Extract relevant variance indices from lavaan
model through lavaan::parameterEstimates (when estimate = "sigma",
est column)) or lavaan::standardizedsolution (when estimate = "r2",
est.std column). R2 values are then calculated as 1 - est.std, and
the new p values for the R2, with the following formula:
stats::pnorm((1 - est) / se).
Arguments
- fit
lavaan fit object to extract covariance indices from
- estimate
What estimate to use, either the standardized estimate ("r2", default), or unstandardized estimate ("sigma2").
- nice_table
Logical, whether to print the table as a rempsyc::nice_table as well as print the reference values at the bottom of the table.
- ...
Arguments to be passed to rempsyc::nice_table
Value
A dataframe of covariances/correlation, including the covaried variables, the covariance/correlation, and corresponding p-value.
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"
#>
(regression <- list(
ageyr = c("visual", "textual", "speed"),
grade = c("visual", "textual", "speed")
))
#> $ageyr
#> [1] "visual" "textual" "speed"
#>
#> $grade
#> [1] "visual" "textual" "speed"
#>
(covariance <- list(speed = "textual", ageyr = "grade"))
#> $speed
#> [1] "textual"
#>
#> $ageyr
#> [1] "grade"
#>
HS.model <- write_lavaan(
regression = regression, covariance = covariance,
latent = latent, label = TRUE
)
cat(HS.model)
#> ##################################################
#> # [-----Latent variables (measurement model)-----]
#>
#> visual =~ x1 + x2 + x3
#> textual =~ x4 + x5 + x6
#> speed =~ x7 + x8 + x9
#>
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> ageyr ~ visual + textual + speed
#> grade ~ visual + textual + speed
#>
#> ##################################################
#> # [------------------Covariances-----------------]
#>
#> speed ~~ textual
#> ageyr ~~ grade
#>
library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_var(fit)
#> Variable p R2 CI_lower CI_upper
#> 18 x1 0 0.605 0.436 0.773
#> 19 x2 0 0.180 0.081 0.280
#> 20 x3 0 0.333 0.208 0.458
#> 21 x4 0 0.732 0.659 0.806
#> 22 x5 0 0.736 0.663 0.809
#> 23 x6 0 0.689 0.612 0.765
#> 24 x7 0 0.359 0.241 0.476
#> 25 x8 0 0.563 0.428 0.699
#> 26 x9 0 0.390 0.270 0.511
#> 27 ageyr 0 0.161 0.068 0.255
#> 28 grade 0 0.191 0.095 0.287
#> 29 visual NaN 0.000 0.000 0.000
#> 30 textual NaN 0.000 0.000 0.000
#> 31 speed 0 0.000 0.000 0.000