Setup

#this is to check to see if package are installed and if not to install them or just load them if they are installed!
if(!require(pacman)) {install.packages(c("pacman", "remotes"))}

if(!require(papaja)) {remotes::install_github("crsh/papaja")}

pacman::p_load(here, tinylabels, apaTables, tidyverse, gtsummary, car, GGally, MASS, rcompanion, moments, utils, sjPlot, interactions, kableExtra, report, papaja, bookdown)

p_load_gh("crsh/papaja@devel")


#get relative path
path =  here("RESSOURCES") #this is really important ! you juste need to put the name of YOUR folder and here() will find the relative path for you !
setwd(path) #set working directory

# load data
base  <- read.csv("data.csv")


session = utils::sessionInfo() # get session informations

#opts_chunk$set(echo = F, message=F, warning=F) # set echo F for all

options(scipen = 666, warn=-1, contrasts=c("contr.sum","contr.poly"), knitr.kable.NA = '')  #remove scientific notation # remove warnings #set contrasts to sum ! 
set.seed(666) #set random seed

`%notin%` <- Negate(`%in%`)

# panderOptions('knitr.auto.asis', FALSE) #remove auto styling

\(~\) \(~\)

Introduction

Blabla citation in parentesis (James 1890) blbabla, or citation in text James (1890)

\(~\) \(~\)

Clean up

\(~\) \(~\)

base$Priming = as.factor(base$Priming); base$Product = as.factor(base$Product);

\(~\) \(~\)

Demographics

\(~\) \(~\)

base[c("Age", "Gender", "Profession")] %>%  tbl_summary(statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} / {N} ({p}%)"),)  %>% modify_caption("**Table 1. Demographics **") %>%bold_labels()

**Table 1. Demographics **
Characteristic	N = 296¹
Age	22 (4)
Gender
Autre/NA	2 / 296 (0.7%)
Femme	239 / 296 (81%)
Homme	55 / 296 (19%)
Profession
Actif.ve	3 / 296 (1.0%)
Etudiant.e	291 / 296 (98%)
Les deux	2 / 296 (0.7%)
¹ Mean (SD); n / N (%)

\(~\) \(~\)

Inspections

\(~\) \(~\)

base %>% 
  keep(is.numeric) %>%
  gather() %>%
  ggplot(aes(value)) +
  facet_wrap(~key, scales = "free") + 
  geom_histogram()

#or GGally

\(~\) \(~\)

ggplot(base, aes(x=Priming, y=Age)) +
  geom_violin(trim=FALSE, fill='#A4A4A4', color="darkred")+
  geom_boxplot(width=0.1) + theme_minimal()

\(~\) \(~\)

Statistics

\(~\) \(~\)

Reccomendation

base_recco = filter(base, id %notin% c("13594")) #remove huge outliers, see appendix

final_recco = lm(Reccomend ~Priming*Product + Behav_enviro + Product:Behav_enviro,  data = base_recco)

apa_lm <- papaja::apa_print(anova(final_recco))
papaja::apa_table(apa_lm$table,  caption = "Anova table for Reccomendation.", label =NULL)

(#tab:Report_Regression)

Anova table for Reccomendation.

Effect	\(F\)	\(\mathit{df}_1\)	\(\mathit{df}_2\)	\(\mathit{MSE}\)	\(p\)	\(\hat{\eta}^2_G\)
Priming	0.76	1	289	2.44	.383	.003
Product	32.23	1	289	2.44	< .001	.100
Behav enviro	33.11	1	289	2.44	< .001	.103
Priming \(\times\) Product	0.06	1	289	2.44	.808	.000
Product \(\times\) Behav enviro	5.30	1	289	2.44	.022	.018

sjPlot::plot_model(final_recco)

\(~\) \(~\)

interactions::interact_plot(final_recco, pred = Behav_enviro, modx = Product, interval = TRUE)

\(~\) \(~\)

Conclusions

\(~\) \(~\)

Example for reporting :

Product (\(F(1, 289) = 32.23\), \(\mathit{MSE} = 2.44\), \(p < .001\), \(\hat{\eta}^2_G = .100\)) and Behav_enviro (\(F(1, 289) = 33.11\), \(\mathit{MSE} = 2.44\), \(p < .001\), \(\hat{\eta}^2_G = .103\)) affected reccomendation. However, the effect of Behav_enviro differed by Product, \(F(1, 289) = 5.30\), \(\mathit{MSE} = 2.44\), \(p = .022\), \(\hat{\eta}^2_G = .018\).

\(~\) \(~\)

Appendix

\(~\) \(~\)

Visual check normality

plotNormalHistogram(base$Reccomend, main = "Reccomendation", sub = paste("skewness =", round(skewness(base$Reccomend, na.rm = TRUE),2)))

\(~\) \(~\)

Diagnostics Plots for GLM Reccomendation with outliers

\(~\) \(~\)

Diagnostics Plots for GLM Reccomendation with outliers

\(~\) \(~\)

Model selection for Reccomendation

Stepwise Model Path Analysis of Deviance Table

Initial Model:

Reccomend ~ Priming * Product * Decision_mode + Age + Priming *

Product * Behav_enviro

Final Model:

Reccomend ~ Priming + Product + Behav_enviro + Product:Behav_enviro

Step	Df	Deviance	Resid. Df	Resid. Dev	AIC
			282	698.9267	280.4583
Priming:Product:Behav_enviro	1	0.1006993	283	699.0274	278.5008
Priming:Behav_enviro	1	0.4823183	284	699.5097	276.7043
Age	1	0.8370115	285	700.3467	275.0570
Priming:Product:Decision_mode	1	4.7384372	286	705.0851	275.0462
Priming:Decision_mode	1	0.0761253	287	705.1613	273.0781
Priming:Product	1	0.1041078	288	705.2654	271.1216
Product:Decision_mode	1	0.8932071	289	706.1586	269.4950
Decision_mode	1	0.2413509	290	706.3999	267.5958

\(~\) \(~\)

Packages

\(~\) \(~\)

report::report_packages()

  - papaja (version 0.1.0.9997; Aust et al., 2020)
  - GGally (version 2.1.1; Barret Schloerke et al., 2021)
  - tinylabels (version 0.2.1; Barth, 2021)
  - apaTables (version 2.0.8; David Stanley, 2021)
  - ggplot2 (version 3.3.3; Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.)
  - stringr (version 1.4.0; Hadley Wickham, 2019)
  - forcats (version 0.5.1; Hadley Wickham, 2021)
  - tidyr (version 1.1.3; Hadley Wickham, 2021)
  - readr (version 1.4.0; Hadley Wickham and Jim Hester, 2020)
  - dplyr (version 1.0.6; Hadley Wickham et al., 2021)
  - kableExtra (version 1.3.4; Hao Zhu, 2021)
  - car (version 3.0.10; John Fox and Sanford Weisberg, 2019)
  - carData (version 3.0.4; John Fox, Sanford Weisberg and Brad Price, 2020)
  - here (version 1.0.1; Kirill Müller, 2020)
  - tibble (version 3.1.2; Kirill Müller and Hadley Wickham, 2021)
  - purrr (version 0.3.4; Lionel Henry and Hadley Wickham, 2020)
  - interactions (version 1.1.3; Long JA, 2019)
  - sjPlot (version 2.8.8; Lüdecke D, 2021)
  - moments (version 0.14; Lukasz Komsta and Frederick Novomestky, 2015)
  - report (version 0.3.5; Makowski et al., 2020)
  - R (version 4.0.1; R Core Team, 2020)
  - pacman (version 0.5.1; Rinker et al., 2017)
  - rcompanion (version 2.4.1; Salvatore Mangiafico, 2021)
  - gtsummary (version 1.5.0; Sjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange Reproducible summary tables with the gtsummary package. The R Journal 2021;13:570–80. https://doi.org/10.32614/RJ-2021-053.)
  - MASS (version 7.3.51.6; Venables et al., 2002)
  - tidyverse (version 1.3.1; Wickham et al., 2019)
  - bookdown (version 0.22; Yihui Xie, 2021)

\(~\) \(~\)

References

\(~\) \(~\)

James, William. 1890. “The Perception of Reality.” Principles of Psychology 2: 283–324.

ANALYSIS REPORT

XXX

07 décembre, 2021

Setup

Introduction

Clean up

Demographics

Inspections

Statistics

Reccomendation

Conclusions

Appendix

Visual check normality

Diagnostics Plots for GLM Reccomendation with outliers

Diagnostics Plots for GLM Reccomendation with outliers

Model selection for Reccomendation

Packages

References