misty (version 0.6.7)

dominance.manual: Dominance Analysis, Manually Inputting a Correlation Matrix

Description

This function conducts dominance analysis (Budescu, 1993; Azen & Budescu, 2003) based on a (model-implied) correlation matrix of the manifest or latent variables. Note that the function only provides general dominance.

Usage

dominance.manual(x, out = NULL, digits = 3, write = NULL, append = TRUE,
                 check = TRUE, output = TRUE)

Value

Returns an object of class misty.object, which is a list with following entries:

call

function call

type

type of analysis

x

correlation matrix specified in x

args

specification of function arguments

result

results table for the general dominance

Arguments

x

a matrix or data frame with the (model-implied) correlation matrix of the manifest or latent variables. Note that column names need to represent the variables names in x.

out

a character string representing the outcome variable. By default, the first row and column represents the outcome variable.

digits

an integer value indicating the number of decimal places to be used for displaying results. Note that the percentage relative importance of predictors are printed with digits minus 1 decimal places.

write

a character string naming a file for writing the output into either a text file with file extension ".txt" (e.g., "Output.txt") or Excel file with file extension ".xlsx" (e.g., "Output.xlsx"). If the file name does not contain any file extension, an Excel file will be written.

append

logical: if TRUE (default), output will be appended to an existing text file with extension .txt specified in write, if FALSE existing text file will be overwritten.

check

logical: if TRUE (default), argument specification is checked.

output

logical: if TRUE (default), output is shown.

Author

Takuya Yanagida

References

Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129–148. https://doi.org/10.1037/1082-989X.8.2.129

Bolker, B., Warnes, G., & Lumley, T. (2022). gtools: Various R Programming Tools. R package version 3.9.4, https://CRAN.R-project.org/package=gtools

Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542–551. https://doi.org/10.1037/0033-2909.114.3.542

Gu, X. (2022). Assessing the relative importance of predictors in latent regression models. Structural Equation Modeling: A Multidisciplinary Journal, 4, 569-583. https://doi.org/10.1080/10705511.2021.2025377

See Also

dominance, std.coef, write.result

Examples

if (FALSE) {
#----------------------------------------------------------------------------
# Linear model

# Example 1a: Dominance analysis, 'mpg' predicted by 'cyl', 'disp', and 'hp'
dominance.manual(cor(mtcars[, c("mpg", "cyl", "disp", "hp")]))

# Example 1b: Equivalent results using the dominance() function
mod <- lm(mpg ~ cyl + disp + hp, data = mtcars)
dominance(mod)

# Example 1c: Dominance analysis, 'hp' predicted by 'mpg', 'cyl', and 'disp'
dominance.manual(cor(mtcars[, c("mpg", "cyl", "disp", "hp")]), out = "hp")

# Example 1d: Write results into a text file
dominance.manual(cor(mtcars[, c("mpg", "cyl", "disp", "hp")]),
                 write = "Dominance_Manual.txt")

#----------------------------------------------------------------------------
# Example 2: Structural equation modeling

library(lavaan)

#.............
# Latent variables

# Model specification
model <- '# Measurement model
          ind60 =~ x1 + x2 + x3
          dem60 =~ y1 + y2 + y3 + y4
          dem65 =~ y5 + y6 + y7 + y8
          # regressions
          ind60 ~ dem60 + dem65'

# Model estimation
fit <- sem(model, data = PoliticalDemocracy)

# Model-implied correlation matrix of the latent variables
fit.cor <- lavInspect(fit, what = "cor.lv")

# Dominance analysis
dominance.manual(fit.cor)

#.............
# Example 3: Latent and manifest variables

# Model specification, convert manifest to latent variable
model <- '# Measurement model
          ind60 =~ x1 + x2 + x3
          dem60 =~ y1 + y2 + y3 + y4
          # Manifest as latent variable
          ly5 =~ 1*y5
          y5 ~~ 0*y5
          # Regressions
          ind60 ~ dem60 + ly5'

# Model estimation
fit <- sem(model, data = PoliticalDemocracy)

# Model-implied correlation matrix of the latent variables
fit.cor <- lavInspect(fit, what = "cor.lv")

# Dominance analysis
dominance.manual(fit.cor)

#----------------------------------------------------------------------------
# Example 4: Multilevel modeling

# Model specification
model <- 'level: 1
            fw =~ y1 + y2 + y3
            # Manifest as latent variables
            lx1 =~ 1*x1
            lx2 =~ 1*x2
            lx3 =~ 1*x3
            x1 ~~ 0*x1
            x2 ~~ 0*x2
            x3 ~~ 0*x3
            # Regression
            fw ~ lx1 + lx2 + lx3
          level: 2
            fb =~ y1 + y2 + y3
            # Manifest as latent variables
            lw1 =~ 1*w1
            lw2 =~ 1*w2
            # Regression
            fb ~ lw1 + lw2'

# Model estimation
fit <- sem(model, data = Demo.twolevel, cluster = "cluster")

# Model-implied correlation matrix of the latent variables
fit.cor <- lavInspect(fit, what = "cor.lv")

# Dominance analysis Within
dominance.manual(fit.cor$within)

# Dominance analysis Between
dominance.manual(fit.cor$cluster)

#----------------------------------------------------------------------------
# Example 5: Mplus
#
# In Mplus, the model-impied correlation matrix of the latent variables
# can be requested by OUTPUT: TECH4 and imported into R by using the
# MplusAuomtation package, for example:

library(MplusAutomation)

# Read Mplus output
output <- readModels()

# Extract model-implied correlation matrix of the latent variables
fit.cor <- output$tech4$latCorEst
}