Linear mixed-effects model fit by REML
Data: structure(list(Unnamed..0 = 0:55, ID = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("al", "an", "ca", "da", "fu", "je", "sa"), class = "factor"), ER = c(0.002, 0.2, 0.002, 0.002, 0.002, 0.002, 0.1, 0.05, 0.002, 0.002, 0.05, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.05, 0.05, 0.0526315789473684, 0.002, 0.05, 0.1, 0.05, 0.002, 0.002, 0.105263157894737, 0.002, 0.002, 0.1, 0.05, 0.002, 0.002, 0.05, 0.002, 0.002, 0.05, 0.002, 0.002, 0.05, 0.002, 0.002, 0.05, 0.05, 0.002, 0.002, 0.002, 0.002, 0.002, 0.1, 0.05, 0.05, 0.002, 0.002, 0.1, 0.05), scrambling = c(0L, 0L, 6L, 10L, 14L, 18L, 22L, 26L, 0L, 0L, 6L, 10L, 14L, 18L, 22L, 26L, 0L, 0L, 6L, 10L, 14L, 18L, 22L, 26L, 0L, 0L, 6L, 10L, 14L, 18L, 22L, 26L, 0L, 0L, 6L, 10L, 14L, 18L, 22L, 26L, 0L, 0L, 6L, 10L, 14L, 18L, 22L, 26L, 0L, 0L, 6L, 10L, 14L, 18L, 22L, 26L), intensity = c(100L, 40L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 40L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 40L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 40L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 40L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 40L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 40L, 100L, 100L, 100L, 100L, 100L, 100L), COI = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label = c("emotion-easy", "emotion-hard", "scrambling-06", "scrambling-10", "scrambling-14", "scrambling-18", "scrambling-22", "scrambling-26"), class = "factor")), .Names = c("Unnamed..0", "ID", "ER", "scrambling", "intensity", "COI"), row.names = c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55"), class = "data.frame")
AIC BIC logLik
-140.5159 -121.8039 80.25796
Random effects:
Formula: ~1 | ID
(Intercept) Residual
StdDev: 0.008392986 0.03785994
Fixed effects: ER ~ COI
Value Std.Error DF t-value p-value
(Intercept) 0.00200000 0.01465712 42 0.1364525 0.8921
COIemotion-hard 0.05600000 0.02023699 42 2.7672098 0.0084
COIscrambling-06 0.04218045 0.02023699 42 2.0843242 0.0433
COIscrambling-10 0.02094737 0.02023699 42 1.0351029 0.3065
COIscrambling-14 0.00685714 0.02023699 42 0.3388420 0.7364
COIscrambling-18 0.02085714 0.02023699 42 1.0306445 0.3086
COIscrambling-22 0.04885714 0.02023699 42 2.4142493 0.0202
COIscrambling-26 0.02742857 0.02023699 42 1.3553681 0.1825
Correlation:
(Intr) COImt- COI-06 COI-10 COI-14 COI-18 COI-22
COIemotion-hard -0.69
COIscrambling-06 -0.69 0.50
COIscrambling-10 -0.69 0.50 0.50
COIscrambling-14 -0.69 0.50 0.50 0.50
COIscrambling-18 -0.69 0.50 0.50 0.50 0.50
COIscrambling-22 -0.69 0.50 0.50 0.50 0.50 0.50
COIscrambling-26 -0.69 0.50 0.50 0.50 0.50 0.50 0.50
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-1.5034871 -0.5653853 -0.1091885 0.4340204 3.6380447
Number of Observations: 56
Number of Groups: 7