Full_data <- read_csv("Full_data_for_j.csv")## New names:
## Rows: 259 Columns: 271
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): Group dbl (270): ...1, ID, B_IUS_1, B_IUS_2, B_IUS_3, B_IUS_4, B_IUS_5,
## B_IUS_6, B...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`IUS_columns <- Full_data %>%
dplyr::select("A_PRE_IUS_total", "B_POST_IUS_total", "C_W1_IUS_total", "D_M1_IUS_total", "E_M3_IUS_total", "Group")
IUS_columns_long <- IUS_columns %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total, C_W1_IUS_total, D_M1_IUS_total, E_M3_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
summary_IUS <- IUS_columns_long %>% # calculating means and sds
group_by(Group, Time) %>%
summarise(
mean_value = mean(IUS_Score, na.rm = TRUE),
sd_value = sd(IUS_Score, na.rm = TRUE),
.groups = 'drop')
round_IUS <- summary_IUS %>% # rounding to 2 decimal places
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(round_IUS)## # A tibble: 15 × 4
## Group Time mean_value sd_value
## <chr> <chr> <dbl> <dbl>
## 1 A_ECs A_PRE_IUS_total 41.1 6.74
## 2 A_ECs B_POST_IUS_total 40.8 9.19
## 3 A_ECs C_W1_IUS_total 42.0 8.52
## 4 A_ECs D_M1_IUS_total 43.4 8.08
## 5 A_ECs E_M3_IUS_total 41.4 7.94
## 6 B_Controls A_PRE_IUS_total 42.0 9.45
## 7 B_Controls B_POST_IUS_total 38.2 10.7
## 8 B_Controls C_W1_IUS_total 40.2 10.5
## 9 B_Controls D_M1_IUS_total 40.5 10.9
## 10 B_Controls E_M3_IUS_total 39.8 10.5
## 11 C_Intervention A_PRE_IUS_total 43.1 9.14
## 12 C_Intervention B_POST_IUS_total 36.8 11.1
## 13 C_Intervention C_W1_IUS_total 38.8 10.1
## 14 C_Intervention D_M1_IUS_total 38.4 10.3
## 15 C_Intervention E_M3_IUS_total 40.1 10.1PHQ_columns <- Full_data %>%
dplyr::select("A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total", "E_M3_PHQ_total", "Group")
PHQ_columns_long <- PHQ_columns %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total, D_M1_PHQ_total, E_M3_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
summary_PHQ <- PHQ_columns_long %>% # calculating means and sds
group_by(Group, Time) %>%
summarise(
mean_value = mean(PHQ_Score, na.rm = TRUE),
sd_value = sd(PHQ_Score, na.rm = TRUE),
.groups = 'drop')
round_PHQ <- summary_PHQ %>% # rounding to 2 decimal places
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(round_PHQ)## # A tibble: 12 × 4
## Group Time mean_value sd_value
## <chr> <chr> <dbl> <dbl>
## 1 A_ECs A_PRE_PHQ_total 9.96 5.77
## 2 A_ECs C_W1_PHQ_total 9.92 5.78
## 3 A_ECs D_M1_PHQ_total 10.4 6.58
## 4 A_ECs E_M3_PHQ_total 10.0 6.09
## 5 B_Controls A_PRE_PHQ_total 9.44 6.16
## 6 B_Controls C_W1_PHQ_total 8.6 6.13
## 7 B_Controls D_M1_PHQ_total 8.15 6.27
## 8 B_Controls E_M3_PHQ_total 8.84 6.12
## 9 C_Intervention A_PRE_PHQ_total 10.7 5.62
## 10 C_Intervention C_W1_PHQ_total 9.22 5.78
## 11 C_Intervention D_M1_PHQ_total 8.36 6.15
## 12 C_Intervention E_M3_PHQ_total 9.52 6GAD_columns <- Full_data %>%
dplyr::select("A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total", "E_M3_GAD_total", "Group")
GAD_columns_long <- GAD_columns %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total, D_M1_GAD_total, E_M3_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
summary_GAD <- GAD_columns_long %>% # calculating means and sds
group_by(Group, Time) %>%
summarise(
mean_value = mean(GAD_Score, na.rm = TRUE),
sd_value = sd(GAD_Score, na.rm = TRUE),
.groups = 'drop')
round_GAD <- summary_GAD %>% # rounding to 2 decimal places
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(round_GAD)## # A tibble: 12 × 4
## Group Time mean_value sd_value
## <chr> <chr> <dbl> <dbl>
## 1 A_ECs A_PRE_GAD_total 8.02 4.85
## 2 A_ECs C_W1_GAD_total 8.38 5.25
## 3 A_ECs D_M1_GAD_total 9.14 6.13
## 4 A_ECs E_M3_GAD_total 9.13 5.99
## 5 B_Controls A_PRE_GAD_total 8.49 5.79
## 6 B_Controls C_W1_GAD_total 7.9 5.82
## 7 B_Controls D_M1_GAD_total 7.34 5.9
## 8 B_Controls E_M3_GAD_total 7.85 6.05
## 9 C_Intervention A_PRE_GAD_total 9.28 5.47
## 10 C_Intervention C_W1_GAD_total 8.26 5.94
## 11 C_Intervention D_M1_GAD_total 7.6 5.78
## 12 C_Intervention E_M3_GAD_total 9.16 6.04GM_columns <- Full_data %>%
dplyr::select("A_PRE_GM", "B_POST_GM", "C_W1_GM", "D_M1_GM", "E_M3_GM", "Group")
GM_columns_long <- GM_columns %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM, C_W1_GM, D_M1_GM, E_M3_GM),
names_to = "Time",
values_to = "GM_Score")
summary_GM <- GM_columns_long %>% # calculating means and sds
group_by(Group, Time) %>%
summarise(
mean_value = mean(GM_Score, na.rm = TRUE),
sd_value = sd(GM_Score, na.rm = TRUE),
.groups = 'drop')
round_GM <- summary_GM %>% # rounding to 2 decimal places
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(round_GM)## # A tibble: 15 × 4
## Group Time mean_value sd_value
## <chr> <chr> <dbl> <dbl>
## 1 A_ECs A_PRE_GM 4.26 1.4
## 2 A_ECs B_POST_GM 4.22 1.5
## 3 A_ECs C_W1_GM 4.33 1.58
## 4 A_ECs D_M1_GM 4.32 1.38
## 5 A_ECs E_M3_GM 4.25 1.64
## 6 B_Controls A_PRE_GM 3.86 1.43
## 7 B_Controls B_POST_GM 4.35 1.49
## 8 B_Controls C_W1_GM 4.17 1.54
## 9 B_Controls D_M1_GM 4.19 1.46
## 10 B_Controls E_M3_GM 4.25 1.5
## 11 C_Intervention A_PRE_GM 4.1 1.36
## 12 C_Intervention B_POST_GM 4.78 1.49
## 13 C_Intervention C_W1_GM 4.67 1.33
## 14 C_Intervention D_M1_GM 4.76 1.39
## 15 C_Intervention E_M3_GM 4.89 1.28FI_columns <- Full_data %>%
dplyr::select("A_PRE_FI_total", "C_W1_FI_total", "D_M1_FI_total", "E_M3_FI_total", "Group")
FI_columns_long <- FI_columns %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total, D_M1_FI_total, E_M3_FI_total),
names_to = "Time",
values_to = "FI_Score")
summary_FI <- FI_columns_long %>% # calculating means and sds
group_by(Group, Time) %>%
summarise(
mean_value = mean(FI_Score, na.rm = TRUE),
sd_value = sd(FI_Score, na.rm = TRUE),
.groups = 'drop')
round_FI <- summary_FI %>% # rounding to 2 decimal places
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(round_FI)## # A tibble: 12 × 4
## Group Time mean_value sd_value
## <chr> <chr> <dbl> <dbl>
## 1 A_ECs A_PRE_FI_total 9.86 3.65
## 2 A_ECs C_W1_FI_total 9.98 4.01
## 3 A_ECs D_M1_FI_total 9.73 3.9
## 4 A_ECs E_M3_FI_total 9.43 3.61
## 5 B_Controls A_PRE_FI_total 10.0 4.12
## 6 B_Controls C_W1_FI_total 9.87 4.12
## 7 B_Controls D_M1_FI_total 9.37 4.4
## 8 B_Controls E_M3_FI_total 8.96 4.32
## 9 C_Intervention A_PRE_FI_total 10.5 3.91
## 10 C_Intervention C_W1_FI_total 9.5 3.77
## 11 C_Intervention D_M1_FI_total 9.29 4.04
## 12 C_Intervention E_M3_FI_total 9.22 3.78NA_columns <- Full_data %>%
dplyr::select("A_PRE_negaff", "B_POST_negaff", "C_W1_negaff", "D_M1_negaff", "E_M3_negaff", "Group")
NA_columns_long <- NA_columns %>%
pivot_longer(cols = c(A_PRE_negaff, B_POST_negaff, C_W1_negaff, D_M1_negaff, E_M3_negaff),
names_to = "Time",
values_to = "NA_Score")
summary_NA <- NA_columns_long %>% # calculating means and sds
group_by(Group, Time) %>%
summarise(
mean_value = mean(NA_Score, na.rm = TRUE),
sd_value = sd(NA_Score, na.rm = TRUE),
.groups = 'drop')
round_NA <- summary_NA %>% # rounding to 2 decimal places
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(round_NA)## # A tibble: 15 × 4
## Group Time mean_value sd_value
## <chr> <chr> <dbl> <dbl>
## 1 A_ECs A_PRE_negaff 40.3 39.1
## 2 A_ECs B_POST_negaff 40.3 41.3
## 3 A_ECs C_W1_negaff 25.6 40.7
## 4 A_ECs D_M1_negaff 19.9 48.6
## 5 A_ECs E_M3_negaff 17.2 45.7
## 6 B_Controls A_PRE_negaff 37.3 44
## 7 B_Controls B_POST_negaff 57.3 34.8
## 8 B_Controls C_W1_negaff 30.8 48.9
## 9 B_Controls D_M1_negaff 30.3 49.4
## 10 B_Controls E_M3_negaff 30.1 50.1
## 11 C_Intervention A_PRE_negaff 29.6 42.7
## 12 C_Intervention B_POST_negaff 58.7 34.0
## 13 C_Intervention C_W1_negaff 24.6 46.7
## 14 C_Intervention D_M1_negaff 23.1 47.2
## 15 C_Intervention E_M3_negaff 26.2 47.6tsummary_IUS <- IUS_columns_long %>%
group_by(Time) %>%
summarise(mean_value = mean(IUS_Score, na.rm = TRUE),
sd_value = sd(IUS_Score, na.rm = TRUE),
.groups = 'drop')
tround_IUS <- tsummary_IUS %>%
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(tround_IUS)## # A tibble: 5 × 3
## Time mean_value sd_value
## <chr> <dbl> <dbl>
## 1 A_PRE_IUS_total 42.3 8.86
## 2 B_POST_IUS_total 38.1 10.7
## 3 C_W1_IUS_total 40.0 9.99
## 4 D_M1_IUS_total 40.2 10.3
## 5 E_M3_IUS_total 40.2 9.89tsummary_PHQ <- PHQ_columns_long %>%
group_by(Time) %>%
summarise(mean_value = mean(PHQ_Score, na.rm = TRUE),
sd_value = sd(PHQ_Score, na.rm = TRUE),
.groups = 'drop')
tround_PHQ <- tsummary_PHQ %>%
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(tround_PHQ)## # A tibble: 4 × 3
## Time mean_value sd_value
## <chr> <dbl> <dbl>
## 1 A_PRE_PHQ_total 10.0 5.88
## 2 C_W1_PHQ_total 9.1 5.92
## 3 D_M1_PHQ_total 8.65 6.31
## 4 E_M3_PHQ_total 9.31 6.06tsummary_GAD <- GAD_columns_long %>%
group_by(Time) %>%
summarise(mean_value = mean(GAD_Score, na.rm = TRUE),
sd_value = sd(GAD_Score, na.rm = TRUE),
.groups = 'drop')
tround_GAD <- tsummary_GAD %>%
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(tround_GAD)## # A tibble: 4 × 3
## Time mean_value sd_value
## <chr> <dbl> <dbl>
## 1 A_PRE_GAD_total 8.71 5.49
## 2 C_W1_GAD_total 8.14 5.75
## 3 D_M1_GAD_total 7.79 5.91
## 4 E_M3_GAD_total 8.57 6.04tsummary_GM <- GM_columns_long %>%
group_by(Time) %>%
summarise(mean_value = mean(GM_Score, na.rm = TRUE),
sd_value = sd(GM_Score, na.rm = TRUE),
.groups = 'drop')
tround_GM <- tsummary_GM %>%
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(tround_GM)## # A tibble: 5 × 3
## Time mean_value sd_value
## <chr> <dbl> <dbl>
## 1 A_PRE_GM 4.03 1.4
## 2 B_POST_GM 4.49 1.51
## 3 C_W1_GM 4.4 1.48
## 4 D_M1_GM 4.44 1.44
## 5 E_M3_GM 4.49 1.48tsummary_FI <- FI_columns_long %>%
group_by(Time) %>%
summarise(mean_value = mean(FI_Score, na.rm = TRUE),
sd_value = sd(FI_Score, na.rm = TRUE),
.groups = 'drop')
tround_FI <- tsummary_FI %>%
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(tround_FI)## # A tibble: 4 × 3
## Time mean_value sd_value
## <chr> <dbl> <dbl>
## 1 A_PRE_FI_total 10.2 3.94
## 2 C_W1_FI_total 9.75 3.95
## 3 D_M1_FI_total 9.41 4.15
## 4 E_M3_FI_total 9.14 3.98tsummary_NA <- NA_columns_long %>%
group_by(Time) %>%
summarise(mean_value = mean(NA_Score, na.rm = TRUE),
sd_value = sd(NA_Score, na.rm = TRUE),
.groups = 'drop')
tround_NA <- tsummary_NA %>%
mutate(mean_value = round(mean_value, 2),
sd_value = round(sd_value, 2))
print(tround_NA)## # A tibble: 5 × 3
## Time mean_value sd_value
## <chr> <dbl> <dbl>
## 1 A_PRE_negaff 34.8 42.6
## 2 B_POST_negaff 54.6 36.4
## 3 C_W1_negaff 27.4 46.5
## 4 D_M1_negaff 25.5 48.4
## 5 E_M3_negaff 26.3 48.4# Post
moderation_GM_Mood_BP <- lm(negaff_BP_change ~ Group*A_PRE_GM, data = Full_data)
anova(moderation_GM_Mood_BP)## Analysis of Variance Table
##
## Response: negaff_BP_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 29097 14548.3 14.0609 1.625e-06 ***
## A_PRE_GM 1 0 0.0 0.0000 0.9988
## Group:A_PRE_GM 2 1150 575.1 0.5558 0.5743
## Residuals 252 260736 1034.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1 week
moderation_GM_Mood_1W <- lm(negaff_B1W_change ~ Group*A_PRE_GM, data = Full_data)
anova(moderation_GM_Mood_1W)## Analysis of Variance Table
##
## Response: negaff_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 2952 1476.0 0.6511 0.5224
## A_PRE_GM 1 1516 1515.6 0.6686 0.4143
## Group:A_PRE_GM 2 449 224.5 0.0990 0.9057
## Residuals 244 553135 2266.9# 1 month
moderation_GM_Mood_1M <- lm(negaff_B1M_change ~ Group*A_PRE_GM, data = Full_data)
anova(moderation_GM_Mood_1M)## Analysis of Variance Table
##
## Response: negaff_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 7881 3940.5 1.6652 0.1915
## A_PRE_GM 1 904 904.4 0.3822 0.5371
## Group:A_PRE_GM 2 2246 1122.9 0.4745 0.6228
## Residuals 222 525329 2366.3# 3 months
moderation_GM_Mood_3M <- lm(negaff_B3M_change ~ Group*A_PRE_GM, data = Full_data)
anova(moderation_GM_Mood_3M)## Analysis of Variance Table
##
## Response: negaff_B3M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 16030 8014.8 3.2114 0.04226 *
## A_PRE_GM 1 31 31.1 0.0124 0.91127
## Group:A_PRE_GM 2 3619 1809.5 0.7250 0.48550
## Residuals 213 531596 2495.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Post
moderation_RTQ_mood_BP <- lm(negaff_BP_change ~ Group*A_PRE_RTQ_n_total, data = Full_data)
anova(moderation_RTQ_mood_BP)## Analysis of Variance Table
##
## Response: negaff_BP_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 29097 14548.3 14.5373 1.059e-06 ***
## A_PRE_RTQ_n_total 1 9445 9445.1 9.4379 0.002359 **
## Group:A_PRE_RTQ_n_total 2 250 124.8 0.1247 0.882792
## Residuals 252 252191 1000.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1 week
moderation_RTQ_mood_1W <- lm(negaff_B1W_change ~ Group*A_PRE_RTQ_n_total, data = Full_data)
anova(moderation_RTQ_mood_1W)## Analysis of Variance Table
##
## Response: negaff_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 2952 1476.00 0.6497 0.5231
## A_PRE_RTQ_n_total 1 44 44.33 0.0195 0.8890
## Group:A_PRE_RTQ_n_total 2 728 363.87 0.1602 0.8521
## Residuals 244 554328 2271.83# 1 month
moderation_RTQ_mood_1M <- lm(negaff_B1M_change ~ Group*A_PRE_RTQ_n_total, data = Full_data)
anova(moderation_RTQ_mood_1M)## Analysis of Variance Table
##
## Response: negaff_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 7881 3940.5 1.6563 0.1932
## A_PRE_RTQ_n_total 1 216 215.7 0.0907 0.7636
## Group:A_PRE_RTQ_n_total 2 104 52.0 0.0219 0.9784
## Residuals 222 528159 2379.1# 3 months
moderation_RTQ_mood_3M <- lm(negaff_B3M_change ~ Group*A_PRE_RTQ_n_total, data = Full_data)
anova(moderation_RTQ_mood_3M)## Analysis of Variance Table
##
## Response: negaff_B3M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 16030 8014.8 3.2606 0.04029 *
## A_PRE_RTQ_n_total 1 1634 1633.5 0.6645 0.41587
## Group:A_PRE_RTQ_n_total 2 10041 5020.7 2.0425 0.13223
## Residuals 213 523572 2458.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Post
moderation_ERQ_mood_BP <- lm(negaff_BP_change ~ Group*A_PRE_ERQ_R_total, data = Full_data)
anova(moderation_ERQ_mood_BP)## Analysis of Variance Table
##
## Response: negaff_BP_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 29097 14548.3 14.1469 1.504e-06 ***
## A_PRE_ERQ_R_total 1 1459 1458.8 1.4185 0.2348
## Group:A_PRE_ERQ_R_total 2 1276 638.1 0.6205 0.5385
## Residuals 252 259151 1028.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1 week
moderation_ERQ_mood_1W <- lm(negaff_B1W_change ~ Group*A_PRE_ERQ_R_total, data = Full_data)
anova(moderation_ERQ_mood_1W)## Analysis of Variance Table
##
## Response: negaff_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 2952 1476.0 0.6614 0.5170
## A_PRE_ERQ_R_total 1 326 325.6 0.1459 0.7028
## Group:A_PRE_ERQ_R_total 2 10272 5135.8 2.3014 0.1023
## Residuals 244 544502 2231.6# 1 month
moderation_ERQ_mood_1M <- lm(negaff_B1M_change ~ Group*A_PRE_ERQ_R_total, data = Full_data)
anova(moderation_ERQ_mood_1M)## Analysis of Variance Table
##
## Response: negaff_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 7881 3940.5 1.6676 0.1911
## A_PRE_ERQ_R_total 1 127 126.5 0.0535 0.8172
## Group:A_PRE_ERQ_R_total 2 3776 1888.0 0.7990 0.4511
## Residuals 222 524576 2363.0# 3 months
moderation_ERQ_mood_3M <- lm(negaff_B3M_change ~ Group*A_PRE_ERQ_R_total, data = Full_data)
anova(moderation_ERQ_mood_3M)## Analysis of Variance Table
##
## Response: negaff_B3M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 16030 8014.8 3.2218 0.04183 *
## A_PRE_ERQ_R_total 1 46 45.8 0.0184 0.89221
## Group:A_PRE_ERQ_R_total 2 5332 2665.9 1.0717 0.34427
## Residuals 213 529869 2487.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1full_lm = lm(negaff_BP_change ~ Group*A_PRE_GM, Full_data)
null_lm = lm(negaff_BP_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0004257513full_lm = lm(negaff_B1W_change ~ Group*A_PRE_GM, Full_data)
null_lm = lm(negaff_B1W_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0003940627full_lm = lm(negaff_B1M_change ~ Group*A_PRE_GM, Full_data)
null_lm = lm(negaff_B1M_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0005742467full_lm = lm(negaff_B3M_change ~ Group*A_PRE_GM, Full_data)
null_lm = lm(negaff_B3M_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0006527475full_lm = lm(negaff_BP_change ~ Group*A_PRE_RTQ_n_total, Full_data)
null_lm = lm(negaff_BP_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.03132396full_lm = lm(negaff_B1W_change ~ Group*A_PRE_RTQ_n_total, Full_data)
null_lm = lm(negaff_B1W_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0003010566full_lm = lm(negaff_B1M_change ~ Group*A_PRE_RTQ_n_total, Full_data)
null_lm = lm(negaff_B1M_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0003112151full_lm = lm(negaff_B3M_change ~ Group*A_PRE_RTQ_n_total, Full_data)
null_lm = lm(negaff_B3M_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.003452178full_lm = lm(negaff_BP_change ~ Group*A_PRE_ERQ_R_total, Full_data)
null_lm = lm(negaff_BP_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0009348534full_lm = lm(negaff_B1W_change ~ Group*A_PRE_ERQ_R_total, Full_data)
null_lm = lm(negaff_B1W_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.002815015full_lm = lm(negaff_B1M_change ~ Group*A_PRE_ERQ_R_total, Full_data)
null_lm = lm(negaff_B1M_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0006761588full_lm = lm(negaff_B3M_change ~ Group*A_PRE_ERQ_R_total, Full_data)
null_lm = lm(negaff_B3M_change ~ Group, Full_data)
BF_BIC = exp((BIC(null_lm) - BIC(full_lm))/2)
BF_BIC## [1] 0.0009322721