# Putting data file into Long Form before running LME so I can use time and memory_type as factors
# Load the data
data <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/merged_data_with_master_columns.xlsx")
# Identify columns with all NA values
na_columns <- colnames(data)[apply(data, 2, function(x) all(is.na(x)))]
# Remove columns with all NA values - remove irrelevant CBS variables from master sheet that are not relevant to these participants
data_filtered <- data %>%
select(-one_of(na_columns))
# Convert to long format
long_data <- data_filtered %>%
pivot_longer(cols = matches("\\bDet\\d|\\bSeq\\d"),
names_to = "mem_type",
values_to = "mem_score") %>%
separate(mem_type, into = c("mem_type", "Time"), sep = "(?<=[a-z])(?=[0-9])") %>%
mutate(Time = parse_number(Time))
# Export to Excel
write_xlsx(long_data, "C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long.xlsx")# Mean Center Key Variables
# Load the data
data <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long.xlsx")
# Mean center the specified variables
variables_to_center <- c("final_score_raw_gr", "final_score_raw_oo", "final_score_raw_ro", "avg_score_raw_pa", "Time")
for (var in variables_to_center) {
data <- data %>%
mutate(!!paste0("c.", var) := !!sym(var) - mean(!!sym(var), na.rm = TRUE))
}
# Export to Excel
write_xlsx(data, "C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")Gender is not yet included as a factor in the LME because I am missing that data for Nick’s participants. I will add gender to the models once I receive that data and update the dataset.
Time and all CBS variables are mean centered. Multicollinearity was checked for the models that included multiple CBS variables
The following LME including Nick’s sample of young participants and also Steph’s sleep participants.
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for participants with values "young" and "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(group %in% c("young", "sleep"))
# Specify the LME model with random effects
cbs_all <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_gr +
c.Time * group * mem_type * c.final_score_raw_oo +
c.Time * group * mem_type * c.final_score_raw_ro +
c.Time * group * mem_type * c.avg_score_raw_pa +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_all, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 1775.1808 1 77.37 < 2.2e-16 ***
## c.Time 31.5741 1 495.00 3.208e-08 ***
## group 0.1314 1 77.37 0.7179897
## mem_type 0.8308 1 495.00 0.3624907
## c.final_score_raw_gr 1.6127 1 77.37 0.2079246
## c.final_score_raw_oo 0.3242 1 77.37 0.5707563
## c.final_score_raw_ro 0.0508 1 77.37 0.8222608
## c.avg_score_raw_pa 2.4482 1 77.37 0.1217374
## c.Time:group 1.0677 1 495.00 0.3019677
## c.Time:mem_type 11.1132 1 495.00 0.0009214 ***
## group:mem_type 0.0066 1 495.00 0.9351093
## c.Time:c.final_score_raw_gr 0.2978 1 495.00 0.5854954
## group:c.final_score_raw_gr 0.2204 1 77.37 0.6400654
## mem_type:c.final_score_raw_gr 0.1807 1 495.00 0.6709251
## c.Time:c.final_score_raw_oo 5.8377 1 495.00 0.0160474 *
## group:c.final_score_raw_oo 0.7853 1 77.37 0.3782583
## mem_type:c.final_score_raw_oo 0.0161 1 495.00 0.8990546
## c.Time:c.final_score_raw_ro 0.2227 1 495.00 0.6372221
## group:c.final_score_raw_ro 0.3505 1 77.37 0.5555831
## mem_type:c.final_score_raw_ro 1.2685 1 495.00 0.2605989
## c.Time:c.avg_score_raw_pa 2.4563 1 495.00 0.1176931
## group:c.avg_score_raw_pa 0.0656 1 77.37 0.7985606
## mem_type:c.avg_score_raw_pa 0.2847 1 495.00 0.5938660
## c.Time:group:mem_type 0.9344 1 495.00 0.3341837
## c.Time:group:c.final_score_raw_gr 0.0080 1 495.00 0.9285627
## c.Time:mem_type:c.final_score_raw_gr 0.5249 1 495.00 0.4691027
## group:mem_type:c.final_score_raw_gr 0.2448 1 495.00 0.6210038
## c.Time:group:c.final_score_raw_oo 2.6274 1 495.00 0.1056710
## c.Time:mem_type:c.final_score_raw_oo 5.1384 1 495.00 0.0238324 *
## group:mem_type:c.final_score_raw_oo 0.2971 1 495.00 0.5859759
## c.Time:group:c.final_score_raw_ro 1.3107 1 495.00 0.2528163
## c.Time:mem_type:c.final_score_raw_ro 0.9787 1 495.00 0.3229997
## group:mem_type:c.final_score_raw_ro 0.0186 1 495.00 0.8916700
## c.Time:group:c.avg_score_raw_pa 0.6835 1 495.00 0.4087738
## c.Time:mem_type:c.avg_score_raw_pa 1.2017 1 495.00 0.2735202
## group:mem_type:c.avg_score_raw_pa 3.8855 1 495.00 0.0492622 *
## c.Time:group:mem_type:c.final_score_raw_gr 1.0964 1 495.00 0.2955663
## c.Time:group:mem_type:c.final_score_raw_oo 3.2389 1 495.00 0.0725181 .
## c.Time:group:mem_type:c.final_score_raw_ro 0.0272 1 495.00 0.8690804
## c.Time:group:mem_type:c.avg_score_raw_pa 0.7626 1 495.00 0.3829401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_all)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 0.10 | [0.07, 1.00]
## group | 2.50e-03 | [0.00, 1.00]
## mem_type | 3.51e-03 | [0.00, 1.00]
## c.final_score_raw_gr | 0.05 | [0.00, 1.00]
## c.final_score_raw_oo | 5.82e-04 | [0.00, 1.00]
## c.final_score_raw_ro | 0.01 | [0.00, 1.00]
## c.avg_score_raw_pa | 0.14 | [0.03, 1.00]
## c.Time:group | 4.94e-04 | [0.00, 1.00]
## c.Time:mem_type | 0.07 | [0.04, 1.00]
## group:mem_type | 1.34e-05 | [0.00, 1.00]
## c.Time:c.final_score_raw_gr | 3.62e-03 | [0.00, 1.00]
## group:c.final_score_raw_gr | 1.78e-03 | [0.00, 1.00]
## mem_type:c.final_score_raw_gr | 2.61e-03 | [0.00, 1.00]
## c.Time:c.final_score_raw_oo | 4.42e-03 | [0.00, 1.00]
## group:c.final_score_raw_oo | 8.83e-03 | [0.00, 1.00]
## mem_type:c.final_score_raw_oo | 2.16e-04 | [0.00, 1.00]
## c.Time:c.final_score_raw_ro | 2.01e-03 | [0.00, 1.00]
## group:c.final_score_raw_ro | 6.65e-03 | [0.00, 1.00]
## mem_type:c.final_score_raw_ro | 8.23e-03 | [0.00, 1.00]
## c.Time:c.avg_score_raw_pa | 0.02 | [0.01, 1.00]
## group:c.avg_score_raw_pa | 1.66e-03 | [0.00, 1.00]
## mem_type:c.avg_score_raw_pa | 0.02 | [0.00, 1.00]
## c.Time:group:mem_type | 1.88e-03 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_gr | 1.71e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_gr | 4.70e-06 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_gr | 4.94e-04 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_oo | 4.90e-04 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_oo | 8.77e-03 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_oo | 6.00e-04 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_ro | 6.39e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_ro | 4.50e-03 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_ro | 3.75e-05 | [0.00, 1.00]
## c.Time:group:c.avg_score_raw_pa | 8.36e-03 | [0.00, 1.00]
## c.Time:mem_type:c.avg_score_raw_pa | 1.59e-03 | [0.00, 1.00]
## group:mem_type:c.avg_score_raw_pa | 7.79e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_gr | 2.21e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_oo | 6.50e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_ro | 5.49e-05 | [0.00, 1.00]
## c.Time:group:mem_type:c.avg_score_raw_pa | 1.54e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].# check collinearity (VIF values)
check_collinearity(cbs_all)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## group 1.34 [1.24, 1.49] 1.16 0.74
## mem_type 3.00 [2.66, 3.41] 1.73 0.33
## c.final_score_raw_gr 2.81 [2.50, 3.19] 1.68 0.36
## c.final_score_raw_oo 4.29 [3.77, 4.90] 2.07 0.23
## c.final_score_raw_ro 3.82 [3.37, 4.37] 1.96 0.26
## c.avg_score_raw_pa 3.29 [2.91, 3.75] 1.81 0.30
## group:mem_type 3.09 [2.74, 3.51] 1.76 0.32
## group:c.final_score_raw_gr 2.75 [2.44, 3.11] 1.66 0.36
## mem_type:c.final_score_raw_gr 3.00 [2.66, 3.41] 1.73 0.33
## group:c.final_score_raw_oo 4.21 [3.70, 4.81] 2.05 0.24
## mem_type:c.final_score_raw_oo 4.30 [3.78, 4.91] 2.07 0.23
## group:c.final_score_raw_ro 3.84 [3.38, 4.38] 1.96 0.26
## mem_type:c.final_score_raw_ro 3.99 [3.52, 4.56] 2.00 0.25
## group:c.avg_score_raw_pa 3.42 [3.02, 3.89] 1.85 0.29
## mem_type:c.avg_score_raw_pa 3.63 [3.20, 4.13] 1.90 0.28
## group:mem_type:c.final_score_raw_gr 2.79 [2.48, 3.16] 1.67 0.36
## group:mem_type:c.final_score_raw_oo 4.22 [3.71, 4.83] 2.05 0.24
## group:mem_type:c.final_score_raw_ro 3.92 [3.46, 4.48] 1.98 0.25
## group:mem_type:c.avg_score_raw_pa 3.68 [3.25, 4.20] 1.92 0.27
## Tolerance 95% CI
## [0.67, 0.81]
## [0.29, 0.38]
## [0.31, 0.40]
## [0.20, 0.27]
## [0.23, 0.30]
## [0.27, 0.34]
## [0.28, 0.37]
## [0.32, 0.41]
## [0.29, 0.38]
## [0.21, 0.27]
## [0.20, 0.26]
## [0.23, 0.30]
## [0.22, 0.28]
## [0.26, 0.33]
## [0.24, 0.31]
## [0.32, 0.40]
## [0.21, 0.27]
## [0.22, 0.29]
## [0.24, 0.31]
##
## Moderate Correlation
##
## Term VIF VIF 95% CI Increased SE
## c.Time 6.01 [5.25, 6.90] 2.45
## c.Time:group 5.95 [5.20, 6.83] 2.44
## c.Time:mem_type 6.01 [5.25, 6.90] 2.45
## c.Time:c.final_score_raw_gr 5.53 [4.84, 6.34] 2.35
## c.Time:c.final_score_raw_oo 7.87 [6.86, 9.06] 2.81
## c.Time:c.final_score_raw_ro 7.34 [6.40, 8.45] 2.71
## c.Time:c.avg_score_raw_pa 6.70 [5.85, 7.70] 2.59
## c.Time:group:mem_type 5.95 [5.20, 6.83] 2.44
## c.Time:group:c.final_score_raw_gr 5.12 [4.48, 5.86] 2.26
## c.Time:mem_type:c.final_score_raw_gr 5.53 [4.84, 6.34] 2.35
## c.Time:group:c.final_score_raw_oo 7.74 [6.74, 8.90] 2.78
## c.Time:mem_type:c.final_score_raw_oo 7.87 [6.86, 9.06] 2.81
## c.Time:group:c.final_score_raw_ro 7.20 [6.28, 8.28] 2.68
## c.Time:mem_type:c.final_score_raw_ro 7.34 [6.40, 8.45] 2.71
## c.Time:group:c.avg_score_raw_pa 6.79 [5.93, 7.81] 2.61
## c.Time:mem_type:c.avg_score_raw_pa 6.70 [5.85, 7.70] 2.59
## c.Time:group:mem_type:c.final_score_raw_gr 5.12 [4.48, 5.86] 2.26
## c.Time:group:mem_type:c.final_score_raw_oo 7.74 [6.74, 8.90] 2.78
## c.Time:group:mem_type:c.final_score_raw_ro 7.20 [6.28, 8.28] 2.68
## c.Time:group:mem_type:c.avg_score_raw_pa 6.79 [5.93, 7.81] 2.61
## Tolerance Tolerance 95% CI
## 0.17 [0.15, 0.19]
## 0.17 [0.15, 0.19]
## 0.17 [0.15, 0.19]
## 0.18 [0.16, 0.21]
## 0.13 [0.11, 0.15]
## 0.14 [0.12, 0.16]
## 0.15 [0.13, 0.17]
## 0.17 [0.15, 0.19]
## 0.20 [0.17, 0.22]
## 0.18 [0.16, 0.21]
## 0.13 [0.11, 0.15]
## 0.13 [0.11, 0.15]
## 0.14 [0.12, 0.16]
## 0.14 [0.12, 0.16]
## 0.15 [0.13, 0.17]
## 0.15 [0.13, 0.17]
## 0.20 [0.17, 0.22]
## 0.13 [0.11, 0.15]
## 0.14 [0.12, 0.16]
## 0.15 [0.13, 0.17]The following LME including Nick’s sample of young participants and also Steph’s sleep participants for Grammatical Reasoning
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for participants with values "young", and "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(group %in% c("young", "sleep"))
# Specify the LME model with random effects
cbs_gr <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_gr +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_gr, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 1617.6538 1 86.8 < 2.2e-16 ***
## c.Time 31.6870 1 534.0 2.929e-08 ***
## group 0.0641 1 86.8 0.8007
## mem_type 1.3878 1 534.0 0.2393
## c.final_score_raw_gr 2.6435 1 86.8 0.1076
## c.Time:group 0.3123 1 534.0 0.5765
## c.Time:mem_type 10.6041 1 534.0 0.0012 **
## group:mem_type 0.3609 1 534.0 0.5483
## c.Time:c.final_score_raw_gr 0.0640 1 534.0 0.8004
## group:c.final_score_raw_gr 0.1950 1 86.8 0.6599
## mem_type:c.final_score_raw_gr 0.3163 1 534.0 0.5741
## c.Time:group:mem_type 0.5344 1 534.0 0.4651
## c.Time:group:c.final_score_raw_gr 0.2404 1 534.0 0.6241
## c.Time:mem_type:c.final_score_raw_gr 1.0109 1 534.0 0.3151
## group:mem_type:c.final_score_raw_gr 0.9413 1 534.0 0.3324
## c.Time:group:mem_type:c.final_score_raw_gr 2.2237 1 534.0 0.1365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_gr)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 0.10 | [0.06, 1.00]
## group | 1.09e-04 | [0.00, 1.00]
## mem_type | 0.01 | [0.00, 1.00]
## c.final_score_raw_gr | 0.08 | [0.01, 1.00]
## c.Time:group | 6.58e-06 | [0.00, 1.00]
## c.Time:mem_type | 0.06 | [0.03, 1.00]
## group:mem_type | 6.75e-04 | [0.00, 1.00]
## c.Time:c.final_score_raw_gr | 2.86e-03 | [0.00, 1.00]
## group:c.final_score_raw_gr | 4.34e-04 | [0.00, 1.00]
## mem_type:c.final_score_raw_gr | 6.10e-03 | [0.00, 1.00]
## c.Time:group:mem_type | 1.00e-03 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_gr | 1.19e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_gr | 2.19e-07 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_gr | 1.76e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_gr | 4.15e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The following LME including Nick’s sample of young participants and also Steph’s sleep participants for Odd One Out
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for participants with values "young", and "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(group %in% c("young", "sleep"))
# Specify the LME model with random effects
cbs_oo <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_oo +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_oo, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 1827.2409 1 84.37 < 2.2e-16 ***
## c.Time 37.0448 1 527.00 2.224e-09 ***
## group 0.4171 1 84.37 0.5201277
## mem_type 2.5226 1 527.00 0.1128244
## c.final_score_raw_oo 0.0476 1 84.37 0.8278789
## c.Time:group 1.0010 1 527.00 0.3175214
## c.Time:mem_type 11.1754 1 527.00 0.0008878 ***
## group:mem_type 0.6309 1 527.00 0.4273801
## c.Time:c.final_score_raw_oo 6.9364 1 527.00 0.0086942 **
## group:c.final_score_raw_oo 0.0610 1 84.37 0.8054709
## mem_type:c.final_score_raw_oo 0.0027 1 527.00 0.9587045
## c.Time:group:mem_type 1.5876 1 527.00 0.2082326
## c.Time:group:c.final_score_raw_oo 4.4250 1 527.00 0.0358900 *
## c.Time:mem_type:c.final_score_raw_oo 4.2830 1 527.00 0.0389822 *
## group:mem_type:c.final_score_raw_oo 0.3027 1 527.00 0.5824458
## c.Time:group:mem_type:c.final_score_raw_oo 3.3398 1 527.00 0.0681873 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_oo)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 0.12 | [0.08, 1.00]
## group | 2.79e-03 | [0.00, 1.00]
## mem_type | 0.02 | [0.00, 1.00]
## c.final_score_raw_oo | 1.32e-03 | [0.00, 1.00]
## c.Time:group | 4.56e-05 | [0.00, 1.00]
## c.Time:mem_type | 0.07 | [0.04, 1.00]
## group:mem_type | 1.20e-03 | [0.00, 1.00]
## c.Time:c.final_score_raw_oo | 5.53e-03 | [0.00, 1.00]
## group:c.final_score_raw_oo | 1.51e-04 | [0.00, 1.00]
## mem_type:c.final_score_raw_oo | 7.76e-04 | [0.00, 1.00]
## c.Time:group:mem_type | 3.00e-03 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_oo | 2.49e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_oo | 5.77e-03 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_oo | 5.74e-04 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_oo | 6.30e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The following LME including Nick’s sample of young participants and also Steph’s sleep participants for Rotations Task
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for participants with values "young" and "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(group %in% c("young", "sleep"))
# Specify the LME model with random effects
cbs_ro <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_ro +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_ro, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 1824.8234 1 85 < 2.2e-16 ***
## c.Time 35.0338 1 527 5.842e-09 ***
## group 0.4609 1 85 0.49904
## mem_type 1.5943 1 527 0.20727
## c.final_score_raw_ro 0.0091 1 85 0.92426
## c.Time:group 0.8778 1 527 0.34923
## c.Time:mem_type 9.6928 1 527 0.00195 **
## group:mem_type 0.3808 1 527 0.53746
## c.Time:c.final_score_raw_ro 0.8782 1 527 0.34912
## group:c.final_score_raw_ro 0.1579 1 85 0.69206
## mem_type:c.final_score_raw_ro 1.2412 1 527 0.26574
## c.Time:group:mem_type 1.7920 1 527 0.18126
## c.Time:group:c.final_score_raw_ro 2.5452 1 527 0.11123
## c.Time:mem_type:c.final_score_raw_ro 0.1325 1 527 0.71596
## group:mem_type:c.final_score_raw_ro 0.0236 1 527 0.87785
## c.Time:group:mem_type:c.final_score_raw_ro 0.1808 1 527 0.67085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_ro)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 0.11 | [0.07, 1.00]
## group | 3.85e-03 | [0.00, 1.00]
## mem_type | 0.01 | [0.00, 1.00]
## c.final_score_raw_ro | 9.00e-03 | [0.00, 1.00]
## c.Time:group | 3.55e-07 | [0.00, 1.00]
## c.Time:mem_type | 0.07 | [0.04, 1.00]
## group:mem_type | 7.22e-04 | [0.00, 1.00]
## c.Time:c.final_score_raw_ro | 1.82e-03 | [0.00, 1.00]
## group:c.final_score_raw_ro | 2.84e-03 | [0.00, 1.00]
## mem_type:c.final_score_raw_ro | 7.59e-03 | [0.00, 1.00]
## c.Time:group:mem_type | 3.39e-03 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_ro | 6.32e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_ro | 2.01e-03 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_ro | 4.49e-05 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_ro | 3.43e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The following LME including Nick’s sample of young participants and also Steph’s sleep participants for Paired Associates
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for participants with values "young" and "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(group %in% c("young", "sleep"))
# Specify the LME model with random effects
cbs_pa <- lmer(mem_score ~
c.Time * group * mem_type * c.avg_score_raw_pa +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_pa, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 2187.5199 1 90.72 < 2.2e-16 ***
## c.Time 46.6813 1 548.00 2.229e-11 ***
## group 0.5571 1 90.72 0.457346
## mem_type 2.1316 1 548.00 0.144866
## c.avg_score_raw_pa 6.8504 1 90.72 0.010382 *
## c.Time:group 0.3008 1 548.00 0.583625
## c.Time:mem_type 8.6566 1 548.00 0.003397 **
## group:mem_type 0.0345 1 548.00 0.852620
## c.Time:c.avg_score_raw_pa 2.6100 1 548.00 0.106770
## group:c.avg_score_raw_pa 0.2886 1 90.72 0.592414
## mem_type:c.avg_score_raw_pa 0.3055 1 548.00 0.580708
## c.Time:group:mem_type 2.2508 1 548.00 0.134118
## c.Time:group:c.avg_score_raw_pa 0.5110 1 548.00 0.475006
## c.Time:mem_type:c.avg_score_raw_pa 0.4382 1 548.00 0.508249
## group:mem_type:c.avg_score_raw_pa 5.2586 1 548.00 0.022216 *
## c.Time:group:mem_type:c.avg_score_raw_pa 0.3331 1 548.00 0.564102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_pa)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ------------------------------------------------------------------------
## c.Time | 0.14 | [0.10, 1.00]
## group | 9.14e-03 | [0.00, 1.00]
## mem_type | 7.43e-03 | [0.00, 1.00]
## c.avg_score_raw_pa | 0.23 | [0.10, 1.00]
## c.Time:group | 9.57e-04 | [0.00, 1.00]
## c.Time:mem_type | 0.06 | [0.03, 1.00]
## group:mem_type | 6.30e-05 | [0.00, 1.00]
## c.Time:c.avg_score_raw_pa | 0.02 | [0.00, 1.00]
## group:c.avg_score_raw_pa | 2.52e-04 | [0.00, 1.00]
## mem_type:c.avg_score_raw_pa | 0.02 | [0.00, 1.00]
## c.Time:group:mem_type | 4.09e-03 | [0.00, 1.00]
## c.Time:group:c.avg_score_raw_pa | 4.58e-03 | [0.00, 1.00]
## c.Time:mem_type:c.avg_score_raw_pa | 3.96e-04 | [0.00, 1.00]
## group:mem_type:c.avg_score_raw_pa | 9.50e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.avg_score_raw_pa | 6.07e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The following LME includes the LME for Steph’s Sample only for T1-T2 for all CBS variables
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for values in the "Time" column that are "1" or "2" and for participants with values "control" or "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(Time %in% c(1, 2), group %in% c("control", "sleep"))
# Specify the LME model with random effects
cbs_steph <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_gr +
c.Time * group * mem_type * c.final_score_raw_oo +
c.Time * group * mem_type * c.final_score_raw_ro +
c.Time * group * mem_type * c.avg_score_raw_pa +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_steph, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 808.2824 1 189.25 < 2e-16 ***
## c.Time 0.5846 1 144.00 0.44577
## group 0.1876 1 189.25 0.66544
## mem_type 0.0358 1 144.00 0.85015
## c.final_score_raw_gr 0.4781 1 189.25 0.49012
## c.final_score_raw_oo 0.0602 1 189.25 0.80650
## c.final_score_raw_ro 0.7075 1 189.25 0.40134
## c.avg_score_raw_pa 0.0108 1 189.25 0.91738
## c.Time:group 0.2520 1 144.00 0.61642
## c.Time:mem_type 0.3324 1 144.00 0.56515
## group:mem_type 0.0088 1 144.00 0.92551
## c.Time:c.final_score_raw_gr 0.0591 1 144.00 0.80833
## group:c.final_score_raw_gr 0.2845 1 189.25 0.59439
## mem_type:c.final_score_raw_gr 1.7065 1 144.00 0.19352
## c.Time:c.final_score_raw_oo 0.3891 1 144.00 0.53376
## group:c.final_score_raw_oo 0.2793 1 189.25 0.59778
## mem_type:c.final_score_raw_oo 3.4307 1 144.00 0.06604 .
## c.Time:c.final_score_raw_ro 1.1877 1 144.00 0.27760
## group:c.final_score_raw_ro 0.2462 1 189.25 0.62037
## mem_type:c.final_score_raw_ro 2.1594 1 144.00 0.14388
## c.Time:c.avg_score_raw_pa 0.0747 1 144.00 0.78500
## group:c.avg_score_raw_pa 0.1160 1 189.25 0.73375
## mem_type:c.avg_score_raw_pa 1.4074 1 144.00 0.23744
## c.Time:group:mem_type 0.0277 1 144.00 0.86812
## c.Time:group:c.final_score_raw_gr 0.0551 1 144.00 0.81471
## c.Time:mem_type:c.final_score_raw_gr 0.4052 1 144.00 0.52541
## group:mem_type:c.final_score_raw_gr 1.8791 1 144.00 0.17257
## c.Time:group:c.final_score_raw_oo 1.6322 1 144.00 0.20345
## c.Time:mem_type:c.final_score_raw_oo 0.6578 1 144.00 0.41869
## group:mem_type:c.final_score_raw_oo 1.9869 1 144.00 0.16082
## c.Time:group:c.final_score_raw_ro 0.5156 1 144.00 0.47387
## c.Time:mem_type:c.final_score_raw_ro 1.9275 1 144.00 0.16718
## group:mem_type:c.final_score_raw_ro 0.3739 1 144.00 0.54186
## c.Time:group:c.avg_score_raw_pa 0.0815 1 144.00 0.77564
## c.Time:mem_type:c.avg_score_raw_pa 0.4520 1 144.00 0.50248
## group:mem_type:c.avg_score_raw_pa 1.8658 1 144.00 0.17408
## c.Time:group:mem_type:c.final_score_raw_gr 0.5120 1 144.00 0.47545
## c.Time:group:mem_type:c.final_score_raw_oo 1.6042 1 144.00 0.20735
## c.Time:group:mem_type:c.final_score_raw_ro 1.2628 1 144.00 0.26299
## c.Time:group:mem_type:c.avg_score_raw_pa 0.7872 1 144.00 0.37642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_steph)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 1.67e-04 | [0.00, 1.00]
## group | 1.57e-03 | [0.00, 1.00]
## mem_type | 2.17e-04 | [0.00, 1.00]
## c.final_score_raw_gr | 0.02 | [0.00, 1.00]
## c.final_score_raw_oo | 0.02 | [0.00, 1.00]
## c.final_score_raw_ro | 5.17e-04 | [0.00, 1.00]
## c.avg_score_raw_pa | 7.76e-03 | [0.00, 1.00]
## c.Time:group | 5.30e-03 | [0.00, 1.00]
## c.Time:mem_type | 6.76e-03 | [0.00, 1.00]
## group:mem_type | 6.09e-05 | [0.00, 1.00]
## c.Time:c.final_score_raw_gr | 2.52e-03 | [0.00, 1.00]
## group:c.final_score_raw_gr | 0.02 | [0.00, 1.00]
## mem_type:c.final_score_raw_gr | 2.61e-03 | [0.00, 1.00]
## c.Time:c.final_score_raw_oo | 0.04 | [0.01, 1.00]
## group:c.final_score_raw_oo | 1.36e-03 | [0.00, 1.00]
## mem_type:c.final_score_raw_oo | 4.61e-03 | [0.00, 1.00]
## c.Time:c.final_score_raw_ro | 6.87e-04 | [0.00, 1.00]
## group:c.final_score_raw_ro | 1.49e-04 | [0.00, 1.00]
## mem_type:c.final_score_raw_ro | 0.01 | [0.00, 1.00]
## c.Time:c.avg_score_raw_pa | 5.11e-06 | [0.00, 1.00]
## group:c.avg_score_raw_pa | 2.90e-03 | [0.00, 1.00]
## mem_type:c.avg_score_raw_pa | 1.94e-03 | [0.00, 1.00]
## c.Time:group:mem_type | 1.92e-04 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_gr | 7.56e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_gr | 4.39e-04 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_gr | 0.01 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_oo | 2.02e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_oo | 5.90e-04 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_oo | 0.01 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_ro | 8.13e-05 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_ro | 3.89e-03 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_ro | 2.59e-03 | [0.00, 1.00]
## c.Time:group:c.avg_score_raw_pa | 1.62e-03 | [0.00, 1.00]
## c.Time:mem_type:c.avg_score_raw_pa | 2.41e-04 | [0.00, 1.00]
## group:mem_type:c.avg_score_raw_pa | 0.01 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_gr | 3.54e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_oo | 0.01 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_ro | 8.69e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.avg_score_raw_pa | 5.44e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].# check collinearity (VIF values)
check_collinearity(cbs_steph)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## c.Time 4.84 [ 4.03, 5.87] 2.20 0.21 [0.17, 0.25]
## group 3.91 [ 3.27, 4.72] 1.98 0.26 [0.21, 0.31]
##
## Moderate Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## c.final_score_raw_gr 9.88 [ 8.11, 12.09] 3.14 0.10
## c.final_score_raw_oo 5.42 [ 4.50, 6.59] 2.33 0.18
## c.final_score_raw_ro 6.83 [ 5.64, 8.33] 2.61 0.15
## c.Time:group 7.49 [ 6.18, 9.15] 2.74 0.13
## group:c.final_score_raw_gr 9.39 [ 7.71, 11.49] 3.06 0.11
## c.Time:c.final_score_raw_oo 6.64 [ 5.48, 8.09] 2.58 0.15
## group:c.final_score_raw_oo 5.62 [ 4.66, 6.84] 2.37 0.18
## c.Time:c.final_score_raw_ro 8.39 [ 6.90, 10.25] 2.90 0.12
## group:c.final_score_raw_ro 6.68 [ 5.52, 8.14] 2.58 0.15
## c.Time:group:c.final_score_raw_oo 6.89 [ 5.69, 8.40] 2.62 0.15
## c.Time:group:c.final_score_raw_ro 8.31 [ 6.84, 10.15] 2.88 0.12
## Tolerance 95% CI
## [0.08, 0.12]
## [0.15, 0.22]
## [0.12, 0.18]
## [0.11, 0.16]
## [0.09, 0.13]
## [0.12, 0.18]
## [0.15, 0.21]
## [0.10, 0.14]
## [0.12, 0.18]
## [0.12, 0.18]
## [0.10, 0.15]
##
## High Correlation
##
## Term VIF VIF 95% CI Increased SE
## mem_type 12.10 [ 9.91, 14.84] 3.48
## c.avg_score_raw_pa 11.06 [ 9.07, 13.55] 3.33
## c.Time:mem_type 14.53 [11.87, 17.83] 3.81
## group:mem_type 14.19 [11.60, 17.41] 3.77
## c.Time:c.final_score_raw_gr 12.86 [10.52, 15.77] 3.59
## mem_type:c.final_score_raw_gr 20.65 [16.84, 25.39] 4.54
## mem_type:c.final_score_raw_oo 10.29 [ 8.44, 12.60] 3.21
## mem_type:c.final_score_raw_ro 13.03 [10.66, 15.98] 3.61
## c.Time:c.avg_score_raw_pa 13.77 [11.26, 16.89] 3.71
## group:c.avg_score_raw_pa 10.27 [ 8.42, 12.57] 3.20
## mem_type:c.avg_score_raw_pa 21.55 [17.57, 26.50] 4.64
## c.Time:group:mem_type 16.42 [13.41, 20.17] 4.05
## c.Time:group:c.final_score_raw_gr 11.98 [ 9.81, 14.69] 3.46
## c.Time:mem_type:c.final_score_raw_gr 23.25 [18.94, 28.59] 4.82
## group:mem_type:c.final_score_raw_gr 19.04 [15.53, 23.39] 4.36
## c.Time:mem_type:c.final_score_raw_oo 11.51 [ 9.43, 14.10] 3.39
## group:mem_type:c.final_score_raw_oo 10.68 [ 8.76, 13.08] 3.27
## c.Time:mem_type:c.final_score_raw_ro 14.57 [11.91, 17.88] 3.82
## group:mem_type:c.final_score_raw_ro 13.00 [10.64, 15.94] 3.61
## c.Time:group:c.avg_score_raw_pa 12.76 [10.44, 15.65] 3.57
## c.Time:mem_type:c.avg_score_raw_pa 24.15 [19.67, 29.70] 4.91
## group:mem_type:c.avg_score_raw_pa 19.96 [16.28, 24.54] 4.47
## c.Time:group:mem_type:c.final_score_raw_gr 21.39 [17.43, 26.29] 4.62
## c.Time:group:mem_type:c.final_score_raw_oo 11.94 [ 9.78, 14.64] 3.46
## c.Time:group:mem_type:c.final_score_raw_ro 14.56 [11.90, 17.87] 3.82
## c.Time:group:mem_type:c.avg_score_raw_pa 22.36 [18.22, 27.50] 4.73
## Tolerance Tolerance 95% CI
## 0.08 [0.07, 0.10]
## 0.09 [0.07, 0.11]
## 0.07 [0.06, 0.08]
## 0.07 [0.06, 0.09]
## 0.08 [0.06, 0.10]
## 0.05 [0.04, 0.06]
## 0.10 [0.08, 0.12]
## 0.08 [0.06, 0.09]
## 0.07 [0.06, 0.09]
## 0.10 [0.08, 0.12]
## 0.05 [0.04, 0.06]
## 0.06 [0.05, 0.07]
## 0.08 [0.07, 0.10]
## 0.04 [0.03, 0.05]
## 0.05 [0.04, 0.06]
## 0.09 [0.07, 0.11]
## 0.09 [0.08, 0.11]
## 0.07 [0.06, 0.08]
## 0.08 [0.06, 0.09]
## 0.08 [0.06, 0.10]
## 0.04 [0.03, 0.05]
## 0.05 [0.04, 0.06]
## 0.05 [0.04, 0.06]
## 0.08 [0.07, 0.10]
## 0.07 [0.06, 0.08]
## 0.04 [0.04, 0.05]The following LME includes the LME for Steph’s Sample only for T1-T2 for Grammatical Reasoning task only
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for values in the "Time" column that are "1" or "2" and for participants with values "control" or "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(Time %in% c(1, 2), group %in% c("control", "sleep"))
# Specify the LME model with random effects
cbs_steph_gr <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_gr +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_steph_gr, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 846.3081 1 218.01 <2e-16 ***
## c.Time 0.3562 1 165.00 0.5514
## group 0.1540 1 218.01 0.6951
## mem_type 0.0597 1 165.00 0.8072
## c.final_score_raw_gr 0.4097 1 218.01 0.5228
## c.Time:group 0.1315 1 165.00 0.7174
## c.Time:mem_type 0.0616 1 165.00 0.8043
## group:mem_type 0.0602 1 165.00 0.8064
## c.Time:c.final_score_raw_gr 0.0348 1 165.00 0.8523
## group:c.final_score_raw_gr 1.2621 1 218.01 0.2625
## mem_type:c.final_score_raw_gr 0.0535 1 165.00 0.8174
## c.Time:group:mem_type 0.1146 1 165.00 0.7354
## c.Time:group:c.final_score_raw_gr 0.8477 1 165.00 0.3585
## c.Time:mem_type:c.final_score_raw_gr 0.0250 1 165.00 0.8747
## group:mem_type:c.final_score_raw_gr 0.1930 1 165.00 0.6610
## c.Time:group:mem_type:c.final_score_raw_gr 0.0109 1 165.00 0.9172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_steph_gr)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 1.40e-07 | [0.00, 1.00]
## group | 2.87e-03 | [0.00, 1.00]
## mem_type | 6.78e-05 | [0.00, 1.00]
## c.final_score_raw_gr | 0.04 | [0.01, 1.00]
## c.Time:group | 4.37e-03 | [0.00, 1.00]
## c.Time:mem_type | 2.92e-03 | [0.00, 1.00]
## group:mem_type | 3.65e-04 | [0.00, 1.00]
## c.Time:c.final_score_raw_gr | 6.56e-03 | [0.00, 1.00]
## group:c.final_score_raw_gr | 0.02 | [0.00, 1.00]
## mem_type:c.final_score_raw_gr | 6.32e-05 | [0.00, 1.00]
## c.Time:group:mem_type | 6.94e-04 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_gr | 8.62e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_gr | 9.65e-05 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_gr | 1.17e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_gr | 6.58e-05 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The following LME includes the LME for Steph’s Sample only for T1-T2 for Odd One Out task only
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for values in the "Time" column that are "1" or "2" and for participants with values "control" or "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(Time %in% c(1, 2), group %in% c("control", "sleep"))
# Specify the LME model with random effects
cbs_steph_oo <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_oo +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_steph_oo, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 927.8011 1 208.38 <2e-16 ***
## c.Time 0.4508 1 162.00 0.5029
## group 0.6513 1 208.38 0.4206
## mem_type 0.0672 1 162.00 0.7958
## c.final_score_raw_oo 0.0595 1 208.38 0.8076
## c.Time:group 0.4254 1 162.00 0.5152
## c.Time:mem_type 0.1203 1 162.00 0.7292
## group:mem_type 0.1463 1 162.00 0.7026
## c.Time:c.final_score_raw_oo 0.3758 1 162.00 0.5407
## group:c.final_score_raw_oo 0.6043 1 208.38 0.4378
## mem_type:c.final_score_raw_oo 3.2588 1 162.00 0.0729 .
## c.Time:group:mem_type 0.1212 1 162.00 0.7281
## c.Time:group:c.final_score_raw_oo 2.0834 1 162.00 0.1508
## c.Time:mem_type:c.final_score_raw_oo 0.6781 1 162.00 0.4115
## group:mem_type:c.final_score_raw_oo 2.2720 1 162.00 0.1337
## c.Time:group:mem_type:c.final_score_raw_oo 1.6228 1 162.00 0.2045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_steph_oo)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 9.34e-04 | [0.00, 1.00]
## group | 0.01 | [0.00, 1.00]
## mem_type | 7.47e-08 | [0.00, 1.00]
## c.final_score_raw_oo | 0.02 | [0.00, 1.00]
## c.Time:group | 9.87e-03 | [0.00, 1.00]
## c.Time:mem_type | 4.50e-03 | [0.00, 1.00]
## group:mem_type | 9.03e-04 | [0.00, 1.00]
## c.Time:c.final_score_raw_oo | 0.05 | [0.01, 1.00]
## group:c.final_score_raw_oo | 2.05e-04 | [0.00, 1.00]
## mem_type:c.final_score_raw_oo | 3.05e-03 | [0.00, 1.00]
## c.Time:group:mem_type | 7.48e-04 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_oo | 3.62e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_oo | 4.34e-04 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_oo | 0.01 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_oo | 9.92e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The following LME includes the LME for Steph’s Sample only for T1-T2 for Rotations task only
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for values in the "Time" column that are "1" or "2" and for participants with values "control" or "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(Time %in% c(1, 2), group %in% c("control", "sleep"))
# Specify the LME model with random effects
cbs_steph_ro <- lmer(mem_score ~
c.Time * group * mem_type * c.final_score_raw_ro +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_steph_ro, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 886.1451 1 218.01 <2e-16 ***
## c.Time 0.8552 1 168.00 0.3564
## group 1.3060 1 218.01 0.2544
## mem_type 0.0351 1 168.00 0.8516
## c.final_score_raw_ro 0.1743 1 218.01 0.6767
## c.Time:group 1.0805 1 168.00 0.3001
## c.Time:mem_type 0.3767 1 168.00 0.5402
## group:mem_type 0.0541 1 168.00 0.8163
## c.Time:c.final_score_raw_ro 0.8039 1 168.00 0.3712
## group:c.final_score_raw_ro 0.0004 1 218.01 0.9834
## mem_type:c.final_score_raw_ro 2.3156 1 168.00 0.1300
## c.Time:group:mem_type 0.0100 1 168.00 0.9203
## c.Time:group:c.final_score_raw_ro 0.0198 1 168.00 0.8881
## c.Time:mem_type:c.final_score_raw_ro 1.5053 1 168.00 0.2216
## group:mem_type:c.final_score_raw_ro 0.2698 1 168.00 0.6041
## c.Time:group:mem_type:c.final_score_raw_ro 0.6141 1 168.00 0.4344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_steph_ro)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------------------------------
## c.Time | 1.89e-03 | [0.00, 1.00]
## group | 0.02 | [0.00, 1.00]
## mem_type | 9.07e-06 | [0.00, 1.00]
## c.final_score_raw_ro | 1.15e-03 | [0.00, 1.00]
## c.Time:group | 0.01 | [0.00, 1.00]
## c.Time:mem_type | 5.80e-03 | [0.00, 1.00]
## group:mem_type | 3.22e-04 | [0.00, 1.00]
## c.Time:c.final_score_raw_ro | 2.42e-03 | [0.00, 1.00]
## group:c.final_score_raw_ro | 1.14e-03 | [0.00, 1.00]
## mem_type:c.final_score_raw_ro | 0.01 | [0.00, 1.00]
## c.Time:group:mem_type | 5.97e-05 | [0.00, 1.00]
## c.Time:group:c.final_score_raw_ro | 2.03e-03 | [0.00, 1.00]
## c.Time:mem_type:c.final_score_raw_ro | 4.12e-03 | [0.00, 1.00]
## group:mem_type:c.final_score_raw_ro | 1.60e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.final_score_raw_ro | 3.64e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The following LME includes the LME for Steph’s Sample only for T1-T2 for Paired Associates task only
# Load the modified data from the Excel file
LME_long_with_centered_variables <- read_excel("C:/Users/narde/Documents/Baycrest/Baytour Analyses/LME_long_with_centered_variables.xlsx")
# Filter data for values in the "Time" column that are "1" or "2" and for participants with values "control" or "sleep" in the "group" column
LME_long_with_centered_variables_filtered <- LME_long_with_centered_variables %>%
filter(Time %in% c(1, 2), group %in% c("control", "sleep"))
# Specify the LME model with random effects
cbs_steph_ro <- lmer(mem_score ~
c.Time * group * mem_type * c.avg_score_raw_pa +
(1|subject),
data = LME_long_with_centered_variables_filtered)
# get ANOVA output
Anova(cbs_steph_ro, type = "III", test.statistic = "F")## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: mem_score
## F Df Df.res Pr(>F)
## (Intercept) 885.5215 1 225.22 <2e-16 ***
## c.Time 0.5325 1 171.00 0.4666
## group 1.0378 1 225.22 0.3094
## mem_type 0.2273 1 171.00 0.6341
## c.avg_score_raw_pa 0.0825 1 225.22 0.7742
## c.Time:group 1.1527 1 171.00 0.2845
## c.Time:mem_type 0.1270 1 171.00 0.7220
## group:mem_type 0.3032 1 171.00 0.5826
## c.Time:c.avg_score_raw_pa 0.2520 1 171.00 0.6163
## group:c.avg_score_raw_pa 0.2044 1 225.22 0.6516
## mem_type:c.avg_score_raw_pa 0.5942 1 171.00 0.4419
## c.Time:group:mem_type 0.0752 1 171.00 0.7842
## c.Time:group:c.avg_score_raw_pa 0.0742 1 171.00 0.7856
## c.Time:mem_type:c.avg_score_raw_pa 0.4977 1 171.00 0.4815
## group:mem_type:c.avg_score_raw_pa 0.7038 1 171.00 0.4027
## c.Time:group:mem_type:c.avg_score_raw_pa 0.5981 1 171.00 0.4404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# eta squares
eta_squared(cbs_steph_ro)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ------------------------------------------------------------------------
## c.Time | 3.76e-03 | [0.00, 1.00]
## group | 0.02 | [0.00, 1.00]
## mem_type | 1.27e-04 | [0.00, 1.00]
## c.avg_score_raw_pa | 0.01 | [0.00, 1.00]
## c.Time:group | 0.02 | [0.00, 1.00]
## c.Time:mem_type | 3.69e-03 | [0.00, 1.00]
## group:mem_type | 1.77e-03 | [0.00, 1.00]
## c.Time:c.avg_score_raw_pa | 9.13e-04 | [0.00, 1.00]
## group:c.avg_score_raw_pa | 4.83e-05 | [0.00, 1.00]
## mem_type:c.avg_score_raw_pa | 8.38e-04 | [0.00, 1.00]
## c.Time:group:mem_type | 4.40e-04 | [0.00, 1.00]
## c.Time:group:c.avg_score_raw_pa | 8.80e-04 | [0.00, 1.00]
## c.Time:mem_type:c.avg_score_raw_pa | 6.80e-04 | [0.00, 1.00]
## group:mem_type:c.avg_score_raw_pa | 4.10e-03 | [0.00, 1.00]
## c.Time:group:mem_type:c.avg_score_raw_pa | 3.49e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].