- [[r - ggplot connect points in graphs with geom_line]] Pilot results for registered report ```r > df1_long[, n_distinct(participant), by = .(condition)] condition V1 1: Effort 59 2: Performance 60 ``` ![[Pasted image 20200927173528.png]] ```r # Simplest model m0 <- brm(choice ~ condition, df1_long[section == "Reward"]) hdi(m0, ci = 0.95) ``` b = -13.79, 95% HPD = [-21.84, -5.98], d = 0.63, BF = 29.79 (p < .001) ```r # Covariate model m0 <- brm(choice_reward ~ condition + choice_time0, df1) # beta hdi(m0, ci = 0.95) # hpd ``` condition: b = -14.69, 95% HPD = [-22.14, -7.35], d = 0.73, BF = 144.07 (p < .001) ```r # Difference/change score model df1[, change := choice_reward - choice_time0] m0 <- brm(change ~ condition, df1) # beta ``` condition: b = -15.41, 95% HPD = [-23.37, -7.44], d = 0.70, BF = 107.86 (p < .001) ```r # interaction of condition and section m0 <- brm(choice ~ section * condition, df1_long) # beta ``` condition:section interaction: b = -15.35 , 95% HPD = [-25.50, -4.88], d = 0.38, BF = 9.67 (p = .005) ```r # effort condition, section effect c <- "Effort" m0 <- brm(choice ~ section, df1_long[condition == c]) # beta ``` section: b = 15.59, 95% HPD = [8.29, 23.21], d = 0.75, BF = 225.90 (p < .001) ```r # performance condition, section effect c <- "Effort" m0 <- brm(choice ~ section, df1_long[condition == c]) # beta ``` section: b = 0.13, 95% HPD = [-6.71, 7.08], d = 0.01, BF = 0.19 (p = .971)