BF Penguin full

Data preparation

Required R libraries

#rm(list = ls())

library(dplyr, quietly = TRUE)
library(BayesFactor, quietly = TRUE)
set.seed(123)

Loading the dataset

setwd(dir = "/Users/ivanropovik/OneDrive/MANUSCRIPTS/2017 penguin project_sexual orientation/Datasets")
full.data <- read.csv(file = "penguin_full.csv", header = TRUE, sep = ";")
#View(full.data)
data.na <- full.data %>% select(DEQ, csi, cbt, seconds, urald, sinod, sex, romrec, sexpref, Zmonog)

Listwise deletion

data <- data.na[complete.cases(data.na),]

Dichotomizing the sexpref variable

data$sexpref10[data$sexpref == 1] <- 1
data$sexpref10[data$sexpref == 2] <- 2
data$sexpref10[data$sexpref == 3] <- 2
data$sexpref10[data$sexpref == 4] <- 2

Calculation of interaction terms

data <- data %>% mutate(
  int_rom_alpha = DEQ * romrec,
  int_rom_beta = csi * romrec,
  int_sexpref_alpha = DEQ * sexpref10,
  int_sexpref_beta = csi * sexpref10,
  int_monog_alpha = DEQ * Zmonog,
  int_monog_beta = csi * Zmonog,
  int_sex_alpha = DEQ * sex,
  int_sex_beta = csi * sex,
  int_sex_rom_alphaXW = DEQ * romrec,
  int_sex_rom_alphaXZ = DEQ * sex,
  int_sex_rom_alphaWZ = romrec * sex,
  int_sex_rom_alphaXWZ = DEQ * romrec * sex
)

BF options

Prior width (r scale)

r.scale <- sqrt(2)/4

Number of iterations

n.iter <- 100000

CSI mediation

Alpha path

bfmodel1 <- regressionBF(csi ~ DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
alpha.path <- bfmodel1[31]/bfmodel1[30]

BF for the alpha path

paste(ifelse(extractBF(alpha.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.path)$bf > 1, extractBF(alpha.path)$bf, 1/extractBF(alpha.path)$bf), 2), sep = "")

## [1] "BF10 = 2485152275.58"

Bayesian posterior estimate of the alpha path

round(summary(posterior(alpha.path, iterations =  n.iter))$statistics[1], 3)

## [1] 0.015

Posterior probability of the alpha path

post.odds.alpha <- newPriorOdds(alpha.path) * alpha.path
round(extractProbabilities(as.BFprobability(post.odds.alpha)[1])$probs, 3)

## [1] 1

Beta path

bfmodel2 <- regressionBF(cbt ~ csi + DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
beta.path <- bfmodel2[63]/bfmodel2[62]

BF for the beta path

paste(ifelse(extractBF(beta.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(beta.path)$bf > 1, extractBF(beta.path)$bf, 1/extractBF(beta.path)$bf), 2), sep = "")

## [1] "BF10 = 1077.4"

Bayesian posterior estimate of the beta path

round(summary(posterior(beta.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.042

Posterior probability of the beta path

post.odds.beta <- newPriorOdds(beta.path) * beta.path
round(extractProbabilities(as.BFprobability(post.odds.beta)[1])$probs, 3)

## [1] 0.999

Tau path

tau.path <- bfmodel2[63]/bfmodel2[61]

BF for the tau path

paste(ifelse(extractBF(tau.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(tau.path)$bf > 1, extractBF(tau.path)$bf, 1/extractBF(tau.path)$bf), 2), sep = "")

## [1] "BF10 = 524197.11"

Bayesian posterior estimate of the tau path

round(summary(posterior(tau.path, iterations = n.iter))$statistic[2], 3)

## [1] -0.004

Posterior probability of the tau path

post.odds.tau <- newPriorOdds(tau.path) * tau.path
round(extractProbabilities(as.BFprobability(post.odds.tau)[1])$probs, 3)

## [1] 1

BF for the mediation - the indirect (ab) path

post.prob.med <- extractBF(alpha.path)$bf/(extractBF(alpha.path)$bf + 1) * extractBF(beta.path)$bf/(extractBF(beta.path)$bf + 1)
bf.med <- post.prob.med/(1 - post.prob.med)

paste(ifelse(bf.med > 1, "BF10 = ", "BF01 = "), round(ifelse(bf.med > 1, bf.med, 1/bf.med), 2), sep = "")

## [1] "BF10 = 1077.4"

Romantic - moderated CSI mediation

Moderation of the alpha path

bfmodel1rom <- regressionBF(csi ~ DEQ + romrec + int_rom_alpha + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
alpha.rom <- bfmodel1rom[127]/bfmodel1rom[124]

BF for the moderation of the alpha path

paste(ifelse(extractBF(alpha.rom)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.rom)$bf > 1, extractBF(alpha.rom)$bf, 1/extractBF(alpha.rom)$bf), 2), sep = "")

## [1] "BF10 = 18197.74"

Bayesian posterior estimate of the moderation effect of the alpha path

round(summary(posterior(alpha.rom, iterations = n.iter))$statistics[1], 3)

## [1] 0.012

Posterior probability for the moderation effect of the alpha path

post.odds.alpha.rom <- newPriorOdds(alpha.rom) * alpha.rom
round(extractProbabilities(as.BFprobability(post.odds.alpha.rom)[1])$probs, 3)

## [1] 1

Moderation of the beta path

bfmodel2rom <- regressionBF(cbt ~ csi + DEQ + romrec + int_rom_beta + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
beta.rom <- bfmodel2rom[255]/bfmodel2rom[251]

BF for the moderation of the beta path

paste(ifelse(extractBF(beta.rom)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(beta.rom)$bf > 1, extractBF(beta.rom)$bf, 1/extractBF(beta.rom)$bf), 2), sep = "")

## [1] "BF10 = 1.92"

Bayesian posterior estimate of the moderation effect of the beta path

round(summary(posterior(beta.rom, iterations = n.iter))$statistic[1], 3)

## [1] 0.039

Posterior probability for the moderation effect of the beta path

post.odds.beta.rom <- newPriorOdds(beta.rom) * beta.rom
round(extractProbabilities(as.BFprobability(post.odds.beta.rom)[1])$probs, 3)

## [1] 0.658

Sexual orientation - moderated CSI mediation

Moderation of the alpha path

bfmodel1sexpref <- regressionBF(csi ~ DEQ + sexpref10 + int_sexpref_alpha + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
alpha.sexpref <- bfmodel1sexpref[127]/bfmodel1sexpref[124]

BF for the moderation of the alpha path

paste(ifelse(extractBF(alpha.sexpref)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.sexpref)$bf > 1, extractBF(alpha.sexpref)$bf, 1/extractBF(alpha.sexpref)$bf), 2), sep = "")

## [1] "BF01 = 4.13"

Bayesian posterior estimate of the moderation effect of the alpha path

round(summary(posterior(alpha.sexpref, iterations =  n.iter))$statistics[1], 3)

## [1] 0.026

Posterior probability for the moderation effect of the alpha path

post.odds.alpha.sexpref <- newPriorOdds(alpha.sexpref) * alpha.sexpref
round(extractProbabilities(as.BFprobability(post.odds.alpha.sexpref)[1])$probs, 3)

## [1] 0.195

Moderation of the beta path

bfmodel2sexpref <- regressionBF(cbt ~ csi + DEQ + sexpref10 + int_sexpref_beta + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
beta.sexpref <- bfmodel2sexpref[255]/bfmodel2sexpref[251]

BF for the moderation of the beta path

paste(ifelse(extractBF(beta.sexpref)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(beta.sexpref)$bf > 1, extractBF(beta.sexpref)$bf, 1/extractBF(beta.sexpref)$bf), 2), sep = "")

## [1] "BF01 = 6.41"

Bayesian posterior estimate of the moderation effect of the beta path

round(summary(posterior(beta.sexpref, iterations = n.iter))$statistic[1], 3)

## [1] 0.021

Posterior probability for the moderation effect of the beta path

post.odds.beta.sexpref <- newPriorOdds(beta.sexpref) * beta.sexpref
round(extractProbabilities(as.BFprobability(post.odds.beta.sexpref)[1])$probs, 3)

## [1] 0.135

Monogamy - moderated CSI mediation

Moderation of the alpha path

bfmodel1monog <- regressionBF(csi ~ DEQ + Zmonog + int_monog_alpha + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
alpha.monog <- bfmodel1monog[127]/bfmodel1monog[124]

BF for the moderation of the alpha path

paste(ifelse(extractBF(alpha.monog)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.monog)$bf > 1, extractBF(alpha.monog)$bf, 1/extractBF(alpha.monog)$bf), 2), sep = "")

## [1] "BF10 = 2.26"

Bayesian posterior estimate of the moderation effect of the alpha path

round(summary(posterior(alpha.monog, iterations =  n.iter))$statistics[1], 3)

## [1] 0.015

Posterior probability for the moderation effect of the alpha path

post.odds.alpha.monog <- newPriorOdds(alpha.monog) * alpha.monog
round(extractProbabilities(as.BFprobability(post.odds.alpha.monog)[1])$probs, 3)

## [1] 0.693

Moderation of the beta path

bfmodel2monog <- regressionBF(cbt ~ csi + DEQ + Zmonog + int_monog_beta + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
beta.monog <- bfmodel2monog[255]/bfmodel2monog[251]

BF for the moderation of the beta path

paste(ifelse(extractBF(beta.monog)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(beta.monog)$bf > 1, extractBF(beta.monog)$bf, 1/extractBF(beta.monog)$bf), 2), sep = "")

## [1] "BF01 = 7.49"

Bayesian posterior estimate of the moderation effect of the beta path

round(summary(posterior(beta.monog, iterations = n.iter))$statistic[1], 3)

## [1] 0.042

Posterior probability for the moderation effect of the beta path

post.odds.beta.monog <- newPriorOdds(beta.monog) * beta.monog
round(extractProbabilities(as.BFprobability(post.odds.beta.monog)[1])$probs, 3)

## [1] 0.118

Monogamy mediation

Alpha path

bfmodel1 <- regressionBF(Zmonog ~ DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
alpha.path <- bfmodel1[31]/bfmodel1[30]

BF for the alpha path

paste(ifelse(extractBF(alpha.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.path)$bf > 1, extractBF(alpha.path)$bf, 1/extractBF(alpha.path)$bf), 2), sep = "")

## [1] "BF10 = 3.88"

Bayesian posterior estimate of the alpha path

round(summary(posterior(alpha.path, iterations =  n.iter))$statistics[1], 3)

## [1] 0.005

Posterior probability of the alpha path

post.odds.alpha <- newPriorOdds(alpha.path) * alpha.path
round(extractProbabilities(as.BFprobability(post.odds.alpha)[1])$probs, 3)

## [1] 0.795

Beta path

bfmodel2 <- regressionBF(cbt ~ Zmonog + DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
beta.path <- bfmodel2[63]/bfmodel2[62]

BF for the beta path

paste(ifelse(extractBF(beta.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(beta.path)$bf > 1, extractBF(beta.path)$bf, 1/extractBF(beta.path)$bf), 2), sep = "")

## [1] "BF01 = 7.35"

Bayesian posterior estimate of the beta path

round(summary(posterior(beta.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.007

Posterior probability of the beta path

post.odds.beta <- newPriorOdds(beta.path) * beta.path
round(extractProbabilities(as.BFprobability(post.odds.beta)[1])$probs, 3)

## [1] 0.12

Tau path

tau.path <- bfmodel2[63]/bfmodel2[61]

BF for the tau path

paste(ifelse(extractBF(tau.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(tau.path)$bf > 1, extractBF(tau.path)$bf, 1/extractBF(tau.path)$bf), 2), sep = "")

## [1] "BF10 = 13375"

Bayesian posterior estimate of the tau path

round(summary(posterior(tau.path, iterations = n.iter))$statistic[2], 3)

## [1] -0.004

Posterior probability of the tau path

post.odds.tau <- newPriorOdds(tau.path) * tau.path
round(extractProbabilities(as.BFprobability(post.odds.tau)[1])$probs, 3)

## [1] 1

BF for the mediation - the indirect (ab) path

post.prob.med <- extractBF(alpha.path)$bf/(extractBF(alpha.path)$bf + 1) * extractBF(beta.path)$bf/(extractBF(beta.path)$bf + 1)
bf.med <- post.prob.med/(1 - post.prob.med)

paste(ifelse(bf.med > 1, "BF10 = ", "BF01 = "), round(ifelse(bf.med > 1, bf.med, 1/bf.med), 2), sep = "")

## [1] "BF01 = 9.51"

MON->CSI double mediation

DEQ_CBT path

bfmodel1 <- regressionBF(cbt ~ DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
DEQ_CBT.path <- bfmodel1[31]/bfmodel1[30]

BF for the DEQ_CBT path

paste(ifelse(extractBF(DEQ_CBT.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(DEQ_CBT.path)$bf > 1, extractBF(DEQ_CBT.path)$bf, 1/extractBF(DEQ_CBT.path)$bf), 2), sep = "")

## [1] "BF10 = 10842.56"

Bayesian posterior estimate of the DEQ_CBT path

round(summary(posterior(DEQ_CBT.path, iterations =  n.iter))$statistics[1], 3)

## [1] -0.004

Posterior probability of the DEQ_CBT path

post.odds.DEQ_CBT <- newPriorOdds(DEQ_CBT.path) * DEQ_CBT.path
round(extractProbabilities(as.BFprobability(post.odds.DEQ_CBT)[1])$probs, 3)

## [1] 1

DEQ_MON path

bfmodel2 <- regressionBF(Zmonog ~ DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
DEQ_MON.path <- bfmodel2[31]/bfmodel2[30]

BF for the DEQ_MON path

paste(ifelse(extractBF(DEQ_MON.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(DEQ_MON.path)$bf > 1, extractBF(DEQ_MON.path)$bf, 1/extractBF(DEQ_MON.path)$bf), 2), sep = "")

## [1] "BF10 = 3.88"

Bayesian posterior estimate of the DEQ_MON path

round(summary(posterior(DEQ_MON.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.005

Posterior probability of the DEQ_MON path

post.odds.DEQ_MON <- newPriorOdds(DEQ_MON.path) * DEQ_MON.path
round(extractProbabilities(as.BFprobability(post.odds.DEQ_MON)[1])$probs, 3)

## [1] 0.795

DEQ_CSI path

bfmodel3 <- regressionBF(csi ~ DEQ + Zmonog + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
DEQ_CSI.path <- bfmodel3[63]/bfmodel3[62]

BF for the DEQ_CSI path

paste(ifelse(extractBF(DEQ_CSI.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(DEQ_CSI.path)$bf > 1, extractBF(DEQ_CSI.path)$bf, 1/extractBF(DEQ_CSI.path)$bf), 2), sep = "")

## [1] "BF10 = 515653225.09"

Bayesian posterior estimate of the DEQ_CSI path

round(summary(posterior(DEQ_CSI.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.014

Posterior probability of the DEQ_CSI path

post.odds.DEQ_CSI <- newPriorOdds(DEQ_CSI.path) * DEQ_CSI.path
round(extractProbabilities(as.BFprobability(post.odds.DEQ_CSI)[1])$probs, 3)

## [1] 1

MON_CSI path

bfmodel4 <- regressionBF(csi ~ Zmonog + DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
MON_CSI.path <- bfmodel4[63]/bfmodel4[62]

BF for the MON_CSI path

paste(ifelse(extractBF(MON_CSI.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(MON_CSI.path)$bf > 1, extractBF(MON_CSI.path)$bf, 1/extractBF(MON_CSI.path)$bf), 2), sep = "")

## [1] "BF10 = 81.41"

Bayesian posterior estimate of the MON_CSI path

round(summary(posterior(MON_CSI.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.114

Posterior probability of the MON_CSI path

post.odds.MON_CSI <- newPriorOdds(MON_CSI.path) * MON_CSI.path
round(extractProbabilities(as.BFprobability(post.odds.MON_CSI)[1])$probs, 3)

## [1] 0.988

MON_CBT path

bfmodel5 <- regressionBF(cbt ~ Zmonog + DEQ + csi + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
MON_CBT.path <- bfmodel5[127]/bfmodel5[126]

BF for the MON_CBT path

paste(ifelse(extractBF(MON_CBT.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(MON_CBT.path)$bf > 1, extractBF(MON_CBT.path)$bf, 1/extractBF(MON_CBT.path)$bf), 2), sep = "")

## [1] "BF01 = 8.16"

Bayesian posterior estimate of the MON_CBT path

round(summary(posterior(MON_CBT.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.002

Posterior probability of the MON_CBT path

post.odds.MON_CBT <- newPriorOdds(MON_CBT.path) * MON_CBT.path
round(extractProbabilities(as.BFprobability(post.odds.MON_CBT)[1])$probs, 3)

## [1] 0.109

CSI_CBT path

bfmodel6 <- regressionBF(cbt ~ csi + DEQ + Zmonog + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
CSI_CBT.path <- bfmodel6[127]/bfmodel6[126]

BF for the CSI_CBT path

paste(ifelse(extractBF(CSI_CBT.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(CSI_CBT.path)$bf > 1, extractBF(CSI_CBT.path)$bf, 1/extractBF(CSI_CBT.path)$bf), 2), sep = "")

## [1] "BF10 = 970.66"

Bayesian posterior estimate of the CSI_CBT path

round(summary(posterior(CSI_CBT.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.042

Posterior probability of the CSI_CBT path

post.odds.CSI_CBT <- newPriorOdds(CSI_CBT.path) * CSI_CBT.path
round(extractProbabilities(as.BFprobability(post.odds.CSI_CBT)[1])$probs, 3)

## [1] 0.999

CSI->MON double mediation

DEQ_CBT path

bfmodel1 <- regressionBF(cbt ~ DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
DEQ_CBT.path <- bfmodel1[31]/bfmodel1[30]

BF for the DEQ_CBT path

paste(ifelse(extractBF(DEQ_CBT.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(DEQ_CBT.path)$bf > 1, extractBF(DEQ_CBT.path)$bf, 1/extractBF(DEQ_CBT.path)$bf), 2), sep = "")

## [1] "BF10 = 10842.56"

Bayesian posterior estimate of the DEQ_CBT path

round(summary(posterior(DEQ_CBT.path, iterations =  n.iter))$statistics[1], 3)

## [1] -0.004

Posterior probability of the DEQ_CBT path

post.odds.DEQ_CBT <- newPriorOdds(DEQ_CBT.path) * DEQ_CBT.path
round(extractProbabilities(as.BFprobability(post.odds.DEQ_CBT)[1])$probs, 3)

## [1] 1

DEQ_CSI path

bfmodel2 <- regressionBF(csi ~ DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
DEQ_CSI.path <- bfmodel2[31]/bfmodel2[30]

BF for the DEQ_CSI path

paste(ifelse(extractBF(DEQ_CSI.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(DEQ_CSI.path)$bf > 1, extractBF(DEQ_CSI.path)$bf, 1/extractBF(DEQ_CSI.path)$bf), 2), sep = "")

## [1] "BF10 = 2485152275.58"

Bayesian posterior estimate of the DEQ_CSI path

round(summary(posterior(DEQ_CSI.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.015

Posterior probability of the DEQ_CSI path

post.odds.DEQ_CSI <- newPriorOdds(DEQ_CSI.path) * DEQ_CSI.path
round(extractProbabilities(as.BFprobability(post.odds.DEQ_CSI)[1])$probs, 3)

## [1] 1

DEQ_MON path

bfmodel3 <- regressionBF(Zmonog ~ DEQ + csi + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
DEQ_MON.path <- bfmodel3[63]/bfmodel3[62]

BF for the DEQ_MON path

paste(ifelse(extractBF(DEQ_MON.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(DEQ_MON.path)$bf > 1, extractBF(DEQ_MON.path)$bf, 1/extractBF(DEQ_MON.path)$bf), 2), sep = "")

## [1] "BF01 = 1.19"

Bayesian posterior estimate of the DEQ_MON path

round(summary(posterior(DEQ_MON.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.003

Posterior probability of the DEQ_MON path

post.odds.DEQ_MON <- newPriorOdds(DEQ_MON.path) * DEQ_MON.path
round(extractProbabilities(as.BFprobability(post.odds.DEQ_MON)[1])$probs, 3)

## [1] 0.456

CSI_MON path

bfmodel4 <- regressionBF(Zmonog ~ csi + DEQ + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
CSI_MON.path <- bfmodel4[63]/bfmodel4[62]

BF for the CSI_MON path

paste(ifelse(extractBF(CSI_MON.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(CSI_MON.path)$bf > 1, extractBF(CSI_MON.path)$bf, 1/extractBF(CSI_MON.path)$bf), 2), sep = "")

## [1] "BF10 = 119.7"

Bayesian posterior estimate of the CSI_MON path

round(summary(posterior(CSI_MON.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.085

Posterior probability of the CSI_MON path

post.odds.CSI_MON <- newPriorOdds(CSI_MON.path) * CSI_MON.path
round(extractProbabilities(as.BFprobability(post.odds.CSI_MON)[1])$probs, 3)

## [1] 0.992

CSI_CBT path

bfmodel5 <- regressionBF(cbt ~ csi + DEQ + Zmonog + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
CSI_CBT.path <- bfmodel5[127]/bfmodel5[126]

BF for the CSI_CBT path

paste(ifelse(extractBF(CSI_CBT.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(CSI_CBT.path)$bf > 1, extractBF(CSI_CBT.path)$bf, 1/extractBF(CSI_CBT.path)$bf), 2), sep = "")

## [1] "BF10 = 970.66"

Bayesian posterior estimate of the CSI_CBT path

round(summary(posterior(CSI_CBT.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.042

Posterior probability of the CSI_CBT path

post.odds.CSI_CBT <- newPriorOdds(CSI_CBT.path) * CSI_CBT.path
round(extractProbabilities(as.BFprobability(post.odds.CSI_CBT)[1])$probs, 3)

## [1] 0.999

MON_CBT path

bfmodel6 <- regressionBF(cbt ~ Zmonog + DEQ + csi + seconds + urald + sinod + sex, data, rscaleCont = r.scale)
MON_CBT.path <- bfmodel6[127]/bfmodel6[126]

BF for the MON_CBT path

paste(ifelse(extractBF(MON_CBT.path)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(MON_CBT.path)$bf > 1, extractBF(MON_CBT.path)$bf, 1/extractBF(MON_CBT.path)$bf), 2), sep = "")

## [1] "BF01 = 8.16"

Bayesian posterior estimate of the MON_CBT path

round(summary(posterior(MON_CBT.path, iterations = n.iter))$statistic[1], 3)

## [1] 0.002

Posterior probability of the MON_CBT path

post.odds.MON_CBT <- newPriorOdds(MON_CBT.path) * MON_CBT.path
round(extractProbabilities(as.BFprobability(post.odds.MON_CBT)[1])$probs, 3)

## [1] 0.109

Sex - moderated CSI mediation

Moderation of the alpha path

bfmodel1sex <- regressionBF(csi ~ DEQ + sex + int_sex_alpha + seconds + urald + sinod, data, rscaleCont = r.scale)
alpha.sex <- bfmodel1sex[63]/bfmodel1sex[60]

BF for the moderation of the alpha path

paste(ifelse(extractBF(alpha.sex)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.sex)$bf > 1, extractBF(alpha.sex)$bf, 1/extractBF(alpha.sex)$bf), 2), sep = "")

## [1] "BF10 = 259.37"

Bayesian posterior estimate of the moderation effect of the alpha path

round(summary(posterior(alpha.sex, iterations = n.iter))$statistics[1], 3)

## [1] -0.013

Posterior probability for the moderation effect of the alpha path

post.odds.alpha.sex <- newPriorOdds(alpha.sex) * alpha.sex
round(extractProbabilities(as.BFprobability(post.odds.alpha.sex)[1])$probs, 3)

## [1] 0.996

Moderation of the beta path

bfmodel2sex <- regressionBF(cbt ~ csi + DEQ + sex + int_sex_beta + seconds + urald + sinod, data, rscaleCont = r.scale)
beta.sex <- bfmodel2sex[127]/bfmodel2sex[123]

BF for the moderation of the beta path

paste(ifelse(extractBF(beta.sex)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(beta.sex)$bf > 1, extractBF(beta.sex)$bf, 1/extractBF(beta.sex)$bf), 2), sep = "")

## [1] "BF01 = 4.34"

Bayesian posterior estimate of the moderation effect of the beta path

round(summary(posterior(beta.sex, iterations = n.iter))$statistic[1], 3)

## [1] 0.078

Posterior probability for the moderation effect of the beta path

post.odds.beta.sex <- newPriorOdds(beta.sex) * beta.sex
round(extractProbabilities(as.BFprobability(post.odds.beta.sex)[1])$probs, 3)

## [1] 0.187

Sex - moderated moderation (by romantic) of the CSI mediation (M11)

Moderated moderation of the alpha path

bfmodel1sex_rom <- regressionBF(csi ~ DEQ + romrec + sex + int_sex_rom_alphaXW + int_sex_rom_alphaXZ + int_sex_rom_alphaWZ + int_sex_rom_alphaXWZ + seconds + urald + sinod, data, rscaleCont = r.scale)
alpha.3way <- bfmodel1sex_rom[1023]/bfmodel1sex_rom[1016]

BF for the moderation of the alpha path

paste(ifelse(extractBF(alpha.3way)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.3way)$bf > 1, extractBF(alpha.3way)$bf, 1/extractBF(alpha.3way)$bf), 2), sep = "")

## [1] "BF01 = 7.14"

Bayesian posterior estimate of the moderation effect of the alpha path

round(summary(posterior(alpha.3way, iterations = n.iter))$statistics[1], 3)

## [1] -0.013

Posterior probability for the moderation effect of the alpha path

post.odds.alpha.3way <- newPriorOdds(alpha.3way) * alpha.3way
round(extractProbabilities(as.BFprobability(post.odds.alpha.3way)[1])$probs, 3)

## [1] 0.123

Sex - moderated CSI mediation controlling for romantic

Moderation of the alpha path

bfmodel1sex_rom <- regressionBF(csi ~ DEQ + sex + int_sex_alpha + romrec + seconds + urald + sinod, data, rscaleCont = r.scale)
alpha.sex <- bfmodel1sex_rom[127]/bfmodel1sex_rom[124]

BF for the moderation of the alpha path

paste(ifelse(extractBF(alpha.sex)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(alpha.sex)$bf > 1, extractBF(alpha.sex)$bf, 1/extractBF(alpha.sex)$bf), 2), sep = "")

## [1] "BF10 = 78.38"

Bayesian posterior estimate of the moderation effect of the alpha path

round(summary(posterior(alpha.sex, iterations = n.iter))$statistics[1], 3)

## [1] -0.012

Posterior probability for the moderation effect of the alpha path

post.odds.alpha.sex <- newPriorOdds(alpha.sex) * alpha.sex
round(extractProbabilities(as.BFprobability(post.odds.alpha.sex)[1])$probs, 3)

## [1] 0.987

Moderation of the beta path

bfmodel2sex_rom <- regressionBF(cbt ~ csi + DEQ + sex + int_sex_beta + romrec + seconds + urald + sinod, data, rscaleCont = r.scale)
beta.sex <- bfmodel2sex_rom[255]/bfmodel2sex_rom[251]

BF for the moderation of the beta path

paste(ifelse(extractBF(beta.sex)$bf > 1, "BF10 = ", "BF01 = "),
      round(ifelse(extractBF(beta.sex)$bf > 1, extractBF(beta.sex)$bf, 1/extractBF(beta.sex)$bf), 2), sep = "")

## [1] "BF01 = 4.01"

Bayesian posterior estimate of the moderation effect of the beta path

round(summary(posterior(beta.sex, iterations = n.iter))$statistic[1], 3)

## [1] 0.08

Posterior probability for the moderation effect of the beta path

post.odds.beta.sex <- newPriorOdds(beta.sex) * beta.sex
round(extractProbabilities(as.BFprobability(post.odds.beta.sex)[1])$probs, 3)

## [1] 0.199

sessionInfo()

## R version 3.3.2 (2016-10-31)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Sierra 10.12.6
## 
## locale:
## [1] sk_SK.UTF-8/sk_SK.UTF-8/sk_SK.UTF-8/C/sk_SK.UTF-8/sk_SK.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] BayesFactor_0.9.12-2 Matrix_1.2-8         coda_0.19-1         
## [4] dplyr_0.5.0         
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.10       knitr_1.15.1       magrittr_1.5      
##  [4] lattice_0.20-34    R6_2.2.0           pbapply_1.3-2     
##  [7] stringr_1.2.0      tools_3.3.2        parallel_3.3.2    
## [10] grid_3.3.2         DBI_0.6            htmltools_0.3.5   
## [13] MatrixModels_0.4-1 gtools_3.5.0       yaml_2.1.14       
## [16] lazyeval_0.2.0     assertthat_0.1     rprojroot_1.2     
## [19] digest_0.6.12      tibble_1.2         evaluate_0.10     
## [22] rmarkdown_1.3      stringi_1.1.2      backports_1.0.5   
## [25] mvtnorm_1.0-6

BF Penguin full

Ivan Ropovik

Thu Jun 8 22:57:43 2017

Data preparation

Required R libraries

Loading the dataset

Listwise deletion

Dichotomizing the sexpref variable

Calculation of interaction terms

BF options

Prior width (r scale)

Number of iterations

CSI mediation

Alpha path

Beta path

Tau path

BF for the mediation - the indirect (ab) path

Romantic - moderated CSI mediation

Moderation of the alpha path

Moderation of the beta path

Sexual orientation - moderated CSI mediation

Moderation of the alpha path

Moderation of the beta path

Monogamy - moderated CSI mediation

Moderation of the alpha path

Moderation of the beta path

Monogamy mediation

Alpha path

Beta path

Tau path

BF for the mediation - the indirect (ab) path

MON->CSI double mediation

DEQ_CBT path

DEQ_MON path

DEQ_CSI path

MON_CSI path

MON_CBT path

CSI_CBT path

CSI->MON double mediation

DEQ_CBT path

DEQ_CSI path

DEQ_MON path

CSI_MON path

CSI_CBT path

MON_CBT path

Sex - moderated CSI mediation

Moderation of the alpha path

Moderation of the beta path

Sex - moderated moderation (by romantic) of the CSI mediation (M11)

Moderated moderation of the alpha path

Sex - moderated CSI mediation controlling for romantic

Moderation of the alpha path

Moderation of the beta path