Dashboard de Resultados Paper ambiguity

Introducción

Este panel muestra la relación entre μ (expectativas), σ (desviación) y contribuciones a partir del experimento.

Los gráficos a continuación fueron generados en R usando plotly, ggplot2, y rmarkdown.

options(repos = c(CRAN = "https://cran.rstudio.com/"))
install.packages("paletteer")

## Warning: unable to access index for repository https://cran.rstudio.com/src/contrib:
##   no fue posible abrir la URL 'https://cran.rstudio.com/src/contrib/PACKAGES'

## Warning: package 'paletteer' is not available for this version of R
## 
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages

## Warning: unable to access index for repository https://cran.rstudio.com/bin/windows/contrib/4.4:
##   no fue posible abrir la URL 'https://cran.rstudio.com/bin/windows/contrib/4.4/PACKAGES'

library(plotly)

## Cargando paquete requerido: ggplot2

## 
## Adjuntando el paquete: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
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## The following object is masked from 'package:graphics':
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library(akima)
library(ggplot2)
library(dplyr)

## 
## Adjuntando el paquete: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(paletteer)

# Definir paleta colores

# Obtener 6 colores de la paleta cartography::blue.pal
blue_cartography <- as.character(paletteer_dynamic("cartography::blue.pal", 20)[seq(1, 20, length.out = 6)])

# Para plotly: armar escala personalizada
colorscale_cartography <- lapply(seq_along(blue_cartography), function(i) {
  list((i - 1) / (length(blue_cartography) - 1), blue_cartography[[i]])
})

# Para ggplot: asignar colores a valores discretos
blue_cartography_named <- setNames(blue_cartography, as.character(0:5))

setwd("C:/Users/dajorrat/Dropbox/Universidad Loyola Andalucía/Experimento Cabrales MPaz PBG/Outputs")  # Ajustar si es necesario
data <- read.csv("data_bruta_3d_all.csv", sep = ",")

data %>%
  summarise(across(c(expcont, cont_pgg, sd), list(
    mean = ~mean(.x, na.rm = TRUE),
    sd = ~sd(.x, na.rm = TRUE),
    min = ~min(.x, na.rm = TRUE),
    max = ~max(.x, na.rm = TRUE),
    n = ~sum(!is.na(.x))
  )))

##   expcont_mean expcont_sd expcont_min expcont_max expcont_n cont_pgg_mean
## 1     2.242833   1.201923           0           5      1500      2.721333
##   cont_pgg_sd cont_pgg_min cont_pgg_max cont_pgg_n   sd_mean     sd_sd sd_min
## 1    1.386949            0            5       1500 0.3907576 0.4923561      0
##     sd_max sd_n
## 1 2.886751 1500

Results

#Figure 1. Average contributions by ambiguity condition

#Figure 2. Average contributions and SEM according to terciles of σ

#Figure 3. Average contributions and SEM according to terciles of μ

#Figure 4. Average contributions by extremes terciles of μ and σ

Gráfico interactivo: Contributions en función de μ y σ

Para entender mejor las interacciones, se procede a realizar un gráfico 3D para ver cómo cambia las contribuciones cuando cambian μ y σ. Hay tres alternativas

#Alternativa 1: datos brutos.

#Alternativa 2: Mapa de calor por categorías reales (sin interpolar)

#Alternativa 3: linear prediction of marginals effects

#Alternativa 4: logit model of high contribution (=1 if contribution > p50 of the contributions) and the marginal effects

Descargar el gráfico

Podés descargar el gráfico haciendo clic derecho sobre él y eligiendo “Guardar imagen como…” o usar las opciones del botón superior derecho del gráfico interactivo.

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