Print Method for Object of Class semmcci
Usage
# S3 method for semmcci
print(x, alpha = NULL, digits = 4, ...)Arguments
- x
an object of class
semmcci.- alpha
Numeric vector. Significance level \(\alpha\). If
alpha = NULL, use the argumentalphaused inx.- digits
Integer indicating the number of decimal places to display.
- ...
further arguments.
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)
print(unstd)
#> Monte Carlo Confidence Intervals
#> est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95%
#> cp 0.2643 0.2646 5 -0.2829 -0.2777 -0.2547 0.4166 0.4398 0.4450
#> b 0.5458 0.0674 5 0.5008 0.5008 0.5011 0.6426 0.6453 0.6459
#> a 0.4206 0.2513 5 0.0697 0.0704 0.0735 0.6149 0.6202 0.6214
#> cond~~cond 0.2471 0.0336 5 0.1787 0.1792 0.1813 0.2582 0.2586 0.2587
#> reaction~~reaction 1.8502 0.2348 5 1.6943 1.6959 1.7031 2.2717 2.2997 2.3060
#> pmi~~pmi 1.6964 0.1276 5 1.5665 1.5686 1.5782 1.8779 1.8829 1.8840
#> reaction~1 0.3254 0.4592 5 -0.0541 -0.0532 -0.0493 0.8715 0.8743 0.8749
#> pmi~1 5.4071 0.1391 5 5.2776 5.2787 5.2835 5.6046 5.6136 5.6157
#> cond~1 0.4478 0.0523 5 0.3865 0.3877 0.3931 0.5175 0.5185 0.5187
#> indirect 0.2296 0.1439 5 0.0450 0.0451 0.0459 0.3691 0.3773 0.3791
#> direct 0.2643 0.2646 5 -0.2829 -0.2777 -0.2547 0.4166 0.4398 0.4450
#> total 0.4938 0.2891 5 -0.0057 -0.0047 -0.0003 0.6268 0.6350 0.6368
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 0.0832 5 -0.0924 -0.0907 -0.0832 0.1283 0.1359 0.1376
#> b 0.4632 0.0496 5 0.4084 0.4095 0.4144 0.5310 0.5316 0.5318
#> a 0.1585 0.0795 5 0.0243 0.0246 0.0259 0.1926 0.1930 0.1931
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.7659 0.0496 5 0.7162 0.7162 0.7163 0.8258 0.8292 0.8300
#> pmi~~pmi 0.9749 0.0177 5 0.9627 0.9627 0.9629 0.9993 0.9994 0.9994
#> indirect 0.2093 0.0401 5 0.0129 0.0130 0.0133 0.0977 0.0983 0.0984
#> direct 4.0990 0.0832 5 -0.0924 -0.0907 -0.0832 0.1283 0.1359 0.1376
#> total 0.9009 0.0846 5 -0.0019 -0.0016 -0.0003 0.1912 0.1959 0.1969
# 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 0.0096 0.0133 0.0296 0.6207 0.6217 0.6220
#> 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 0.4843 0.4848 0.4849
#> cond~~cond 0.2471 0.0211 5 0.2068 0.2070 0.2082 0.2567 0.2574 0.2576
#> reaction~~reaction 1.9278 0.2548 5 1.4165 1.4214 1.4433 2.0870 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 0.1185 5 -0.0319 -0.0286 -0.0142 0.2681 0.2714 0.2722
#> direct 0.2706 0.2655 5 0.0096 0.0133 0.0296 0.6207 0.6217 0.6220
#> total 0.4882 0.2215 5 0.2402 0.2433 0.2572 0.7662 0.7695 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 0.0876 5 0.0027 0.0039 0.0092 0.2072 0.2089 0.2093
#> b 0.4932 0.0621 5 0.3578 0.3589 0.3637 0.5207 0.5273 0.5288
#> a 0.1505 0.0920 5 -0.0307 -0.0283 -0.0173 0.2122 0.2171 0.2182
#> cond~~cond 1.0000 0.0000 5 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> reaction~~reaction 0.7440 0.0538 5 0.7066 0.7073 0.7102 0.8321 0.8327 0.8328
#> pmi~~pmi 0.9773 0.0177 5 0.9524 0.9528 0.9546 0.9980 0.9988 0.9990
#> indirect 0.0742 0.0401 5 -0.0110 -0.0100 -0.0055 0.0942 0.0968 0.0973
#> direct 0.0609 0.0876 5 0.0027 0.0039 0.0092 0.2072 0.2089 0.2093
#> total 0.1351 0.0780 5 0.0683 0.0695 0.0747 0.2581 0.2603 0.2609