O.2 Report - Gender

Focus on OXT by Gender

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)

The working directory was changed to E:/Cinetic idei noi/EXPERIMENTE OGL Frontiers (O.2 & O.0.3 & O.0.2) inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

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

# Gender Dataframe
Data_Gen <- rio::import(file.path(wd, "Gen varsta O03 O02 O2.xlsx"), which = "O.2")

Data_Gen_merged <- 
  Data %>%
  tidyr::separate(Indicativ,  c("ID_tag", "ID", "study_tag"), "\\s+") %>%                               # split on white space
  select(-c("ID_tag", "study_tag")) %>%
  mutate(ID = as.numeric(as.character(ID))) %>%
  dplyr::left_join(., Data_Gen, by = c("ID")) %>%
  select(1:7, Gen, Varsta, everything())

Data_fem <- 
  Data_Gen_merged %>%
  filter(Gen == "F")

Data_masc <- 
  Data_Gen_merged %>%
  filter(Gen == "M")

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, test_method = "t.test"){
  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 = test_method, 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 Descriptives - Gen Varsta

describe(Data_Gen_merged$Varsta)

prop.table(table(Data_Gen_merged$Gen))

        F         M 
0.7333333 0.2666667 

t.test(Data_Gen_merged$Varsta ~ Data_Gen_merged$Gen) %>%
  tidy()

  
wilcox.test(Data_Gen_merged$Varsta ~ Data_Gen_merged$Gen) %>%
  tidy()

cannot compute exact p-value with ties

psych::describeBy(Data_Gen_merged$Varsta, group = Data_Gen_merged$Gen)

 Descriptive statistics by group 
group: F

------------------------------------------------------------------------------------------------------------- 
group: M

5 Analyses

5.1 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

5.1.0.1 Correlations with D_Ox_Poz

as.tibble() is deprecated, use as_tibble() (but mind the new semantics). This warning is displayed once per session.

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

5.1.0.2 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

5.1.0.3 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

5.1.0.4 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

5.1.0.5 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

5.1.0.6 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

5.1.0.7 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

5.1.0.8 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

5.1.0.9 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

5.1.0.10 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

5.1.0.11 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

5.1.0.12 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

5.1.0.13 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

5.1.0.14 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

5.1.0.15 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

5.1.0.16 Correlations with D_TrustTot_Neg

## Simple before-after analyses with t test
func_t_box(Data, "Indicativ", "Ox_pre_Poz", "Ox_post_Poz")  # sig  
func_t_box(Data, "Indicativ", "Ox_pre_Neg", "Ox_post_Neg")  # sig
func_t_box(Data, "Indicativ", "Cort_pre_Poz", "Cort_post_Poz")  # nu  
func_t_box(Data, "Indicativ", "Cort_pre_Neg", "Cort_post_Neg")  # sig

func_t_box(Data, "Indicativ", "VasS_prePoz", "VasS_postPoz")  # sig  
func_t_box(Data, "Indicativ", "VasS_preNeg", "VasS_postNeg")  # nu
func_t_box(Data, "Indicativ", "VasB_prePoz", "VasB_postPoz")  # sig  
func_t_box(Data, "Indicativ", "VasB_preNeg", "VasB_postNeg")  # nu
func_t_box(Data, "Indicativ", "Sam1_prePoz", "Sam1_postPoz")  # sig  
func_t_box(Data, "Indicativ", "Sam1_preNeg", "Sam1_postNeg")  # nu
func_t_box(Data, "Indicativ", "Sam2_prePoz", "Sam2_postPoz")  # nu  
func_t_box(Data, "Indicativ", "Sam2_preNeg", "Sam2_postNeg")  # nu
func_t_box(Data, "Indicativ", "IOS_prePoz", "IOS_postPoz")  # sig  
func_t_box(Data, "Indicativ", "IOS_preNeg", "IOS_postNeg")  # nu

func_t_box(Data, "Indicativ", "DG_prePozTot", "DG_postPozTot")  # nu  
func_t_box(Data, "Indicativ", "DG_preNegTot", "DG_postNegTot")  # sig

func_t_box(Data, "Indicativ", "TrustMinPozPre", "TrustMinPozPost")  # nu  
func_t_box(Data, "Indicativ", "TrustMinNegPre", "TrustMinNegPost")  # nu
func_t_box(Data, "Indicativ", "TrustTotPozPre", "TrustTotPozPost")  # nu  
func_t_box(Data, "Indicativ", "TrustTotNegPre", "TrustTotNegPost")  # nu

5.2 tChance and ANCOVAPost

## tChance and ANCOVAPost 
func_ancova_multibox(Data, "Indicativ", "Ox_pre_Poz", "Ox_post_Poz", "Ox_pre_Neg", "Ox_post_Neg")
func_ancova_multibox(Data, "Indicativ", "Cort_pre_Poz", "Cort_post_Poz", "Cort_pre_Neg", "Cort_post_Neg")

func_ancova_multibox(Data, "Indicativ", "VasS_prePoz", "VasS_postPoz", "VasS_preNeg", "VasS_postNeg")
func_ancova_multibox(Data, "Indicativ", "VasB_prePoz", "VasB_postPoz", "VasB_preNeg", "VasB_postNeg")
func_ancova_multibox(Data, "Indicativ", "Sam1_prePoz", "Sam1_postPoz", "Sam1_preNeg", "Sam1_postNeg")
func_ancova_multibox(Data, "Indicativ", "Sam2_prePoz", "Sam2_postPoz", "Sam2_preNeg", "Sam2_postNeg")
func_ancova_multibox(Data, "Indicativ", "IOS_prePoz", "IOS_postPoz", "IOS_preNeg", "IOS_postNeg") 

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

func_ancova_multibox(Data, "Indicativ", "TrustMinPozPre", "TrustMinPozPost", "TrustMinNegPre", "TrustMinNegPost") 
func_ancova_multibox(Data, "Indicativ", "TrustTotPozPre", "TrustTotPozPost", "TrustTotNegPre", "TrustTotNegPost") 

–>

5.3 Simple before-after analyses with t test by Gender

# Females
cat("### Females")

5.3.1 Females

# func_t_box(Data_fem, "ID", "Ox_pre_Poz", "Ox_post_Poz", test_method = "wilcox.test")     # ns 
func_t_box(Data_fem, "ID", "Ox_pre_Poz", "Ox_post_Poz")  # ns

funs() is soft deprecated as of dplyr 0.8.0 Please use a list of either functions or lambdas:

# Simple named list: list(mean = mean, median = median)

# Auto named with tibble::lst(): tibble::lst(mean, median)

# Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE)) This warning is displayed once per session.#### Ox_pre_Poz Ox_post_Poz

func_t_box(Data_fem, "ID", "Ox_pre_Neg", "Ox_post_Neg")     # sig

5.3.1.1 Ox_pre_Neg Ox_post_Neg

# func_t_box(Data_fem, "ID", "Cort_pre_Poz", "Cort_post_Poz")  # ns  
# func_t_box(Data_fem, "ID", "Cort_pre_Neg", "Cort_post_Neg")  # sig

func_t_box(Data_fem, "ID", "VasS_prePoz", "VasS_postPoz")  # sig  

5.3.1.2 VasS_prePoz VasS_postPoz

func_t_box(Data_fem, "ID", "VasS_preNeg", "VasS_postNeg")  # ns

5.3.1.3 VasS_preNeg VasS_postNeg

func_t_box(Data_fem, "ID", "VasB_prePoz", "VasB_postPoz")  # sig  

5.3.1.4 VasB_prePoz VasB_postPoz

func_t_box(Data_fem, "ID", "VasB_preNeg", "VasB_postNeg")  # ns

5.3.1.5 VasB_preNeg VasB_postNeg

# func_t_box(Data_fem, "ID", "Sam1_prePoz", "Sam1_postPoz")  # sig  
# func_t_box(Data_fem, "ID", "Sam1_preNeg", "Sam1_postNeg")  # nu
# func_t_box(Data_fem, "ID", "Sam2_prePoz", "Sam2_postPoz")  # nu  
# func_t_box(Data_fem, "ID", "Sam2_preNeg", "Sam2_postNeg")  # nu
func_t_box(Data_fem, "ID", "IOS_prePoz", "IOS_postPoz")  # sig  

5.3.1.6 IOS_prePoz IOS_postPoz

func_t_box(Data_fem, "ID", "IOS_preNeg", "IOS_postNeg")  # ns

5.3.1.7 IOS_preNeg IOS_postNeg

func_t_box(Data_fem, "ID", "DG_prePozTot", "DG_postPozTot")  # ns  

5.3.1.8 DG_prePozTot DG_postPozTot

func_t_box(Data_fem, "ID", "DG_preNegTot", "DG_postNegTot")  # ns

5.3.1.9 DG_preNegTot DG_postNegTot

func_t_box(Data_fem, "ID", "TrustMinPozPre", "TrustMinPozPost")  # ns  

5.3.1.10 TrustMinPozPre TrustMinPozPost

func_t_box(Data_fem, "ID", "TrustMinNegPre", "TrustMinNegPost")  # ns

5.3.1.11 TrustMinNegPre TrustMinNegPost

func_t_box(Data_fem, "ID", "TrustTotPozPre", "TrustTotPozPost")  # ns  

5.3.1.12 TrustTotPozPre TrustTotPozPost

func_t_box(Data_fem, "ID", "TrustTotNegPre", "TrustTotNegPost")  # ns

5.3.1.13 TrustTotNegPre TrustTotNegPost

## tChance and ANCOVAPost 
func_ancova_multibox(Data, "Indicativ", "Ox_pre_Poz", "Ox_post_Poz", "Ox_pre_Neg", "Ox_post_Neg")
func_ancova_multibox(Data, "Indicativ", "Cort_pre_Poz", "Cort_post_Poz", "Cort_pre_Neg", "Cort_post_Neg")

func_ancova_multibox(Data, "Indicativ", "VasS_prePoz", "VasS_postPoz", "VasS_preNeg", "VasS_postNeg")
func_ancova_multibox(Data, "Indicativ", "VasB_prePoz", "VasB_postPoz", "VasB_preNeg", "VasB_postNeg")
func_ancova_multibox(Data, "Indicativ", "Sam1_prePoz", "Sam1_postPoz", "Sam1_preNeg", "Sam1_postNeg")
func_ancova_multibox(Data, "Indicativ", "Sam2_prePoz", "Sam2_postPoz", "Sam2_preNeg", "Sam2_postNeg")
func_ancova_multibox(Data, "Indicativ", "IOS_prePoz", "IOS_postPoz", "IOS_preNeg", "IOS_postNeg") 

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

func_ancova_multibox(Data, "Indicativ", "TrustMinPozPre", "TrustMinPozPost", "TrustMinNegPre", "TrustMinNegPost") 
func_ancova_multibox(Data, "Indicativ", "TrustTotPozPre", "TrustTotPozPost", "TrustTotNegPre", "TrustTotNegPost") 

–>

## 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()

5.4 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()

5.5 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()

5.6 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()

–>

6 Session Info

R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)

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] rio_0.5.16         plyr_1.8.4         summarytools_0.8.8 DT_0.5             ggpubr_0.2         magrittr_1.5       broom_0.5.2       
 [8] papaja_0.1.0.9842  psych_1.8.12       forcats_0.4.0      stringr_1.4.0      dplyr_0.8.3        purrr_0.3.2        readr_1.3.1       
[15] tidyr_1.0.0        tibble_2.1.3       ggplot2_3.2.1      tidyverse_1.2.1    pacman_0.5.1      

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2         lubridate_1.7.4    lattice_0.20-38    assertthat_0.2.1   zeallot_0.1.0      digest_0.6.21      R6_2.4.0          
 [8] cellranger_1.1.0   backports_1.1.4    httr_1.4.0         pillar_1.4.2       rlang_0.4.0        curl_3.2           lazyeval_0.2.2    
[15] readxl_1.1.0       data.table_1.11.8  rstudioapi_0.8     labeling_0.3       foreign_0.8-71     pander_0.6.3       htmlwidgets_1.3   
[22] RCurl_1.95-4.11    munsell_0.5.0      compiler_3.6.1     modelr_0.1.5       xfun_0.9           pkgconfig_2.0.3    mnormt_1.5-5      
[29] htmltools_0.3.6    tidyselect_0.2.5   codetools_0.2-16   matrixStats_0.54.0 crayon_1.3.4       withr_2.1.2        bitops_1.0-6      
[36] grid_3.6.1         nlme_3.1-140       jsonlite_1.6       gtable_0.3.0       lifecycle_0.1.0    scales_1.0.0       zip_1.0.0         
[43] cli_1.1.0          stringi_1.4.3      ggsignif_0.4.0     pryr_0.1.4         xml2_1.2.0         ellipsis_0.3.0     rapportools_1.0   
[50] generics_0.0.2     vctrs_0.2.0        openxlsx_4.1.0     tools_3.6.1        glue_1.3.1         hms_0.5.1          parallel_3.6.1    
[57] colorspace_1.4-1   rvest_0.3.2        knitr_1.25         haven_2.1.1       

 

---

A work by Claudiu Papasteri

claudiu.papasteri@gmail.com