# ============================================================
# ANALISIS LINEAR MIXED EFFECT
# Psychological Flexibility dan Emotional Well-Being
# ============================================================
# Library
library(readr)
library(dplyr)
library(ggplot2)
library(lme4)
library(lmerTest)
library(broom.mixed)
library(performance)
# Membaca data
# Membaca data
library(readr)
Dataset_longitudinal_GLS <- read_csv2("Dataset_longitudinal_GLS.csv")
View(Dataset_longitudinal_GLS)
str(Dataset_longitudinal_GLS)
## spc_tbl_ [546 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : num [1:546] 1 1 1 2 2 2 3 3 3 4 ...
## $ IDUser : num [1:546] 230642 230642 230642 274870 274870 ...
## $ Time_Point : num [1:546] 1 2 3 1 2 3 1 2 3 1 ...
## $ RespondPrePost: num [1:546] 1 1 1 1 1 1 1 1 1 1 ...
## $ Completers : num [1:546] 1 1 1 1 1 1 1 1 1 1 ...
## $ Groups : num [1:546] 0 0 0 0 0 0 0 0 0 0 ...
## $ Sex_at_birth : num [1:546] 1 1 1 1 1 1 1 1 1 1 ...
## $ Emot_Well_T : num [1:546] 5.58e+14 5.63e+14 4.58e+14 5.82e+14 5.61e+14 ...
## $ Psych_Infle_T : num [1:546] 5.75e+13 5.06e+14 5.86e+14 4.53e+14 4.80e+14 ...
## - attr(*, "spec")=
## .. cols(
## .. id = col_double(),
## .. IDUser = col_double(),
## .. Time_Point = col_double(),
## .. RespondPrePost = col_double(),
## .. Completers = col_double(),
## .. Groups = col_double(),
## .. Sex_at_birth = col_double(),
## .. Emot_Well_T = col_number(),
## .. Psych_Infle_T = col_number()
## .. )
## - attr(*, "problems")=<externalptr>
dim(Dataset_longitudinal_GLS)
## [1] 546 9
names(Dataset_longitudinal_GLS)
## [1] "id" "IDUser" "Time_Point" "RespondPrePost"
## [5] "Completers" "Groups" "Sex_at_birth" "Emot_Well_T"
## [9] "Psych_Infle_T"
head(Dataset_longitudinal_GLS)
## # A tibble: 6 × 9
## id IDUser Time_Point RespondPrePost Completers Groups Sex_at_birth
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 230642 1 1 1 0 1
## 2 1 230642 2 1 1 0 1
## 3 1 230642 3 1 1 0 1
## 4 2 274870 1 1 1 0 1
## 5 2 274870 2 1 1 0 1
## 6 2 274870 3 1 1 0 1
## # ℹ 2 more variables: Emot_Well_T <dbl>, Psych_Infle_T <dbl>
colSums(is.na(Dataset_longitudinal_GLS))
## id IDUser Time_Point RespondPrePost Completers
## 0 0 0 0 0
## Groups Sex_at_birth Emot_Well_T Psych_Infle_T
## 0 0 74 51
# Persiapan variabel
Dataset_longitudinal_GLS <- Dataset_longitudinal_GLS %>%
mutate(
id = factor(id),
Time_Point = factor(
Time_Point,
levels = c(1, 2, 3),
labels = c("Pre", "Post", "Follow-up")
),
Groups = factor(
Groups,
levels = c(0, 1),
labels = c("Wait-list Control", "Experimental")
)
)
levels(Dataset_longitudinal_GLS$id)
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
## [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
## [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
## [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
## [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
## [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
## [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
## [97] "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108"
## [109] "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144"
## [145] "145" "146" "147" "148" "149" "150" "151" "152" "153" "154" "155" "156"
## [157] "157" "158" "159" "160" "161" "162" "163" "164" "165" "166" "167" "168"
## [169] "169" "170" "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182"
levels(Dataset_longitudinal_GLS$Time_Point)
## [1] "Pre" "Post" "Follow-up"
levels(Dataset_longitudinal_GLS$Groups)
## [1] "Wait-list Control" "Experimental"
unique(Dataset_longitudinal_GLS$Time_Point)
## [1] Pre Post Follow-up
## Levels: Pre Post Follow-up
unique(Dataset_longitudinal_GLS$Groups)
## [1] Wait-list Control Experimental
## Levels: Wait-list Control Experimental
# 4.1 Statistik Deskriptif
# Statistik deskriptif Psychological Flexibility
deskriptif_pf <- Dataset_longitudinal_GLS %>%
group_by(Groups, Time_Point) %>%
summarise(
n = sum(!is.na(Psych_Infle_T)),
mean = mean(Psych_Infle_T, na.rm = TRUE),
sd = sd(Psych_Infle_T, na.rm = TRUE),
min = min(Psych_Infle_T, na.rm = TRUE),
max = max(Psych_Infle_T, na.rm = TRUE),
.groups = "drop"
)
deskriptif_pf
## # A tibble: 6 × 7
## Groups Time_Point n mean sd min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Wait-list Control Pre 85 4.52e14 1.60e14 6.39e12 6.92e14
## 2 Wait-list Control Post 82 4.63e14 1.45e14 3.41e11 7.03e14
## 3 Wait-list Control Follow-up 65 4.42e14 1.27e14 4.48e13 7.14e14
## 4 Experimental Pre 97 4.60e14 1.65e14 5.27e12 7.24e14
## 5 Experimental Post 89 4.03e14 1.62e14 3.04e12 6.76e14
## 6 Experimental Follow-up 77 3.94e14 1.53e14 3.04e12 6.56e14
# Statistik deskriptif Emotional Well-Being
deskriptif_ewb <- Dataset_longitudinal_GLS %>%
group_by(Groups, Time_Point) %>%
summarise(
n = sum(!is.na(Emot_Well_T)),
mean = mean(Emot_Well_T, na.rm = TRUE),
sd = sd(Emot_Well_T, na.rm = TRUE),
min = min(Emot_Well_T, na.rm = TRUE),
max = max(Emot_Well_T, na.rm = TRUE),
.groups = "drop"
)
deskriptif_ewb
## # A tibble: 6 × 7
## Groups Time_Point n mean sd min max
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Wait-list Control Pre 83 4.65e14 1.58e14 5.87e12 6.56e14
## 2 Wait-list Control Post 81 4.70e14 1.52e14 6.36e12 6.64e14
## 3 Wait-list Control Follow-up 63 5.14e14 1.52e14 5.52e12 6.55e14
## 4 Experimental Pre 94 4.29e14 1.94e14 3.86e12 6.72e14
## 5 Experimental Post 82 4.84e14 1.73e14 5.28e13 6.82e14
## 6 Experimental Follow-up 69 5.41e14 1.15e14 6.01e13 6.72e14
# Menyimpan hasil statistik deskriptif
write.csv(
deskriptif_pf,
"Tabel_4_1_Deskriptif_Psychological_Flexibility.csv",
row.names = FALSE
)
write.csv(
deskriptif_ewb,
"Tabel_4_2_Deskriptif_Emotional_Well_Being.csv",
row.names = FALSE
)
# 4.2 Visualisasi Data
# Rata-rata Psychological Flexibility
ggplot(
Dataset_longitudinal_GLS,
aes(
x = Time_Point,
y = Psych_Infle_T,
group = Groups,
linetype = Groups
)
) +
stat_summary(
fun = mean,
geom = "line",
linewidth = 1
) +
stat_summary(
fun = mean,
geom = "point",
size = 3
) +
labs(
title = "Rata-rata Psychological Flexibility Berdasarkan Waktu dan Kelompok",
x = "Waktu Pengukuran",
y = "Psychological Flexibility",
linetype = "Kelompok"
) +
theme_minimal()
# Perubahan Psychological Flexibility setiap individu
ggplot(
Dataset_longitudinal_GLS,
aes(
x = Time_Point,
y = Psych_Infle_T,
group = id
)
) +
geom_line(alpha = 0.2) +
geom_point(alpha = 0.3) +
facet_wrap(~ Groups) +
labs(
title = "Perubahan Psychological Flexibility Individu",
x = "Waktu Pengukuran",
y = "Psychological Flexibility"
) +
theme_minimal()
# Rata-rata Emotional Well-Being
ggplot(
Dataset_longitudinal_GLS,
aes(
x = Time_Point,
y = Emot_Well_T,
group = Groups,
linetype = Groups
)
) +
stat_summary(
fun = mean,
geom = "line",
linewidth = 1
) +
stat_summary(
fun = mean,
geom = "point",
size = 3
) +
labs(
title = "Rata-rata Emotional Well-Being Berdasarkan Waktu dan Kelompok",
x = "Waktu Pengukuran",
y = "Emotional Well-Being",
linetype = "Kelompok"
) +
theme_minimal()
# Perubahan Emotional Well-Being setiap individu
ggplot(
Dataset_longitudinal_GLS,
aes(
x = Time_Point,
y = Emot_Well_T,
group = id
)
) +
geom_line(alpha = 0.2) +
geom_point(alpha = 0.3) +
facet_wrap(~ Groups) +
labs(
title = "Perubahan Emotional Well-Being Individu",
x = "Waktu Pengukuran",
y = "Emotional Well-Being"
) +
theme_minimal()
# 4.3 Pembentukan Model Linear Mixed Effect
# Model Psychological Flexibility
model_pf <- lmer(
Psych_Infle_T ~ Time_Point * Groups + (1 | id),
data = Dataset_longitudinal_GLS,
REML = FALSE
)
summary(model_pf)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: Psych_Infle_T ~ Time_Point * Groups + (1 | id)
## Data: Dataset_longitudinal_GLS
##
## AIC BIC logLik -2*log(L) df.resid
## 33741.3 33774.9 -16862.6 33725.3 487
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0368 -0.2782 0.1353 0.5509 1.9129
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 4.097e+27 6.401e+13
## Residual 1.925e+28 1.387e+14
## Number of obs: 495, groups: id, 182
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.521e+14 1.657e+13 7.949e+02 27.281
## Time_PointPost 1.085e+13 2.151e+13 3.646e+06 0.504
## Time_PointFollow-up -1.180e+13 2.309e+13 1.196e+05 -0.511
## GroupsExperimental 7.453e+12 2.270e+13 7.949e+02 0.328
## Time_PointPost:GroupsExperimental -6.715e+13 2.967e+13 1.328e+06 -2.263
## Time_PointFollow-up:GroupsExperimental -5.546e+13 3.146e+13 1.271e+05 -1.763
## Pr(>|t|)
## (Intercept) <2e-16 ***
## Time_PointPost 0.6140
## Time_PointFollow-up 0.6092
## GroupsExperimental 0.7428
## Time_PointPost:GroupsExperimental 0.0236 *
## Time_PointFollow-up:GroupsExperimental 0.0779 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Tm_PnP Tm_PF- GrpsEx T_PP:G
## Time_PntPst -0.635
## Tm_PntFllw- -0.592 0.459
## GrpsExprmnt -0.730 0.464 0.432
## Tm_PntPs:GE 0.460 -0.725 -0.333 -0.631
## Tm_PntF-:GE 0.434 -0.337 -0.734 -0.595 0.460
coef_pf <- summary(model_pf)$coefficients
coef_pf
## Estimate Std. Error df
## (Intercept) 4.521252e+14 1.657276e+13 794.8791
## Time_PointPost 1.084779e+13 2.150929e+13 3646199.0649
## Time_PointFollow-up -1.180353e+13 2.309132e+13 119628.8680
## GroupsExperimental 7.453108e+12 2.270099e+13 794.8791
## Time_PointPost:GroupsExperimental -6.714805e+13 2.967318e+13 1328465.9353
## Time_PointFollow-up:GroupsExperimental -5.546196e+13 3.146100e+13 127074.5428
## t value Pr(>|t|)
## (Intercept) 27.2812215 3.567441e-116
## Time_PointPost 0.5043304 6.140293e-01
## Time_PointFollow-up -0.5111676 6.092346e-01
## GroupsExperimental 0.3283165 7.427588e-01
## Time_PointPost:GroupsExperimental -2.2629207 2.364074e-02
## Time_PointFollow-up:GroupsExperimental -1.7628797 7.792318e-02
# Model Emotional Well-Being
model_ewb <- lmer(
Emot_Well_T ~ Time_Point * Groups + (1 | id),
data = Dataset_longitudinal_GLS,
REML = FALSE
)
summary(model_ewb)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: Emot_Well_T ~ Time_Point * Groups + (1 | id)
## Data: Dataset_longitudinal_GLS
##
## AIC BIC logLik -2*log(L) df.resid
## 32220.9 32254.2 -16102.5 32204.9 464
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1293 -0.2635 0.2475 0.5645 1.6445
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 4.902e+27 7.001e+13
## Residual 2.095e+28 1.448e+14
## Number of obs: 472, groups: id, 181
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.642e+14 1.764e+13 6.791e+02 26.316
## Time_PointPost 6.242e+12 2.268e+13 9.225e+05 0.275
## Time_PointFollow-up 5.170e+13 2.448e+13 8.447e+04 2.111
## GroupsExperimental -3.534e+13 2.421e+13 6.784e+02 -1.460
## Time_PointPost:GroupsExperimental 4.523e+13 3.164e+13 3.048e+05 1.430
## Time_PointFollow-up:GroupsExperimental 6.033e+13 3.378e+13 7.074e+04 1.786
## Pr(>|t|)
## (Intercept) <2e-16 ***
## Time_PointPost 0.7832
## Time_PointFollow-up 0.0347 *
## GroupsExperimental 0.1447
## Time_PointPost:GroupsExperimental 0.1528
## Time_PointFollow-up:GroupsExperimental 0.0741 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Tm_PnP Tm_PF- GrpsEx T_PP:G
## Time_PntPst -0.634
## Tm_PntFllw- -0.586 0.459
## GrpsExprmnt -0.729 0.462 0.427
## Tm_PntPs:GE 0.454 -0.717 -0.329 -0.623
## Tm_PntF-:GE 0.425 -0.332 -0.725 -0.583 0.452
coef_ewb <- summary(model_ewb)$coefficients
coef_ewb
## Estimate Std. Error df
## (Intercept) 4.641987e+14 1.763972e+13 679.0568
## Time_PointPost 6.241809e+12 2.268308e+13 922494.8593
## Time_PointFollow-up 5.169579e+13 2.448392e+13 84465.1156
## GroupsExperimental -3.534170e+13 2.420626e+13 678.3670
## Time_PointPost:GroupsExperimental 4.523305e+13 3.163740e+13 304837.3041
## Time_PointFollow-up:GroupsExperimental 6.032549e+13 3.377747e+13 70736.6615
## t value Pr(>|t|)
## (Intercept) 26.3155413 9.378589e-106
## Time_PointPost 0.2751746 7.831821e-01
## Time_PointFollow-up 2.1114178 3.473935e-02
## GroupsExperimental -1.4600228 1.447468e-01
## Time_PointPost:GroupsExperimental 1.4297336 1.527945e-01
## Time_PointFollow-up:GroupsExperimental 1.7859680 7.410872e-02
# Formula model
formula(model_pf)
## Psych_Infle_T ~ Time_Point * Groups + (1 | id)
formula(model_ewb)
## Emot_Well_T ~ Time_Point * Groups + (1 | id)
# 4.4 Estimasi Parameter dan Kesesuaian Model
# Interval kepercayaan model Psychological Flexibility
confint(
model_pf,
method = "Wald"
)
## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 4.196432e+14 4.846072e+14
## Time_PointPost -3.130964e+13 5.300522e+13
## Time_PointFollow-up -5.706168e+13 3.345461e+13
## GroupsExperimental -3.704001e+13 5.194623e+13
## Time_PointPost:GroupsExperimental -1.253064e+14 -8.989689e+12
## Time_PointFollow-up:GroupsExperimental -1.171244e+14 6.200469e+12
# Interval kepercayaan model Emotional Well-Being
confint(
model_ewb,
method = "Wald"
)
## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 4.296255e+14 4.987719e+14
## Time_PointPost -3.821621e+13 5.069983e+13
## Time_PointFollow-up 3.708183e+12 9.968340e+13
## GroupsExperimental -8.278510e+13 1.210171e+13
## Time_PointPost:GroupsExperimental -1.677511e+13 1.072412e+14
## Time_PointFollow-up:GroupsExperimental -5.877144e+12 1.265281e+14
# Ukuran model Psychological Flexibility
AIC(model_pf)
## [1] 33741.26
BIC(model_pf)
## [1] 33774.9
logLik(model_pf)
## 'log Lik.' -16862.63 (df=8)
# Ukuran model Emotional Well-Being
AIC(model_ewb)
## [1] 32220.92
BIC(model_ewb)
## [1] 32254.18
logLik(model_ewb)
## 'log Lik.' -16102.46 (df=8)
# Fixed effect Psychological Flexibility
fixed_pf <- broom.mixed::tidy(
model_pf,
effects = "fixed",
conf.int = TRUE
)
fixed_pf
## # A tibble: 6 × 9
## effect term estimate std.error statistic df p.value conf.low conf.high
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed (Inte… 4.52e14 1.66e13 27.3 7.95e2 3.57e-116 4.20e14 4.85e14
## 2 fixed Time_… 1.08e13 2.15e13 0.504 3.65e6 6.14e- 1 -3.13e13 5.30e13
## 3 fixed Time_… -1.18e13 2.31e13 -0.511 1.20e5 6.09e- 1 -5.71e13 3.35e13
## 4 fixed Group… 7.45e12 2.27e13 0.328 7.95e2 7.43e- 1 -3.71e13 5.20e13
## 5 fixed Time_… -6.71e13 2.97e13 -2.26 1.33e6 2.36e- 2 -1.25e14 -8.99e12
## 6 fixed Time_… -5.55e13 3.15e13 -1.76 1.27e5 7.79e- 2 -1.17e14 6.20e12
# Fixed effect Emotional Well-Being
fixed_ewb <- broom.mixed::tidy(
model_ewb,
effects = "fixed",
conf.int = TRUE
)
fixed_ewb
## # A tibble: 6 × 9
## effect term estimate std.error statistic df p.value conf.low conf.high
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed (Inte… 4.64e14 1.76e13 26.3 6.79e2 9.38e-106 4.30e14 4.99e14
## 2 fixed Time_… 6.24e12 2.27e13 0.275 9.22e5 7.83e- 1 -3.82e13 5.07e13
## 3 fixed Time_… 5.17e13 2.45e13 2.11 8.45e4 3.47e- 2 3.71e12 9.97e13
## 4 fixed Group… -3.53e13 2.42e13 -1.46 6.78e2 1.45e- 1 -8.29e13 1.22e13
## 5 fixed Time_… 4.52e13 3.16e13 1.43 3.05e5 1.53e- 1 -1.68e13 1.07e14
## 6 fixed Time_… 6.03e13 3.38e13 1.79 7.07e4 7.41e- 2 -5.88e12 1.27e14
# Menyimpan hasil estimasi parameter
write.csv(
fixed_pf,
"Hasil_Fixed_Effect_Psychological_Flexibility.csv",
row.names = FALSE
)
write.csv(
fixed_ewb,
"Hasil_Fixed_Effect_Emotional_Well_Being.csv",
row.names = FALSE
)
# 4.5 Random Effect
# Random effect Psychological Flexibility
ranef(model_pf)
## $id
## (Intercept)
## 1 -2.674432e+13
## 2 7.354523e+12
## 3 -4.722147e+13
## 4 -5.413846e+13
## 5 -4.032778e+13
## 6 4.671021e+13
## 7 8.730831e+12
## 8 1.076752e+13
## 9 8.738764e+12
## 10 5.227363e+13
## 11 -9.667991e+11
## 12 4.538151e+13
## 13 1.353865e+13
## 14 -3.338276e+13
## 15 3.148090e+13
## 16 -3.755665e+13
## 17 6.606580e+13
## 18 -4.791731e+13
## 19 2.875738e+13
## 20 -4.809251e+13
## 21 6.882635e+13
## 22 1.979154e+13
## 23 8.748717e+13
## 24 3.223385e+13
## 25 -4.151445e+13
## 26 -2.995918e+13
## 27 3.497060e+13
## 28 -4.608007e+13
## 29 1.771518e+13
## 30 7.394196e+12
## 31 -7.824534e+12
## 32 1.150461e+13
## 33 -7.655844e+13
## 34 3.224708e+13
## 35 -3.549615e+13
## 36 -1.405098e+13
## 37 1.908885e+13
## 38 -1.269054e+13
## 39 4.257335e+13
## 40 -5.428934e+13
## 41 5.847891e+13
## 42 3.084961e+13
## 43 -3.858506e+13
## 44 -7.055014e+13
## 45 -2.177600e+13
## 46 -6.428778e+13
## 47 7.145934e+13
## 48 -5.741306e+13
## 49 1.779921e+12
## 50 1.136911e+13
## 51 -1.484686e+13
## 52 -2.815422e+12
## 53 2.521947e+13
## 54 9.300684e+12
## 55 2.521682e+13
## 56 -2.042616e+13
## 57 -7.541532e+13
## 58 2.380878e+13
## 59 2.933517e+13
## 60 -5.296635e+13
## 61 -5.570683e+12
## 62 -3.342640e+13
## 63 4.869340e+13
## 64 2.040107e+13
## 65 3.559071e+13
## 66 -1.127059e+13
## 67 -1.695496e+13
## 68 -9.617421e+13
## 69 -4.582877e+13
## 70 4.457241e+13
## 71 -1.959218e+13
## 72 -6.036014e+13
## 73 5.525268e+13
## 74 -2.706021e+13
## 75 6.664359e+13
## 76 1.413760e+13
## 77 -1.069942e+13
## 78 8.117549e+13
## 79 7.633328e+13
## 80 -2.520487e+13
## 81 -2.867076e+13
## 82 -3.834723e+13
## 83 5.489340e+13
## 84 -5.175673e+12
## 85 2.796680e+13
## 86 6.734364e+13
## 87 -2.158663e+13
## 88 -3.697357e+13
## 89 2.586134e+13
## 90 -1.349171e+13
## 91 3.147225e+13
## 92 -2.450216e+13
## 93 -9.566361e+12
## 94 2.404901e+13
## 95 2.590482e+13
## 96 3.430598e+13
## 97 4.203750e+12
## 98 2.242923e+13
## 99 -3.791263e+13
## 100 -5.871983e+11
## 101 -1.963548e+13
## 102 5.896833e+13
## 103 4.456741e+12
## 104 -9.318929e+12
## 105 -2.200360e+13
## 106 -1.172048e+13
## 107 1.526408e+13
## 108 3.244135e+12
## 109 -3.965769e+13
## 110 7.073290e+13
## 111 -1.185425e+13
## 112 -5.246618e+13
## 113 3.247174e+12
## 114 -1.185425e+13
## 115 1.052919e+13
## 116 -5.651306e+13
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## 118 3.983661e+13
## 119 5.017346e+13
## 120 -6.125700e+12
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## 122 -1.269054e+13
## 123 -5.658501e+13
## 124 -4.880389e+12
## 125 2.311666e+13
## 126 2.529942e+13
## 127 5.904348e+13
## 128 5.344465e+12
## 129 5.283449e+12
## 130 9.308619e+12
## 131 4.523544e+13
## 132 4.899348e+13
## 133 4.451423e+13
## 134 1.040726e+14
## 135 1.977568e+13
## 136 3.639400e+11
## 137 -2.452333e+13
## 138 -4.435982e+13
## 139 -4.042624e+13
## 140 -1.024815e+13
## 141 -6.456833e+13
## 142 -8.006852e+13
## 143 -6.035351e+13
## 144 -4.149380e+13
## 145 8.028131e+12
## 146 -8.651164e+12
## 147 -7.257326e+12
## 148 5.974618e+13
## 149 -3.685032e+12
## 150 -9.212675e+13
## 151 3.141418e+13
## 152 -2.818244e+13
## 153 -2.195996e+13
## 154 2.949711e+13
## 155 1.633359e+13
## 156 1.689815e+13
## 157 2.404749e+13
## 158 -1.826006e+13
## 159 -1.268261e+13
## 160 -2.441321e+13
## 161 -9.562196e+11
## 162 2.656668e+13
## 163 1.275336e+13
## 164 5.658197e+13
## 165 7.937600e+12
## 166 -3.024648e+12
## 167 -2.373437e+13
## 168 -1.829688e+13
## 169 -3.006558e+13
## 170 -5.798421e+12
## 171 -2.996116e+13
## 172 -4.425497e+13
## 173 -6.551981e+12
## 174 -2.076196e+13
## 175 1.165105e+12
## 176 -8.240502e+12
## 177 3.766707e+13
## 178 -2.591021e+13
## 179 -5.467508e+12
## 180 -5.091698e+13
## 181 2.452999e+13
## 182 -3.438532e+13
##
## with conditional variances for "id"
VarCorr(model_pf)
## Groups Name Std.Dev.
## id (Intercept) 6.4009e+13
## Residual 1.3874e+14
as.data.frame(
VarCorr(model_pf)
)
## grp var1 var2 vcov sdcor
## 1 id (Intercept) <NA> 4.097103e+27 6.400862e+13
## 2 Residual <NA> <NA> 1.924870e+28 1.387397e+14
# Random effect Emotional Well-Being
ranef(model_ewb)
## $id
## (Intercept)
## 1 1.776951e+13
## 2 4.036343e+13
## 3 -1.126451e+13
## 4 4.716990e+13
## 5 4.880585e+13
## 6 -4.936637e+13
## 7 3.144740e+13
## 8 -6.296028e+13
## 9 3.122324e+13
## 10 -9.399002e+11
## 11 2.925270e+13
## 12 -1.286523e+13
## 13 5.552226e+12
## 14 -2.489466e+13
## 15 -6.806380e+12
## 16 3.661714e+13
## 17 -9.284785e+13
## 18 4.102973e+13
## 19 -2.043332e+13
## 20 3.101586e+13
## 21 -9.591719e+13
## 22 1.449763e+13
## 23 -5.756785e+13
## 24 1.911304e+13
## 25 4.846832e+13
## 26 2.989731e+13
## 27 -1.774802e+13
## 28 -6.003953e+13
## 29 4.013461e+13
## 30 3.105714e+13
## 31 6.799860e+12
## 32 8.587714e+12
## 33 3.797410e+13
## 34 -4.968816e+13
## 35 3.686917e+13
## 36 5.061321e+13
## 37 -1.356875e+13
## 38 5.261375e+13
## 39 -7.467125e+12
## 40 4.179048e+13
## 41 -9.556762e+13
## 42 1.282832e+13
## 43 3.396214e+13
## 44 6.505407e+13
## 45 -2.777885e+13
## 46 5.101549e+13
## 47 -3.129506e+13
## 48 -6.166216e+13
## 49 5.163827e+13
## 50 1.587405e+13
## 51 -2.263968e+13
## 52 -6.499562e+13
## 53 5.525601e+12
## 54 5.274891e+12
## 55 -4.885846e+13
## 56 2.414895e+13
## 57 7.905343e+13
## 58 -1.117936e+12
## 59 -1.974542e+11
## 60 1.818324e+13
## 61 -4.017294e+13
## 62 -4.690106e+13
## 63 5.663860e+12
## 64 2.488551e+13
## 65 3.388169e+13
## 66 -3.345375e+13
## 67 3.851686e+13
## 68 -1.332376e+14
## 69 -5.485801e+13
## 70 -1.049131e+13
## 71 -6.269079e+12
## 72 4.879380e+13
## 73 -3.461855e+12
## 74 2.873555e+13
## 75 -1.154119e+13
## 76 4.074308e+13
## 77 5.317290e+13
## 78 -6.379475e+12
## 79 -8.417104e+13
## 80 9.120076e+12
## 81 2.751753e+13
## 82 -2.002240e+13
## 83 -1.522377e+13
## 84 4.107474e+13
## 85 5.776168e+13
## 86 -4.046318e+13
## 87 2.896325e+13
## 88 4.317074e+13
## 89 8.891461e+12
## 90 3.574891e+13
## 91 1.083555e+13
## 93 -8.040810e+13
## 94 -2.035606e+13
## 95 -8.045111e+13
## 96 8.613728e+12
## 97 2.116888e+13
## 98 -7.639549e+13
## 99 4.347303e+13
## 100 2.435376e+13
## 101 4.134535e+13
## 102 -4.280958e+13
## 103 7.806972e+11
## 104 2.916438e+13
## 105 2.777646e+13
## 106 2.942159e+13
## 107 -3.101344e+12
## 108 5.134448e+13
## 109 -5.115514e+12
## 110 -2.660447e+13
## 111 -7.028047e+13
## 112 -7.156672e+13
## 113 -1.981352e+13
## 114 -2.322208e+13
## 115 1.957105e+13
## 116 4.806724e+13
## 117 -2.814602e+13
## 118 -6.633595e+13
## 119 -8.115368e+13
## 120 2.364953e+13
## 121 -6.896863e+13
## 122 3.306130e+13
## 123 3.992220e+13
## 124 6.454830e+12
## 125 3.488947e+13
## 126 -1.691328e+13
## 127 -6.673917e+13
## 128 -1.463623e+13
## 129 3.031701e+12
## 130 5.451396e+12
## 131 1.052570e+12
## 132 -8.556588e+12
## 133 1.910244e+13
## 134 -4.410930e+13
## 135 -3.130629e+13
## 136 6.304213e+13
## 137 -2.194131e+13
## 138 -2.912637e+13
## 139 -4.662672e+13
## 140 3.783072e+13
## 141 6.222854e+13
## 142 5.012294e+13
## 143 7.921687e+12
## 144 3.483876e+13
## 145 1.629907e+13
## 146 1.798415e+13
## 147 2.212639e+13
## 148 -7.475924e+13
## 149 -3.702402e+13
## 150 -2.059265e+13
## 151 4.212037e+13
## 152 -3.092129e+13
## 153 -7.400235e+13
## 154 3.686402e+13
## 155 1.811530e+13
## 156 3.389165e+13
## 157 1.555414e+13
## 158 1.452871e+13
## 159 5.353203e+13
## 160 -3.406510e+13
## 161 -3.707035e+13
## 162 -1.066385e+14
## 163 -3.307163e+13
## 164 -9.152819e+13
## 165 6.127389e+12
## 166 2.724620e+13
## 167 5.720186e+13
## 168 4.404403e+13
## 169 5.992214e+13
## 170 4.422274e+13
## 171 -7.753619e+13
## 172 5.431250e+13
## 173 6.112971e+13
## 174 4.557839e+13
## 175 1.856222e+13
## 176 2.628217e+12
## 177 1.067035e+13
## 178 -1.009265e+13
## 179 2.447528e+13
## 180 -1.748116e+13
## 181 1.703587e+13
## 182 -5.595056e+13
##
## with conditional variances for "id"
VarCorr(model_ewb)
## Groups Name Std.Dev.
## id (Intercept) 7.0012e+13
## Residual 1.4475e+14
as.data.frame(
VarCorr(model_ewb)
)
## grp var1 var2 vcov sdcor
## 1 id (Intercept) <NA> 4.901729e+27 7.001235e+13
## 2 Residual <NA> <NA> 2.095386e+28 1.447545e+14
# Random intercept setiap individu
random_pf <- ranef(
model_pf,
condVar = TRUE
)
random_ewb <- ranef(
model_ewb,
condVar = TRUE
)
random_pf
## $id
## (Intercept)
## 1 -2.674432e+13
## 2 7.354523e+12
## 3 -4.722147e+13
## 4 -5.413846e+13
## 5 -4.032778e+13
## 6 4.671021e+13
## 7 8.730831e+12
## 8 1.076752e+13
## 9 8.738764e+12
## 10 5.227363e+13
## 11 -9.667991e+11
## 12 4.538151e+13
## 13 1.353865e+13
## 14 -3.338276e+13
## 15 3.148090e+13
## 16 -3.755665e+13
## 17 6.606580e+13
## 18 -4.791731e+13
## 19 2.875738e+13
## 20 -4.809251e+13
## 21 6.882635e+13
## 22 1.979154e+13
## 23 8.748717e+13
## 24 3.223385e+13
## 25 -4.151445e+13
## 26 -2.995918e+13
## 27 3.497060e+13
## 28 -4.608007e+13
## 29 1.771518e+13
## 30 7.394196e+12
## 31 -7.824534e+12
## 32 1.150461e+13
## 33 -7.655844e+13
## 34 3.224708e+13
## 35 -3.549615e+13
## 36 -1.405098e+13
## 37 1.908885e+13
## 38 -1.269054e+13
## 39 4.257335e+13
## 40 -5.428934e+13
## 41 5.847891e+13
## 42 3.084961e+13
## 43 -3.858506e+13
## 44 -7.055014e+13
## 45 -2.177600e+13
## 46 -6.428778e+13
## 47 7.145934e+13
## 48 -5.741306e+13
## 49 1.779921e+12
## 50 1.136911e+13
## 51 -1.484686e+13
## 52 -2.815422e+12
## 53 2.521947e+13
## 54 9.300684e+12
## 55 2.521682e+13
## 56 -2.042616e+13
## 57 -7.541532e+13
## 58 2.380878e+13
## 59 2.933517e+13
## 60 -5.296635e+13
## 61 -5.570683e+12
## 62 -3.342640e+13
## 63 4.869340e+13
## 64 2.040107e+13
## 65 3.559071e+13
## 66 -1.127059e+13
## 67 -1.695496e+13
## 68 -9.617421e+13
## 69 -4.582877e+13
## 70 4.457241e+13
## 71 -1.959218e+13
## 72 -6.036014e+13
## 73 5.525268e+13
## 74 -2.706021e+13
## 75 6.664359e+13
## 76 1.413760e+13
## 77 -1.069942e+13
## 78 8.117549e+13
## 79 7.633328e+13
## 80 -2.520487e+13
## 81 -2.867076e+13
## 82 -3.834723e+13
## 83 5.489340e+13
## 84 -5.175673e+12
## 85 2.796680e+13
## 86 6.734364e+13
## 87 -2.158663e+13
## 88 -3.697357e+13
## 89 2.586134e+13
## 90 -1.349171e+13
## 91 3.147225e+13
## 92 -2.450216e+13
## 93 -9.566361e+12
## 94 2.404901e+13
## 95 2.590482e+13
## 96 3.430598e+13
## 97 4.203750e+12
## 98 2.242923e+13
## 99 -3.791263e+13
## 100 -5.871983e+11
## 101 -1.963548e+13
## 102 5.896833e+13
## 103 4.456741e+12
## 104 -9.318929e+12
## 105 -2.200360e+13
## 106 -1.172048e+13
## 107 1.526408e+13
## 108 3.244135e+12
## 109 -3.965769e+13
## 110 7.073290e+13
## 111 -1.185425e+13
## 112 -5.246618e+13
## 113 3.247174e+12
## 114 -1.185425e+13
## 115 1.052919e+13
## 116 -5.651306e+13
## 117 3.655260e+13
## 118 3.983661e+13
## 119 5.017346e+13
## 120 -6.125700e+12
## 121 7.153869e+13
## 122 -1.269054e+13
## 123 -5.658501e+13
## 124 -4.880389e+12
## 125 2.311666e+13
## 126 2.529942e+13
## 127 5.904348e+13
## 128 5.344465e+12
## 129 5.283449e+12
## 130 9.308619e+12
## 131 4.523544e+13
## 132 4.899348e+13
## 133 4.451423e+13
## 134 1.040726e+14
## 135 1.977568e+13
## 136 3.639400e+11
## 137 -2.452333e+13
## 138 -4.435982e+13
## 139 -4.042624e+13
## 140 -1.024815e+13
## 141 -6.456833e+13
## 142 -8.006852e+13
## 143 -6.035351e+13
## 144 -4.149380e+13
## 145 8.028131e+12
## 146 -8.651164e+12
## 147 -7.257326e+12
## 148 5.974618e+13
## 149 -3.685032e+12
## 150 -9.212675e+13
## 151 3.141418e+13
## 152 -2.818244e+13
## 153 -2.195996e+13
## 154 2.949711e+13
## 155 1.633359e+13
## 156 1.689815e+13
## 157 2.404749e+13
## 158 -1.826006e+13
## 159 -1.268261e+13
## 160 -2.441321e+13
## 161 -9.562196e+11
## 162 2.656668e+13
## 163 1.275336e+13
## 164 5.658197e+13
## 165 7.937600e+12
## 166 -3.024648e+12
## 167 -2.373437e+13
## 168 -1.829688e+13
## 169 -3.006558e+13
## 170 -5.798421e+12
## 171 -2.996116e+13
## 172 -4.425497e+13
## 173 -6.551981e+12
## 174 -2.076196e+13
## 175 1.165105e+12
## 176 -8.240502e+12
## 177 3.766707e+13
## 178 -2.591021e+13
## 179 -5.467508e+12
## 180 -5.091698e+13
## 181 2.452999e+13
## 182 -3.438532e+13
##
## with conditional variances for "id"
random_ewb
## $id
## (Intercept)
## 1 1.776951e+13
## 2 4.036343e+13
## 3 -1.126451e+13
## 4 4.716990e+13
## 5 4.880585e+13
## 6 -4.936637e+13
## 7 3.144740e+13
## 8 -6.296028e+13
## 9 3.122324e+13
## 10 -9.399002e+11
## 11 2.925270e+13
## 12 -1.286523e+13
## 13 5.552226e+12
## 14 -2.489466e+13
## 15 -6.806380e+12
## 16 3.661714e+13
## 17 -9.284785e+13
## 18 4.102973e+13
## 19 -2.043332e+13
## 20 3.101586e+13
## 21 -9.591719e+13
## 22 1.449763e+13
## 23 -5.756785e+13
## 24 1.911304e+13
## 25 4.846832e+13
## 26 2.989731e+13
## 27 -1.774802e+13
## 28 -6.003953e+13
## 29 4.013461e+13
## 30 3.105714e+13
## 31 6.799860e+12
## 32 8.587714e+12
## 33 3.797410e+13
## 34 -4.968816e+13
## 35 3.686917e+13
## 36 5.061321e+13
## 37 -1.356875e+13
## 38 5.261375e+13
## 39 -7.467125e+12
## 40 4.179048e+13
## 41 -9.556762e+13
## 42 1.282832e+13
## 43 3.396214e+13
## 44 6.505407e+13
## 45 -2.777885e+13
## 46 5.101549e+13
## 47 -3.129506e+13
## 48 -6.166216e+13
## 49 5.163827e+13
## 50 1.587405e+13
## 51 -2.263968e+13
## 52 -6.499562e+13
## 53 5.525601e+12
## 54 5.274891e+12
## 55 -4.885846e+13
## 56 2.414895e+13
## 57 7.905343e+13
## 58 -1.117936e+12
## 59 -1.974542e+11
## 60 1.818324e+13
## 61 -4.017294e+13
## 62 -4.690106e+13
## 63 5.663860e+12
## 64 2.488551e+13
## 65 3.388169e+13
## 66 -3.345375e+13
## 67 3.851686e+13
## 68 -1.332376e+14
## 69 -5.485801e+13
## 70 -1.049131e+13
## 71 -6.269079e+12
## 72 4.879380e+13
## 73 -3.461855e+12
## 74 2.873555e+13
## 75 -1.154119e+13
## 76 4.074308e+13
## 77 5.317290e+13
## 78 -6.379475e+12
## 79 -8.417104e+13
## 80 9.120076e+12
## 81 2.751753e+13
## 82 -2.002240e+13
## 83 -1.522377e+13
## 84 4.107474e+13
## 85 5.776168e+13
## 86 -4.046318e+13
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## 89 8.891461e+12
## 90 3.574891e+13
## 91 1.083555e+13
## 93 -8.040810e+13
## 94 -2.035606e+13
## 95 -8.045111e+13
## 96 8.613728e+12
## 97 2.116888e+13
## 98 -7.639549e+13
## 99 4.347303e+13
## 100 2.435376e+13
## 101 4.134535e+13
## 102 -4.280958e+13
## 103 7.806972e+11
## 104 2.916438e+13
## 105 2.777646e+13
## 106 2.942159e+13
## 107 -3.101344e+12
## 108 5.134448e+13
## 109 -5.115514e+12
## 110 -2.660447e+13
## 111 -7.028047e+13
## 112 -7.156672e+13
## 113 -1.981352e+13
## 114 -2.322208e+13
## 115 1.957105e+13
## 116 4.806724e+13
## 117 -2.814602e+13
## 118 -6.633595e+13
## 119 -8.115368e+13
## 120 2.364953e+13
## 121 -6.896863e+13
## 122 3.306130e+13
## 123 3.992220e+13
## 124 6.454830e+12
## 125 3.488947e+13
## 126 -1.691328e+13
## 127 -6.673917e+13
## 128 -1.463623e+13
## 129 3.031701e+12
## 130 5.451396e+12
## 131 1.052570e+12
## 132 -8.556588e+12
## 133 1.910244e+13
## 134 -4.410930e+13
## 135 -3.130629e+13
## 136 6.304213e+13
## 137 -2.194131e+13
## 138 -2.912637e+13
## 139 -4.662672e+13
## 140 3.783072e+13
## 141 6.222854e+13
## 142 5.012294e+13
## 143 7.921687e+12
## 144 3.483876e+13
## 145 1.629907e+13
## 146 1.798415e+13
## 147 2.212639e+13
## 148 -7.475924e+13
## 149 -3.702402e+13
## 150 -2.059265e+13
## 151 4.212037e+13
## 152 -3.092129e+13
## 153 -7.400235e+13
## 154 3.686402e+13
## 155 1.811530e+13
## 156 3.389165e+13
## 157 1.555414e+13
## 158 1.452871e+13
## 159 5.353203e+13
## 160 -3.406510e+13
## 161 -3.707035e+13
## 162 -1.066385e+14
## 163 -3.307163e+13
## 164 -9.152819e+13
## 165 6.127389e+12
## 166 2.724620e+13
## 167 5.720186e+13
## 168 4.404403e+13
## 169 5.992214e+13
## 170 4.422274e+13
## 171 -7.753619e+13
## 172 5.431250e+13
## 173 6.112971e+13
## 174 4.557839e+13
## 175 1.856222e+13
## 176 2.628217e+12
## 177 1.067035e+13
## 178 -1.009265e+13
## 179 2.447528e+13
## 180 -1.748116e+13
## 181 1.703587e+13
## 182 -5.595056e+13
##
## with conditional variances for "id"
# Menyimpan hasil random effect
random_pf_df <- as.data.frame(
ranef(model_pf)
)
random_ewb_df <- as.data.frame(
ranef(model_ewb)
)
write.csv(
random_pf_df,
"Hasil_Random_Effect_Psychological_Flexibility.csv",
row.names = FALSE
)
write.csv(
random_ewb_df,
"Hasil_Random_Effect_Emotional_Well_Being.csv",
row.names = FALSE
)
# Intraclass Correlation Coefficient (ICC)
performance::icc(model_pf)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.175
## Unadjusted ICC: 0.170
performance::icc(model_ewb)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.190
## Unadjusted ICC: 0.181
# 4.6 Pengujian Fixed Effect
# Pengujian fixed effect Psychological Flexibility
anova_pf <- anova(model_pf)
anova_pf
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time_Point 1.2503e+29 6.2513e+28 2 290910 3.2476 0.03887 *
## Groups 8.5277e+28 8.5277e+28 1 179 4.4303 0.03670 *
## Time_Point:Groups 1.1129e+29 5.5645e+28 2 290910 2.8909 0.05553 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_pf)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: Psych_Infle_T ~ Time_Point * Groups + (1 | id)
## Data: Dataset_longitudinal_GLS
##
## AIC BIC logLik -2*log(L) df.resid
## 33741.3 33774.9 -16862.6 33725.3 487
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0368 -0.2782 0.1353 0.5509 1.9129
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 4.097e+27 6.401e+13
## Residual 1.925e+28 1.387e+14
## Number of obs: 495, groups: id, 182
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.521e+14 1.657e+13 7.949e+02 27.281
## Time_PointPost 1.085e+13 2.151e+13 3.646e+06 0.504
## Time_PointFollow-up -1.180e+13 2.309e+13 1.196e+05 -0.511
## GroupsExperimental 7.453e+12 2.270e+13 7.949e+02 0.328
## Time_PointPost:GroupsExperimental -6.715e+13 2.967e+13 1.328e+06 -2.263
## Time_PointFollow-up:GroupsExperimental -5.546e+13 3.146e+13 1.271e+05 -1.763
## Pr(>|t|)
## (Intercept) <2e-16 ***
## Time_PointPost 0.6140
## Time_PointFollow-up 0.6092
## GroupsExperimental 0.7428
## Time_PointPost:GroupsExperimental 0.0236 *
## Time_PointFollow-up:GroupsExperimental 0.0779 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Tm_PnP Tm_PF- GrpsEx T_PP:G
## Time_PntPst -0.635
## Tm_PntFllw- -0.592 0.459
## GrpsExprmnt -0.730 0.464 0.432
## Tm_PntPs:GE 0.460 -0.725 -0.333 -0.631
## Tm_PntF-:GE 0.434 -0.337 -0.734 -0.595 0.460
# Pengujian fixed effect Emotional Well-Being
anova_ewb <- anova(model_ewb)
anova_ewb
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time_Point 4.9583e+29 2.4791e+29 2 126663 11.8314 7.28e-06 ***
## Groups 1.7162e+24 1.7162e+24 1 163 0.0001 0.9928
## Time_Point:Groups 7.7027e+28 3.8513e+28 2 126663 1.8380 0.1591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_ewb)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: Emot_Well_T ~ Time_Point * Groups + (1 | id)
## Data: Dataset_longitudinal_GLS
##
## AIC BIC logLik -2*log(L) df.resid
## 32220.9 32254.2 -16102.5 32204.9 464
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1293 -0.2635 0.2475 0.5645 1.6445
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 4.902e+27 7.001e+13
## Residual 2.095e+28 1.448e+14
## Number of obs: 472, groups: id, 181
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.642e+14 1.764e+13 6.791e+02 26.316
## Time_PointPost 6.242e+12 2.268e+13 9.225e+05 0.275
## Time_PointFollow-up 5.170e+13 2.448e+13 8.447e+04 2.111
## GroupsExperimental -3.534e+13 2.421e+13 6.784e+02 -1.460
## Time_PointPost:GroupsExperimental 4.523e+13 3.164e+13 3.048e+05 1.430
## Time_PointFollow-up:GroupsExperimental 6.033e+13 3.378e+13 7.074e+04 1.786
## Pr(>|t|)
## (Intercept) <2e-16 ***
## Time_PointPost 0.7832
## Time_PointFollow-up 0.0347 *
## GroupsExperimental 0.1447
## Time_PointPost:GroupsExperimental 0.1528
## Time_PointFollow-up:GroupsExperimental 0.0741 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Tm_PnP Tm_PF- GrpsEx T_PP:G
## Time_PntPst -0.634
## Tm_PntFllw- -0.586 0.459
## GrpsExprmnt -0.729 0.462 0.427
## Tm_PntPs:GE 0.454 -0.717 -0.329 -0.623
## Tm_PntF-:GE 0.425 -0.332 -0.725 -0.583 0.452
# Menyimpan hasil pengujian fixed effect
write.csv(
as.data.frame(anova_pf),
"Pengujian_Fixed_Effect_Psychological_Flexibility.csv",
row.names = TRUE
)
write.csv(
as.data.frame(anova_ewb),
"Pengujian_Fixed_Effect_Emotional_Well_Being.csv",
row.names = TRUE
)
# Pengujian interaksi Time dan Group
grep(
"Time_Point.*:Groups",
rownames(coef_pf),
value = TRUE
)
## [1] "Time_PointPost:GroupsExperimental"
## [2] "Time_PointFollow-up:GroupsExperimental"
grep(
"Time_Point.*:Groups",
rownames(coef_ewb),
value = TRUE
)
## [1] "Time_PointPost:GroupsExperimental"
## [2] "Time_PointFollow-up:GroupsExperimental"
# Model tanpa interaksi Psychological Flexibility
model_pf_no_interaction <- lmer(
Psych_Infle_T ~ Time_Point + Groups + (1 | id),
data = Dataset_longitudinal_GLS,
REML = FALSE
)
anova(
model_pf_no_interaction,
model_pf
)
## Data: Dataset_longitudinal_GLS
## Models:
## model_pf_no_interaction: Psych_Infle_T ~ Time_Point + Groups + (1 | id)
## model_pf: Psych_Infle_T ~ Time_Point * Groups + (1 | id)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## model_pf_no_interaction 6 33743 33768 -16866 33731
## model_pf 8 33741 33775 -16863 33725 5.7338 2 0.05688
##
## model_pf_no_interaction
## model_pf .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Model tanpa interaksi Emotional Well-Being
model_ewb_no_interaction <- lmer(
Emot_Well_T ~ Time_Point + Groups + (1 | id),
data = Dataset_longitudinal_GLS,
REML = FALSE
)
anova(
model_ewb_no_interaction,
model_ewb
)
## Data: Dataset_longitudinal_GLS
## Models:
## model_ewb_no_interaction: Emot_Well_T ~ Time_Point + Groups + (1 | id)
## model_ewb: Emot_Well_T ~ Time_Point * Groups + (1 | id)
## npar AIC BIC logLik -2*log(L) Chisq Df Pr(>Chisq)
## model_ewb_no_interaction 6 32221 32246 -16104 32209
## model_ewb 8 32221 32254 -16102 32205 3.6607 2 0.1604