data

BayesianStatistics/2025/data/make_TAP.R

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+# Tap 1 -------------------------------------------------------------------
+
+
+
+set.seed(5)
+df <- data.frame(rt=rgamma(5000, 2, 1))
+df$confidence <- c(rbeta(2500, 0.5, 2) / 2, (rbeta(2500, 2, 0.5) / 2) + 0.5)
+
+plot(bayestestR::estimate_density(df$rt))
+
+# write.csv(df, "tap1a.csv", row.names=FALSE)
+write.csv(df, "tap1a_resit.csv", row.names=FALSE)
+
+set.seed(6)
+df <- data.frame(reading_time=rgamma(5000, 3, 1))*2
+df$certainty <- c(rbeta(2500, 0.5, 2) / 2, (rbeta(2500, 2, 0.5) / 2) + 0.5)
+
+plot(bayestestR::estimate_density(df$reading_time))
+
+write.csv(df, "tap1a.csv", row.names=FALSE)
+# write.csv(df, "tap1a_resit.csv", row.names=FALSE)
+
+
+# TAP 2 -------------------------------------------------------------------
+
+
+df <- read.csv("https://raw.githubusercontent.com/DominiqueMakowski/PHQ4R/main/study2/data/data.csv")
+df$Interoception <- rowSums(df[grep("IAS_\\d", names(df))])
+df <-  filter(df, Gender %in% c("Female", "Male"))
+
+
+# lm(STAI5_General ~ Age * Gender, data = df) |>
+#   parameters::parameters()
+#
+# plot(estimate_relation(m))
+
+
+df |>
+  dplyr::select(Age, Gender, Anxiety=STAI5_General) |>
+  write.csv("tap2.csv", row.names=FALSE)
+
+
+# lm(BDI2_Total ~ Interoception * Gender, data = df) |>
+#   parameters::parameters()
+#
+# plot(estimate_relation(m))
+
+df |>
+  dplyr::select(Interoception, Gender, Depression=BDI2_Total) |>
+  write.csv("tap2_resit.csv", row.names=FALSE)