Extract relevant regression indices from lavaan model through
lavaan::parameterEstimates with standardized = TRUE. In this
case, the beta (B) represents the resulting std.all column.
See "Value" section for more details.
Arguments
- fit
lavaan fit object to extract fit indices from
- estimate
What estimate to use, either the standardized estimate ("B", default), or unstandardized estimate ("b").
- 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, including the outcome ("lhs"), predictor ("rhs"), standardized regression coefficient ("std.all"), corresponding p-value, as well as the unstandardized regression coefficient ("est") and its confidence interval ("ci.lower", "ci.upper").
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"
#>
HS.model <- write_lavaan(latent = latent, regression = regression)
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
#>
library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_reg(fit)
#> Outcome Predictor p B CI_lower CI_upper
#> 10 ageyr visual 0.513 -0.058 -0.230 0.115
#> 11 ageyr textual 0.000 -0.304 -0.437 -0.171
#> 12 ageyr speed 0.000 0.354 0.208 0.500
#> 13 grade visual 0.818 0.020 -0.150 0.189
#> 14 grade textual 0.148 0.099 -0.035 0.233
#> 15 grade speed 0.000 0.389 0.248 0.531