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This vignette demonstrates the use of contrasts in the
pinSearch() function based on a real data set.
summary(lui_sim)
#> class1 class2 class3 class4
#> Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
#> 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
#> Median :2.000 Median :2.000 Median :2.000 Median :1.000
#> Mean :1.779 Mean :1.577 Mean :1.703 Mean :1.432
#> 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
#> Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
#> class5 class6 class7 class8 class9
#> Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
#> 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
#> Median :1.000 Median :1.000 Median :2.000 Median :2.00 Median :2.000
#> Mean :1.379 Mean :1.223 Mean :1.655 Mean :1.58 Mean :1.638
#> 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:2.000
#> Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.00 Max. :2.000
#> class10 class11 class12 class13
#> Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
#> 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
#> Median :2.000 Median :2.000 Median :2.000 Median :1.000
#> Mean :1.513 Mean :1.722 Mean :1.653 Mean :1.356
#> 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
#> Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
#> class14 class15 group
#> Min. :1.000 Min. :1.000 Min. :1.000
#> 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
#> Median :2.000 Median :1.000 Median :4.000
#> Mean :1.742 Mean :1.436 Mean :3.615
#> 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:5.000
#> Max. :2.000 Max. :2.000 Max. :6.000config_mod <- "
f1 =~ class1 + class2 + class3 + class4 + class5 + class6 +
class7 + class8 + class9 + class10 + class11 + class12 +
class13 + class14 + class15
"
config_fit <- cfa(config_mod, data = lui_sim, group = "group", ordered = TRUE)
# Release covariance between class1 and class2
config_fit2 <- update(config_fit, c(config_mod, "class1 ~~ class2"))
anova(config_fit, config_fit2)
#>
#> Scaled and Shifted Chi-Squared Difference Test (method = "satorra.2000")
#>
#> lavaan->lavTestLRT():
#> lavaan NOTE: The "Chisq" column contains standard test statistics, not the
#> robust test that should be reported per model. A robust difference test is
#> a function of two standard (not robust) statistics.
#>
#> Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
#> config_fit2 534 354.80
#> config_fit 540 411.35 36.784 0.2357 6 1.94e-06 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Forward search
| group | lhs | rhs | type |
|---|---|---|---|
| 6 | f1 | class3 | loadings |
| 6 | f1 | class6 | loadings |
| 2 | f1 | class15 | loadings |
| 6 | f1 | class8 | loadings |
| 1 | f1 | class13 | loadings |
| 2 | f1 | class11 | loadings |
| 4 | f1 | class7 | loadings |
| 4 | f1 | class4 | loadings |
| 5 | f1 | class5 | loadings |
| 2 | f1 | class14 | loadings |
| 1 | f1 | class3 | loadings |
| 3 | f1 | class9 | loadings |
| 2 | f1 | class5 | loadings |
| 5 | f1 | class6 | loadings |
| 5 | f1 | class15 | loadings |
| 4 | f1 | class8 | loadings |
| 1 | f1 | class12 | loadings |
| 2 | f1 | class8 | loadings |
| 4 | class14 | t1 | thresholds |
| 3 | class3 | t1 | thresholds |
| 2 | class9 | t1 | thresholds |
| 5 | class8 | t1 | thresholds |
| 2 | class10 | t1 | thresholds |
| 2 | class12 | t1 | thresholds |
| 1 | class10 | t1 | thresholds |
| 4 | class1 | t1 | thresholds |
| 1 | class15 | t1 | thresholds |
| 4 | class2 | t1 | thresholds |
| 3 | class8 | t1 | thresholds |
# Obtain fmacs effect size
(f_omni <- pin_effsize(ps[[1]]))
#> class1-f1 class2-f1 class3-f1 class4-f1 class5-f1 class6-f1 class7-f1
#> fmacs 0.1199234 0.1105238 0.2381024 0.09092262 0.04957459 0.1858849 0.09464019
#> class8-f1 class9-f1 class10-f1 class11-f1 class12-f1 class13-f1
#> fmacs 0.1744948 0.1149935 0.126812 0.0353763 0.1087859 0.0708557
#> class14-f1 class15-f1
#> fmacs 0.1860244 0.142588
# fmacs by gender
(f_gender <- pin_effsize(ps[[1]], group_factor = c(1, 1, 1, 2, 2, 2)))
#> class1-f1 class2-f1 class3-f1 class4-f1 class5-f1 class6-f1
#> fmacs 0.03579041 0.03298516 0.1443592 0.02713531 0.03031336 0.04858438
#> class7-f1 class8-f1 class9-f1 class10-f1 class11-f1 class12-f1
#> fmacs 0.0282448 0.03521963 0.083309 0.04836829 0.01813981 0.01888187
#> class13-f1 class14-f1 class15-f1
#> fmacs 0.02702558 0.03136801 0.04376062
# fmacs by ethnicity
(f_eth <- pin_effsize(ps[[1]], group_factor = c(1, 2, 3, 1, 2, 3)))
#> class1-f1 class2-f1 class3-f1 class4-f1 class5-f1 class6-f1 class7-f1
#> fmacs 0.0580995 0.05354566 0.1249914 0.04404945 0.01246479 0.1131955 0.04585051
#> class8-f1 class9-f1 class10-f1 class11-f1 class12-f1 class13-f1
#> fmacs 0.08975193 0.07570749 0.07851749 0.02558737 0.08675476 0.04387133
#> class14-f1 class15-f1
#> fmacs 0.07596325 0.1148166
# interaction (using contrast matrix)
contr <- local({
gen <- factor(c("F", "M"))
contrasts(gen) <- contr.sum(length(gen))
eth <- factor(1:3)
contrasts(eth) <- contr.sum(length(eth))
model.matrix(~ gen * eth, data = expand.grid(eth = eth, gen = gen))
})
(f_int <- pin_effsize(ps[[1]], contrast = contr[, 5:6, drop = FALSE]))
#> class1-f1 class2-f1 class3-f1 class4-f1 class5-f1 class6-f1 class7-f1
#> fmacs 0.0580995 0.05354566 0.1641827 0.04404945 0.04276027 0.1131955 0.04585051
#> class8-f1 class9-f1 class10-f1 class11-f1 class12-f1 class13-f1
#> fmacs 0.1310042 0.07570749 0.07851749 0.02558737 0.08675476 0.04387133
#> class14-f1 class15-f1
#> fmacs 0.1208021 0.07983613# Render as table
item_names <- gsub("class", replacement = "Item ", colnames(f_omni)) |>
gsub(pattern = "-f1", replacement = "")
t(rbind(f_omni, f_gender, f_eth, f_int)) |>
as.data.frame() |>
`dimnames<-`(list(
item_names,
c("Overall", "Gender", "Ethnicity", "Gender x Ethnicity")
)) |>
knitr::kable(digits = 2, caption = "$f_\\text{MACS}$ effect sizes")| Overall | Gender | Ethnicity | Gender x Ethnicity | |
|---|---|---|---|---|
| Item 1 | 0.12 | 0.04 | 0.06 | 0.06 |
| Item 2 | 0.11 | 0.03 | 0.05 | 0.05 |
| Item 3 | 0.24 | 0.14 | 0.12 | 0.16 |
| Item 4 | 0.09 | 0.03 | 0.04 | 0.04 |
| Item 5 | 0.05 | 0.03 | 0.01 | 0.04 |
| Item 6 | 0.19 | 0.05 | 0.11 | 0.11 |
| Item 7 | 0.09 | 0.03 | 0.05 | 0.05 |
| Item 8 | 0.17 | 0.04 | 0.09 | 0.13 |
| Item 9 | 0.11 | 0.08 | 0.08 | 0.08 |
| Item 10 | 0.13 | 0.05 | 0.08 | 0.08 |
| Item 11 | 0.04 | 0.02 | 0.03 | 0.03 |
| Item 12 | 0.11 | 0.02 | 0.09 | 0.09 |
| Item 13 | 0.07 | 0.03 | 0.04 | 0.04 |
| Item 14 | 0.19 | 0.03 | 0.08 | 0.12 |
| Item 15 | 0.14 | 0.04 | 0.11 | 0.08 |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.