Extract relevant correlation indices from lavaan model through
lavaan::parameterEstimates with standardized = TRUE. In this
case, the correlation coefficient (r) represents the resulting
std.all column.
Arguments
- fit
lavaan fit object to extract correlations from
- nice_table
Logical, whether to print the table as a
rempsyc::nice_tableas well as print the reference values at the bottom of the table.- ...
Arguments to be passed to
rempsyc::nice_table
Value
A dataframe of correlations, including the correlated variables, the correlation, and the corresponding p-value.
Examples
(latent <- list(visual = paste0("x", 1:3),
textual = paste0("x", 4:6),
speed = paste0("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_cor(fit)
#> Variable.1 Variable.2 r p
#> 16 textual speed 0.268 0.001
#> 17 ageyr grade 0.533 0.000
#> 32 visual textual 0.458 0.000
#> 33 visual speed 0.438 0.000