Vector-based lavaan syntax interpreter.
Usage
write_lavaan(
mediation = NULL,
regression = NULL,
covariance = NULL,
indirect = NULL,
latent = NULL,
intercept = NULL,
threshold = NULL,
constraint.equal = NULL,
constraint.smaller = NULL,
constraint.larger = NULL,
custom = NULL,
label = FALSE,
use.letters = FALSE
)Arguments
- mediation
Mediation indicators (
~symbol: "is regressed on"). Differs from argumentregressionbecause path names can be optionally specified automatically with argumentlabel.- regression
Regression indicators (
~symbol: "is regressed on").- covariance
(Residual) (co)variance indicators (
~~symbol: "is correlated with").- indirect
Indirect effect indicators (
:=symbol: "indirect effect defined as"). If a named list is provided, with names "IV" (independent variables), "M" (mediator), and "DV" (dependent variables),write_lavaanattempts to write indirect effects automatically. In this case, themediationargument must be specified too.- latent
Latent variable indicators (
=~symbol: "is measured by").- intercept
Intercept indicators (
~ 1symbol: "intercept").- threshold
Threshold indicators (
|symbol: "threshold").- constraint.equal
Equality indicators (
==symbol).- constraint.smaller
Smaller than indicators (
<symbol).- constraint.larger
Greater than indicators (
>symbol).- custom
Custom specifications. Takes a single string just like regular
lavaansyntax would. Always added at the end of the model.- label
Logical, whether to display path names for the mediation argument.
- use.letters
Logical, for the labels, whether to use letters instead of the variable names.
See also
The corresponding vignette: https://lavaanextra.remi-theriault.com/articles/write_lavaan.html
Examples
x <- paste0("x", 1:9)
(latent <- list(
visual = x[1:3],
textual = x[4:6],
speed = x[7:9]
))
#> $visual
#> [1] "x1" "x2" "x3"
#>
#> $textual
#> [1] "x4" "x5" "x6"
#>
#> $speed
#> [1] "x7" "x8" "x9"
#>
HS.model <- write_lavaan(latent = latent)
cat(HS.model)
#> ##################################################
#> # [-----Latent variables (measurement model)-----]
#>
#> visual =~ x1 + x2 + x3
#> textual =~ x4 + x5 + x6
#> speed =~ x7 + x8 + x9
#>
library(lavaan)
fit <- lavaan(HS.model,
data = HolzingerSwineford1939,
auto.var = TRUE, auto.fix.first = TRUE,
auto.cov.lv.x = TRUE
)
summary(fit, fit.measures = TRUE)
#> lavaan 0.6-18 ended normally after 35 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 21
#>
#> Number of observations 301
#>
#> Model Test User Model:
#>
#> Test statistic 85.306
#> Degrees of freedom 24
#> P-value (Chi-square) 0.000
#>
#> Model Test Baseline Model:
#>
#> Test statistic 918.852
#> Degrees of freedom 36
#> P-value 0.000
#>
#> User Model versus Baseline Model:
#>
#> Comparative Fit Index (CFI) 0.931
#> Tucker-Lewis Index (TLI) 0.896
#>
#> Loglikelihood and Information Criteria:
#>
#> Loglikelihood user model (H0) -3737.745
#> Loglikelihood unrestricted model (H1) -3695.092
#>
#> Akaike (AIC) 7517.490
#> Bayesian (BIC) 7595.339
#> Sample-size adjusted Bayesian (SABIC) 7528.739
#>
#> Root Mean Square Error of Approximation:
#>
#> RMSEA 0.092
#> 90 Percent confidence interval - lower 0.071
#> 90 Percent confidence interval - upper 0.114
#> P-value H_0: RMSEA <= 0.050 0.001
#> P-value H_0: RMSEA >= 0.080 0.840
#>
#> Standardized Root Mean Square Residual:
#>
#> SRMR 0.065
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> visual =~
#> x1 1.000
#> x2 0.554 0.100 5.554 0.000
#> x3 0.729 0.109 6.685 0.000
#> textual =~
#> x4 1.000
#> x5 1.113 0.065 17.014 0.000
#> x6 0.926 0.055 16.703 0.000
#> speed =~
#> x7 1.000
#> x8 1.180 0.165 7.152 0.000
#> x9 1.082 0.151 7.155 0.000
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> visual ~~
#> textual 0.408 0.074 5.552 0.000
#> speed 0.262 0.056 4.660 0.000
#> textual ~~
#> speed 0.173 0.049 3.518 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x1 0.549 0.114 4.833 0.000
#> .x2 1.134 0.102 11.146 0.000
#> .x3 0.844 0.091 9.317 0.000
#> .x4 0.371 0.048 7.779 0.000
#> .x5 0.446 0.058 7.642 0.000
#> .x6 0.356 0.043 8.277 0.000
#> .x7 0.799 0.081 9.823 0.000
#> .x8 0.488 0.074 6.573 0.000
#> .x9 0.566 0.071 8.003 0.000
#> visual 0.809 0.145 5.564 0.000
#> textual 0.979 0.112 8.737 0.000
#> speed 0.384 0.086 4.451 0.000
#>