# CARREGAR BIBLIOTECAS

library(summarytools)
library(qwraps2)
library(knitr)
library(Hmisc)
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
## 
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:summarytools':
## 
##     label, label<-
## The following objects are masked from 'package:base':
## 
##     format.pval, units
library(nortest)

library(data.table)
library(ggplot2)
library(tidyverse)
## -- Attaching packages --------------------------------------------------------- tidyverse 1.2.1 --
## v tibble  1.4.2     v purrr   0.2.5
## v tidyr   0.8.1     v dplyr   0.7.8
## v readr   1.1.1     v stringr 1.3.1
## v tibble  1.4.2     v forcats 0.3.0
## -- Conflicts ------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::between()   masks data.table::between()
## x dplyr::filter()    masks stats::filter()
## x dplyr::first()     masks data.table::first()
## x dplyr::lag()       masks stats::lag()
## x dplyr::last()      masks data.table::last()
## x dplyr::src()       masks Hmisc::src()
## x dplyr::summarize() masks Hmisc::summarize()
## x purrr::transpose() masks data.table::transpose()
# CARREGAR BANCO DE DADOS

bd_final = fread(file.choose()) # bd_final2_wide
#names(bd_final)
#glimpse(bd_final)

attach(bd_final)


# CRIAR/EDITAR VARIÁVEIS
#names(bd_final[,c(105:125,128:148)]) 
bd_final = bd_final %>% mutate(
  scared = case_when(scared == 0 ~ 1, 
                     scared == 1 ~ 2, 
                     scared == 2 ~ 3,
                     scared == 3 ~ 4, 
                     scared == 4 ~ 5),
  terrified = case_when(terrified == 0 ~ 1, 
                        terrified == 1 ~ 2,
                        terrified == 2 ~ 3, 
                        terrified == 3 ~ 4,
                        terrified == 4 ~ 5),
  nervous = case_when(nervous == 0 ~ 1, 
                      nervous == 1 ~ 2, 
                      nervous == 2 ~ 3,
                      nervous == 3 ~ 4, 
                      nervous == 4 ~ 5),
  restless = case_when(restless == 0 ~ 1, 
                       restless == 1 ~ 2, 
                       restless == 2 ~ 3,
                       restless == 3 ~ 4, 
                       restless == 4 ~ 5),
  trembling = case_when(trembling == 0 ~ 1, 
                        trembling == 1 ~ 2,
                        trembling == 2 ~ 3,
                        trembling == 3 ~ 4, 
                        trembling == 4 ~ 5),
  angry = case_when(angry == 0 ~ 1,
                    angry == 1 ~ 2, 
                    angry == 2 ~ 3,
                    angry == 3 ~ 4, 
                    angry == 4 ~ 5),
  unfriendly = case_when(unfriendly == 0 ~ 1,
                         unfriendly == 1 ~ 2, 
                         unfriendly == 2 ~ 3,
                         unfriendly == 3 ~ 4,
                         unfriendly == 4 ~ 5),
  irritable = case_when(irritable == 0 ~ 1,
                        irritable == 1 ~ 2,
                        irritable == 2 ~ 3,
                        irritable == 3 ~ 4, 
                        irritable == 4 ~ 5),
  fill_with_disdain = case_when(fill_with_disdain == 0 ~ 1,
                                fill_with_disdain == 1 ~ 2, 
                                fill_with_disdain == 2 ~ 3,
                                fill_with_disdain == 3 ~ 4, 
                                fill_with_disdain == 4 ~ 5),
  bored = case_when(bored == 0 ~ 1, 
                    bored == 1 ~ 2, 
                    bored == 2 ~ 3, 
                    bored == 3 ~ 4, 
                    bored == 4 ~ 5),
  hating = case_when(hating == 0 ~ 1, 
                     hating == 1 ~ 2, 
                     hating == 2 ~ 3, 
                     hating == 3 ~ 4, 
                     hating == 4 ~ 5),
  proud = case_when(proud == 0 ~ 1, 
                    proud == 1 ~ 2, 
                    proud == 2 ~ 3,
                    proud == 3 ~ 4,
                    proud == 4 ~ 5),
  strong = case_when(strong == 0 ~ 1, 
                     strong == 1 ~ 2, 
                     strong == 2 ~ 3, 
                     strong == 3 ~ 4, 
                     strong == 4 ~ 5),
  audacious = case_when(audacious == 0 ~ 1, 
                        audacious == 1 ~ 2, 
                        audacious == 2 ~ 3, 
                        audacious == 3 ~ 4, 
                        audacious == 4 ~ 5),
  confident = case_when(confident == 0 ~ 1, 
                        confident == 1 ~ 2, 
                        confident == 2 ~ 3,
                        confident == 3 ~ 4, 
                        confident == 4 ~ 5),
  fearless = case_when(fearless == 0 ~ 1,
                       fearless == 1 ~ 2, 
                       fearless == 2 ~ 3, 
                       fearless == 3 ~ 4, 
                       fearless == 4 ~ 5),
  calm = case_when(calm == 0 ~ 1, 
                   calm == 1 ~ 2, 
                   calm == 2 ~ 3, 
                   calm == 3 ~ 4, 
                   calm == 4 ~ 5),
  relaxed = case_when(relaxed == 0 ~ 1, 
                      relaxed == 1 ~ 2, 
                      relaxed == 2 ~ 3, 
                      relaxed == 3 ~ 4, 
                      relaxed == 4 ~ 5),
  freely = case_when(freely == 0 ~ 1,
                     freely == 1 ~ 2, 
                     freely == 2 ~ 3, 
                     freely == 3 ~ 4, 
                     freely == 4 ~ 5),
  scared_av2 = case_when(scared_av2 == 0 ~ 1, 
                         scared_av2 == 1 ~ 2, 
                         scared_av2 == 2 ~ 3,
                         scared_av2 == 3 ~ 4, 
                         scared_av2 == 4 ~ 5),
  terrified_av2 = case_when(terrified_av2 == 0 ~ 1, 
                            terrified_av2 == 1 ~ 2,
                            terrified_av2 == 2 ~ 3, 
                            terrified_av2 == 3 ~ 4,
                            terrified_av2 == 4 ~ 5),
  nervous_av2 = case_when(nervous_av2 == 0 ~ 1, 
                          nervous_av2 == 1 ~ 2, 
                          nervous_av2 == 2 ~ 3,
                          nervous_av2 == 3 ~ 4, 
                          nervous_av2 == 4 ~ 5),
  restless_av2 = case_when(restless_av2 == 0 ~ 1, 
                           restless_av2 == 1 ~ 2, 
                           restless_av2 == 2 ~ 3,
                           restless_av2 == 3 ~ 4, 
                           restless_av2 == 4 ~ 5),
  trembling_av2 = case_when(trembling_av2 == 0 ~ 1, 
                            trembling_av2 == 1 ~ 2,
                            trembling_av2 == 2 ~ 3,
                            trembling_av2 == 3 ~ 4, 
                            trembling_av2 == 4 ~ 5),
  angry_av2 = case_when(angry_av2 == 0 ~ 1,
                        angry_av2 == 1 ~ 2, 
                        angry_av2 == 2 ~ 3,
                        angry_av2 == 3 ~ 4, 
                        angry_av2 == 4 ~ 5),
  unfriendly_av2 = case_when(unfriendly_av2 == 0 ~ 1,
                             unfriendly_av2 == 1 ~ 2, 
                             unfriendly_av2 == 2 ~ 3,
                             unfriendly_av2 == 3 ~ 4,
                             unfriendly_av2 == 4 ~ 5),
  irritable_av2 = case_when(irritable_av2 == 0 ~ 1,
                            irritable_av2 == 1 ~ 2,
                            irritable_av2 == 2 ~ 3,
                            irritable_av2 == 3 ~ 4, 
                            irritable_av2 == 4 ~ 5),
  fill_with_disdain_av2 = case_when(fill_with_disdain_av2 == 0 ~ 1,
                                    fill_with_disdain_av2 == 1 ~ 2, 
                                    fill_with_disdain_av2 == 2 ~ 3,
                                    fill_with_disdain_av2 == 3 ~ 4, 
                                    fill_with_disdain_av2 == 4 ~ 5),
  bored_av2 = case_when(bored_av2 == 0 ~ 1, 
                        bored_av2 == 1 ~ 2, 
                        bored_av2 == 2 ~ 3, 
                        bored_av2 == 3 ~ 4, 
                        bored_av2 == 4 ~ 5),
  hating_av2 = case_when(hating_av2 == 0 ~ 1, 
                         hating_av2 == 1 ~ 2, 
                         hating_av2 == 2 ~ 3, 
                         hating_av2 == 3 ~ 4, 
                         hating_av2 == 4 ~ 5),
  proud_av2 = case_when(proud_av2 == 0 ~ 1, 
                        proud_av2 == 1 ~ 2, 
                        proud_av2 == 2 ~ 3,
                        proud_av2 == 3 ~ 4,
                        proud_av2 == 4 ~ 5),
  strong_av2 = case_when(strong_av2 == 0 ~ 1, 
                         strong_av2 == 1 ~ 2, 
                         strong_av2 == 2 ~ 3, 
                         strong_av2 == 3 ~ 4, 
                         strong_av2 == 4 ~ 5),
  audacious_av2 = case_when(audacious_av2 == 0 ~ 1, 
                            audacious_av2 == 1 ~ 2, 
                            audacious_av2 == 2 ~ 3, 
                            audacious_av2 == 3 ~ 4, 
                            audacious_av2 == 4 ~ 5),
  confident_av2 = case_when(confident_av2 == 0 ~ 1, 
                            confident_av2 == 1 ~ 2, 
                            confident_av2 == 2 ~ 3,
                            confident_av2 == 3 ~ 4, 
                            confident_av2 == 4 ~ 5),
  fearless_av2 = case_when(fearless_av2 == 0 ~ 1,
                           fearless_av2 == 1 ~ 2, 
                           fearless_av2 == 2 ~ 3, 
                           fearless_av2 == 3 ~ 4, 
                           fearless_av2 == 4 ~ 5),
  calm_av2 = case_when(calm_av2 == 0 ~ 1, 
                       calm_av2 == 1 ~ 2, 
                       calm_av2 == 2 ~ 3, 
                       calm_av2 == 3 ~ 4, 
                       calm_av2 == 4 ~ 5),
  relaxed_av2 = case_when(relaxed_av2 == 0 ~ 1, 
                          relaxed_av2 == 1 ~ 2, 
                          relaxed_av2 == 2 ~ 3, 
                          relaxed_av2 == 3 ~ 4, 
                          relaxed_av2 == 4 ~ 5),
  freely_av2 = case_when(freely_av2 == 0 ~ 1,
                         freely_av2 == 1 ~ 2, 
                         freely_av2 == 2 ~ 3, 
                         freely_av2 == 3 ~ 4, 
                         freely_av2 == 4 ~ 5))

bd_final = bd_final %>% mutate(
  medo_av1 = scared + terrified + nervous + restless + trembling,
  hostilidade_av1 = angry + unfriendly + irritable + fill_with_disdain + bored + hating,
  assertividade_av1 = proud + strong + audacious + confident + fearless,
  serenidade_av1 = calm + relaxed + freely,
  
  medo_av2 = scared_av2 + terrified_av2 + nervous_av2 + restless_av2 + trembling_av2,
  hostilidade_av2 = angry_av2 + unfriendly_av2 + irritable_av2 + fill_with_disdain_av2 + 
    bored_av2 + hating_av2,
  assertividade_av2 = proud_av2 + strong_av2 + audacious_av2 + confident_av2 + fearless_av2,
  serenidade_av2 = calm_av2 + relaxed_av2 + freely_av2)

bd_final = bd_final %>% mutate(
  negative_affects_av1 = medo_av1 + hostilidade_av1,
  positive_affects_av1 = assertividade_av1 + serenidade_av1,
  negative_affects_av2 = medo_av2 + hostilidade_av2,
  positive_affects_av2 = assertividade_av2 + serenidade_av2)

#<média/delta sono>

bd_final$md_sono_av1 <- rowMeans(bd_final[,c(150:172)], na.rm = T)
bd_final$md_sono_av2 <- rowMeans(bd_final[,c(173:195)], na.rm = T)


##############################
###### ANALISES PARTE 1 ######
##############################


detach(bd_final)
attach(bd_final)


# OVERVIEW (vars numericas)

bd_vars_num = bd_final %>% select(
  age,weight,imc,height,height,ansiedade_apego,evitacao_apego,cuidado_materno_pnts,
  cuidado_paterno_pnts,protecao_materno_pnts,protecao_paterno_pnts,cortisol_value,
  cortisol_value_av2,hamilton_av1,hamilton_av2,medo_av1,hostilidade_av1,
  assertividade_av1,serenidade_av1,medo_av2,hostilidade_av2,assertividade_av2,
  serenidade_av2,negative_affects_av1,positive_affects_av1,negative_affects_av2,
  positive_affects_av2,md_sono_av1,md_sono_av2)

print(
  dfSummary(bd_vars_num, graph.magnif = 0.75), 
  method = "render")

Data Frame Summary

bd_vars_num

N: 26
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 age [numeric] mean (sd) : 38.21 (10.31) min < med < max : 19.6 < 36.95 < 60.2 IQR (CV) : 14.8 (0.27) 25 distinct values 26 (100%) 0 (0%)
2 weight [numeric] mean (sd) : 83.01 (17.81) min < med < max : 56 < 84 < 135 IQR (CV) : 25.38 (0.21) 24 distinct values 26 (100%) 0 (0%)
3 imc [numeric] mean (sd) : 26.9 (5.39) min < med < max : 17.28 < 26.19 < 41.67 IQR (CV) : 3.41 (0.2) 26 distinct values 26 (100%) 0 (0%)
4 height [numeric] mean (sd) : 1.76 (0.07) min < med < max : 1.64 < 1.75 < 1.91 IQR (CV) : 0.07 (0.04) 16 distinct values 26 (100%) 0 (0%)
5 ansiedade_apego [integer] mean (sd) : 24.23 (6.54) min < med < max : 12 < 25 < 35 IQR (CV) : 6.75 (0.27) 17 distinct values 26 (100%) 0 (0%)
6 evitacao_apego [integer] mean (sd) : 20.77 (4.29) min < med < max : 13 < 21 < 29 IQR (CV) : 5.75 (0.21) 15 distinct values 26 (100%) 0 (0%)
7 cuidado_materno_pnts [integer] mean (sd) : 22 (8.3) min < med < max : 5 < 20.5 < 36 IQR (CV) : 14.25 (0.38) 17 distinct values 26 (100%) 0 (0%)
8 cuidado_paterno_pnts [integer] mean (sd) : 17.85 (9.89) min < med < max : 0 < 17.5 < 36 IQR (CV) : 12 (0.55) 20 distinct values 26 (100%) 0 (0%)
9 protecao_materno_pnts [integer] mean (sd) : 20.96 (7.83) min < med < max : 5 < 18.5 < 35 IQR (CV) : 11.75 (0.37) 18 distinct values 26 (100%) 0 (0%)
10 protecao_paterno_pnts [integer] mean (sd) : 16.04 (7.83) min < med < max : 2 < 18 < 34 IQR (CV) : 11 (0.49) 15 distinct values 26 (100%) 0 (0%)
11 cortisol_value [numeric] mean (sd) : 5.34 (1.24) min < med < max : 3.22 < 5.2 < 8.59 IQR (CV) : 1.49 (0.23) 24 distinct values 25 (96.15%) 1 (3.85%)
12 cortisol_value_av2 [numeric] mean (sd) : 4.45 (0.93) min < med < max : 2.89 < 4.34 < 6.84 IQR (CV) : 0.84 (0.21) 26 distinct values 26 (100%) 0 (0%)
13 hamilton_av1 [integer] mean (sd) : 29.23 (9.21) min < med < max : 14 < 28.5 < 49 IQR (CV) : 10.75 (0.32) 17 distinct values 26 (100%) 0 (0%)
14 hamilton_av2 [integer] mean (sd) : 20.42 (10.96) min < med < max : 3 < 20 < 51 IQR (CV) : 13.75 (0.54) 19 distinct values 26 (100%) 0 (0%)
15 medo_av1 [numeric] mean (sd) : 15.23 (3.83) min < med < max : 8 < 16 < 25 IQR (CV) : 4.75 (0.25) 13 distinct values 26 (100%) 0 (0%)
16 hostilidade_av1 [numeric] mean (sd) : 16.19 (6) min < med < max : 6 < 17 < 28 IQR (CV) : 9.75 (0.37) 16 distinct values 26 (100%) 0 (0%)
17 assertividade_av1 [numeric] mean (sd) : 10.35 (3.63) min < med < max : 5 < 10.5 < 18 IQR (CV) : 4.75 (0.35) 12 distinct values 26 (100%) 0 (0%)
18 serenidade_av1 [numeric] mean (sd) : 5.65 (1.6) min < med < max : 3 < 6 < 9 IQR (CV) : 2.75 (0.28) 3 : 2 (7.7%) 4 : 5 (19.2%) 5 : 5 (19.2%) 6 : 6 (23.1%) 7 : 6 (23.1%) 9 : 2 (7.7%) 26 (100%) 0 (0%)
19 medo_av2 [numeric] mean (sd) : 11.42 (3.98) min < med < max : 5 < 11 < 19 IQR (CV) : 5.75 (0.35) 14 distinct values 26 (100%) 0 (0%)
20 hostilidade_av2 [numeric] mean (sd) : 12.65 (5.24) min < med < max : 6 < 12.5 < 25 IQR (CV) : 5 (0.41) 14 distinct values 26 (100%) 0 (0%)
21 assertividade_av2 [numeric] mean (sd) : 11.62 (3.7) min < med < max : 5 < 11 < 19 IQR (CV) : 4.75 (0.32) 14 distinct values 26 (100%) 0 (0%)
22 serenidade_av2 [numeric] mean (sd) : 7.27 (2.05) min < med < max : 4 < 7 < 12 IQR (CV) : 3 (0.28) 4 : 3 (11.5%) 5 : 2 (7.7%) 6 : 5 (19.2%) 7 : 4 (15.4%) 8 : 4 (15.4%) 9 : 5 (19.2%) 10 : 2 (7.7%) 12 : 1 (3.8%) 26 (100%) 0 (0%)
23 negative_affects_av1 [numeric] mean (sd) : 31.42 (8.96) min < med < max : 18 < 33 < 53 IQR (CV) : 13.5 (0.29) 20 distinct values 26 (100%) 0 (0%)
24 positive_affects_av1 [numeric] mean (sd) : 16 (4.7) min < med < max : 8 < 16 < 27 IQR (CV) : 6 (0.29) 15 distinct values 26 (100%) 0 (0%)
25 negative_affects_av2 [numeric] mean (sd) : 24.08 (8) min < med < max : 11 < 24 < 42 IQR (CV) : 10.75 (0.33) 20 distinct values 26 (100%) 0 (0%)
26 positive_affects_av2 [numeric] mean (sd) : 18.88 (5.18) min < med < max : 9 < 18.5 < 28 IQR (CV) : 7.75 (0.27) 16 distinct values 26 (100%) 0 (0%)
27 md_sono_av1 [numeric] mean (sd) : 8.08 (1.1) min < med < max : 6.51 < 8.12 < 10.24 IQR (CV) : 1.73 (0.14) 25 distinct values 25 (96.15%) 1 (3.85%)
28 md_sono_av2 [numeric] mean (sd) : 7.8 (1.13) min < med < max : 6.22 < 7.64 < 10.54 IQR (CV) : 1.33 (0.15) 25 distinct values 25 (96.15%) 1 (3.85%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-03

print(
  descr(bd_vars_num, transpose = TRUE, omit.headings = TRUE, style = "rmarkdown"),
  method = "render") # stats = c("mean","sd","med","iqr","min","max","skewness","kurtosis")
Mean Std.Dev Min Q1 Median Q3 Max MAD IQR CV Skewness SE.Skewness Kurtosis N.Valid Pct.Valid
age 38.21 10.31 19.60 30.60 36.95 47.20 60.20 9.49 14.80 0.27 0.33 0.46 -0.68 26.00 100.00
weight 83.01 17.81 56.00 66.00 84.00 92.50 135.00 19.27 25.38 0.21 0.71 0.46 0.63 26.00 100.00
imc 26.90 5.39 17.28 24.54 26.19 28.09 41.67 2.63 3.41 0.20 0.83 0.46 0.66 26.00 100.00
height 1.76 0.07 1.64 1.72 1.75 1.80 1.91 0.07 0.07 0.04 0.32 0.46 -0.47 26.00 100.00
ansiedade_apego 24.23 6.54 12.00 22.00 25.00 29.00 35.00 5.19 6.75 0.27 -0.47 0.46 -0.68 26.00 100.00
evitacao_apego 20.77 4.29 13.00 17.00 21.00 23.00 29.00 4.45 5.75 0.21 -0.07 0.46 -0.90 26.00 100.00
cuidado_materno_pnts 22.00 8.30 5.00 15.00 20.50 30.00 36.00 8.15 14.25 0.38 0.08 0.46 -1.06 26.00 100.00
cuidado_paterno_pnts 17.85 9.89 0.00 13.00 17.50 26.00 36.00 7.41 12.00 0.55 -0.09 0.46 -0.68 26.00 100.00
protecao_materno_pnts 20.96 7.83 5.00 16.00 18.50 29.00 35.00 6.67 11.75 0.37 0.22 0.46 -1.00 26.00 100.00
protecao_paterno_pnts 16.04 7.83 2.00 10.00 18.00 21.00 34.00 6.67 11.00 0.49 -0.11 0.46 -0.65 26.00 100.00
cortisol_value 5.34 1.24 3.22 4.50 5.20 5.99 8.59 1.17 1.49 0.23 0.57 0.46 -0.07 25.00 96.15
cortisol_value_av2 4.45 0.93 2.89 3.85 4.34 4.72 6.84 0.67 0.84 0.21 0.54 0.46 0.01 26.00 100.00
hamilton_av1 29.23 9.21 14.00 23.00 28.50 34.00 49.00 8.15 10.75 0.32 0.55 0.46 -0.28 26.00 100.00
hamilton_av2 20.42 10.96 3.00 13.00 20.00 28.00 51.00 11.12 13.75 0.54 0.77 0.46 0.30 26.00 100.00
medo_av1 15.23 3.83 8.00 13.00 16.00 18.00 25.00 2.97 4.75 0.25 0.15 0.46 -0.10 26.00 100.00
hostilidade_av1 16.19 6.00 6.00 10.00 17.00 20.00 28.00 7.41 9.75 0.37 0.15 0.46 -1.06 26.00 100.00
assertividade_av1 10.35 3.63 5.00 8.00 10.50 13.00 18.00 3.71 4.75 0.35 0.18 0.46 -0.83 26.00 100.00
serenidade_av1 5.65 1.60 3.00 4.00 6.00 7.00 9.00 1.48 2.75 0.28 0.27 0.46 -0.60 26.00 100.00
medo_av2 11.42 3.98 5.00 8.00 11.00 14.00 19.00 4.45 5.75 0.35 0.21 0.46 -0.95 26.00 100.00
hostilidade_av2 12.65 5.24 6.00 9.00 12.50 14.00 25.00 5.19 5.00 0.41 0.80 0.46 -0.28 26.00 100.00
assertividade_av2 11.62 3.70 5.00 9.00 11.00 14.00 19.00 2.97 4.75 0.32 0.33 0.46 -0.69 26.00 100.00
serenidade_av2 7.27 2.05 4.00 6.00 7.00 9.00 12.00 2.22 3.00 0.28 0.16 0.46 -0.68 26.00 100.00
negative_affects_av1 31.42 8.96 18.00 23.00 33.00 37.00 53.00 11.12 13.50 0.29 0.32 0.46 -0.65 26.00 100.00
positive_affects_av1 16.00 4.70 8.00 13.00 16.00 19.00 27.00 4.45 6.00 0.29 0.22 0.46 -0.67 26.00 100.00
negative_affects_av2 24.08 8.00 11.00 17.00 24.00 28.00 42.00 8.15 10.75 0.33 0.48 0.46 -0.40 26.00 100.00
positive_affects_av2 18.88 5.18 9.00 15.00 18.50 23.00 28.00 5.93 7.75 0.27 0.13 0.46 -1.06 26.00 100.00
md_sono_av1 8.08 1.10 6.51 7.18 8.12 8.91 10.24 1.39 1.73 0.14 0.24 0.46 -1.21 25.00 96.15
md_sono_av2 7.80 1.13 6.22 6.93 7.64 8.27 10.54 1.05 1.33 0.15 0.71 0.46 -0.30 25.00 96.15

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-03

# OVERVIEW (vars categoricas)

bd_vars_cat = bd_final %>% select(
  gender, education_level, labor_status,
  living_dt___0,living_dt___1,living_dt___2,living_dt___3,living_dt___4,living_dt___5,
  children,children_amount,
  health_issues___1,health_issues___2,health_issues___3,health_issues___4,
  health_issues___5,health_issues___6,health_issues___7,health_issues___8,
  psycho_treatment, previous_surgery,
  physical_exercises,physical_exercices_freq,
  physical_exercices_type___1,physical_exercices_type___2,
  physical_exercices_type___3,physical_exercices_type___4,
  physical_exercices_type___5,physical_exercices_type___6,
  physical_exercices_type___7,physical_exercices_type___8,
  physical_exercices_type___9,physical_exercices_type___10,physical_exercices_type___11,
  snore_habit,snore_claims,sleep_difficulties,sleep_maintenance,sleep_satisfaction,
  stay_at_home,being_alone,going_out,go_to_gym,reading,electronic_media)

col_names <- names(bd_vars_cat) # ver: str(bd_vars_cat)
bd_vars_cat[,names(bd_vars_cat)] = lapply(bd_vars_cat[,col_names], factor)

print(
  dfSummary(bd_vars_cat, graph.magnif = 0.75), 
  method = "render")

Data Frame Summary

bd_vars_cat

N: 26
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 gender [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
2 education_level [factor] 1. 5 2. 6 3. 7 4. 8 5. 9 7 (26.9%) 5 (19.2%) 8 (30.8%) 5 (19.2%) 1 (3.8%) 26 (100%) 0 (0%)
3 labor_status [factor] 1. 1 2. 2 3. 3 4. 4 14 (53.8%) 6 (23.1%) 1 (3.8%) 5 (19.2%) 26 (100%) 0 (0%)
4 living_dt___0 [factor] 1. 0 2. 1 20 (76.9%) 6 (23.1%) 26 (100%) 0 (0%)
5 living_dt___1 [factor] 1. 0 2. 1 15 (57.7%) 11 (42.3%) 26 (100%) 0 (0%)
6 living_dt___2 [factor] 1. 0 2. 1 20 (76.9%) 6 (23.1%) 26 (100%) 0 (0%)
7 living_dt___3 [factor] 1. 0 2. 1 20 (76.9%) 6 (23.1%) 26 (100%) 0 (0%)
8 living_dt___4 [factor] 1. 0 2. 1 21 (80.8%) 5 (19.2%) 26 (100%) 0 (0%)
9 living_dt___5 [factor] 1. 0 2. 1 23 (88.5%) 3 (11.5%) 26 (100%) 0 (0%)
10 children [factor] 1. 0 2. 1 20 (76.9%) 6 (23.1%) 26 (100%) 0 (0%)
11 children_amount [factor] 1. 1 2. 2 5 (83.3%) 1 (16.7%) 6 (23.08%) 20 (76.92%)
12 health_issues___1 [factor] 1. 0 2. 1 21 (80.8%) 5 (19.2%) 26 (100%) 0 (0%)
13 health_issues___2 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
14 health_issues___3 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
15 health_issues___4 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
16 health_issues___5 [factor] 1. 0 2. 1 24 (92.3%) 2 (7.7%) 26 (100%) 0 (0%)
17 health_issues___6 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
18 health_issues___7 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
19 health_issues___8 [factor] 1. 0 2. 1 7 (26.9%) 19 (73.1%) 26 (100%) 0 (0%)
20 psycho_treatment [factor] 1. 0 2. 1 8 (30.8%) 18 (69.2%) 26 (100%) 0 (0%)
21 previous_surgery [factor] 1. 0 2. 1 14 (53.8%) 12 (46.2%) 26 (100%) 0 (0%)
22 physical_exercises [factor] 1. 0 2. 1 15 (57.7%) 11 (42.3%) 26 (100%) 0 (0%)
23 physical_exercices_freq [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 3 (27.3%) 2 (18.2%) 3 (27.3%) 1 (9.1%) 1 (9.1%) 1 (9.1%) 11 (42.31%) 15 (57.69%)
24 physical_exercices_type___1 [factor] 1. 0 2. 1 17 (65.4%) 9 (34.6%) 26 (100%) 0 (0%)
25 physical_exercices_type___2 [factor] 1. 0 2. 1 21 (80.8%) 5 (19.2%) 26 (100%) 0 (0%)
26 physical_exercices_type___3 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
27 physical_exercices_type___4 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
28 physical_exercices_type___5 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
29 physical_exercices_type___6 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
30 physical_exercices_type___7 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
31 physical_exercices_type___8 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
32 physical_exercices_type___9 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
33 physical_exercices_type___10 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
34 physical_exercices_type___11 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
35 snore_habit [factor] 1. 0 2. 1 11 (42.3%) 15 (57.7%) 26 (100%) 0 (0%)
36 snore_claims [factor] 1. 0 2. 1 14 (53.8%) 12 (46.2%) 26 (100%) 0 (0%)
37 sleep_difficulties [factor] 1. 0 2. 1 8 (30.8%) 18 (69.2%) 26 (100%) 0 (0%)
38 sleep_maintenance [factor] 1. 0 2. 1 10 (38.5%) 16 (61.5%) 26 (100%) 0 (0%)
39 sleep_satisfaction [factor] 1. 0 2. 1 4 (15.4%) 22 (84.6%) 26 (100%) 0 (0%)
40 stay_at_home [factor] 1. 2 2. 3 3. 4 4. 5 1 (3.8%) 2 (7.7%) 18 (69.2%) 5 (19.2%) 26 (100%) 0 (0%)
41 being_alone [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 2 (7.7%) 4 (15.4%) 5 (19.2%) 12 (46.2%) 3 (11.5%) 26 (100%) 0 (0%)
42 going_out [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 1 (3.8%) 2 (7.7%) 9 (34.6%) 12 (46.2%) 2 (7.7%) 26 (100%) 0 (0%)
43 go_to_gym [factor] 1. 1 2. 2 3. 3 4. 4 3 (11.5%) 9 (34.6%) 9 (34.6%) 5 (19.2%) 26 (100%) 0 (0%)
44 reading [factor] 1. 2 2. 3 3. 4 4. 5 3 (11.5%) 10 (38.5%) 10 (38.5%) 3 (11.5%) 26 (100%) 0 (0%)
45 electronic_media [factor] 1. 2 2. 3 3. 4 4. 5 2 (7.7%) 3 (11.5%) 17 (65.4%) 4 (15.4%) 26 (100%) 0 (0%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-03

# OVERVIEW (vars categoricas - MINI)

bd_vars_mini = bd_final %>% select(
  mini_edm___0,mini_edm___1,mini_edm___2,mini_edm___3,
  mini_distimia___1,mini_suicidio,
  mini_hipomania___0,mini_hipomania___1,mini_hipomania___2,
  mini_mania___0,mini_mania___1,mini_mania___2,
  mini_panico___0,mini_panico___1,mini_panico___2,mini_panico___3,
  mini_agorafobia___0,mini_agorafobia___1,mini_agorafobia___2,mini_agorafobia___3,
  mini_agorafobia___4,mini_acompanha_psi,mini_medicacoes,
  mini_fobia_social___1,mini_toc___1,mini_tept___1,
  mini_alcool___0,mini_alcool___1,mini_alcool___2,
  mini_substancio___0,mini_substancio___1,mini_substancio___2,
  mini_psicose___0,mini_psicose___1,mini_psicose___2,
  mini_bulimia___0,mini_bulimia___1,mini_bulimia___2,
  mini_ansiedade___1,mini_personalidade___1)

col_names <- names(bd_vars_mini)
bd_vars_mini[,names(bd_vars_mini)] = lapply(bd_vars_mini[,col_names], factor)

print(
  dfSummary(bd_vars_mini, graph.magnif = 0.75), 
  method = "render")

Data Frame Summary

bd_vars_mini

N: 26
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 mini_edm___0 [factor] 1. 0 2. 1 19 (73.1%) 7 (26.9%) 26 (100%) 0 (0%)
2 mini_edm___1 [factor] 1. 0 2. 1 7 (26.9%) 19 (73.1%) 26 (100%) 0 (0%)
3 mini_edm___2 [factor] 1. 0 2. 1 13 (50.0%) 13 (50.0%) 26 (100%) 0 (0%)
4 mini_edm___3 [factor] 1. 0 2. 1 9 (34.6%) 17 (65.4%) 26 (100%) 0 (0%)
5 mini_distimia___1 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
6 mini_suicidio [factor] 1. 0 2. 1 3. 2 4. 3 12 (46.2%) 6 (23.1%) 2 (7.7%) 6 (23.1%) 26 (100%) 0 (0%)
7 mini_hipomania___0 [factor] 1. 0 2. 1 11 (42.3%) 15 (57.7%) 26 (100%) 0 (0%)
8 mini_hipomania___1 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
9 mini_hipomania___2 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
10 mini_mania___0 [factor] 1. 0 2. 1 15 (57.7%) 11 (42.3%) 26 (100%) 0 (0%)
11 mini_mania___1 [factor] 1. 0 2. 1 14 (53.8%) 12 (46.2%) 26 (100%) 0 (0%)
12 mini_mania___2 [factor] 1. 0 2. 1 21 (80.8%) 5 (19.2%) 26 (100%) 0 (0%)
13 mini_panico___0 [factor] 1. 0 2. 1 23 (88.5%) 3 (11.5%) 26 (100%) 0 (0%)
14 mini_panico___1 [factor] 1. 0 2. 1 4 (15.4%) 22 (84.6%) 26 (100%) 0 (0%)
15 mini_panico___2 [factor] 1. 0 2. 1 10 (38.5%) 16 (61.5%) 26 (100%) 0 (0%)
16 mini_panico___3 [factor] 1. 0 2. 1 6 (23.1%) 20 (76.9%) 26 (100%) 0 (0%)
17 mini_agorafobia___0 [factor] 1. 0 2. 1 22 (84.6%) 4 (15.4%) 26 (100%) 0 (0%)
18 mini_agorafobia___1 [factor] 1. 0 2. 1 12 (46.2%) 14 (53.8%) 26 (100%) 0 (0%)
19 mini_agorafobia___2 [factor] 1. 0 2. 1 19 (73.1%) 7 (26.9%) 26 (100%) 0 (0%)
20 mini_agorafobia___3 [factor] 1. 0 2. 1 15 (57.7%) 11 (42.3%) 26 (100%) 0 (0%)
21 mini_agorafobia___4 [factor] 1. 0 2. 1 24 (92.3%) 2 (7.7%) 26 (100%) 0 (0%)
22 mini_acompanha_psi [factor] 1. 0 2. 1 3. 2 4. 3 1 (25.0%) 1 (25.0%) 1 (25.0%) 1 (25.0%) 4 (15.38%) 22 (84.62%)
23 mini_medicacoes [factor] 1. 0 2. 2 1 (12.5%) 7 (87.5%) 8 (30.77%) 18 (69.23%)
24 mini_fobia_social___1 [factor] 1. 0 2. 1 12 (46.2%) 14 (53.8%) 26 (100%) 0 (0%)
25 mini_toc___1 [factor] 1. 0 2. 1 17 (65.4%) 9 (34.6%) 26 (100%) 0 (0%)
26 mini_tept___1 [factor] 1. 0 2. 1 24 (92.3%) 2 (7.7%) 26 (100%) 0 (0%)
27 mini_alcool___0 [factor] 1. 0 2. 1 3 (11.5%) 23 (88.5%) 26 (100%) 0 (0%)
28 mini_alcool___1 [factor] 1. 0 2. 1 23 (88.5%) 3 (11.5%) 26 (100%) 0 (0%)
29 mini_alcool___2 [factor] 1. 0 2. 1 24 (92.3%) 2 (7.7%) 26 (100%) 0 (0%)
30 mini_substancio___0 [factor] 1. 0 2. 1 1 (3.8%) 25 (96.2%) 26 (100%) 0 (0%)
31 mini_substancio___1 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
32 mini_substancio___2 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)
33 mini_psicose___0 [factor] 1. 0 2. 1 7 (26.9%) 19 (73.1%) 26 (100%) 0 (0%)
34 mini_psicose___1 [factor] 1. 0 2. 1 20 (76.9%) 6 (23.1%) 26 (100%) 0 (0%)
35 mini_psicose___2 [factor] 1. 0 2. 1 20 (76.9%) 6 (23.1%) 26 (100%) 0 (0%)
36 mini_bulimia___0 [factor] 1. 1 26 (100.0%) 26 (100%) 0 (0%)
37 mini_bulimia___1 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
38 mini_bulimia___2 [factor] 1. 0 26 (100.0%) 26 (100%) 0 (0%)
39 mini_ansiedade___1 [factor] 1. 1 26 (100.0%) 26 (100%) 0 (0%)
40 mini_personalidade___1 [factor] 1. 0 2. 1 25 (96.2%) 1 (3.8%) 26 (100%) 0 (0%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-03

### <fazer descritivas para vars entrevistas inicial e final + recategorizar vars mini?>


##############################
###### ANALISES PARTE 2 ######
##############################


# TESTES DE NORMALIDADE (vars desfecho no baseline) 

A = matrix(c(
  round(lillie.test(cortisol_value)$p.value, 4), # ver: lillie.test(cortisol_value)
  round(lillie.test(md_sono_av1)$p.value, 4), 
  round(lillie.test(hamilton_av1)$p.value, 4),
#  round(lillie.test(ansiedade_apego)$p.value, 4), # só baseline
#  round(lillie.test(evitacao_apego)$p.value, 4), # só baseline
  round(lillie.test(negative_affects_av1)$p.value, 4),
  round(lillie.test(positive_affects_av1)$p.value, 4),
  round(lillie.test(medo_av1)$p.value, 4),
  round(lillie.test(hostilidade_av1)$p.value, 4),
  round(lillie.test(assertividade_av1)$p.value, 4),
  round(lillie.test(serenidade_av1)$p.value, 4),
#  round(lillie.test(cuidado_materno_pnts)$p.value, 4), # categorizada (só baseline)
#  round(lillie.test(cuidado_paterno_pnts)$p.value, 4), # categorizada (só baseline)
#  round(lillie.test(protecao_materno_pnts)$p.value, 4), # categorizada (só baseline)
#  round(lillie.test(protecao_paterno_pnts)$p.value, 4), # categorizada (só baseline)
  
  round(shapiro.test(md_sono_av1)$p.value, 4), # ver: shapiro.test(md_sono_av1)
  round(shapiro.test(cortisol_value)$p.value, 4),
  round(shapiro.test(hamilton_av1)$p.value, 4),
#  round(shapiro.test(ansiedade_apego)$p.value, 4), # só baseline
#  round(shapiro.test(evitacao_apego)$p.value, 4), # só baseline
  round(shapiro.test(negative_affects_av1)$p.value, 4),
  round(shapiro.test(positive_affects_av1)$p.value, 4),
  round(shapiro.test(medo_av1)$p.value, 4),
  round(shapiro.test(hostilidade_av1)$p.value, 4),
  round(shapiro.test(assertividade_av1)$p.value, 4),
  round(shapiro.test(serenidade_av1)$p.value, 4)),
#  round(shapiro.test(cuidado_materno_pnts)$p.value, 4), # categorizada (só baseline)
#  round(shapiro.test(cuidado_paterno_pnts)$p.value, 4), # categorizada (só baseline)
#  round(shapiro.test(protecao_materno_pnts)$p.value, 4), # categorizada (só baseline)
#  round(shapiro.test(protecao_paterno_pnts)$p.value, 4)
  nrow=9, ncol=2, byrow=F)

dimnames(A) = list(
  c("cortisol_cat_av1",
    "md_sono_av1",
    "hamilton_av1",
#    "ansiedade_apego",
#    "evitacao_apego",
    "negative_affects_av1",
    "positive_affects_av1",
    "medo_av1",
    "hostilidade_av1",
    "assertividade_av1",
    "serenidade_av1"),
#    "cuidado_materno_pnts",
#    "cuidado_paterno_pnts",
#    "protecao_materno_pnts",
#    "protecao_paterno_pnts"),
  c("Teste Kolmogorov-Smirnov com correção de Lilliefors (valor-p)", 
    "Teste de Shapiro-Wilk (valor-p)"))

knitr::kable(A, format="markdown")
Teste Kolmogorov-Smirnov com correção de Lilliefors (valor-p) Teste de Shapiro-Wilk (valor-p)
cortisol_cat_av1 0.9315 0.2658
md_sono_av1 0.1691 0.7405
hamilton_av1 0.2550 0.1314
negative_affects_av1 0.1521 0.2797
positive_affects_av1 0.3538 0.7127
medo_av1 0.4621 0.6034
hostilidade_av1 0.1925 0.3148
assertividade_av1 0.5050 0.3444
serenidade_av1 0.3808 0.1323 
### <Não há evidência para rejeitar a hipótese nula de normalidade das vars.>

### <incluir histogramas e qqnorm/qqline?>


##############################
###### ANALISES PARTE 3 ######
##############################


# ANALISES DESCRITIVAS (vars numericas selecionadas)

B = matrix(c(
  median_iqr(age, na_rm=T, show_n="never"),
  median_iqr(weight, na_rm=T, show_n="never"),
  median_iqr(height, na_rm=T, show_n="never"),
  median_iqr(imc, na_rm=T, show_n="never"),
  median_iqr(ansiedade_apego, na_rm=T, show_n="never"),
  median_iqr(evitacao_apego, na_rm=T, show_n="never"),
  median_iqr(cuidado_materno_pnts, na_rm=T, show_n="never"),
  median_iqr(cuidado_paterno_pnts, na_rm=T, show_n="never"),
  median_iqr(protecao_materno_pnts, na_rm=T, show_n="never"),
  median_iqr(protecao_paterno_pnts, na_rm=T, show_n="never"),
  
  median_iqr(cortisol_value, na_rm=T, show_n="never"),
  median_iqr(cortisol_value_av2, na_rm=T, show_n="never"),
  median_iqr(md_sono_av1, na_rm=T, show_n="never"),
  median_iqr(md_sono_av2, na_rm=T, show_n="never"),
  median_iqr(hamilton_av1, na_rm=T, show_n="never"),
  median_iqr(hamilton_av2, na_rm=T, show_n="never"),
  
  median_iqr(medo_av1, na_rm=T, show_n="never"),
  median_iqr(medo_av2, na_rm=T, show_n="never"),
  median_iqr(hostilidade_av1, na_rm=T, show_n="never"),
  median_iqr(hostilidade_av2, na_rm=T, show_n="never"),
  median_iqr(assertividade_av1, na_rm=T, show_n="never"),
  median_iqr(assertividade_av2, na_rm=T, show_n="never"),
  median_iqr(serenidade_av1, na_rm=T, show_n="never"),
  median_iqr(serenidade_av2, na_rm=T, show_n="never"),
  median_iqr(negative_affects_av1, na_rm=T, show_n="never"),
  median_iqr(negative_affects_av2, na_rm=T, show_n="never"),
  median_iqr(positive_affects_av1, na_rm=T, show_n="never"),
  median_iqr(positive_affects_av2, na_rm=T, show_n="never"),
  
  mean_sd(age, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(weight, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(height, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(imc, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(ansiedade_apego, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(evitacao_apego, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(cuidado_materno_pnts, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(cuidado_paterno_pnts, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(protecao_materno_pnts, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(protecao_paterno_pnts, na_rm=T, show_n="never", denote_sd = "paren"),
  
  mean_sd(cortisol_value, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(cortisol_value_av2, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(md_sono_av1, na_rm=T, show_n="never"),
  mean_sd(md_sono_av2, na_rm=T, show_n="never"),
  mean_sd(hamilton_av1, na_rm=T, show_n="never", denote_sd = "paren"),
  mean_sd(hamilton_av2, na_rm=T, show_n="never", denote_sd = "paren"),
  
  mean_sd(medo_av1, na_rm=T, show_n="never"),
  mean_sd(medo_av2, na_rm=T, show_n="never"),
  mean_sd(hostilidade_av1, na_rm=T, show_n="never"),
  mean_sd(hostilidade_av2, na_rm=T, show_n="never"),
  mean_sd(assertividade_av1, na_rm=T, show_n="never"),
  mean_sd(assertividade_av2, na_rm=T, show_n="never"),
  mean_sd(serenidade_av1, na_rm=T, show_n="never"),
  mean_sd(serenidade_av2, na_rm=T, show_n="never"),
  mean_sd(negative_affects_av1, na_rm=T, show_n="never"),
  mean_sd(negative_affects_av2, na_rm=T, show_n="never"),
  mean_sd(positive_affects_av1, na_rm=T, show_n="never"),
  mean_sd(positive_affects_av2, na_rm=T, show_n="never")),
  nrow=28, ncol=2, byrow=F)

dimnames(B) = list(
  c("Idade, y", 
    "Peso, Kg", 
    "Estatura, m", 
    "IMC, Kg/m²",
    "Ansiedade_apego, pnts", 
    "Evitacao_apego, pnts", 
    "Cuidado_materno, pnts", 
    "Cuidado_paterno, pnts", 
    "Protecao_materno, pnts", 
    "Protecao_paterno, pnts", 
    "Cortisol_value_av1, ng/L", 
    "Cortisol_value_av2, ng/L",
    "Sono_av1, hrs",
    "Sono_av2, hrs",
    "Hamilton_av1, pnts", 
    "Hamilton_av2, pnts",
    "Medo_av1, pnts",
    "Medo_av2, pnts",
    "Hostilidade_av1, pnts",
    "Hostilidade_av2, pnts",
    "Assertividade_av1, pnts",
    "Assertividade_av2, pnts",
    "Serenidade_av1, pnts",
    "Serenidade_av2, pnts",
    "Negative affects_av1, pnts",
    "Negative affects_av2, pnts",
    "Positive affects_av1, pnts",
    "Positive affects_av2, pnts"),
  c("mediana (IIQ)", "média ± dp"))

knitr::kable(B, format="markdown")
mediana (IIQ) média ± dp
Idade, y 36.95 (30.90, 45.70) 38.21 (10.31)
Peso, Kg 84.00 (67.00, 92.38) 83.01 (17.81)
Estatura, m 1.75 (1.72, 1.80) 1.76 (0.07)
IMC, Kg/m² 26.19 (24.57, 27.98) 26.90 (5.39)
Ansiedade_apego, pnts 25.00 (22.00, 28.75) 24.23 (6.54)
Evitacao_apego, pnts 21.00 (17.25, 23.00) 20.77 (4.29)
Cuidado_materno, pnts 20.50 (15.25, 29.50) 22.00 (8.30)
Cuidado_paterno, pnts 17.50 (13.00, 25.00) 17.85 (9.89)
Protecao_materno, pnts 18.50 (16.00, 27.75) 20.96 (7.83)
Protecao_paterno, pnts 18.00 (10.00, 21.00) 16.04 (7.83)
Cortisol_value_av1, ng/L 5.20 (4.50, 5.99) 5.34 (1.24)
Cortisol_value_av2, ng/L 4.34 (3.87, 4.71) 4.45 (0.93)
Sono_av1, hrs 8.12 (7.18, 8.91) 8.08 \(\pm\) 1.10
Sono_av2, hrs 7.64 (6.93, 8.27) 7.80 \(\pm\) 1.13
Hamilton_av1, pnts 28.50 (23.00, 33.75) 29.23 (9.21)
Hamilton_av2, pnts 20.00 (13.00, 26.75) 20.42 (10.96)
Medo_av1, pnts 16.00 (13.00, 17.75) 15.23 \(\pm\) 3.83
Medo_av2, pnts 11.00 (8.25, 14.00) 11.42 \(\pm\) 3.98
Hostilidade_av1, pnts 17.00 (10.25, 20.00) 16.19 \(\pm\) 6.00
Hostilidade_av2, pnts 12.50 (9.00, 14.00) 12.65 \(\pm\) 5.24
Assertividade_av1, pnts 10.50 (8.00, 12.75) 10.35 \(\pm\) 3.63
Assertividade_av2, pnts 11.00 (9.00, 13.75) 11.62 \(\pm\) 3.70
Serenidade_av1, pnts 6.00 (4.25, 7.00) 5.65 \(\pm\) 1.60
Serenidade_av2, pnts 7.00 (6.00, 9.00) 7.27 \(\pm\) 2.05
Negative affects_av1, pnts 33.00 (23.25, 36.75) 31.42 \(\pm\) 8.96
Negative affects_av2, pnts 24.00 (17.25, 28.00) 24.08 \(\pm\) 8.00
Positive affects_av1, pnts 16.00 (13.00, 19.00) 16.00 \(\pm\) 4.70
Positive affects_av2, pnts 18.50 (15.00, 22.75) 18.88 \(\pm\) 5.18 
# ANALISES DESCRITIVAS (vars categoricas selecionadas)

# <fazer>


##############################
###### ANALISES PARTE 4 ######
##############################


## ANALISES GRAFICAS DA VARIAVEL SONO


# GRUPO TRATAMENTO (media de sono: baseline x encerramento)

trat = bd_final %>% filter(random==1) %>% select(md_sono_av1, md_sono_av2)
trat = rename(trat, baseline = "md_sono_av1", encerramento = "md_sono_av2")
trat$baseline = round(trat$baseline, 1)
trat$encerramento = round(trat$encerramento, 1)
trat$id=as.factor(c(1:13))

trat=melt(trat)
## Using id as id variables
ggplot(data=trat, aes(x=variable, y=value, group=id, colour=id)) +
  geom_line() + geom_point() +
  xlab("Sono") + ylab("Média de horas") # id=3 é NA
## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).

# GRUPO CONTROLE (media de sono: baseline x encerramento)

cont = bd_final %>% filter(random==0) %>% select(md_sono_av1, md_sono_av2)
cont = rename(cont, baseline = "md_sono_av1", encerramento = "md_sono_av2")
cont$baseline = round(cont$baseline,1)
cont$encerramento = round(cont$encerramento,1)
cont$id=as.factor(c(1:13))

cont=melt(cont)
## Using id as id variables
ggplot(data=cont, aes(x=variable, y=value, group=id, colour=id)) +
  geom_line() + geom_point() +
  xlab("Sono") + ylab("Média de horas") # id=11 é NA
## Warning: Removed 1 rows containing missing values (geom_path).

## Warning: Removed 1 rows containing missing values (geom_point).

# DISTRIBUIÇÃO INDIVIDUAL DO TEMPO DE SONO A CADA DIA (baseline x encerramento)

bd_sono_baseline = bd_final %>% select(ends_with(".x"))
print(
  dfSummary(bd_sono_baseline, graph.magnif = 0.75), 
  method = "render")

Data Frame Summary

bd_sono_baseline

N: 26
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 4.x [numeric] mean (sd) : 7.66 (2.27) min < med < max : 0 < 8.2 < 11 IQR (CV) : 2.3 (0.3) 15 distinct values 25 (96.15%) 1 (3.85%)
2 5.x [numeric] mean (sd) : 7.39 (1.88) min < med < max : 3.1 < 7 < 11 IQR (CV) : 1.9 (0.25) 23 distinct values 25 (96.15%) 1 (3.85%)
3 6.x [numeric] mean (sd) : 8.04 (2.05) min < med < max : 4.5 < 8 < 13.8 IQR (CV) : 2.2 (0.25) 20 distinct values 25 (96.15%) 1 (3.85%)
4 7.x [numeric] mean (sd) : 8.64 (2.54) min < med < max : 5.5 < 8.2 < 17.7 IQR (CV) : 3 (0.29) 20 distinct values 25 (96.15%) 1 (3.85%)
5 8.x [numeric] mean (sd) : 8.2 (1.78) min < med < max : 4.2 < 8.5 < 11 IQR (CV) : 2.3 (0.22) 17 distinct values 25 (96.15%) 1 (3.85%)
6 9.x [numeric] mean (sd) : 7.44 (2.04) min < med < max : 1.9 < 7.5 < 11.2 IQR (CV) : 1.6 (0.27) 21 distinct values 25 (96.15%) 1 (3.85%)
7 10.x [numeric] mean (sd) : 8.36 (1.69) min < med < max : 4.4 < 8 < 12.6 IQR (CV) : 1.9 (0.2) 21 distinct values 25 (96.15%) 1 (3.85%)
8 11.x [numeric] mean (sd) : 8.08 (1.73) min < med < max : 5.4 < 8 < 12.4 IQR (CV) : 2.2 (0.21) 17 distinct values 25 (96.15%) 1 (3.85%)
9 12.x [numeric] mean (sd) : 8.45 (2.47) min < med < max : 4.6 < 8 < 17.5 IQR (CV) : 2.5 (0.29) 14 distinct values 25 (96.15%) 1 (3.85%)
10 13.x [numeric] mean (sd) : 7.58 (2.09) min < med < max : 0 < 7 < 11 IQR (CV) : 2.1 (0.28) 18 distinct values 25 (96.15%) 1 (3.85%)
11 14.x [numeric] mean (sd) : 8.29 (1.98) min < med < max : 5.2 < 8.2 < 13.5 IQR (CV) : 2.4 (0.24) 21 distinct values 25 (96.15%) 1 (3.85%)
12 15.x [numeric] mean (sd) : 8.53 (2.14) min < med < max : 5.7 < 8 < 14 IQR (CV) : 2 (0.25) 18 distinct values 25 (96.15%) 1 (3.85%)
13 16.x [numeric] mean (sd) : 8.38 (2.6) min < med < max : 3.2 < 8.7 < 14.3 IQR (CV) : 2.6 (0.31) 21 distinct values 25 (96.15%) 1 (3.85%)
14 17.x [numeric] mean (sd) : 7.98 (1.47) min < med < max : 5.5 < 8 < 10.4 IQR (CV) : 2.2 (0.18) 18 distinct values 25 (96.15%) 1 (3.85%)
15 18.x [numeric] mean (sd) : 7.78 (2.03) min < med < max : 4.3 < 7.7 < 12.7 IQR (CV) : 2.7 (0.26) 22 distinct values 25 (96.15%) 1 (3.85%)
16 19.x [numeric] mean (sd) : 8.04 (1.6) min < med < max : 4.2 < 8 < 10.5 IQR (CV) : 2.3 (0.2) 20 distinct values 25 (96.15%) 1 (3.85%)
17 20.x [numeric] mean (sd) : 7.94 (1.78) min < med < max : 3 < 8 < 10.7 IQR (CV) : 2.4 (0.22) 19 distinct values 25 (96.15%) 1 (3.85%)
18 21.x [numeric] mean (sd) : 8.13 (1.68) min < med < max : 5.2 < 8 < 11.2 IQR (CV) : 2.7 (0.21) 21 distinct values 25 (96.15%) 1 (3.85%)
19 22.x [numeric] mean (sd) : 8.64 (2.3) min < med < max : 4.3 < 8.8 < 13 IQR (CV) : 2.6 (0.27) 21 distinct values 25 (96.15%) 1 (3.85%)
20 23.x [numeric] mean (sd) : 8.42 (2.03) min < med < max : 5.3 < 9 < 12 IQR (CV) : 3.3 (0.24) 18 distinct values 25 (96.15%) 1 (3.85%)
21 24.x [numeric] mean (sd) : 7.79 (1.86) min < med < max : 4.3 < 7.6 < 11.5 IQR (CV) : 2.5 (0.24) 21 distinct values 25 (96.15%) 1 (3.85%)
22 25.x [numeric] mean (sd) : 7.7 (1.63) min < med < max : 3.5 < 8 < 10.8 IQR (CV) : 1.9 (0.21) 18 distinct values 25 (96.15%) 1 (3.85%)
23 26.x [numeric] mean (sd) : 8.4 (2.36) min < med < max : 4 < 8 < 15 IQR (CV) : 2.8 (0.28) 22 distinct values 25 (96.15%) 1 (3.85%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-03

bd_sono_encerramento = bd_final %>% select(ends_with(".y"))
print(
  dfSummary(bd_sono_encerramento, graph.magnif = 0.75), 
  method = "render")

Data Frame Summary

bd_sono_encerramento

N: 26
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 4.y [numeric] mean (sd) : 7.84 (1.85) min < med < max : 4.8 < 7.5 < 11.8 IQR (CV) : 2.3 (0.24) 19 distinct values 25 (96.15%) 1 (3.85%)
2 5.y [numeric] mean (sd) : 7.77 (1.85) min < med < max : 5.3 < 7.3 < 12.8 IQR (CV) : 2 (0.24) 17 distinct values 25 (96.15%) 1 (3.85%)
3 6.y [numeric] mean (sd) : 7.82 (1.8) min < med < max : 4.5 < 8 < 11.8 IQR (CV) : 2.3 (0.23) 20 distinct values 25 (96.15%) 1 (3.85%)
4 7.y [numeric] mean (sd) : 7.82 (2.11) min < med < max : 3 < 7.5 < 12.9 IQR (CV) : 2.6 (0.27) 20 distinct values 25 (96.15%) 1 (3.85%)
5 8.y [numeric] mean (sd) : 8.42 (2.3) min < med < max : 1 < 8.6 < 14.3 IQR (CV) : 1.98 (0.27) 21 distinct values 24 (92.31%) 2 (7.69%)
6 9.y [numeric] mean (sd) : 8.05 (2.11) min < med < max : 4.7 < 8 < 12.1 IQR (CV) : 2.5 (0.26) 21 distinct values 25 (96.15%) 1 (3.85%)
7 10.y [numeric] mean (sd) : 7.52 (1.56) min < med < max : 5.5 < 6.75 < 10.7 IQR (CV) : 2.25 (0.21) 18 distinct values 24 (92.31%) 2 (7.69%)
8 11.y [numeric] mean (sd) : 7.22 (1.64) min < med < max : 4.3 < 6.9 < 11 IQR (CV) : 2.2 (0.23) 21 distinct values 25 (96.15%) 1 (3.85%)
9 12.y [numeric] mean (sd) : 8.19 (1.81) min < med < max : 5.5 < 7.8 < 12 IQR (CV) : 1.8 (0.22) 20 distinct values 25 (96.15%) 1 (3.85%)
10 13.y [numeric] mean (sd) : 7.94 (1.83) min < med < max : 4.4 < 8.2 < 11.5 IQR (CV) : 2.8 (0.23) 19 distinct values 25 (96.15%) 1 (3.85%)
11 14.y [numeric] mean (sd) : 8.16 (1.73) min < med < max : 4.5 < 8.2 < 11.6 IQR (CV) : 1.8 (0.21) 20 distinct values 25 (96.15%) 1 (3.85%)
12 15.y [numeric] mean (sd) : 8.47 (2.01) min < med < max : 5 < 8 < 14 IQR (CV) : 2.3 (0.24) 19 distinct values 25 (96.15%) 1 (3.85%)
13 16.y [numeric] mean (sd) : 7.93 (2.16) min < med < max : 2 < 7.7 < 11 IQR (CV) : 2.5 (0.27) 19 distinct values 25 (96.15%) 1 (3.85%)
14 17.y [numeric] mean (sd) : 7.82 (2.82) min < med < max : 2.8 < 7.4 < 15.7 IQR (CV) : 2.8 (0.36) 20 distinct values 24 (92.31%) 2 (7.69%)
15 18.y [numeric] mean (sd) : 7.78 (1.45) min < med < max : 4.8 < 8.1 < 10 IQR (CV) : 2.1 (0.19) 19 distinct values 25 (96.15%) 1 (3.85%)
16 19.y [numeric] mean (sd) : 7.57 (1.79) min < med < max : 3.3 < 7.5 < 11.5 IQR (CV) : 2.4 (0.24) 21 distinct values 25 (96.15%) 1 (3.85%)
17 20.y [numeric] mean (sd) : 7.17 (2.08) min < med < max : 2.5 < 7.1 < 10.5 IQR (CV) : 2.25 (0.29) 21 distinct values 24 (92.31%) 2 (7.69%)
18 21.y [numeric] mean (sd) : 7.31 (1.63) min < med < max : 3.5 < 7.8 < 9.9 IQR (CV) : 2.7 (0.22) 19 distinct values 25 (96.15%) 1 (3.85%)
19 22.y [numeric] mean (sd) : 8.24 (1.68) min < med < max : 5.5 < 7.8 < 12 IQR (CV) : 2.7 (0.2) 18 distinct values 25 (96.15%) 1 (3.85%)
20 23.y [numeric] mean (sd) : 8.21 (1.54) min < med < max : 4.3 < 8 < 11.8 IQR (CV) : 2 (0.19) 16 distinct values 25 (96.15%) 1 (3.85%)
21 24.y [numeric] mean (sd) : 7.38 (1.78) min < med < max : 4 < 7.2 < 11 IQR (CV) : 2.9 (0.24) 19 distinct values 25 (96.15%) 1 (3.85%)
22 25.y [numeric] mean (sd) : 7.31 (1.57) min < med < max : 5 < 7.2 < 11 IQR (CV) : 1.4 (0.21) 19 distinct values 25 (96.15%) 1 (3.85%)
23 26.y [numeric] mean (sd) : 7.31 (1.61) min < med < max : 4.9 < 6.8 < 11 IQR (CV) : 2 (0.22) 20 distinct values 25 (96.15%) 1 (3.85%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-03

##############################
###### ANALISES PARTE 5 ######
##############################


detach(bd_final)
attach(bd_final)


# ANALISES DESCRITIVAS POR GRUPO DE TRATAMENTO

by(gender %in% 0, random, n_perc, na_rm=T)
## random: 0
## [1] "13 (100.00\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "13 (100.00\\%)"
by(age, random, mean_sd, na_rm=T)
## random: 0
## [1] "36.92 $\\pm$ 11.38"
## -------------------------------------------------------- 
## random: 1
## [1] "39.49 $\\pm$ 9.40"
by(race %in% 1, random, n_perc, na_rm=T)
## random: 0
## [1] "7 (53.85\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "11 (84.62\\%)"
by(education_level >= 7, random, n_perc, na_rm=T)
## random: 0
## [1] "5 (38.46\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "9 (69.23\\%)"
by(labor_status %in% 1, random, n_perc, na_rm=T)
## random: 0
## [1] "6 (46.15\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "8 (61.54\\%)"
by(health_issues___8 %in% 1, random, n_perc, na_rm=T)
## random: 0
## [1] "11 (84.62\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "8 (61.54\\%)"
by(psycho_treatment %in% 1, random, n_perc, na_rm=T)
## random: 0
## [1] "9 (69.23\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "9 (69.23\\%)"
by(physical_exercises %in% 1, random, n_perc, na_rm=T)
## random: 0
## [1] "4 (30.77\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "7 (53.85\\%)"
by(weight, random, mean_sd, na_rm=T) # confere: mean(weight[random==0]); sd(weight[random==0])
## random: 0
## [1] "77.98 $\\pm$ 14.19"
## -------------------------------------------------------- 
## random: 1
## [1] "88.04 $\\pm$ 20.12"
by(height, random, mean_sd, na_rm=T)
## random: 0
## [1] "1.75 $\\pm$ 0.05"
## -------------------------------------------------------- 
## random: 1
## [1] "1.76 $\\pm$ 0.09"
by(imc, random, mean_sd, na_rm=T)
## random: 0
## [1] "25.59 $\\pm$ 4.54"
## -------------------------------------------------------- 
## random: 1
## [1] "28.21 $\\pm$ 6.02"
by(ansiedade_apego, random, mean_sd, na_rm=T)
## random: 0
## [1] "24.69 $\\pm$ 6.54"
## -------------------------------------------------------- 
## random: 1
## [1] "23.77 $\\pm$ 6.78"
by(evitacao_apego, random, mean_sd, na_rm=T)
## random: 0
## [1] "21.15 $\\pm$ 4.36"
## -------------------------------------------------------- 
## random: 1
## [1] "20.38 $\\pm$ 4.37"
by(cuidado_materno_cat, random, n_perc, na_rm=T)
## random: 0
## [1] "5 (38.46\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "3 (23.08\\%)"
by(protecao_materno_cat, random, n_perc, na_rm=T)
## random: 0
## [1] "11 (84.62\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "12 (92.31\\%)"
by(cuidado_paterno_cat, random, n_perc, na_rm=T)
## random: 0
## [1] "2 (15.38\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "5 (38.46\\%)"
by(protecao_paterno_cat, random, n_perc, na_rm=T)
## random: 0
## [1] "11 (84.62\\%)"
## -------------------------------------------------------- 
## random: 1
## [1] "6 (46.15\\%)"
by(cortisol_value, random, mean_sd, na_rm=T)
## random: 0
## [1] "12; 4.87 $\\pm$ 1.10"
## -------------------------------------------------------- 
## random: 1
## [1] "5.78 $\\pm$ 1.24"
by(md_sono_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "12; 7.87 $\\pm$ 1.09"
## -------------------------------------------------------- 
## random: 1
## [1] "8.28 $\\pm$ 1.11"
by(hamilton_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "31.15 $\\pm$ 10.52"
## -------------------------------------------------------- 
## random: 1
## [1] "27.31 $\\pm$ 7.62"
by(negative_affects_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "34.08 $\\pm$ 10.09"
## -------------------------------------------------------- 
## random: 1
## [1] "28.77 $\\pm$ 7.10"
by(medo_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "16.31 $\\pm$ 4.21"
## -------------------------------------------------------- 
## random: 1
## [1] "14.15 $\\pm$ 3.21"
by(hostilidade_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "17.77 $\\pm$ 6.43"
## -------------------------------------------------------- 
## random: 1
## [1] "14.62 $\\pm$ 5.32"
by(positive_affects_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "15.00 $\\pm$ 4.92"
## -------------------------------------------------------- 
## random: 1
## [1] "17.00 $\\pm$ 4.43"
by(assertividade_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "9.46 $\\pm$ 3.86"
## -------------------------------------------------------- 
## random: 1
## [1] "11.23 $\\pm$ 3.30"
by(serenidade_av1, random, mean_sd, na_rm=T)
## random: 0
## [1] "5.54 $\\pm$ 1.33"
## -------------------------------------------------------- 
## random: 1
## [1] "5.77 $\\pm$ 1.88"
# TESTES DE HIPOTESE: diferença das medidas de interesse entre grupos de tratamento no baseline

# fisher.test(gender, random) só homens

round(fisher.test(race %in% 1, random)$p.value, 4) # ver: fisher.test(race %in% 1, random)
## [1] 0.2016
round(fisher.test(education_level >= 7, random)$p.value, 4) # ftable(education_level >= 7, random)
## [1] 0.2377
round(fisher.test(labor_status %in% 1, random)$p.value, 4)
## [1] 0.6951
round(fisher.test(health_issues___8 %in% 1, random)$p.value, 4)
## [1] 0.3783
round(fisher.test(psycho_treatment %in% 1, random)$p.value, 4) # table(psycho_treatment, random)
## [1] 1
round(fisher.test(physical_exercises %in% 1, random)$p.value, 4) # table(psycho_treatment, random)
## [1] 0.4283
round(fisher.test(cuidado_materno_cat %in% 1, random)$p.value, 4)
## [1] 0.6728
round(fisher.test(protecao_materno_cat %in% 1, random)$p.value, 4) # ftable(protecao_materno_cat, random)
## [1] 1
round(fisher.test(cuidado_paterno_cat %in% 1, random)$p.value, 4)
## [1] 0.3783
round(fisher.test(protecao_paterno_cat %in% 1, random)$p.value, 4)
## [1] 0.0968
round(t.test(age ~ random)$p.value, 4) # ver: t.test(age ~ random)
## [1] 0.5364
round(t.test(weight ~ random)$p.value, 4)
## [1] 0.1551
round(t.test(height ~ random)$p.value, 4)
## [1] 0.5083
round(t.test(imc ~ random)$p.value, 4) # exemplo: round(t.test(imc ~ random)$p.value,4)
## [1] 0.2241
round(t.test(ansiedade_apego ~ random)$p.value, 4)
## [1] 0.727
round(t.test(evitacao_apego ~ random)$p.value, 4)
## [1] 0.6571
round(t.test(cortisol_value ~ random)$p.value, 4)
## [1] 0.0648
round(t.test(md_sono_av1 ~ random)$p.value, 4)
## [1] 0.3601
round(t.test(hamilton_av1 ~ random)$p.value, 4)
## [1] 0.2973
round(t.test(negative_affects_av1 ~ random)$p.value, 4)
## [1] 0.1353
round(t.test(medo_av1 ~ random)$p.value, 4)
## [1] 0.1564
round(t.test(hostilidade_av1 ~ random)$p.value, 4)
## [1] 0.186
round(t.test(positive_affects_av1 ~ random)$p.value, 4)
## [1] 0.287
round(t.test(assertividade_av1 ~ random)$p.value, 4)
## [1] 0.2215
round(t.test(serenidade_av1 ~ random)$p.value, 4)
## [1] 0.7212
#t.test(cuidado_materno_pnts ~ random)
#t.test(cuidado_paterno_pnts ~ random)
#t.test(protecao_materno_pnts ~ random)
#t.test(protecao_paterno_pnts ~ random)

### <Não há evidência para rejeitar a hipótese nula de igualdade entre medias/proporções das vars.>


##############################
###### ANALISES PARTE 6 ######
##############################


# ANALISES DAS VARIAÇÕES DOS DESFECHOS POR GRUPO DE TRATAMENTO (Tabela 2)

## Média e desvios-padrao

by(cortisol_value_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "4.36 $\\pm$ 1.24"
## -------------------------------------------------------- 
## random: 1
## [1] "4.53 $\\pm$ 0.50"
by(md_sono_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "7.53 $\\pm$ 0.80"
## -------------------------------------------------------- 
## random: 1
## [1] "12; 8.08 $\\pm$ 1.39"
by(hamilton_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "24.38 $\\pm$ 12.45"
## -------------------------------------------------------- 
## random: 1
## [1] "16.46 $\\pm$ 7.81"
by(negative_affects_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "27.38 $\\pm$ 8.34"
## -------------------------------------------------------- 
## random: 1
## [1] "20.77 $\\pm$ 6.33"
by(positive_affects_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "16.38 $\\pm$ 4.25"
## -------------------------------------------------------- 
## random: 1
## [1] "21.38 $\\pm$ 4.93"
by(medo_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "13.00 $\\pm$ 3.42"
## -------------------------------------------------------- 
## random: 1
## [1] "9.85 $\\pm$ 4.00"
by(hostilidade_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "14.38 $\\pm$ 5.66"
## -------------------------------------------------------- 
## random: 1
## [1] "10.92 $\\pm$ 4.31"
by(assertividade_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "9.77 $\\pm$ 2.89"
## -------------------------------------------------------- 
## random: 1
## [1] "13.46 $\\pm$ 3.57"
by(serenidade_av2, random, mean_sd, na_rm=T)
## random: 0
## [1] "6.62 $\\pm$ 1.98"
## -------------------------------------------------------- 
## random: 1
## [1] "7.92 $\\pm$ 1.98"
## variação dos desfechos de interesse

bd_final = bd_final %>% mutate(
  delta_cortisol = cortisol_value_av2 - cortisol_value, # ver: cbind(bd_final$cortisol_value_av2, bd_final$cortisol_value)
  delta_sono = md_sono_av2 - md_sono_av1,
  delta_hamilton = hamilton_av2 - hamilton_av1,
  delta_negative_affects = negative_affects_av2 - negative_affects_av1,
  delta_positive_affects = positive_affects_av2 - positive_affects_av1,
  delta_medo = medo_av2 - medo_av1,
  delta_hostilidade = hostilidade_av2 - hostilidade_av1,
  delta_assertividade = assertividade_av2 - assertividade_av1,
  delta_serenidade = serenidade_av2 - serenidade_av1)

detach(bd_final)
attach(bd_final)


by(delta_cortisol, random, mean_sd, na_rm = T) # mean(bd_final$delta_cortisol[bd_final$random==0], na.rm=T)
## random: 0
## [1] "12; -0.38 $\\pm$ 1.29"
## -------------------------------------------------------- 
## random: 1
## [1] "-1.25 $\\pm$ 1.28"
by(delta_sono, random, mean_sd, na_rm=T)
## random: 0
## [1] "12; -0.34 $\\pm$ 1.16"
## -------------------------------------------------------- 
## random: 1
## [1] "12; -0.21 $\\pm$ 0.46"
by(delta_hamilton, random, mean_sd, na_rm=T)
## random: 0
## [1] "-6.77 $\\pm$ 9.86"
## -------------------------------------------------------- 
## random: 1
## [1] "-10.85 $\\pm$ 8.38"
by(delta_negative_affects, random, mean_sd, na_rm=T)
## random: 0
## [1] "-6.69 $\\pm$ 9.98"
## -------------------------------------------------------- 
## random: 1
## [1] "-8.00 $\\pm$ 6.47"
by(delta_positive_affects, random, mean_sd, na_rm=T)
## random: 0
## [1] "1.38 $\\pm$ 3.99"
## -------------------------------------------------------- 
## random: 1
## [1] "4.38 $\\pm$ 4.93"
by(delta_medo, random, mean_sd, na_rm=T)
## random: 0
## [1] "-3.31 $\\pm$ 3.86"
## -------------------------------------------------------- 
## random: 1
## [1] "-4.31 $\\pm$ 3.59"
by(delta_hostilidade, random, mean_sd, na_rm=T)
## random: 0
## [1] "-3.38 $\\pm$ 6.58"
## -------------------------------------------------------- 
## random: 1
## [1] "-3.69 $\\pm$ 3.47"
by(delta_assertividade, random, mean_sd, na_rm=T)
## random: 0
## [1] "0.31 $\\pm$ 2.69"
## -------------------------------------------------------- 
## random: 1
## [1] "2.23 $\\pm$ 2.83"
by(delta_serenidade, random, mean_sd, na_rm=T)
## random: 0
## [1] "1.08 $\\pm$ 1.89"
## -------------------------------------------------------- 
## random: 1
## [1] "2.15 $\\pm$ 2.51"
round(t.test(delta_cortisol ~ random)$p.value, 4)
## [1] 0.1052
round(t.test(delta_sono ~ random)$p.value, 4)
## [1] 0.7178
round(t.test(delta_hamilton ~ random)$p.value, 4)
## [1] 0.2675
round(t.test(delta_negative_affects ~ random)$p.value, 4)
## [1] 0.6958
round(t.test(delta_positive_affects ~ random)$p.value, 4)
## [1] 0.1014
round(t.test(delta_medo ~ random)$p.value, 4)
## [1] 0.5006
round(t.test(delta_hostilidade ~ random)$p.value, 4)
## [1] 0.8831
round(t.test(delta_assertividade ~ random)$p.value, 4)
## [1] 0.0886
round(t.test(delta_serenidade ~ random)$p.value, 4)
## [1] 0.2297
########################################
################ LIXO ##################
########################################


# view(lapply(bd_final[ ,c(16:25)],freq),footnote = NA)#, file = 'doencas.html')
# freq(bd_final$health_issues___8, style = "rmarkdown")
# freq(bd_final$health_issues___8, style = "rmarkdown", report.nas=F, omit.headings=T, totals=F)
# with(bd_final, view(by(cortisol_value, random, freq), method = "pander"))

# by(weight, random, shapiro.test)
# ks.test(weight, random)
# nortest::lillie.test(imc[random==1])$p.value

#cor.test()
#spearman.test()
#mcnemar.test()
#kruskal.test() # politomica x nominal
#wilcox.test() # ordinal x bin
#prop.test()

#fisher.test() # bin x bin
#t.test() # num x bin