Summary Method for an Object of Class semmcci
Usage
# S3 method for class 'semmcci'
summary(object, alpha = NULL, digits = 4, ...)Value
Returns a matrix of estimates, standard errors, number of Monte Carlo replications, and confidence intervals.
Examples
library(semmcci)
library(lavaan)
# Data ---------------------------------------------------------------------
data("Tal.Or", package = "psych")
df <- mice::ampute(Tal.Or)$amp
# Monte Carlo --------------------------------------------------------------
## Fit Model in lavaan -----------------------------------------------------
model <- "
reaction ~ cp * cond + b * pmi
pmi ~ a * cond
cond ~~ cond
indirect := a * b
direct := cp
total := cp + (a * b)
"
fit <- sem(data = df, model = model, missing = "fiml")
## MC() --------------------------------------------------------------------
unstd <- MC(
fit,
R = 5L # use a large value e.g., 20000L for actual research
)
## Standardized Monte Carlo ------------------------------------------------
std <- MCStd(unstd)
summary(unstd)
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.2758 0.2309 5 0.0487 0.0509 0.0606 0.5772 0.5833 0.5847
#> b 0.5077 0.0907 5 0.3787 0.3793 0.3823 0.5844 0.5875 0.5882
#> a 0.3362 0.3414 5 -0.1192 -0.1139 -0.0904 0.6700 0.6714 0.6717
#> cond~~cond 0.2448 0.0209 5 0.2044 0.2045 0.2048 0.2537 0.2552 0.2556
#> reaction~~reaction 1.8981 0.2880 5 1.5563 1.5594 1.5731 2.2139 2.2189 2.2200
#> pmi~~pmi 1.7406 0.3256 5 1.2691 1.2791 1.3235 2.1329 2.1532 2.1578
#> reaction~1 0.4989 0.6719 5 -0.1871 -0.1841 -0.1709 1.3508 1.4010 1.4123
#> pmi~1 5.4719 0.1467 5 5.3094 5.3111 5.3189 5.6405 5.6426 5.6431
#> cond~1 0.4334 0.0355 5 0.4021 0.4023 0.4031 0.4803 0.4810 0.4812
#> indirect 0.1707 0.1733 5 -0.0450 -0.0424 -0.0306 0.3666 0.3682 0.3685
#> direct 0.2758 0.2309 5 0.0487 0.0509 0.0606 0.5772 0.5833 0.5847
#> total 0.4465 0.3187 5 0.0042 0.0139 0.0567 0.7893 0.7959 0.7974
summary(std)
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.0882 0.0692 5 0.0144 0.0150 0.0181 0.1674 0.1678 0.1679
#> b 0.4362 0.0991 5 0.2756 0.2777 0.2867 0.5153 0.5160 0.5162
#> a 0.1251 0.1204 5 -0.0483 -0.0464 -0.0381 0.2318 0.2328 0.2331
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.7924 0.0879 5 0.7067 0.7069 0.7079 0.9117 0.9218 0.9241
#> pmi~~pmi 0.9844 0.0244 5 0.9457 0.9458 0.9463 0.9976 0.9976 0.9976
#> indirect 0.3224 0.0530 5 -0.0133 -0.0125 -0.0092 0.1124 0.1131 0.1133
#> direct 4.1150 0.0692 5 0.0144 0.0150 0.0181 0.1674 0.1678 0.1679
#> total 0.8761 0.0975 5 0.0013 0.0042 0.0174 0.2382 0.2384 0.2384
# Monte Carlo (Multiple Imputation) ----------------------------------------
## Multiple Imputation -----------------------------------------------------
mi <- mice::mice(
data = df,
print = FALSE,
m = 5L, # use a large value e.g., 100L for actual research,
seed = 42
)
## Fit Model in lavaan -----------------------------------------------------
fit <- sem(data = df, model = model) # use default listwise deletion
## MCMI() ------------------------------------------------------------------
unstd <- MCMI(
fit,
mi = mi,
R = 5L # use a large value e.g., 20000L for actual research
)
## Standardized Monte Carlo ------------------------------------------------
std <- MCStd(unstd)
summary(unstd)
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.2422 0.2558 5 -0.3065 -0.2995 -0.2687 0.3410 0.3467 0.3480
#> b 0.5185 0.0942 5 0.4054 0.4072 0.4149 0.6534 0.6638 0.6662
#> a 0.2893 0.2818 5 0.0032 0.0076 0.0270 0.6602 0.6671 0.6686
#> cond~~cond 0.2447 0.0369 5 0.1633 0.1644 0.1692 0.2541 0.2544 0.2545
#> reaction~~reaction 1.8824 0.3018 5 1.5497 1.5586 1.5980 2.2844 2.2880 2.2889
#> pmi~~pmi 1.7001 0.2032 5 1.5629 1.5689 1.5957 2.0675 2.0692 2.0695
#> indirect 0.1493 0.1626 5 0.0014 0.0035 0.0133 0.3796 0.3835 0.3843
#> direct 0.2422 0.2558 5 -0.3065 -0.2995 -0.2687 0.3410 0.3467 0.3480
#> total 0.3914 0.2073 5 0.0057 0.0106 0.0324 0.4914 0.4933 0.4937
summary(std)
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.0430 0.0758 5 -0.0854 -0.0835 -0.0750 0.1064 0.1081 0.1085
#> b 0.4251 0.0754 5 0.3517 0.3531 0.3592 0.5513 0.5594 0.5613
#> a 0.0947 0.0925 5 0.0012 0.0027 0.0095 0.2237 0.2267 0.2274
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.8140 0.0682 5 0.6802 0.6820 0.6899 0.8625 0.8675 0.8686
#> pmi~~pmi 0.9910 0.0215 5 0.9483 0.9486 0.9498 0.9993 0.9999 1.0000
#> indirect 0.0403 0.0443 5 0.0004 0.0011 0.0041 0.1023 0.1028 0.1029
#> direct 0.0430 0.0758 5 -0.0854 -0.0835 -0.0750 0.1064 0.1081 0.1085
#> total 0.0833 0.0602 5 0.0016 0.0032 0.0101 0.1465 0.1468 0.1468