Print Method for Object of Class semmcci
Usage
# S3 method for class 'semmcci'
print(x, alpha = NULL, digits = 4, ...)Value
Prints 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)
print(unstd)
#> Monte Carlo Confidence Intervals
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.2643 0.2540 5 -0.2660 -0.2610 -0.2387 0.4095 0.4330 0.4383
#> b 0.5458 0.0681 5 0.5002 0.5003 0.5005 0.6426 0.6453 0.6459
#> a 0.4206 0.2039 5 0.0615 0.0656 0.0836 0.5924 0.6056 0.6086
#> cond~~cond 0.2471 0.0334 5 0.1791 0.1795 0.1816 0.2581 0.2585 0.2586
#> reaction~~reaction 1.8502 0.2414 5 1.4342 1.4394 1.4623 2.0532 2.0624 2.0644
#> pmi~~pmi 1.6964 0.1318 5 1.5036 1.5048 1.5100 1.8180 1.8259 1.8277
#> reaction~1 0.3254 0.4607 5 -0.0530 -0.0520 -0.0478 0.8678 0.8690 0.8693
#> pmi~1 5.4071 0.2019 5 5.1967 5.1999 5.2143 5.7063 5.7166 5.7189
#> cond~1 0.4478 0.0519 5 0.3846 0.3859 0.3917 0.5159 0.5169 0.5172
#> indirect 0.2296 0.1090 5 0.0396 0.0415 0.0499 0.3013 0.3039 0.3045
#> direct 0.2643 0.2540 5 -0.2660 -0.2610 -0.2387 0.4095 0.4330 0.4383
#> total 0.4938 0.2729 5 0.0381 0.0383 0.0394 0.6627 0.6846 0.6896
print(std)
#> Standardized Monte Carlo Confidence Intervals
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.0845 0.0793 5 -0.0813 -0.0797 -0.0730 0.1290 0.1370 0.1388
#> b 0.4632 0.0469 5 0.4104 0.4115 0.4162 0.5257 0.5263 0.5264
#> a 0.1585 0.0747 5 0.0212 0.0229 0.0304 0.2193 0.2253 0.2267
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.7659 0.0585 5 0.6816 0.6824 0.6863 0.8336 0.8389 0.8401
#> pmi~~pmi 0.9749 0.0188 5 0.9486 0.9491 0.9514 0.9983 0.9993 0.9995
#> indirect 0.2093 0.0320 5 0.0110 0.0118 0.0152 0.0917 0.0928 0.0930
#> direct 4.0990 0.0793 5 -0.0813 -0.0797 -0.0730 0.1290 0.1370 0.1388
#> total 0.9009 0.0851 5 0.0116 0.0117 0.0119 0.2081 0.2165 0.2183
# 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)
print(unstd)
#> Monte Carlo Confidence Intervals (Multiple Imputation Estimates)
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.2706 0.2655 5 0.0095 0.0132 0.0295 0.6206 0.6217 0.6219
#> b 0.5224 0.0836 5 0.3956 0.3971 0.4035 0.6054 0.6087 0.6095
#> a 0.4154 0.2309 5 -0.0809 -0.0744 -0.0457 0.4845 0.4849 0.4850
#> cond~~cond 0.2471 0.0201 5 0.2396 0.2397 0.2402 0.2852 0.2861 0.2863
#> reaction~~reaction 1.9278 0.2548 5 1.4165 1.4214 1.4433 2.0871 2.1063 2.1106
#> pmi~~pmi 1.6481 0.3208 5 1.1216 1.1281 1.1570 1.9128 1.9247 1.9274
#> indirect 0.2176 0.1186 5 -0.0319 -0.0287 -0.0142 0.2681 0.2714 0.2722
#> direct 0.2706 0.2655 5 0.0095 0.0132 0.0295 0.6206 0.6217 0.6219
#> total 0.4882 0.2215 5 0.2401 0.2433 0.2571 0.7662 0.7694 0.7702
print(std)
#> Standardized Monte Carlo Confidence Intervals
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.0609 0.0901 5 0.0032 0.0043 0.0096 0.2073 0.2078 0.2079
#> b 0.4932 0.0620 5 0.3583 0.3594 0.3642 0.5207 0.5273 0.5288
#> a 0.1505 0.0958 5 -0.0296 -0.0270 -0.0151 0.2173 0.2203 0.2210
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.7440 0.0547 5 0.7068 0.7074 0.7101 0.8340 0.8349 0.8351
#> pmi~~pmi 0.9773 0.0189 5 0.9512 0.9514 0.9526 0.9978 0.9988 0.9991
#> indirect 0.0742 0.0413 5 -0.0106 -0.0095 -0.0047 0.0964 0.0982 0.0985
#> direct 0.0609 0.0901 5 0.0032 0.0043 0.0096 0.2073 0.2078 0.2079
#> total 0.1351 0.0795 5 0.0802 0.0812 0.0854 0.2634 0.2639 0.2641