semTools: indProd – R documentation

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indProd

Make products of indicators using no centering, mean centering, double-mean centering, or residual centering

Description

The indProd function will make products of indicators using no centering, mean centering, double-mean centering, or residual centering. The orthogonalize function is the shortcut of the indProd function to make the residual-centered indicators products.

Usage

indProd(data, var1, var2, var3 = NULL, match = TRUE, meanC = TRUE,
  residualC = FALSE, doubleMC = TRUE, namesProd = NULL)

orthogonalize(data, var1, var2, var3 = NULL, match = TRUE, namesProd = NULL)

Arguments

`data`	The desired data to be transformed.
`var1`	Names or indices of the variables loaded on the first factor
`var2`	Names or indices of the variables loaded on the second factor
`var3`	Names or indices of the variables loaded on the third factor (for three-way interaction)
`match`	Specify `TRUE` to use match-paired approach (Marsh, Wen, & Hau, 2004). If `FALSE`, the resulting products are all possible products.
`meanC`	Specify `TRUE` for mean centering the main effect indicator before making the products
`residualC`	Specify `TRUE` for residual centering the products by the main effect indicators (Little, Bovaird, & Widaman, 2006).
`doubleMC`	Specify `TRUE` for centering the resulting products (Lin et. al., 2010)
`namesProd`	The names of resulting products

Value

The original data attached with the products.

Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com) Alexander Schoemann (East Carolina University; schoemanna@ecu.edu)

References

Marsh, H. W., Wen, Z. & Hau, K. T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9(3), 275–300. doi: 10.1037/1082-989X.9.3.275

Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies. Structural Equation Modeling, 17(3), 374–391. doi: 10.1080/10705511.2010.488999

Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Structural Equation Modeling, 13(4), 497–519. doi: 10.1207/s15328007sem1304_1

Examples

## Mean centering / two-way interaction / match-paired
dat <- indProd(attitude[ , -1], var1 = 1:3, var2 = 4:6)

## Residual centering / two-way interaction / match-paired
dat2 <- indProd(attitude[ , -1], var1 = 1:3, var2 = 4:6, match = FALSE,
                meanC = FALSE, residualC = TRUE, doubleMC = FALSE)

## Double-mean centering / two-way interaction / match-paired
dat3 <- indProd(attitude[ , -1], var1 = 1:3, var2 = 4:6, match = FALSE,
                meanC = TRUE, residualC = FALSE, doubleMC = TRUE)

## Mean centering / three-way interaction / match-paired
dat4 <- indProd(attitude[ , -1], var1 = 1:2, var2 = 3:4, var3 = 5:6)

## Residual centering / three-way interaction / match-paired
dat5 <- orthogonalize(attitude[ , -1], var1 = 1:2, var2 = 3:4, var3 = 5:6,
                      match = FALSE)

## Double-mean centering / three-way interaction / match-paired
dat6 <- indProd(attitude[ , -1], var1 = 1:2, var2 = 3:4, var3 = 5:6,
                match = FALSE, meanC = TRUE, residualC = TRUE,
                doubleMC = TRUE)


## To add product-indicators to multiple-imputed data sets
## Not run: 
HSMiss <- HolzingerSwineford1939[ , c(paste0("x", 1:9), "ageyr","agemo")]
set.seed(12345)
HSMiss$x5 <- ifelse(HSMiss$x5 <= quantile(HSMiss$x5, .3), NA, HSMiss$x5)
age <- HSMiss$ageyr + HSMiss$agemo/12
HSMiss$x9 <- ifelse(age <= quantile(age, .3), NA, HSMiss$x9)
library(Amelia)
set.seed(12345)
HS.amelia <- amelia(HSMiss, m = 3, p2s = FALSE)
imps <- HS.amelia$imputations # extract a list of imputations
## apply indProd() to the list of data.frames
imps2 <- lapply(imps, indProd,
                var1 = c("x1","x2","x3"), var2 = c("x4","x5","x6"))
## verify:
lapply(imps2, head)

## End(Not run)

semTools

Useful Tools for Structural Equation Modeling

v0.5-4

GPL (>= 2)

Authors

Terrence D. Jorgensen [aut, cre] (<https://orcid.org/0000-0001-5111-6773>), Sunthud Pornprasertmanit [aut], Alexander M. Schoemann [aut] (<https://orcid.org/0000-0002-8479-8798>), Yves Rosseel [aut] (<https://orcid.org/0000-0002-4129-4477>), Patrick Miller [ctb], Corbin Quick [ctb], Mauricio Garnier-Villarreal [ctb] (<https://orcid.org/0000-0002-2951-6647>), James Selig [ctb], Aaron Boulton [ctb], Kristopher Preacher [ctb], Donna Coffman [ctb], Mijke Rhemtulla [ctb] (<https://orcid.org/0000-0003-2572-2424>), Alexander Robitzsch [ctb] (<https://orcid.org/0000-0002-8226-3132>), Craig Enders [ctb], Ruben Arslan [ctb] (<https://orcid.org/0000-0002-6670-5658>), Bell Clinton [ctb], Pavel Panko [ctb], Edgar Merkle [ctb] (<https://orcid.org/0000-0001-7158-0653>), Steven Chesnut [ctb], Jarrett Byrnes [ctb], Jason D. Rights [ctb], Ylenio Longo [ctb], Maxwell Mansolf [ctb] (<https://orcid.org/0000-0001-6861-8657>), Mattan S. Ben-Shachar [ctb] (<https://orcid.org/0000-0002-4287-4801>), Mikko Rönkkö [ctb] (<https://orcid.org/0000-0001-7988-7609>)

Initial release