Extract relevant indirect effects indices from lavaan model
through lavaan::parameterEstimates) with
standardized = TRUE. In this case, the beta (B) represents
the resulting std.all column.
Arguments
- fit
lavaan fit object to extract fit indices 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.- underscores_to_arrows
Logical, whether to convert underscorse to arrows in the "Indirect Effect column".
- ...
Arguments to be passed to
rempsyc::nice_table
Value
A dataframe, including the indirect effect, corresponding paths, standardized regression coefficient, and 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"
#>
(mediation <- list(speed = "visual",
textual = "visual",
visual = c("ageyr", "grade")))
#> $speed
#> [1] "visual"
#>
#> $textual
#> [1] "visual"
#>
#> $visual
#> [1] "ageyr" "grade"
#>
(indirect <- list(IV = c("ageyr", "grade"),
M = "visual",
DV = c("speed", "textual")))
#> $IV
#> [1] "ageyr" "grade"
#>
#> $M
#> [1] "visual"
#>
#> $DV
#> [1] "speed" "textual"
#>
HS.model <- write_lavaan(mediation, indirect = indirect,
latent = latent, label = TRUE)
cat(HS.model)
#> ##################################################
#> # [-----Latent variables (measurement model)-----]
#>
#> visual =~ x1 + x2 + x3
#> textual =~ x4 + x5 + x6
#> speed =~ x7 + x8 + x9
#>
#> ##################################################
#> # [-----------Mediations (named paths)-----------]
#>
#> speed ~ visual_speed*visual
#> textual ~ visual_textual*visual
#> visual ~ ageyr_visual*ageyr + grade_visual*grade
#>
#> ##################################################
#> # [--------Mediations (indirect effects)---------]
#>
#> ageyr_visual_speed := ageyr_visual * visual_speed
#> ageyr_visual_textual := ageyr_visual * visual_textual
#> grade_visual_speed := grade_visual * visual_speed
#> grade_visual_textual := grade_visual * visual_textual
#>
library(lavaan)
fit <- lavaan(HS.model, data=HolzingerSwineford1939,
auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE)
lavaan_ind(fit)
#> Indirect.Effect Paths B p
#> 29 ageyr → visual → speed ageyr_visual*visual_speed -0.152 0.001
#> 30 ageyr → visual → textual ageyr_visual*visual_textual -0.154 0.000
#> 31 grade → visual → speed grade_visual*visual_speed 0.249 0.000
#> 32 grade → visual → textual grade_visual*visual_textual 0.253 0.000