizes
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_apology,
                               filename = "predict_experience_apology.doc")
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_circumst,
                               filename = "predict_experience_circumst.doc")
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_stable, #does not run because the models have different sample sizes
                               filename = "predict_experience_stable.doc")
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_injunctive,
                               filename = "predict_experience_injunctive.doc")
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_descriptive,
                               filename = "predict_experience_descriptive.doc")
      apaTables::apa.reg.table(experience_base_model, experience_inc_model_closeness,
                               filename = "predict_experience_closeness.doc")

#for the two models above that did not run, we need to rerun the base model using listwise deletion:
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(person_regret)) %>% 
            lm(anger_feel ~ harm.threat_self + harm.threat_other, data = .) -> experience_base_model_listwise1
      
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(behave_same)) %>% 
            lm(anger_feel ~ harm.threat_self + harm.threat_other, data = .) -> experience_base_model_listwise2
      
      #now generate the APA tables
      apaTables::apa.reg.table(experience_base_model_listwise1, experience_inc_model_regret,
                               filename = "predict_experience_regret.doc")
      apaTables::apa.reg.table(experience_base_model_listwise2, experience_inc_model_stable,
                               filename = "predict_experience_stable.doc")
      

```


**Research Question 1b:** To what extent do the predictors of anger intensity (based on the model above) vary depending on whether the anger event is perceived as justified or unjustified?

```{r, increment-feel2}
#A base model predicting anger experience from perceptions of harm/threat X anger type
      experience_int_model <- lm(anger_feel ~ harm.threat_self * Condition + harm.threat_other * Condition,
                     data = df1)
      summary(experience_int_model)
      confint(experience_int_model) %>% round(2)

#Testing the incremental associations (above and beyond harm/threat) for each of the other predictors interacting with the anger type:
      #fairness/justification
      experience_int_inc_model_fairness <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + fair_just*Condition,
                     data = df1)
      summary(experience_int_inc_model_fairness)
      confint(experience_int_inc_model_fairness) %>% round(2)
      anova(experience_int_model, experience_int_inc_model_fairness)
      
      #perception of target's regret
      experience_int_inc_model_regret <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + person_regret*Condition,
                     data = df1)
      summary(experience_int_inc_model_regret)
      anova(experience_int_model_listwise1, experience_int_inc_model_regret)
      
      #perception of target's tendency to apologize
      experience_int_inc_model_apology <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + person_apologize*Condition,
                     data = df1)
      summary(experience_int_inc_model_apology)
      
      #perception that target's behavior had an internal cause
      experience_int_inc_model_circumst <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + cause_circumst*Condition,
                     data = df1)
      summary(experience_int_inc_model_circumst)
      
      #perception that target would behave the same (i.e., stable characteristic)
      experience_int_inc_model_stable <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + behave_same*Condition,
                     data = df1)
      summary(experience_int_inc_model_stable)
      confint(experience_int_inc_model_stable)

      #behavior violated injunctive norms
      experience_int_inc_model_injunctive <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + injunctive*Condition,
                     data = df1)
      summary(experience_int_inc_model_injunctive)      

      #behavior violated descriptive norms
      experience_int_inc_model_descriptive <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + descriptive*Condition,
                     data = df1)
      summary(experience_int_inc_model_descriptive) 

      #relationship closeness prior to the event
      experience_int_inc_model_closeness <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + prior_closeness*Condition,
                     data = df1)
      summary(experience_int_inc_model_closeness) 
      
```

Reporting the models in APA style (for the manuscript)

```{r, eval=FALSE}
#save the results for each model in a word doc
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_fairness,
                               filename = "predict_experience_fairness_int.doc")
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_regret,
                               filename = "predict_experience_regret") #does not run because the models have different sample sizes
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_apology,
                               filename = "predict_experience_apology_int.doc")
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_circumst,
                               filename = "predict_experience_circumst_int.doc")
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_stable, #does not run because the models have different sample sizes
                               filename = "predict_experience_stable_int.doc")
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_injunctive,
                               filename = "predict_experience_injunctive_int.doc")
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_descriptive,
                               filename = "predict_experience_descriptive_int.doc")
      apaTables::apa.reg.table(experience_int_model, experience_int_inc_model_closeness,
                               filename = "predict_experience_closeness_int.doc")

#for the two models above that did not run, we need to rerun the base model using listwise deletion:
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(person_regret)) %>% 
            lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition, data = .) -> experience_int_model_listwise1
      
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(behave_same)) %>% 
            lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition, data = .) -> experience_int_model_listwise2
      
      #now generate the APA tables
      apaTables::apa.reg.table(experience_int_model_listwise1, experience_int_inc_model_regret,
                               filename = "predict_experience_regret_int.doc")
      apaTables::apa.reg.table(experience_int_model_listwise2, experience_int_inc_model_stable,
                               filename = "predict_experience_stable_int.doc")
      

```

### Simultaneous Prediction

**Research Question 2a:** What is the unique association between various characteristics of the anger eliciting event/person (i.e., perception of harm/threat to self and to others, perception of fairness/justification, perception of target’s regret, perception of target's tendency to apologize, causal attributions, norm violations, relationship closeness prior to the event) and participants’ anger experience?

```{r, pred-experience}

#Testing for multicolinearity
  #check the correlation matrix for r > .9
    df1 %>% 
      select(harm.threat_self, harm.threat_other, fair_just, 
             person_regret, person_apologize, cause_circumst, behave_same, injunctive, descriptive, prior_closeness) %>% 
      cor(use = "pairwise.complete.obs") %>% 
      round(2)

  #check for tolerance < .1 and VIFs > 5
    feel_model <- lm(anger_feel ~ harm.threat_self + harm.threat_other + fair_just + person_regret + person_apologize + cause_circumst + behave_same + injunctive + descriptive + prior_closeness,
                     data = df1)

    olsrr::ols_vif_tol(feel_model)
    
  #None of the three criteria above is violated -- proceed to interpreting the model outcome:
      summary(feel_model)
    
    #Save results into a file for the regression Table in the manuscript
      #reg_report(feel_model) %>% write.csv("predict_experience_simultaneous.csv")

```

**Research Question 2b:** To what extent do the predictors of anger intensity (based on the model above) vary depending on whether the anger event is perceived as justified or unjustified?

```{r, pred-experience-mod}
feel_model_interaction <- lm(anger_feel ~ harm.threat_self*Condition + harm.threat_other*Condition + fair_just*Condition + person_regret*Condition + person_apologize*Condition + cause_circumst*Condition + behave_same*Condition + injunctive*Condition + descriptive*Condition + prior_closeness*Condition,
                     data = df1)

  summary(feel_model_interaction)

    #Save results into a file for the regression Table in the manuscript
      #reg_report(feel_model_interaction) %>% write.csv("predict_experience_simultaneous_interaction.csv")
```

# Predicting Anger Expression

## Incremental Association Beyond Harm/Threat

**Research Question 1a:** What is the association between various characteristics of the anger eliciting event/person (i.e., perception of fairness/justification, perception of target’s regret, perception of target's tendency to apologize, causal attributions, norm violations, relationship closeness prior to the event) and participants’ anger expression over and above perception of harm/threat (to self and to others)?

```{r, increment-express}
#A base model predicting anger expression from perceptions of harm/threat
      expression_base_model <- lm(anger_express ~ harm.threat_self + harm.threat_other,
                     data = df1)
      summary(expression_base_model)
      
#Testing the incremental associations (above and beyond harm/threat) for each of the other predictors separately:
      #fairness/justification
      expression_inc_model_fairness <- lm(anger_express ~ harm.threat_self + harm.threat_other + fair_just,
                     data = df1)
      summary(expression_inc_model_fairness)
      
      #perception of target's regret
      expression_inc_model_regret <- lm(anger_express ~ harm.threat_self + harm.threat_other + person_regret,
                     data = df1)
      summary(expression_inc_model_regret)
      
      #perception of target's tendency to apologize
      expression_inc_model_apology <- lm(anger_express ~ harm.threat_self + harm.threat_other + person_apologize,
                     data = df1)
      summary(expression_inc_model_apology)
      
      #perception that target's behavior had an internal cause
      expression_inc_model_circumst <- lm(anger_express ~ harm.threat_self + harm.threat_other + cause_circumst,
                     data = df1)
      summary(expression_inc_model_circumst)
      
      #perception that target would behave the same (i.e., stable characteristic)
      expression_inc_model_stable <- lm(anger_express ~ harm.threat_self + harm.threat_other + behave_same,
                     data = df1)
      summary(expression_inc_model_stable)

      #behavior violated injunctive norms
      expression_inc_model_injunctive <- lm(anger_express ~ harm.threat_self + harm.threat_other + injunctive,
                     data = df1)
      summary(expression_inc_model_injunctive)      

      #behavior violated descriptive norms
      expression_inc_model_descriptive <- lm(anger_express ~ harm.threat_self + harm.threat_other + descriptive,
                     data = df1)
      summary(expression_inc_model_descriptive) 

      #relationship closeness prior to the event
      expression_inc_model_closeness <- lm(anger_express ~ harm.threat_self + harm.threat_other + prior_closeness,
                     data = df1)
      summary(expression_inc_model_closeness) 

```

Reporting the models in APA style (for the manuscript)

```{r, eval=FALSE}
#save the results for each model in a word doc
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_fairness,
                               filename = "predict_expression_fairness.doc")
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_regret,
                               filename = "predict_expression_regret") #does not run because the models have different sample sizes
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_apology,
                               filename = "predict_expression_apology.doc")
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_circumst,
                               filename = "predict_expression_circumst.doc")
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_stable, #does not run because the models have different sample sizes
                               filename = "predict_expression_stable.doc")
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_injunctive,
                               filename = "predict_expression_injunctive.doc")
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_descriptive,
                               filename = "predict_expression_descriptive.doc")
      apaTables::apa.reg.table(expression_base_model, expression_inc_model_closeness,
                               filename = "predict_expression_closeness.doc")

#for the two models above that did not run, we need to rerun the base model using listwise deletion:
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(person_regret)) %>% 
            lm(anger_express ~ harm.threat_self + harm.threat_other, data = .) -> expression_base_model_listwise1
      
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(behave_same)) %>% 
            lm(anger_express ~ harm.threat_self + harm.threat_other, data = .) -> expression_base_model_listwise2
      
      #now generate the APA tables
      apaTables::apa.reg.table(expression_base_model_listwise1, expression_inc_model_regret,
                               filename = "predict_expression_regret.doc")
      apaTables::apa.reg.table(expression_base_model_listwise2, expression_inc_model_stable,
                               filename = "predict_expression_stable.doc")
      

```

**Research Question 1b:** To what extent do the predictors of anger expression (based on the model above) vary depending on whether the anger event is perceived as justified or unjustified?

```{r, increment-express2}
#A base model predicting anger expression from perceptions of harm/threat X anger type
      expression_int_model <- lm(anger_express ~ harm.threat_self * Condition + harm.threat_other * Condition,
                     data = df1)
      summary(expression_int_model)

#Testing the incremental associations (above and beyond harm/threat) for each of the other predictors interacting with the anger type:
      #fairness/justification
      expression_int_inc_model_fairness <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + fair_just*Condition,
                     data = df1)
      summary(expression_int_inc_model_fairness)
      
      #perception of target's regret
      expression_int_inc_model_regret <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + person_regret*Condition,
                     data = df1)
      summary(expression_int_inc_model_regret)
      
      #perception of target's tendency to apologize
      expression_int_inc_model_apology <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + person_apologize*Condition,
                     data = df1)
      summary(expression_int_inc_model_apology)
      
      #perception that target's behavior had an internal cause
      expression_int_inc_model_circumst <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + cause_circumst*Condition,
                     data = df1)
      summary(expression_int_inc_model_circumst)
      
      #perception that target would behave the same (i.e., stable characteristic)
      expression_int_inc_model_stable <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + behave_same*Condition,
                     data = df1)
      summary(expression_int_inc_model_stable)

      #behavior violated injunctive norms
      expression_int_inc_model_injunctive <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + injunctive*Condition,
                     data = df1)
      summary(expression_int_inc_model_injunctive)      

      #behavior violated descriptive norms
      expression_int_inc_model_descriptive <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + descriptive*Condition,
                     data = df1)
      summary(expression_int_inc_model_descriptive) 

      #relationship closeness prior to the event
      expression_int_inc_model_closeness <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + prior_closeness*Condition,
                     data = df1)
      summary(expression_int_inc_model_closeness) 
      
```

Reporting the models in APA style (for the manuscript)

```{r, eval=FALSE}
#save the results for each model in a word doc
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_fairness,
                               filename = "predict_expression_fairness_int.doc")
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_regret,
                               filename = "predict_expression_regret") #does not run because the models have different sample sizes
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_apology,
                               filename = "predict_expression_apology_int.doc")
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_circumst,
                               filename = "predict_expression_circumst_int.doc")
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_stable, #does not run because the models have different sample sizes
                               filename = "predict_expression_stable_int.doc")
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_injunctive,
                               filename = "predict_expression_injunctive_int.doc")
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_descriptive,
                               filename = "predict_expression_descriptive_int.doc")
      apaTables::apa.reg.table(expression_int_model, expression_int_inc_model_closeness,
                               filename = "predict_expression_closeness_int.doc")

#for the two models above that did not run, we need to rerun the base model using listwise deletion:
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(person_regret)) %>% 
            lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition, data = .) -> expression_int_model_listwise1
      
      df1 %>% 
        filter(!is.na(harm.threat_self) & !is.na(harm.threat_other) & !is.na(behave_same)) %>% 
            lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition, data = .) -> expression_int_model_listwise2
      
      #now generate the APA tables
      apaTables::apa.reg.table(expression_int_model_listwise1, expression_int_inc_model_regret,
                               filename = "predict_expression_regret_int.doc")
      apaTables::apa.reg.table(expression_int_model_listwise2, expression_int_inc_model_stable,
                               filename = "predict_expression_stable_int.doc")
      

```


## Simultaneous Prediction

**Research Question 2a:** What is the unique association between various characteristics of the anger eliciting event/person (i.e., perception of harm/threat to self and to others, perception of fairness/justification, perception of target’s regret, perception of target's tendency to apologize, causal attributions, norm violations, relationship closeness prior to the event) and participants’ anger expression?

```{r, pred-express}

  #Testing for multicolinearity: check for tolerance < .1 and VIFs > 5
    express_model <- lm(anger_express ~ harm.threat_self + harm.threat_other + fair_just + person_regret + person_apologize + cause_circumst + behave_same + injunctive + descriptive + prior_closeness,
                     data = df1)

    olsrr::ols_vif_tol(express_model)
    
  #Since none of the multicolinearity criteria is violated, evaluate the model:
    summary(express_model)
    
    #Save results into a file for the regression Table in the manuscript
      #reg_report(express_model) %>% write.csv("predict_expression_simultaneous.csv")
```

**Research Question 2b:** To what extent do the predictors of anger expressivity (based on the model above) vary depending on whether the anger event is perceived as justified or unjustified?

```{r, pred-express-mod}
express_model_interaction <- lm(anger_express ~ harm.threat_self*Condition + harm.threat_other*Condition + fair_just*Condition + person_regret*Condition + person_apologize*Condition + cause_circumst*Condition + behave_same*Condition + injunctive*Condition + descriptive*Condition + prior_closeness*Condition,
                     data = df1)

  summary(express_model_interaction)

    #Save results into a file for the regression Table in the manuscript
      #reg_report(express_model_interaction) %>% write.csv("predict_expression_simultaneous_interaction.csv")
```