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 0.1593 0.3947 5 -0.4627 -0.4597 -0.4463 0.4056 0.4141 0.4160
#> b 0.5192 0.0393 5 0.4710 0.4723 0.4780 0.5721 0.5739 0.5743
#> a 0.4360 0.2370 5 0.2624 0.2672 0.2889 0.8465 0.8498 0.8506
#> cond~~cond 0.2484 0.0102 5 0.2413 0.2415 0.2425 0.2666 0.2668 0.2669
#> reaction~~reaction 1.8979 0.3744 5 1.4834 1.4887 1.5125 2.4561 2.4952 2.5041
#> pmi~~pmi 1.7089 0.2266 5 1.4151 1.4152 1.4154 1.8953 1.9011 1.9024
#> reaction~1 0.4784 0.2342 5 0.0795 0.0805 0.0852 0.5988 0.6059 0.6075
#> pmi~1 5.3712 0.0971 5 5.2141 5.2147 5.2173 5.4487 5.4616 5.4645
#> cond~1 0.4623 0.0504 5 0.3978 0.3995 0.4069 0.5254 0.5267 0.5270
#> indirect 0.2264 0.1141 5 0.1440 0.1469 0.1599 0.4350 0.4381 0.4388
#> direct 0.1593 0.3947 5 -0.4627 -0.4597 -0.4463 0.4056 0.4141 0.4160
#> total 0.3856 0.5007 5 -0.3188 -0.3128 -0.2864 0.8407 0.8522 0.8548
print(std)
#> Standardized Monte Carlo Confidence Intervals
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.0513 0.1305 5 -0.1649 -0.1635 -0.1576 0.1279 0.1299 0.1303
#> b 0.4446 0.0563 5 0.3904 0.3906 0.3913 0.5212 0.5257 0.5268
#> a 0.1640 0.0939 5 0.0974 0.0992 0.1074 0.3360 0.3397 0.3405
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.7923 0.0490 5 0.7121 0.7124 0.7139 0.8256 0.8278 0.8283
#> pmi~~pmi 0.9731 0.0412 5 0.8841 0.8846 0.8869 0.9875 0.9900 0.9905
#> indirect 0.3091 0.0356 5 0.0513 0.0520 0.0555 0.1373 0.1374 0.1375
#> direct 4.0532 0.1305 5 -0.1649 -0.1635 -0.1576 0.1279 0.1299 0.1303
#> total 0.9275 0.1637 5 -0.1136 -0.1115 -0.1021 0.2652 0.2673 0.2678
# 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 0.1080 0.2202 5 -0.0399 -0.0330 -0.0021 0.5231 0.5296 0.5310
#> b 0.5234 0.1310 5 0.3456 0.3469 0.3526 0.6420 0.6433 0.6436
#> a 0.5281 0.1979 5 0.0325 0.0356 0.0496 0.5223 0.5295 0.5312
#> cond~~cond 0.2481 0.0229 5 0.2117 0.2121 0.2140 0.2685 0.2690 0.2692
#> reaction~~reaction 1.9501 0.2810 5 1.5453 1.5510 1.5762 2.2337 2.2428 2.2448
#> pmi~~pmi 1.7127 0.3034 5 1.3450 1.3492 1.3679 2.0915 2.1030 2.1055
#> indirect 0.2754 0.1174 5 0.0113 0.0135 0.0233 0.3210 0.3311 0.3334
#> direct 0.1080 0.2202 5 -0.0399 -0.0330 -0.0021 0.5231 0.5296 0.5310
#> total 0.3834 0.2505 5 0.1114 0.1177 0.1457 0.7351 0.7381 0.7388
print(std)
#> Standardized Monte Carlo Confidence Intervals
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.0385 0.0691 5 -0.0132 -0.0110 -0.0016 0.1597 0.1606 0.1608
#> b 0.4652 0.0761 5 0.3307 0.3317 0.3362 0.5243 0.5315 0.5331
#> a 0.1219 0.0696 5 0.0113 0.0127 0.0191 0.1879 0.1904 0.1909
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.7778 0.0737 5 0.6804 0.6823 0.6906 0.8658 0.8670 0.8673
#> pmi~~pmi 0.9851 0.0143 5 0.9636 0.9637 0.9646 0.9991 0.9997 0.9999
#> indirect 0.0567 0.0356 5 0.0038 0.0044 0.0074 0.0980 0.1011 0.1018
#> direct 0.0385 0.0691 5 -0.0132 -0.0110 -0.0016 0.1597 0.1606 0.1608
#> total 0.0952 0.0748 5 0.0367 0.0386 0.0473 0.2228 0.2236 0.2237