Estimate a modsem model using multiple imputation
modsem_mimpute.RdEstimate a modsem model using multiple imputation
Usage
modsem_mimpute(
model.syntax,
data,
method = "lms",
m = 25,
verbose = interactive(),
se = c("simple", "full"),
...
)Arguments
- model.syntax
lavaansyntax- data
A dataframe with observed variables used in the model.
- method
Method to use:
"lms"latent moderated structural equations (not passed to
lavaan)."qml"quasi maximum likelihood estimation (not passed to
lavaan).
- m
Number of imputations to perform. More imputations will yield better estimates but can also be (a lot) slower.
- verbose
Should progress be printed to the console?
- se
How should corrected standard errors be computed? Alternatives are:
"simple"Uncorrected standard errors are only calculated once, in the first imputation. The standard errors are thereafter corrected using the distribution of the estimated coefficients from the different imputations.
"full"Uncorrected standard errors are calculated and aggregated for each imputation. This can give more accurate results, but can be (a lot) slower. The standard errors are thereafter corrected using the distribution of the estimated coefficients from the different imputations.
- ...
Arguments passed to
modsem.
Details
modsem_impute is currently only available for the DA approaches
(LMS and QML). It performs multiple imputation using Amelia::amelia
and returns aggregated coefficients from the multiple imputations, along with
corrected standard errors.
Examples
m1 <- '
# Outer Model
X =~ x1 + x2 +x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner model
Y ~ X + Z + X:Z
'
oneInt2 <- oneInt
set.seed(123)
k <- 200
I <- sample(nrow(oneInt2), k, replace = TRUE)
J <- sample(ncol(oneInt2), k, replace = TRUE)
for (k_i in seq_along(I)) oneInt2[I[k_i], J[k_i]] <- NA
# \dontrun{
est <- modsem_mimpute(m1, oneInt2, m = 25)
summary(est)
#>
#> modsem (1.0.12) ended normally after 328 iterations
#>
#> Estimator LMS
#> Optimization method EMA-NLMINB
#> Number of model parameters 31
#>
#> Number of observations 2000
#>
#> Loglikelihood and Information Criteria:
#> Loglikelihood -17494.52
#> Akaike (AIC) 35051.04
#> Bayesian (BIC) 35224.67
#>
#> Numerical Integration:
#> Points of integration (per dim) 24
#> Dimensions 1
#> Total points of integration 24
#>
#> Fit Measures for Baseline Model (H0):
#> Loglikelihood -17845.54
#> Akaike (AIC) 35751.07
#> Bayesian (BIC) 35919.1
#> Chi-square 18.20
#> Degrees of Freedom (Chi-square) 24
#> P-value (Chi-square) 0.793
#> RMSEA 0.000
#>
#> Comparative Fit to H0 (LRT test):
#> Loglikelihood change 351.02
#> Difference test (D) 702.03
#> Degrees of freedom (D) 1
#> P-value (D) 0.000
#>
#> R-Squared Interaction Model (H1):
#> Y 0.598
#> R-Squared Baseline Model (H0):
#> Y 0.396
#> R-Squared Change (H1 - H0):
#> Y 0.202
#>
#> Parameter Estimates:
#> Coefficients unstandardized
#> Information Rubin-corrected (m=25)
#> Standard errors standard
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 1.000
#> x2 0.804 0.013 63.357 0.000
#> x3 0.910 0.014 66.726 0.000
#> Z =~
#> z1 1.000
#> z2 0.812 0.012 65.237 0.000
#> z3 0.883 0.013 67.591 0.000
#> Y =~
#> y1 1.000
#> y2 0.800 0.008 105.852 0.000
#> y3 0.899 0.008 110.897 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.671 0.031 21.649 0.000
#> Z 0.570 0.030 18.821 0.000
#> X:Z 0.717 0.028 25.876 0.000
#>
#> Intercepts:
#> Estimate Std.Error z.value P(>|z|)
#> .x1 1.023 0.024 42.681 0.000
#> .x2 1.211 0.020 60.452 0.000
#> .x3 0.917 0.022 41.343 0.000
#> .z1 1.011 0.024 41.507 0.000
#> .z2 1.203 0.020 58.957 0.000
#> .z3 0.916 0.022 41.968 0.000
#> .y1 1.038 0.033 31.463 0.000
#> .y2 1.220 0.027 45.271 0.000
#> .y3 0.953 0.030 31.791 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.199 0.024 8.196 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .x1 0.157 0.009 17.895 0.000
#> .x2 0.161 0.007 22.750 0.000
#> .x3 0.165 0.008 20.227 0.000
#> .z1 0.167 0.009 18.486 0.000
#> .z2 0.159 0.007 22.440 0.000
#> .z3 0.158 0.008 20.620 0.000
#> .y1 0.160 0.009 17.839 0.000
#> .y2 0.154 0.007 22.181 0.000
#> .y3 0.165 0.008 20.459 0.000
#> X 0.982 0.036 26.920 0.000
#> Z 1.015 0.038 26.839 0.000
#> .Y 0.980 0.038 26.085 0.000
#>
# }