O.2 Report

Focus on Oxy

1 Read and Merge

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Read and Merge
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
wd <- "E:/Cinetic idei noi/EXPERIMENTE OGL Frontiers (O.2 & O.0.3 & O.0.2)"
setwd(wd)
DataTrust <- rio::import(file.path(wd, "O.2 REZULTATE\\O.2 Date PrelucrareSPSS cu NEO si STAI", "O.2 Date pt Trust BUN cu NEO si STAIY.xlsx"))
DataDG <- rio::import(file.path(wd, "O.2 REZULTATE\\O.2 Date PrelucrareSPSS cu NEO si STAI", "O.2 Date pt DG BUN cu NEO si STAIY.xlsx"))
DataVAS <- rio::import(file.path(wd, "O.2 REZULTATE\\O.2 VAS,IOS", "O.2 Date PrelucrareSPSS.xlsx"))
DataBIO <- rio::import(file.path(wd, "O.2 BIO", "O.2 Ox si Cortizol.xlsx"))
  
Data_merge1 <- merge(DataVAS, DataTrust)  
Data_merge2 <- merge(Data_merge1, DataDG) 
Data_merge3 <- merge(Data_merge2, DataBIO)
Data <- Data_merge3
test_names <- unique(unlist(lapply(list(DataTrust, DataDG, DataVAS, DataBIO), names)))
merge_names <- names(Data)
if(identical(merge_names[order(merge_names)], test_names[order(test_names)])){    # the order matters in identical()
  cat("**Merge was succesful**")
  rm("Data_merge1", "Data_merge2", "Data_merge3", "DataBIO", "DataDG", "DataTrust", "DataVAS", "test_names", "merge_names")
}else cat("**Merge unsuccesful**") 

Merge was succesful

2 Derive new variables

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Derive new variables
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Data$D_VasS_Poz <- Data[, "VasS_postPoz"] - Data[, "VasS_prePoz"] 
Data$D_VasS_Neg <- Data[, "VasS_postNeg"] - Data[, "VasS_preNeg"]
Data$D_VasB_Poz <- Data[, "VasB_postPoz"] - Data[, "VasB_prePoz"] 
Data$D_VasB_Neg <- Data[, "VasB_postNeg"] - Data[, "VasB_preNeg"]
Data$D_IOS_Poz <- Data[, "IOS_postPoz"] - Data[, "IOS_prePoz"] 
Data$D_IOS_Neg <- Data[, "IOS_postNeg"] - Data[, "IOS_preNeg"]
Data$D_Sam1_Poz <- Data[, "Sam1_postPoz"] - Data[, "Sam1_prePoz"] 
Data$D_Sam1_Neg <- Data[, "Sam1_postNeg"] - Data[, "Sam1_preNeg"]
Data$D_Sam2_Poz <- Data[, "Sam2_postPoz"] - Data[, "Sam2_prePoz"] 
Data$D_Sam2_Neg <- Data[, "Sam2_postNeg"] - Data[, "Sam2_preNeg"]
Data$D_Sam3_Poz <- Data[, "Sam3_postPoz"] - Data[, "Sam3_prePoz"] 
Data$D_Sam3_Neg <- Data[, "Sam3_postNeg"] - Data[, "Sam3_preNeg"]
Data$D_DG_Poz <- Data[, "DG_postPozTot"] - Data[, "DG_prePozTot"] 
Data$D_DG_Neg <- Data[, "DG_postNegTot"] - Data[, "DG_preNegTot"]
Data$D_TrustMin_Poz <- Data[, "TrustMinPozPost"] - Data[, "TrustMinPozPre"] 
Data$D_TrustMin_Neg <- Data[, "TrustMinNegPost"] - Data[, "TrustMinNegPre"]
Data$D_TrustTot_Poz <- Data[, "TrustTotPozPost"] - Data[, "TrustTotPozPre"] 
Data$D_TrustTot_Neg <- Data[, "TrustTotNegPost"] - Data[, "TrustTotNegPre"]
Data$D_Cort_Poz <- Data[, "Cort_post_Poz"] - Data[, "Cort_pre_Poz"] 
Data$D_Cort_Neg <- Data[, "Cort_post_Neg"] - Data[, "Cort_pre_Neg"]
Data$D_Ox_Poz <- Data[, "Ox_post_Poz"] - Data[, "Ox_pre_Poz"] 
Data$D_Ox_Neg <- Data[, "Ox_post_Neg"] - Data[, "Ox_pre_Neg"]

3 Define Functions

subchunkify <- function(g, fig_height=7, fig_width=5) {
  g_deparsed <- paste0(deparse(
    function() {g}
  ), collapse = '')
  
  sub_chunk <- paste0("
  `","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", echo=FALSE}",
  "\n(", 
    g_deparsed
    , ")()",
  "\n`","``
  ")
  
  cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
}

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Define Function for mining correlations
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Function for p-value significance -- both for func_ancova_multibox(), Get_Top_Relationships() and Correlations_With_One()
stars_signif <- function(pval) {
  stars = "ns"
  if(pval <= 0.001)
    stars = "***"
  if(pval > 0.001 & pval <= 0.01)
    stars = "**"
  if(pval > 0.01 & pval <= 0.05)
    stars = "*"
  if(pval > 0.05 & pval <= 0.1)
    stars = "."
  stars
}
## Function that returns correlations of all variables in descending order.
# Arg for threshold with default at .3 will keep only correlantions above .3 and below -.3. Also has threshhold for p-value. 
Get_Top_Relationships <- function(data_set, 
                                  correlation_abs_threshold=0.3,
                                  pvalue_threshold=0.05) {
  require(psych)
  require(dplyr)
  feature_names <- names(data_set)
  # strip var names to index for pair-wise identification
  names(data_set) <- seq(1:ncol(data_set))
  # calculate correlation and significance numbers
  cor_data_df <- psych::corr.test(data_set)
  # apply var names to correlation matrix over index
  rownames(cor_data_df$r) <- feature_names
  colnames(cor_data_df$r) <- feature_names
  # top cor and sig
  relationships_set <- cor_data_df$ci[,c('r','p')]
  # apply var names to data over index pairs
  relationships_set$feature_1 <- feature_names[as.numeric(sapply(strsplit(rownames(relationships_set), "-"), `[`, 1))]
  relationships_set$feature_2 <- feature_names[as.numeric(
    sapply(strsplit(rownames(relationships_set), "-"), `[`, 2))]
  relationships_set <- dplyr::select(relationships_set, feature_1, feature_2, r, p) %>% dplyr::rename(correlation = r, p.value = p)
  # return only the most insteresting relationships
  return(filter(relationships_set, abs(correlation) > correlation_abs_threshold &
                  p.value < pvalue_threshold) %>% 
        arrange(p.value) %>%
        mutate(p.signif = sapply(p.value, function(x) stars_signif(x))))
}
## Function that returns all correlation between numeric variables and one specific variable
Correlations_With_One <- function(data_set,
                            variable,
                            correlation_abs_threshold=0.3,
                            pvalue_threshold=0.05) {
  require(psych)
  require(dplyr)
  # use all numeric columns only
  numeric_cols <- unlist(lapply(data_set, is.numeric))
  data_set <- data_set[, numeric_cols]                               
  # calculate correlation and significance numbers
  cor_data_df <- psych::corr.test(data_set[, names(data_set) != variable], data_set[, variable], minlength = 20, adjust="none")
  # top cor and sig
  relationships_set <- as.data.frame(cbind(cor_data_df$r, cor_data_df$p))     # same as  cor_data_df$ci[,c('r','p')]
  relationships_set <- tibble::rownames_to_column(relationships_set, "Variable")   # relationships_set$Variable <- rownames(relationships_set)
  colnames(relationships_set) <- c("Variable", "correlation", "p.value")
  # return only the most insteresting relationships
  cat("#### Correlations with ", variable, "\n")
  return(filter(relationships_set, abs(correlation) > correlation_abs_threshold &
                  p.value < pvalue_threshold) %>% 
           arrange(p.value) %>%
           mutate(p.signif = sapply(p.value, function(x) stars_signif(x)))) %>%
           tibble::as.tibble()
}  
## Function for ploting correlation data frames resulting from Get_Top_Relationships and Correlations_With_One()
func_dotplot_cor <- function(df){                                        # https://www.r-pkg.org/pkg/ggpubr
  dotplotcor_scale_fill <- function(...){                                # Fix colors to signif factor levels even if missing
    ggplot2:::manual_scale(                                   
      'color', 
      values = setNames(
        c("darkgreen", "green3", "lawngreen", "yellow", "red"), 
        c("***", "**", "*", ".", "ns")), 
      ...
    )
  }                                           
  
  dtoplot_theme <- 
    ggpubr::theme_pubr() +
    theme(axis.text.y = element_text(size = 10))
  
  if(!"Variable" %in% colnames(df)){                                             # in oder to work for both Get_Top_Relationships and Correlations_With_One()
  df <- 
    df %>%                                            
      unite(cor_between, c("feature_1", "feature_2"), sep = " X ")               # unite 2 columns to x name from plot
  }else df <- df %>% dplyr::rename(cor_between = Variable)                       # change Variable to x name from plot
  
  df %>%
    ggpubr::ggdotchart(x = "cor_between", y = "correlation",
                       color = "p.signif",                                       # Color by sig
                       #   palette = c("#00AFBB", "#E7B800", "#FC4E07"),         # Custom color palette
                       sorting = "descending",                                   # Sort value in descending order
                       add = "segments",                                         # Add segments from y = 0 to dots
                       add.params = list(color = "lightgray", size = 2),         # Change segment color and size
                       group = "p.signif",                                       # Order by groups
                       dot.size = 8,                                             # Large dot size
                       xlab = "",
                       rotate = TRUE,                                            # Rotate vertically
                       label = round(.$correlation, 1),                          # Add mpg values as dot labels
                       font.label = list(color = "white", size = 9, 
                                         vjust = 0.5),                           # Adjust label parameters
                       ggtheme = dtoplot_theme) +                                # ggplot2 theme
    dotplotcor_scale_fill() +                                            # Fix colors to signif factor levels even if missing
    geom_hline(yintercept = 0, linetype = 2, color = "lightgray")
}
  
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Define Function for Pre-Post Plots, t Change and ANCOVA Post
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Func t test si boxplot simplu
func_t_box <- function(df, ind, pre_var, post_var){
  df_modif <-
    df %>%
    select(ind, pre_var, post_var) %>% 
    tidyr::drop_na() %>%
    gather(pre_var, post_var, key = "Cond", value = "value") %>% 
    mutate_at(vars(c(1, 2)), funs(as.factor)) %>% 
    mutate(Cond = factor(Cond, levels = c(pre_var, post_var))) 
  
  stat_comp <- ggpubr::compare_means(value ~ Cond, data = df_modif, method = "t.test", paired = TRUE)
  
  stat_comp2 <-
    df_modif %>% 
    do(tidy(t.test(.$value ~ .$Cond,
                   paired = TRUE,
                   data=.)))
  
  plot <- 
    ggpubr::ggpaired(df_modif, x = "Cond", y = "value", id = ind, 
                     color = "Cond", line.color = "gray", line.size = 0.4,
                     palette = c("#00AFBB", "#FC4E07"), legend = "none") +
      stat_summary(fun.data = mean_se,  colour = "darkred") +
      ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label.x = as.numeric(df_modif$Cond)-0.4, label.y = max(df_modif$value)+0.5) + 
      ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label = "p.signif", comparisons = list(c(pre_var, post_var)))
  
  cat(paste0("#### ", pre_var, " ", post_var, "\n", "\n"))
  print(stat_comp)
  print(stat_comp2)
  print(plot)
}
func_ancova_multibox <- function(df, ind, pre_var_c1, post_var_c1, pre_var_c2, post_var_c2){
  
  diff_score_c1 <- paste0(post_var_c1, " - ", pre_var_c1)
  diff_score_c2 <- paste0(post_var_c2, " - ", pre_var_c2)
  
  ## Plots and p-values for t tests
  df_modif <-
    df %>%
    select(ind, pre_var_c1, post_var_c1, pre_var_c2, post_var_c2) %>% 
    tidyr::drop_na() %>%
    gather(pre_var_c1, post_var_c1, pre_var_c2, post_var_c2, key = "Cond", value = "value") %>% 
    mutate_at(vars(c(1, 2)), funs(as.factor)) %>% 
    mutate(Cond = factor(Cond, levels = c(pre_var_c1, post_var_c1, pre_var_c2, post_var_c2))) 
  
  stat_comp <- ggpubr::compare_means(value ~ Cond, data = df_modif, method = "t.test", paired = TRUE, p.adjust.method = "holm")
  
  plot <-
    ggpubr::ggpaired(df_modif, x = "Cond", y = "value", id = ind, 
                     color = "Cond", line.color = "gray", line.size = 0.4,
                     palette = c("#00AFBB", "#FC4E07", "#00AFBB", "#FC4E07"), legend = "none") +
    stat_summary(fun.data = mean_se,  colour = "darkred") +
    ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label = "p.signif", 
                               label.y = c(max(df_modif$value) + 0.1*IQR(df_modif$value),
                                           max(df_modif$value) + 0.1*IQR(df_modif$value),
                                           seq(max(df_modif$value) + 0.3*IQR(df_modif$value), 
                                               max(df_modif$value) + 0.9*IQR(df_modif$value), length.out = 4)),  
                               comparisons = list(c(pre_var_c1, post_var_c1),
                                                  c(pre_var_c2, post_var_c2),
                                                  c(post_var_c1, pre_var_c2),
                                                  c(pre_var_c1, pre_var_c2),
                                                  c(post_var_c1, post_var_c2),
                                                  c(pre_var_c1, post_var_c2)))
  
  ## For ttestChange or ANCOVAChange - we do ttestChange (Post-Pre) here, but it isnt very important
  df_modif2 <-                                 
    df %>%
    select(ind, pre_var_c1, post_var_c1, pre_var_c2, post_var_c2) %>%
    tidyr::drop_na() 
  df_modif2[diff_score_c1] <- df_modif2[, post_var_c1] - df_modif2[, pre_var_c1]
  df_modif2[diff_score_c2] <- df_modif2[, post_var_c2] - df_modif2[, pre_var_c2]
  
  tChange <- t.test(df_modif2[, diff_score_c1], df_modif2[, diff_score_c2], paired = TRUE)
  
  ## For descriptives by 2 factors (PrePost and PozNeg)
  df_modif3 <-
    df %>%
    select(ind, pre_var_c1, post_var_c1, pre_var_c2, post_var_c2) %>%
    tidyr::drop_na() %>%
    gather(pre_var_c1, post_var_c1, pre_var_c2, post_var_c2, key = "Cond", value = "value") %>%
    mutate(PrePost = case_when(stringr::str_detect(.$"Cond", "pre|Pre") ~ "Pre",
                               stringr::str_detect(.$"Cond", "post|Post") ~ "Post",
                               TRUE ~ NA_character_),
           PozNeg = case_when(stringr::str_detect(.$"Cond", "poz|Poz") ~ "Poz",
                              stringr::str_detect(.$"Cond", "neg|Neg") ~ "Neg",
                              TRUE ~ NA_character_)) %>%
    mutate(PrePost = as.factor(PrePost),
           PozNeg = as.factor(PozNeg))
  
  ## For ANCOVAPost - this is what we use
  df_modif4 <-
    df_modif3 %>%
    select(-"Cond") %>%
    spread("PrePost", "value")
  
  ## Models (here we use ANCOVAPost)    # https://m-clark.github.io/docs/mixedModels/anovamixed.html#introduction
  full_ancovaPost <-                                          # this is better than using lm() and glht()
      jmv::ancova(
        formula = Post ~ Pre + PozNeg,
        data = df_modif4,
        homo = TRUE,
        ss = "3",                                                     
        postHoc = ~ PozNeg,
        postHocCorr = list("tukey"),
        effectSize = list("eta", "partEta")
      )
  
      # mod_ancovaPost <- lm(Post ~ Pre + PozNeg, data = df_modif4)            # this is a Covariate Second model
      # mod_ancovaPost_ss3 <- car::Anova(mod_ancovaPost, type = "III")         # Type III sums of squares; see Andy Fields 2012
      # postHocs <- multcomp::glht(mod_ancovaPost, linfct = multcomp::mcp(PozNeg = "Tukey"))  # differences between the adjusted means,
      # sum_postHocs <- summary(postHocs)                                               # use Tukey or Dunnett’s post hoc tests
      # conf_postHocs <- confint(postHocs)
  scatter <-                                                             # Check for homogeneity of regression slopes
    ggplot(df_modif4, aes(Pre, Post, colour = PozNeg)) +
    geom_point(aes(shape = PozNeg), size = 3) +
    geom_smooth(method = "lm", aes(fill = PozNeg), alpha = 0.1)
    
  
  ## Other Models that work for this date
  # mod_ancovaPost <- lm(post ~ pre + treat)      # exactly the same with aov(post ~ pre + treat)
  # summary(mod_ancovaPost)
  # 
  # mod_anovaRM <- aov(score ~ treat*time + Error(id), dflong)
  # summary(mod_anovaRM)
  # 
  # mod_lme <- lme4::lmer(score ~ treat*time + (1|id), data=dflong)
  # anova(lmeModel)
  
  ## Output
  print(plot)
  cat(paste0("#### ", pre_var_c1, " ", post_var_c1, " ", pre_var_c2, " ", post_var_c2, "\n", "\n"))
  
  cat("#### Descriptives")
  psych::describeBy(df_modif3[, "value"], list(df_modif3[, "PrePost"], df_modif3[, "PozNeg"]), mat = TRUE) %>% 
    as.tibble() %>%
    print()
  cat("\n")
  
  print(stat_comp)
  cat("\n")
  
  cat("#### t Change")
  tidy(tChange) %>% print()
  cat("\n")
  
  cat("#### ANCOVA Post")
  cat("\n")
  cat("##### Homogeneity test")
  print(tibble::as.tibble(full_ancovaPost$assump$homo))
  cat("##### ANCOVA output")
  print(tibble::as.tibble(full_ancovaPost$main))
  # tidy(mod_ancovaPost) %>% 
  #   mutate(p.signif = sapply(p.value, function(x) stars_signif(x))) %>% 
  #   print()
  # cat("\n")
  cat("##### Post Hoc")
  print(tibble::as.tibble(full_ancovaPost$postHoc[[1]]))
  # tidy(sum_postHocs) %>% 
  #   mutate(p.signif = sapply(p.value, function(x) stars_signif(x))) %>% 
  #   print()
  cat("\n")
  cat("##### Homogeneity of regression slopes")
  subchunkify(plot(scatter), 5, 5)
}

4 Analyses

4.1 Correlations between Diffrence Scores with other variables

## Correlations between Diffrence Scores with other variables
Correlations_With_One(Data[,-c(1:7)], variable = "D_Ox_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # D_DG_Poz, Neo_C4, Neo_E3

4.1.0.1 Correlations with D_Ox_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_Ox_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.2 Correlations with D_Ox_Neg

Correlations_With_One(Data[,-c(1:7)], variable = "D_Cort_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05) # D_Sam1_Poz, D_TrustTot_Poz, StaiS

4.1.0.3 Correlations with D_Cort_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_Cort_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.4 Correlations with D_Cort_Neg

Correlations_With_One(Data[,-c(1:7)], variable = "D_VasS_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.5 Correlations with D_VasS_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_VasS_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # D_TrustTot_Neg

4.1.0.6 Correlations with D_VasS_Neg

Correlations_With_One(Data[,-c(1:7)], variable = "D_VasB_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # D_Sam1_Poz

4.1.0.7 Correlations with D_VasB_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_VasB_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # D_Sam1_Neg

4.1.0.8 Correlations with D_VasB_Neg

Correlations_With_One(Data[,-c(1:7)], variable = "D_IOS_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.9 Correlations with D_IOS_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_IOS_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.10 Correlations with D_IOS_Neg

Correlations_With_One(Data[,-c(1:7)], variable = "D_DG_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.11 Correlations with D_DG_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_DG_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.12 Correlations with D_DG_Neg

Correlations_With_One(Data[,-c(1:7)], variable = "D_TrustMin_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.13 Correlations with D_TrustMin_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_TrustMin_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.14 Correlations with D_TrustMin_Neg

Correlations_With_One(Data[,-c(1:7)], variable = "D_TrustTot_Poz", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # nimic

4.1.0.15 Correlations with D_TrustTot_Poz

Correlations_With_One(Data[,-c(1:7)], variable = "D_TrustTot_Neg", correlation_abs_threshold = 0.2, pvalue_threshold = 0.05)  # D_VasS_Neg

4.1.0.16 Correlations with D_TrustTot_Neg

4.2 Simple before-after analyses with t test

## Simple before-after analyses with t test
func_t_box(Data, "Indicativ", "Ox_pre_Poz", "Ox_post_Poz")  # sig  

4.2.0.1 Ox_pre_Poz Ox_post_Poz

func_t_box(Data, "Indicativ", "Ox_pre_Neg", "Ox_post_Neg")  # sig

4.2.0.2 Ox_pre_Neg Ox_post_Neg

func_t_box(Data, "Indicativ", "Cort_pre_Poz", "Cort_post_Poz")  # nu  

4.2.0.3 Cort_pre_Poz Cort_post_Poz

func_t_box(Data, "Indicativ", "Cort_pre_Neg", "Cort_post_Neg")  # sig

4.2.0.4 Cort_pre_Neg Cort_post_Neg

func_t_box(Data, "Indicativ", "VasS_prePoz", "VasS_postPoz")  # sig  

4.2.0.5 VasS_prePoz VasS_postPoz

func_t_box(Data, "Indicativ", "VasS_preNeg", "VasS_postNeg")  # nu

4.2.0.6 VasS_preNeg VasS_postNeg

func_t_box(Data, "Indicativ", "VasB_prePoz", "VasB_postPoz")  # sig  

4.2.0.7 VasB_prePoz VasB_postPoz

func_t_box(Data, "Indicativ", "VasB_preNeg", "VasB_postNeg")  # nu

4.2.0.8 VasB_preNeg VasB_postNeg

func_t_box(Data, "Indicativ", "Sam1_prePoz", "Sam1_postPoz")  # sig  

4.2.0.9 Sam1_prePoz Sam1_postPoz

func_t_box(Data, "Indicativ", "Sam1_preNeg", "Sam1_postNeg")  # nu

4.2.0.10 Sam1_preNeg Sam1_postNeg

func_t_box(Data, "Indicativ", "Sam2_prePoz", "Sam2_postPoz")  # nu  

4.2.0.11 Sam2_prePoz Sam2_postPoz

func_t_box(Data, "Indicativ", "Sam2_preNeg", "Sam2_postNeg")  # nu

4.2.0.12 Sam2_preNeg Sam2_postNeg

func_t_box(Data, "Indicativ", "IOS_prePoz", "IOS_postPoz")  # sig  

4.2.0.13 IOS_prePoz IOS_postPoz

func_t_box(Data, "Indicativ", "IOS_preNeg", "IOS_postNeg")  # nu

4.2.0.14 IOS_preNeg IOS_postNeg

func_t_box(Data, "Indicativ", "DG_prePozTot", "DG_postPozTot")  # nu  

4.2.0.15 DG_prePozTot DG_postPozTot

func_t_box(Data, "Indicativ", "DG_preNegTot", "DG_postNegTot")  # sig

4.2.0.16 DG_preNegTot DG_postNegTot

func_t_box(Data, "Indicativ", "TrustMinPozPre", "TrustMinPozPost")  # nu  

4.2.0.17 TrustMinPozPre TrustMinPozPost

func_t_box(Data, "Indicativ", "TrustMinNegPre", "TrustMinNegPost")  # nu

4.2.0.18 TrustMinNegPre TrustMinNegPost

func_t_box(Data, "Indicativ", "TrustTotPozPre", "TrustTotPozPost")  # nu  

4.2.0.19 TrustTotPozPre TrustTotPozPost

func_t_box(Data, "Indicativ", "TrustTotNegPre", "TrustTotNegPost")  # nu

4.2.0.20 TrustTotNegPre TrustTotNegPost

4.3 tChance and ANCOVAPost

## tChance and ANCOVAPost 
func_ancova_multibox(Data, "Indicativ", "Ox_pre_Poz", "Ox_post_Poz", "Ox_pre_Neg", "Ox_post_Neg")

4.3.0.1 Ox_pre_Poz Ox_post_Poz Ox_pre_Neg Ox_post_Neg

4.3.0.2 Descriptives

NA

4.3.0.3 t Change

4.3.0.4 ANCOVA Post

4.3.0.4.1 Homogeneity test

4.3.0.4.2 ANCOVA output

4.3.0.4.3 Post Hoc

4.3.0.4.4 Homogeneity of regression slopes

plot of chunk sub_chunk_5929

func_ancova_multibox(Data, "Indicativ", "Cort_pre_Poz", "Cort_post_Poz", "Cort_pre_Neg", "Cort_post_Neg")

4.3.0.5 Cort_pre_Poz Cort_post_Poz Cort_pre_Neg Cort_post_Neg

4.3.0.6 Descriptives

NA

4.3.0.7 t Change

4.3.0.8 ANCOVA Post

4.3.0.8.1 Homogeneity test

4.3.0.8.2 ANCOVA output

4.3.0.8.3 Post Hoc

4.3.0.8.4 Homogeneity of regression slopes

plot of chunk sub_chunk_7264

func_ancova_multibox(Data, "Indicativ", "VasS_prePoz", "VasS_postPoz", "VasS_preNeg", "VasS_postNeg")

4.3.0.9 VasS_prePoz VasS_postPoz VasS_preNeg VasS_postNeg

4.3.0.10 Descriptives

NA

4.3.0.11 t Change

4.3.0.12 ANCOVA Post

4.3.0.12.1 Homogeneity test

4.3.0.12.2 ANCOVA output

4.3.0.12.3 Post Hoc

4.3.0.12.4 Homogeneity of regression slopes

plot of chunk sub_chunk_3704

func_ancova_multibox(Data, "Indicativ", "VasB_prePoz", "VasB_postPoz", "VasB_preNeg", "VasB_postNeg")

4.3.0.13 VasB_prePoz VasB_postPoz VasB_preNeg VasB_postNeg

4.3.0.14 Descriptives

NA

4.3.0.15 t Change

4.3.0.16 ANCOVA Post

4.3.0.16.1 Homogeneity test

4.3.0.16.2 ANCOVA output

4.3.0.16.3 Post Hoc

4.3.0.16.4 Homogeneity of regression slopes

plot of chunk sub_chunk_5149

func_ancova_multibox(Data, "Indicativ", "Sam1_prePoz", "Sam1_postPoz", "Sam1_preNeg", "Sam1_postNeg")

4.3.0.17 Sam1_prePoz Sam1_postPoz Sam1_preNeg Sam1_postNeg

4.3.0.18 Descriptives

NA

4.3.0.19 t Change

4.3.0.20 ANCOVA Post

4.3.0.20.1 Homogeneity test

4.3.0.20.2 ANCOVA output

4.3.0.20.3 Post Hoc

4.3.0.20.4 Homogeneity of regression slopes

plot of chunk sub_chunk_3776

func_ancova_multibox(Data, "Indicativ", "Sam2_prePoz", "Sam2_postPoz", "Sam2_preNeg", "Sam2_postNeg")

4.3.0.21 Sam2_prePoz Sam2_postPoz Sam2_preNeg Sam2_postNeg

4.3.0.22 Descriptives

NA

4.3.0.23 t Change

4.3.0.24 ANCOVA Post

4.3.0.24.1 Homogeneity test

4.3.0.24.2 ANCOVA output

4.3.0.24.3 Post Hoc

4.3.0.24.4 Homogeneity of regression slopes

plot of chunk sub_chunk_4183

func_ancova_multibox(Data, "Indicativ", "IOS_prePoz", "IOS_postPoz", "IOS_preNeg", "IOS_postNeg") 

4.3.0.25 IOS_prePoz IOS_postPoz IOS_preNeg IOS_postNeg

4.3.0.26 Descriptives

NA

4.3.0.27 t Change

4.3.0.28 ANCOVA Post

4.3.0.28.1 Homogeneity test

4.3.0.28.2 ANCOVA output

4.3.0.28.3 Post Hoc

4.3.0.28.4 Homogeneity of regression slopes

plot of chunk sub_chunk_106

func_ancova_multibox(Data, "Indicativ", "DG_prePozTot", "DG_postPozTot", "DG_preNegTot", "DG_postNegTot") 

4.3.0.29 DG_prePozTot DG_postPozTot DG_preNegTot DG_postNegTot

4.3.0.30 Descriptives

NA

4.3.0.31 t Change

4.3.0.32 ANCOVA Post

4.3.0.32.1 Homogeneity test

4.3.0.32.2 ANCOVA output

4.3.0.32.3 Post Hoc

4.3.0.32.4 Homogeneity of regression slopes

plot of chunk sub_chunk_5322

func_ancova_multibox(Data, "Indicativ", "TrustMinPozPre", "TrustMinPozPost", "TrustMinNegPre", "TrustMinNegPost") 

4.3.0.33 TrustMinPozPre TrustMinPozPost TrustMinNegPre TrustMinNegPost

4.3.0.34 Descriptives

NA

4.3.0.35 t Change

4.3.0.36 ANCOVA Post

4.3.0.36.1 Homogeneity test

4.3.0.36.2 ANCOVA output

4.3.0.36.3 Post Hoc

4.3.0.36.4 Homogeneity of regression slopes

plot of chunk sub_chunk_4321

func_ancova_multibox(Data, "Indicativ", "TrustTotPozPre", "TrustTotPozPost", "TrustTotNegPre", "TrustTotNegPost") 

4.3.0.37 TrustTotPozPre TrustTotPozPost TrustTotNegPre TrustTotNegPost

4.3.0.38 Descriptives

NA

4.3.0.39 t Change

4.3.0.40 ANCOVA Post

4.3.0.40.1 Homogeneity test

4.3.0.40.2 ANCOVA output

4.3.0.40.3 Post Hoc

4.3.0.40.4 Homogeneity of regression slopes

plot of chunk sub_chunk_936

4.4 Correlations with Oxy

## Mining Correlations with Oxy
df_OxyAll_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.05) %>%
    dplyr::as_tibble() %>%
    dplyr::filter_all(any_vars(grepl("Ox", .)))                           # only Oxy, but both Pre and Post Oxy
df_OxyAll_cor %>%              
    print(n = Inf)                                     

df_OxyAll_cor %>%
  func_dotplot_cor()

 
# Correlations only on Pre measures with Oxy -- nothing
df_OxyPre_cor <- 
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
    dplyr::as_tibble() %>%
    filter_at(vars(feature_1, feature_2), all_vars(grepl("pre|Pre", .))) %>%
    dplyr::filter_all(any_vars(grepl("Ox", .))) 
df_OxyPre_cor %>%
  print(n = Inf)

# Correlations only on Post measures with Oxy
df_OxyPost_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
    dplyr::as_tibble() %>%
    filter_at(vars(feature_1, feature_2), all_vars(grepl("post|Post", .))) %>%
    dplyr::filter_all(any_vars(grepl("Ox", .))) 
df_OxyPost_cor %>%
    print(n = Inf)

df_OxyPost_cor %>% 
  func_dotplot_cor()

4.5 Correlations with Stess

## Mining Correlations with VasS
df_VasSAll_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.05) %>%
    dplyr::as_tibble() %>%
    dplyr::filter_all(any_vars(grepl("VasS", .)))                           # only VasS, but both Pre and Post VasS
df_VasSAll_cor %>%              
    print(n = Inf)                                     

# Correlations only on Pre measures with VasS
df_VasSPre_cor <- 
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
    dplyr::as_tibble() %>%
    filter_at(vars(feature_1, feature_2), all_vars(grepl("pre|Pre", .))) %>%
    dplyr::filter_all(any_vars(grepl("VasS", .))) 
df_VasSPre_cor %>%
  print(n = Inf)

df_VasSPre_cor %>% 
  func_dotplot_cor()

# Correlations only on Post measures with VasS
df_VasSPost_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
    dplyr::as_tibble() %>%
    filter_at(vars(feature_1, feature_2), all_vars(grepl("post|Post", .))) %>%
    dplyr::filter_all(any_vars(grepl("VasS", .))) 
df_VasSPost_cor %>%
    print(n = Inf)

df_VasSPost_cor %>% 
  func_dotplot_cor()

4.6 Correlations with Well Being

## Mining Correlations with VasB
df_VasBAll_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.05) %>%
  dplyr::as_tibble() %>%
  dplyr::filter_all(any_vars(grepl("VasB", .)))                           # only VasB, but both Pre and Post VasB
df_VasBAll_cor %>%              
  print(n = Inf)                                     

# Correlations only on Pre measures with VasB
df_VasBPre_cor <- 
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
  dplyr::as_tibble() %>%
  filter_at(vars(feature_1, feature_2), all_vars(grepl("pre|Pre", .))) %>%
  dplyr::filter_all(any_vars(grepl("VasB", .))) 
df_VasBPre_cor %>%
  print(n = Inf)

df_VasBPre_cor %>%
  func_dotplot_cor()

# Correlations only on Post measures with VasB
df_VasBPost_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
  dplyr::as_tibble() %>%
  filter_at(vars(feature_1, feature_2), all_vars(grepl("post|Post", .))) %>%
  dplyr::filter_all(any_vars(grepl("VasB", .))) 
df_VasBPost_cor %>%
  print(n = Inf)

df_VasBPost_cor %>% 
  func_dotplot_cor()

4.7 Correlations with IOS

## Mining Correlations with IOS
df_IOSAll_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.05) %>%
  dplyr::as_tibble() %>%
  dplyr::filter_all(any_vars(grepl("IOS", .)))                           # only IOS, but both Pre and Post IOS
df_IOSAll_cor %>%              
  print(n = Inf)                                     

# Correlations only on Pre measures with IOS
df_IOSPre_cor <- 
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
  dplyr::as_tibble() %>%
  filter_at(vars(feature_1, feature_2), all_vars(grepl("pre|Pre", .))) %>%
  dplyr::filter_all(any_vars(grepl("IOS", .))) 
df_IOSPre_cor %>%
  print(n = Inf)

df_IOSPre_cor %>%
  func_dotplot_cor()

# Correlations only on Post measures with IOS
df_IOSPost_cor <-
  Get_Top_Relationships(Data[,-c(1:7)], correlation_abs_threshold = 0.2, pvalue_threshold = 0.1) %>%
  dplyr::as_tibble() %>%
  filter_at(vars(feature_1, feature_2), all_vars(grepl("post|Post", .))) %>%
  dplyr::filter_all(any_vars(grepl("IOS", .))) 
df_IOSPost_cor %>%
  print(n = Inf)

df_IOSPost_cor %>% 
  func_dotplot_cor()

5 Session Info

R version 3.5.2 (2018-12-20)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=Romanian_Romania.1250  LC_CTYPE=Romanian_Romania.1250    LC_MONETARY=Romanian_Romania.1250 LC_NUMERIC=C                     
[5] LC_TIME=Romanian_Romania.1250    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2     rio_0.5.16         plyr_1.8.4         summarytools_0.9.3 DT_0.5             ggpubr_0.2         magrittr_1.5      
 [8] broom_0.5.1        papaja_0.1.0.9842  psych_1.8.10       forcats_0.3.0      stringr_1.3.1      dplyr_0.7.8        purrr_0.2.5       
[15] readr_1.3.0        tidyr_0.8.2        tibble_1.4.2       ggplot2_3.1.0      tidyverse_1.2.1    pacman_0.5.0      

loaded via a namespace (and not attached):
 [1] nlme_3.1-137       bitops_1.0-6       matrixStats_0.54.0 lubridate_1.7.4    httr_1.4.0         tools_3.5.2        backports_1.1.3   
 [8] R6_2.4.0           lazyeval_0.2.1     colorspace_1.3-2   withr_2.1.2        tidyselect_0.2.5   mnormt_1.5-5       emmeans_1.3.1     
[15] curl_3.3           compiler_3.5.2     cli_1.0.1          rvest_0.3.2        xml2_1.2.0         sandwich_2.5-1     labeling_0.3      
[22] scales_1.0.0       checkmate_1.8.5    mvtnorm_1.0-10     digest_0.6.18      foreign_0.8-71     base64enc_0.1-3    pkgconfig_2.0.2   
[29] htmltools_0.3.6    highr_0.7          htmlwidgets_1.3    rlang_0.3.0.1      readxl_1.1.0       rstudioapi_0.8     pryr_0.1.4        
[36] jmvcore_0.9.5.2    bindr_0.1.1        generics_0.0.2     zoo_1.8-4          jsonlite_1.6       zip_1.0.0          car_3.0-2         
[43] RCurl_1.95-4.11    rapportools_1.0    Matrix_1.2-15      Rcpp_1.0.1         munsell_0.5.0      abind_1.4-5        stringi_1.2.4     
[50] multcomp_1.4-10    yaml_2.2.0         carData_3.0-2      MASS_7.3-51.1      grid_3.5.2         parallel_3.5.2     crayon_1.3.4      
[57] lattice_0.20-38    splines_3.5.2      haven_2.1.0        pander_0.6.3       hms_0.4.2          magick_2.0         knitr_1.21        
[64] pillar_1.3.1       tcltk_3.5.2        rjson_0.2.20       estimability_1.3   ggsignif_0.4.0     codetools_0.2-15   glue_1.3.1        
[71] evaluate_0.12      data.table_1.12.2  modelr_0.1.2       cellranger_1.1.0   gtable_0.2.0       assertthat_0.2.1   xfun_0.4          
[78] openxlsx_4.1.0     xtable_1.8-3       coda_0.19-2        survival_2.43-3    jmv_0.9.6.1        TH.data_1.0-9     

 

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A work by Claudiu Papasteri

claudiu.papasteri@gmail.com