Estructuras fenomonológicas y precisión interoceriva (rho)

Tarea de detección de latidos usando teclado

cat('\014')     # clean terminal

rm(list = ls()) # clean workspace
library(tidyverse)
library(readxl)
library(afex)
library(emmeans)
library(easystats)

my_dodge  <- .3
my_jitter <- .2

theme_set(theme_minimal())

a_posteriori_aov_ez <- function(aov_ez_object, sig_level = .05) {
  factors  <- as.list(rownames(aov_ez_object$anova_table))
  for (j in 1:length(factors)) {
    if (grepl(':', factors[[j]])) {
      factors[[j]] <- unlist(strsplit(factors[[j]], ':'))
    }
  }
  p_values <- aov_ez_object$anova_table$`Pr(>F)`
  for (i in 1:length(p_values)) {
    if (p_values[i] <= sig_level) {
      cat(rep('_', 60), '\n', sep = '')
      print(emmeans(aov_ez_object, factors[[i]], contr = 'pairwise'))
    }
  }
}

a_posteriori_lmer <- function(lmer_obj, sig_level = .05) {
  anova_lmer <- anova(lmer_obj)
  factors  <- as.list(row.names(anova_lmer))
  for (j in 1:length(factors)) {
    if (grepl(':', factors[[j]])) {
      factors[[j]] <- unlist(strsplit(factors[[j]], ':'))
    }
  }
  p_values <- anova_lmer$`Pr(>F)`
  for (i in 1:length(p_values)) {
    if (p_values[i] <= sig_level) {
      cat(rep('_', 60), '\n', sep = '')
      print(emmeans(lmer_obj, factors[[i]], contr = 'pairwise'))
    }
  }
}

rho_df     <- read_csv('rho_wide.csv', show_col_types = FALSE)
struct_df  <- read_excel('ESTRATEGIAS FENO DE INTERO CARDIACA edit.xlsx') |> 
  mutate(Participante = parse_number(ID),
         Grupo        = case_match(substring(ID, 1, 1), 'N' ~ 'normopeso', 'O' ~ 'obeso'))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `Grupo = case_match(substring(ID, 1, 1), "N" ~ "normopeso", "O"
  ~ "obeso")`.
Caused by warning:
! `case_match()` was deprecated in dplyr 1.2.0.
ℹ Please use `recode_values()` instead.

intero_df  <- merge(struct_df, rho_df[c('Participante', 'Intero1', 'Sexo')], by = 'Participante') |> 
  mutate(log_rho      = log(Intero1), 
         Participante = factor(Participante),
         Grupo        = relevel(factor(Grupo), 'obeso')) |> 
  mutate_if(is.character, as.factor)
write.csv(intero_df,  'structures_and_intero_rho.csv',  row.names = FALSE)

Descripción general

addmargins(xtabs(~ Estructura + Grupo + Sexo, data = intero_df))

, , Sexo = Femenino

             Grupo
Estructura    obeso normopeso Sum
  extension      12         5  17
  imaginativa    11        11  22
  pecho           6         4  10
  Sum            29        20  49

, , Sexo = Masculino

             Grupo
Estructura    obeso normopeso Sum
  extension       3         5   8
  imaginativa     4         5   9
  pecho           4         3   7
  Sum            11        13  24

, , Sexo = Sum

             Grupo
Estructura    obeso normopeso Sum
  extension      15        10  25
  imaginativa    15        16  31
  pecho          10         7  17
  Sum            40        33  73

summary(intero_df)

  Participante       ID           Estructura       Grupo       Intero1        
 101    : 1    N101   : 1   extension  :25   obeso    :40   Min.   :0.007103  
 102    : 1    N102   : 1   imaginativa:31   normopeso:33   1st Qu.:0.026686  
 103    : 1    N103   : 1   pecho      :17                  Median :0.046123  
 104    : 1    N104   : 1                                   Mean   :0.061715  
 105    : 1    N105   : 1                                   3rd Qu.:0.080124  
 106    : 1    N106   : 1                                   Max.   :0.241604  
 (Other):67    (Other):67                                                     
        Sexo       log_rho      
 Femenino :49   Min.   :-4.947  
 Masculino:24   1st Qu.:-3.624  
                Median :-3.076  
                Mean   :-3.071  
                3rd Qu.:-2.524  
                Max.   :-1.420  
                                

setdiff(rho_df$Participante, struct_df$Participante)

[1] 130

setdiff(struct_df$Participante, rho_df$Participante)

[1] 136 137 226 242

Estructuras:

struct_anova <- aov_ez('Participante', 'log_rho', intero_df, between = c('Estructura', 'Grupo', 'Sexo'))
afex_plot(
  struct_anova,
  x     = 'Estructura',
  trace = 'Grupo',
  panel = 'Sexo',
  error_arg = list(width = .25),
  dodge     = .3,
  data_arg  = list(
    position = 
      position_jitterdodge(
        jitter.width  = .1, 
        jitter.height = 0, 
        dodge.width   = 0.3  ## needs to be same as dodge
      )),
  mapping   = c('color'),
  point_arg = list(size = 3)
)

Figure 1: Estructuras & log(rho)

ANOVA Estructuras

summary(struct_anova)

Anova Table (Type 3 tests)

Response: log_rho
                      num Df den Df     MSE      F      ges   Pr(>F)   
Estructura                 2     61 0.49046 1.5447 0.048206 0.221596   
Grupo                      1     61 0.49046 0.0277 0.000454 0.868314   
Sexo                       1     61 0.49046 9.7472 0.137775 0.002745 **
Estructura:Grupo           2     61 0.49046 0.7129 0.022839 0.494267   
Estructura:Sexo            2     61 0.49046 1.7172 0.053300 0.188138   
Grupo:Sexo                 1     61 0.49046 0.8134 0.013159 0.370670   
Estructura:Grupo:Sexo      2     61 0.49046 1.4087 0.044149 0.252291   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc tests Estructuras

a_posteriori_lmer(struct_anova)

____________________________________________________________

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 Sexo      emmean    SE df lower.CL upper.CL
 Femenino   -3.28 0.110 61     -3.5    -3.06
 Masculino  -2.71 0.146 61     -3.0    -2.42

Results are averaged over the levels of: Estructura, Grupo 
Confidence level used: 0.95 

$contrasts
 contrast             estimate    SE df t.ratio p.value
 Femenino - Masculino    -0.57 0.183 61  -3.122  0.0027

Results are averaged over the levels of: Estructura, Grupo 

Supuestos estadísticos Estructuras

check_model(struct_anova)

Figure 2: Supuestos estadísticos del modelo