# Extract relevant modification indices along item labels

Source:`R/nice_modindices.R`

`nice_modindices.Rd`

Extract relevant modification indices along item labels, with a similarity score provided to help guide decision-making for removing redundant items with high covariance.

## Arguments

- fit
lavaan fit object to extract modification indices from

- labels
Dataframe of labels. If the original data frame is provided, and that it contains labelled variables, will automatically attempt to extract the correct labels from the dataframe.

- method
Method for distance calculation from stringdist::stringsim. Defaults to

`"lcs"`

.- sort
Logical. If TRUE, sort the output using the values of the modification index values. Higher values appear first. Defaults to

`TRUE`

.- ...
Arguments to be passed to lavaan::modindices

## 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

```
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"
#>
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)
nice_modindices(fit, maximum.number = 5)
#> lhs op rhs mi
#> 1 visual =~ x9 35.699
#> 2 visual =~ x7 16.659
#> 3 x7 ~~ grade 16.511
#> 4 x7 ~~ ageyr 10.030
#> 5 textual =~ x3 9.833
data_labels <- data.frame(
x1 = "I have good visual perception",
x2 = "I have good cube perception",
x3 = "I have good at lozenge perception",
x4 = "I have paragraph comprehension",
x5 = "I am good at sentence completion",
x6 = "I excel at finding the meaning of words",
x7 = "I am quick at doing mental additions",
x8 = "I am quick at counting dots",
x9 = "I am quick at discriminating straight and curved capitals"
)
nice_modindices(fit, maximum.number = 10, labels = data_labels, op = "~~")
#> lhs op rhs mi lhs.labels
#> 1 x7 ~~ grade 16.511 I am quick at doing mental additions
#> 2 x7 ~~ ageyr 10.030 I am quick at doing mental additions
#> 3 x2 ~~ x7 9.673 I have good cube perception
#> 4 x7 ~~ x8 9.479 I am quick at doing mental additions
#> 5 x2 ~~ x3 8.746 I have good cube perception
#> 6 x1 ~~ x9 8.408 I have good visual perception
#> rhs.labels similarity similar
#> 1 <NA> NA FALSE
#> 2 <NA> NA FALSE
#> 3 I am quick at doing mental additions 0.381 FALSE
#> 4 I am quick at counting dots 0.698 TRUE
#> 5 I have good at lozenge perception 0.800 TRUE
#> 6 I am quick at discriminating straight and curved capitals 0.326 FALSE
x <- HolzingerSwineford1939
x <- sjlabelled::set_label(x, label = c(rep("", 6), data_labels))
fit <- sem(HS.model, data = x)
nice_modindices(fit, maximum.number = 10, op = "~~")
#> lhs op rhs mi
#> 1 x7 ~~ grade 16.511
#> 2 x7 ~~ ageyr 10.030
#> 3 x2 ~~ x7 9.673
#> 4 x7 ~~ x8 9.479
#> 5 x2 ~~ x3 8.746
#> 6 x1 ~~ x9 8.408
```