Add paths to an object of class tidy_sem, or replace
existing paths. The paths must be specified as
model.syntax, and separated by commas.
add_paths(model, ...)An object of class tidy_sem.
Paths to add or substitute, specified in
lavaan{model.syntax}, and separated by commas.
An object of class tidy_sem.
Currently, only the lavaan{lavaan} commands ~,
~~, =~, and ~1 are parsed.
This function
relies on lavaan model.syntax to convert syntax strings
to lavaan parameter tables. By default, is uses the arguments
int.ov.free = TRUE, int.lv.free = FALSE, auto.fix.first = TRUE,
auto.fix.single = TRUE, auto.var = TRUE, auto.cov.lv.x = TRUE,
auto.efa = TRUE, auto.th = TRUE, auto.delta = TRUE, auto.cov.y = TRUE,
meanstructure = TRUE, in a similar way
to sem and cfa.
library(lavaan)
#> This is lavaan 0.6-17
#> lavaan is FREE software! Please report any bugs.
df <- iris[, 1:4]
names(df) <- paste0("x_", 1:4)
model <- tidy_sem(df)
model <- measurement(model)
model <- add_paths(model, x =~ a*x_1 + b*x_2 + a*x_3 + b*x_4)
res <- estimate_lavaan(model)
#> Warning: lavaan WARNING: some estimated ov variances are negative
summary(res)
#> lavaan 0.6.17 ended normally after 31 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 11
#>
#> Number of observations 150
#>
#> Model Test User Model:
#>
#> Test statistic 359.420
#> Degrees of freedom 3
#> P-value (Chi-square) 0.000
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> x =~
#> x_1 (a) 1.000
#> x_2 (b) -0.139 0.029 -4.717 0.000
#> x_3 (a) 1.000
#> x_4 (b) 0.752 0.038 19.760 0.000
#>
#> Intercepts:
#> Estimate Std.Err z-value P(>|z|)
#> .x_1 5.843 0.105 55.447 0.000
#> .x_2 3.057 0.035 86.196 0.000
#> .x_3 3.758 0.110 34.094 0.000
#> .x_4 1.199 0.062 19.335 0.000
#> x 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x_1 0.535 0.070 7.689 0.000
#> .x_2 0.167 0.019 8.808 0.000
#> .x_3 0.691 0.085 8.092 0.000
#> .x_4 -0.063 0.021 -2.972 0.003
#> x 1.131 0.168 6.743 0.000
#>