Help for package dySEM

Title:

Dyadic Structural Equation Modeling

Version:

1.4.1

Description:

Scripting of structural equation models via 'lavaan' for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization.

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.3.2

URL:

https://github.com/jsakaluk/dySEM, https://jsakaluk.github.io/dySEM/

BugReports:

https://github.com/jsakaluk/dySEM/issues

Imports:

cli, dplyr, EGAnet, gt, lavaan, lifecycle, magrittr, rlang, semPlot, stringr, tibble

Suggests:

janitor, knitr, rmarkdown, testthat (≥ 3.0.0)

VignetteBuilder:

knitr

LazyData:

true

Depends:

R (≥ 4.1)

Config/testthat/edition:

NeedsCompilation:

Packaged:

2025-12-22 19:37:42 UTC; johnsakaluk

Author:

John Sakaluk

[aut, cre, cph], Omar Camanto

[aut], Christopher Quinn-Nilas

[ctb], Merissa Prine [ctb], Robyn Kilshaw [ctb], Alexandra Fisher [ctb]

Maintainer:

John Sakaluk <jksakaluk@gmail.com>

Repository:

CRAN

Date/Publication:

2025-12-22 20:00:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Value

No return value

Relationship quality and sexual satisfaction of 121 couples

Description

A dataset containing 9 observed indicators of relationship quality (PRQC) and 5 observed indicators of sexual satisfaction from 121 couples in the DRES (Daily Relationship Experiences Study; Raposo, Impett, & Muise, in press)

Usage

data(DRES)

Format

A data frame with 121 rows and 28 variables:

PRQC_1.1: PRQC item 1 for partner 1
PRQC_2.1: PRQC item 2 for partner 1
PRQC_3.1: PRQC item 3 for partner 1
PRQC_4.1: PRQC item 4 for partner 1
PRQC_5.1: PRQC item 5 for partner 1
PRQC_6.1: PRQC item 6 for partner 1
PRQC_7.1: PRQC item 7 for partner 1
PRQC_8.1: PRQC item 8 for partner 1
PRQC_9.1: PRQC item 9 for partner 1
PRQC_1.2: PRQC item 1 for partner 2
PRQC_2.2: PRQC item 2 for partner 2
PRQC_3.2: PRQC item 3 for partner 2
PRQC_4.2: PRQC item 4 for partner 2
PRQC_5.2: PRQC item 5 for partner 2
PRQC_6.2: PRQC item 6 for partner 2
PRQC_7.2: PRQC item 7 for partner 2
PRQC_8.2: PRQC item 8 for partner 2
PRQC_9.2: PRQC item 9 for partner 2
sexsat1.1: sexual satisfaction item 1 for partner 1
sexsat2.1: sexual satisfaction item 2 for partner 1
sexsat3.1: sexual satisfaction item 3 for partner 1
sexsat4.1: sexual satisfaction item 4 for partner 1
sexsat5.1: sexual satisfaction item 5 for partner 1
sexsat1.2: sexual satisfaction item 1 for partner 2
sexsat2.2: sexual satisfaction item 2 for partner 2
sexsat3.2: sexual satisfaction item 3 for partner 2
sexsat4.2: sexual satisfaction item 4 for partner 2
sexsat5.2: sexual satisfaction item 5 for partner 2

References

Raposo, S., Impett, E. A., & Muise, A. (2020). Avoidantly Attached Individuals Are More Exchange‐Oriented and Less Communal in the Bedroom. Archives of Sexual Behavior, 49, 2863–2881. https://doi.org/10.1007/s10508-020-01813-9

Ratings of relational satisfaction and commitment from 282 (M)ixed-sex couples

Description

A data set containing 5 ratings of satisfaction and 5 ratings of commitment for each member of a mixed-sex romantic dyad. Measured using the Investment Model Scale subscales (Rusbult, Martz, & Agnew, 1998). Data are from Sakaluk, Fisher, and Kilshaw's (2021) study of dyadic invariance. Variable names have been re-coded to follow a stem-item-partner ("sip") order, with a delimiter ("_") between the item number and partner distinguishing character.

Usage

data(commitmentM)

Format

A data frame with 282 rows and 20 variables:

sat.g1_f: Satisfaction item 1 for female partner
sat.g2_f: Satisfaction item 2 for female partner
sat.g3_f: Satisfaction item 3 for female partner
sat.g4_f: Satisfaction item 4 for female partner
sat.g5_f: Satisfaction item 5 for female partner
com1_f: Commitment items item 1 for female partner
com2_f: Commitment items item 2 for female partner
com3_f: Commitment items item 3 for female partner
com4_f: Commitment items item 4 for female partner
com5_f: Commitment items item 5 for female partner
sat.g1_m: Satisfaction item 1 for male partner
sat.g2_m: Satisfaction item 2 for male partner
sat.g3_m: Satisfaction item 3 for male partner
sat.g4_m: Satisfaction item 4 for male partner
sat.g5_m: Satisfaction item 5 for male partner
com1_m: Commitment items item 1 for male partner
com2_m: Commitment items item 2 for male partner
com3_m: Commitment items item 3 for male partner
com4_m: Commitment items item 4 for male partner
com5_m: Commitment items item 5 for male partner

References

Sakaluk, J. K., Fisher, A. N., & Kilshaw, R. E.(2021). Dyadic measurement invariance and its importance for replicability in romantic relationship research. Personal Relationships, 28(1), 190-226. .

Ratings of relational satisfaction and commitment from 282 (Q)ueer couples

Description

A data set containing 5 ratings of satisfaction and 5 ratings of commitment for each member of a dyad in which one or more members identify as LGBTQ+. Measured using the Investment Model Scale subscales (Rusbult, Martz, & Agnew, 1998). Data are from Sakaluk, Fisher, and Kilshaw (2021). Variable names follow a stem-partner-item ("spi") order, with a delimiter (".") between the stem and distinguishing partner character, and another delimiter ("_") between the distinguishing partner character and item number.

Usage

data(commitmentQ)

Format

A data frame with 118 rows and 20 variables:

sat.g.1_1: Satisfaction item 1 for partner1
sat.g.1_2: Satisfaction item 2 for partner1
sat.g.1_3: Satisfaction item 3 for partner1
sat.g.1_4: Satisfaction item 4 for partner1
sat.g.1_5: Satisfaction item 5 for partner1
com.1_1: Commitment items item 1 for partner1
com.1_2: Commitment items item 2 for partner1
com.1_3: Commitment items item 3 for partner1
com.1_4: Commitment items item 4 for partner1
com.1_5: Commitment items item 5 for partner1
sat.g.2_1: Satisfaction item 1 for partner 2
sat.g.2_2: Satisfaction item 2 for partner 2
sat.g.2_3: Satisfaction item 3 for partner 2
sat.g.2_4: Satisfaction item 4 for partner 2
sat.g.2_5: Satisfaction item 5 for partner 2
com.2_1: Commitment items item 1 for partner 2
com.2_2: Commitment items item 2 for partner 2
com.2_3: Commitment items item 3 for partner 2
com.2_4: Commitment items item 4 for partner 2
com.2_5: Commitment items item 5 for partner 2

References

A function that performs a score test for relaxing each invariance equality constraint between partners in a given dyadic SEM model.

Description

A function that performs a score test for relaxing each invariance equality constraint between partners in a given dyadic SEM model.

Usage

getConstraintTests(constrainFit, filterSig = FALSE)

Arguments

constrainFit

fitted lavaan model with dyadic invariance equality constraints

filterSig

logical indicating whether to filter for significant constraints (default is FALSE)

Value

a data frame with rows of equality constraints (now with readable param labels) and test statistic, df, and p for whether constraint worsens model fit

Examples

dvn <- scrapeVarCross(dat = commitmentM, x_order = "sip", x_stem = "sat.g",
x_delim2="_", distinguish_1="f", distinguish_2="m")

sat.resids.script <- scriptCor(dvn, lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none")

sat.resids.mod <- lavaan::cfa(sat.resids.script, data = commitmentM, std.lv = FALSE,
auto.fix.first= FALSE, meanstructure = TRUE)

getConstraintTests(sat.resids.mod)

A Function Calculates Omega Total Coefficients from a Dyadic CFA

Description

This function takes the model from fitted scriptCor() scripts and returns omega total coefficients for each dyad member, adapted following Formula 2 in McNeish (2018).

Usage

getDyReliability(dvn, fit)

Arguments

dvn

input dvn list from scrapeVarCross

fit

outputted dyadic cfa lavaan object based on the default (i.e., "configural") dyadCFA() function

Value

a tibble/data frame with calculated omega total coefficients for dyad Member 1 and Member 2

Examples

dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
sat.indist.script <-  scriptCor(dvn, lvname = "Sat")
sat.indist.mod <- lavaan::cfa(sat.indist.script, data = commitmentQ, std.lv = FALSE,
auto.fix.first= FALSE, meanstructure = TRUE)
getDyReliability(dvn, sat.indist.mod)

Calculates dmacs difference in expected indicator scores for between dyad members

Description

Calculates dmacs difference in expected indicator scores for between dyad members

Usage

getDydmacs(dat, dvn, fit, nodewidth = 0.01, lowerLV = -5, upperLV = 5)

Arguments

dat

data frame of indicators

dvn

input dvn list from scrapeVarCross

fit

outputted dyadic cfa lavaan object; should be from a partial-invariance model

nodewidth

space between nodes during quadrature approximation (default = .01)

lowerLV

lowest latent variable value evaluated (default = -5)

upperLV

greatest latent variable value evaluated (default = 5)

Value

vector of d_macs values

Examples

dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
sat.config.script <- scriptCor(dvn, lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none")

sat.config.mod <- lavaan::cfa(sat.config.script, data = commitmentQ, std.lv = FALSE,
auto.fix.first= FALSE, meanstructure = TRUE)
getDydmacs(commitmentQ, dvn, sat.config.mod)

A Function that Computes Corrected Fit Indexes According to the ISAT and INULL Models of Olsen & Kenny (2006)

Description

This function takes the outputted model fit using scriptCor() with model = "indist", as well as scriptISAT(), and scriptINULL() and computes corrected model fit indexes according to the approach outlined by Olsen & Kenny (2006)

Usage

getIndistFit(indmodel, isatmod, inullmod)

Arguments

indmodel

input lavaan model object fitted using dyadCFA(model = "indistinguishable")

isatmod

input lavaan model object fitted using ISAT()

inullmod

input lavaan model object fitted using INULL()

Value

A data frame of the original and corrected chi sq, df, p, rmsea, and tli

Examples

dvn <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2"
)

sat.indist.script <- scriptCor(dvn, lvname = "Sat")
sat.indist.mod <- lavaan::cfa(sat.indist.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

sat.isat.script <- scriptISAT(dvn, lvxname = "Sat")
sat.isat.mod <- lavaan::cfa(sat.isat.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = FALSE
)

sat.inull.script <- scriptINULL(dvn, lvxname = "Sat")
sat.inull.mod <- lavaan::cfa(sat.inull.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = FALSE
)

getIndistFit(sat.indist.mod, sat.isat.mod, sat.inull.mod)

Compare model fit of nested dyadic invariance models in order from most parsimonious (residual) to least parsimonious (configural)

Description

Compare model fit of nested dyadic invariance models in order from most parsimonious (residual) to least parsimonious (configural)

Usage

getInvarCompTable(mods)

Arguments

mods

a list of neted lavaan dyadic invariance models, in the order of residual, intercept, loading, configural

Value

a data frame of model fit statistics for each model, as well as the difference in fit statistics between each model and the previous model

Examples


dvn <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi",
  x_stem = "sat.g", x_delim1 = ".", x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2"
)

sat.residual.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none"
)

sat.intercept.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none"
)

sat.loading.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = c("loadings"), constr_dy_struct = "none"
)

sat.config.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = "none", constr_dy_struct = "none"
)

sat.residual.fit <- lavaan::cfa(sat.residual.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

sat.intercept.fit <- lavaan::cfa(sat.intercept.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

sat.loading.fit <- lavaan::cfa(sat.loading.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

sat.config.fit <- lavaan::cfa(sat.config.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

mods <- list(sat.residual.fit, sat.intercept.fit, sat.loading.fit, sat.config.fit)

getInvarCompTable(mods)

Ratings on the full Investment Model Scale (IMS) from 282 (M)ixed-sex couples

Description

A data set containing 5 ratings for each of (1) satisfaction, (2) quality of alternatives, (3) investment, and 4 (commitment) for each member of a mixed-sex romantic dyad. Measured using the Investment Model Scale subscales (Rusbult, Martz, & Agnew, 1998). Data are from Sakaluk, Fisher, and Kilshaw's (2021) study of dyadic invariance. Variable names have been re-coded to follow a stem-item-partner ("sip") order, with a delimiter ("_") between the item number and partner distinguishing character.

Usage

data(imsM)

Format

A data frame with 282 rows and 40 variables:

sat.g1_f: Satisfaction item 1 for female partner
sat.g2_f: Satisfaction item 2 for female partner
sat.g3_f: Satisfaction item 3 for female partner
sat.g4_f: Satisfaction item 4 for female partner
sat.g5_f: Satisfaction item 5 for female partner
qalt.g1_f: Quality of alternatives item 1 for female partner
qalt.g2_f: Quality of alternatives item 2 for female partner
qalt.g3_f: Quality of alternatives item 3 for female partner
qalt.g4_f: Quality of alternatives item 4 for female partner
qalt.g5_f: Quality of alternatives item 5 for female partner
invest.g1_f: Investment item 1 for female partner
invest.g2_f: Investment item 2 for female partner
invest.g3_f: Investment item 3 for female partner
invest.g4_f: Investment item 4 for female partner
invest.g5_f: Investment item 5 for female partner
com1_f: Commitment items item 1 for female partner
com2_f: Commitment items item 2 for female partner
com3_f: Commitment items item 3 for female partner
com4_f: Commitment items item 4 for female partner
com5_f: Commitment items item 5 for female partner
sat.g1_m: Satisfaction item 1 for male partner
sat.g2_m: Satisfaction item 2 for male partner
sat.g3_m: Satisfaction item 3 for male partner
sat.g4_m: Satisfaction item 4 for male partner
sat.g5_m: Satisfaction item 5 for male partner
qalt.g1_m: Quality of alternatives item 1 for male partner
qalt.g2_m: Quality of alternatives item 2 for male partner
qalt.g3_m: Quality of alternatives item 3 for male partner
qalt.g4_m: Quality of alternatives item 4 for male partner
qalt.g5_m: Quality of alternatives item 5 for male partner
invest.g1_m: Investment item 1 for male partner
invest.g2_m: Investment item 2 for male partner
invest.g3_m: Investment item 3 for male partner
invest.g4_m: Investment item 4 for male partner
invest.g5_m: Investment item 5 for male partner
com1_m: Commitment items item 1 for male partner
com2_m: Commitment items item 2 for male partner
com3_m: Commitment items item 3 for male partner
com4_m: Commitment items item 4 for male partner
com5_m: Commitment items item 5 for male partner

References

Evaluate Invariance Equality Constraints in a specified Dyadic Invariance Model

Description

outputConstraintTab() is used to perform a score test for relaxing each invariance equality constraint between partners in a given dyadic SEM model.

Usage

outputConstraintTab(
  constrainFit,
  filterSig = FALSE,
  gtTab = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

constrainFit

A fitted lavaan model with dyadic invariance equality constraints

filterSig

A logical indicating whether to filter for significant constraints (default is FALSE)

gtTab

A logical input indicating whether to generate the output in gt::gt() table object format (TRUE). By default (FALSE), the output is generated in tibble::tibble() format. Users can also apply the writeTo argument if they wish to export the gt:gt() table object.

writeTo

A character string specifying a directory path to where the gt::gt() table object should be saved. If set to ".", the file will be written to the current working directory. The default is NULL, and examples use a temporary directory created by tempdir(). writeTo is only relevant if gtTab = TRUE.

fileName

A character string specifying a desired base name for the output gt::gt() file. If a fileName is not provided (i.e., fileName = NULL), then a default will be used (i.e., "dySEM_table"). The resulting base name will automatically be appended with a .rtf file extension. fileName is only relevant if gtTab = TRUE and writeTo is specified.

Details

If gtTab = TRUE and writeTo is specified, then output will simultaneously be saved as a .rtf file to the user's specified directory.
If output file is successfully saved, a confirmation message will be printed to the console.
If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

A tibble::tibble() if gtTab = FALSE (default), or gt::gt() object if gtTab = TRUE, with rows of equality constraints (now with readable param labels) and test statistic, df, and p for whether constraint worsens model fit.

Examples

dvn <- scrapeVarCross(
  dat = commitmentM, x_order = "sip", x_stem = "sat.g",
  x_delim2 = "_", distinguish_1 = "f", distinguish_2 = "m"
)

sat.resids.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none"
)

sat.resids.mod <- lavaan::cfa(sat.resids.script,
  data = commitmentM, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

outputConstraintTab(sat.resids.mod,
  filterSig = FALSE,
  gtTab = TRUE, writeTo = tempdir(), fileName = "dCFA_Residual"
)

Compare Model Fit of Nested Dyadic Invariance Models

Description

outputInvarCompTab() is used to compare the model fit of nested dyadic invariance models in order from most parsimonious (residual) to least parsimonious (configural)

Usage

outputInvarCompTab(
  mods,
  parsimonyFirst = FALSE,
  gtTab = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

mods

A list of nested lavaan dyadic invariance models, in the order of residual, intercept, loading, configural

parsimonyFirst

A logical input indicating whether to prioritize the residual dyadic invariance (i.e. most parsimonious measurement model) as the baseline model for nested comparisons, or to prioritize the configural dyadic invariance (i.e. least parsimonious measurement model). Defaults to FALSE (i.e., configural dyadic invariance is the baseline model).

gtTab

writeTo

fileName

Details

If gtTab = TRUE and writeTo is specified, then output will simultaneously be saved as a .rtf file to the user's specified directory.
If output file is successfully saved, a confirmation message will be printed to the console.
If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

A tibble::tibble() if gtTab = FALSE (default), or gt::gt() object if gtTab = TRUE, of model fit statistics for each model, as well as the difference in fit statistics between each model and the previous model

Examples


dvn <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi",
  x_stem = "sat.g", x_delim1 = ".", x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2"
)

sat.residual.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none"
)

sat.intercept.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none"
)

sat.loading.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = c("loadings"), constr_dy_struct = "none"
)

sat.config.script <- scriptCor(dvn,
  lvname = "Sat",
  constr_dy_meas = "none", constr_dy_struct = "none"
)

sat.residual.fit <- lavaan::cfa(sat.residual.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

sat.intercept.fit <- lavaan::cfa(sat.intercept.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

sat.loading.fit <- lavaan::cfa(sat.loading.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

sat.config.fit <- lavaan::cfa(sat.config.script,
  data = commitmentQ,
  std.lv = FALSE, auto.fix.first = FALSE, meanstructure = TRUE
)

mods <- list(sat.residual.fit, sat.intercept.fit, sat.loading.fit, sat.config.fit)

outputInvarCompTab(mods,
  parsimonyFirst = FALSE,
  gtTab = TRUE, writeTo = tempdir(), fileName = "dCFA_Invar_Standard"
)

mods <- list(sat.config.fit, sat.loading.fit, sat.intercept.fit, sat.residual.fit)

outputInvarCompTab(mods,
  parsimonyFirst = TRUE,
  gtTab = TRUE, writeTo = tempdir(), fileName = "dCFA_Invar_Reverse"
)

A Function That Exports Tables and/or SEM Diagrams based on dySEM models

Description

This function takes the model from fitted dySEM() scripts and exports table(s) and/or a path diagram figure of expected output.

Usage

outputModel(
  dvn,
  model = NULL,
  fit,
  table = TRUE,
  tabletype = NULL,
  figure = TRUE,
  figtype = NULL,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross

model

character input specifying type of model to output (e.g., "cfa", "apim", "cfm")

fit

input object from fitted lavaan model

table

logical input of whether table output is desired. Default is TRUE

tabletype

character input of what type of table(s) is(are) desired. options are "measurement" (i.e,, loadings, intercepts,), "structural" (i.e., latent slopes, such as actor/partner effects, k parameters), or "both" (i.e., both measurement and structural tables)

figure

logical input of whether figure output is desired. Default is TRUE

figtype

character input of what type of figure is desired

writeTo

A character string specifying a directory path to where the file(s) should be saved. If set to “.”, the file(s) will be written to the current working directory. The default is NULL (which will throw an error), and examples use a temporary directory created by tempdir().

fileName

A character string specifying a desired base name for the output file(s). If a fileName not provided (i.e., default fileName = NULL), then defaults will be used (e.g., "dySEM_table"/"dySEM_table_Measurement"/"dySEM_table_Structural for tables; "dySEM_figure" for figures). The specified name will be automatically appended with the appropriate file extension (i.e., .rtf for tables; .png for figures).

Details

If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

Ignore console (prints unnecessary semPlot::semPaths details). More importantly, prints word files for the table(s) and/or figure, outputted to the users working directory

Examples

dvnx <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")

sat.config.script <- scriptCor(dvnx, lvname = "Sat", constr_dy_meas = "none",
constr_dy_struct = "none")

sat.config.mod <- lavaan::cfa(sat.config.script, data = commitmentQ, std.lv = FALSE,
auto.fix.first= FALSE, meanstructure = TRUE)

outputModel(dvnx, model = "cfa", fit = sat.config.mod, table = TRUE,
tabletype = "measurement", figure = "TRUE", figtype = "standardized",
writeTo = tempdir(), fileName = "dCFA_configural")
dvnxy <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2",
y_order="spi", y_stem="com", y_delim1 = ".", y_delim2="_")

apim.indist.script <-  scriptAPIM(dvnxy, lvxname = "Sat", lvyname = "Com", est_k = TRUE)

apim.indist.mod <- lavaan::cfa(apim.indist.script, data = commitmentQ, std.lv = FALSE,
auto.fix.first= FALSE, meanstructure = TRUE)

outputModel(dvnxy, model = "apim", fit = apim.indist.mod, table = TRUE,
tabletype = "measurement", figure = "TRUE", figtype = "standardized",
writeTo = tempdir(), fileName = "APIM_indist")

A function That Exports SEM diagrams based on dySEM models

Description

This function takes the model from fitted dySEM scripts and exports .png path diagram figures of expected output.

Usage

outputParamFig(fit, figtype = NULL, writeTo = NULL, fileName = NULL)

Arguments

fit

Input object from fitted lavaan model.

figtype

A character input of what type of model is desired:

"unstandardized" for unstandardized path coefficients.
"standardized" for standardized path coefficients.
"labels" for labelled parameters.

writeTo

A character string specifying a directory path to where the output file of the path diagram should be saved. If set to “.”, the file will be written to the current working directory. The default is NULL (which will throw an error), and examples use a temporary directory created by tempdir().

fileName

A character string specifying a desired base name for the output file. If a fileName not provided (i.e., fileName = NULL), then a default will be used depending on the specified figtype (e.g., "dySEM_figure unstd", "dySEM_figure std", or "dySEM_figure lab"). The specified name will automatically be appended with the .png file extension.

Details

The function uses semPlot::semPaths() to create a qgraph object of the desired SEM path diagram.
After execution, a semPlot::semPaths() message will be printed to the console confirming the directory path of the saved output file.
If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

A qgraph object of the desired SEM path diagram, which is simultaneously exported as a .png file to the specified directory.

Examples

dvnx <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2"
)

sat.config.script <- scriptCor(dvnx,
  lvname = "Sat", constr_dy_meas = "none",
  constr_dy_struct = "none"
)

sat.config.mod <- lavaan::cfa(sat.config.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

outputParamFig(sat.config.mod,
  figtype = "standardized",
  writeTo = tempdir(), fileName = "dCFA_configural"
)

dvnxy <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2",
  y_order = "spi", y_stem = "com", y_delim1 = ".", y_delim2 = "_"
)

apim.indist.script <- scriptAPIM(dvnxy, lvxname = "Sat", lvyname = "Com", est_k = TRUE)

apim.indist.mod <- lavaan::cfa(apim.indist.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

outputParamFig(apim.indist.mod,
  figtype = "standardized",
  writeTo = tempdir(), fileName = "APIM_indist"
)

A Function That Exports Tables based on dySEM models

Description

This function takes the model from fitted dySEM scripts and creates tables of expected output and/or exports them as .rtfs.

Usage

outputParamTab(
  dvn,
  model = NULL,
  fit,
  tabletype = NULL,
  gtTab = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross()

model

A character input specifying type of model to output: "cfa", "bidyc", "apim", "mim", "cfm", or "bidys".

fit

input object from fitted lavaan model.

tabletype

A character input of what type of table(s) is(are) desired. Options are "measurement" (i.e., loadings, intercepts, etc.), "structural" (i.e., latent slopes, such as actor/partner effects, k parameters), "both" (i.e., both measurement and structural tables), or "correlation" (for a table of factor correlations).

gtTab

A logical input indicating whether to generate the table(s) in gt::gt() table object format (TRUE). By default (FALSE), the table(s) are generated in tibble::tibble() format. Users can also apply the writeTo argument if they wish to export the gt:gt() table object(s).

writeTo

A character string specifying a directory path to where the gt::gt() table object(s) should be saved. If set to ".", the file(s) will be written to the current working directory. The default is NULL, and examples use a temporary directory created by tempdir(). writeTo is only relevant if gtTab = TRUE.

fileName

A character string specifying a desired base name for the output gt::gt() file(s). If a fileName is not provided (i.e., fileName = NULL), then defaults will be used (e.g., "dySEM_table", "dySEM_table_measurement", or "dySEM_table_structural) based on the tabletype argument. The resulting base name will automatically be appended with a .rtf file extension. fileName is only relevant if gtTab = TRUE and writeTo is specified.

Details

If gtTab = TRUE and writeTo is specified, then output will simultaneously be saved as a .rtf file to the user's specified directory.
If output file(s) is(are) successfully saved, a confirmation message will be printed to the console.
If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

A tibble::tibble() if gtTab = FALSE (default), or gt::gt() object if gtTab = TRUE, of specified model parameter estimates and corresponding statistical tests.

Examples

dvnx <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2"
)

sat.config.script <- scriptCor(dvnx,
  lvname = "Sat", constr_dy_meas = "none",
  constr_dy_struct = "none"
)

sat.config.mod <- lavaan::cfa(sat.config.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

outputParamTab(dvnx,
  model = "cfa", sat.config.mod, tabletype = "measurement",
  writeTo = tempdir(), fileName = "dCFA_configural"
)

dvnxy <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2",
  y_order = "spi", y_stem = "com", y_delim1 = ".", y_delim2 = "_"
)

apim.indist.script <- scriptAPIM(dvnxy, lvxname = "Sat", lvyname = "Com", est_k = TRUE)

apim.indist.mod <- lavaan::cfa(apim.indist.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

outputParamTab(dvnxy,
  model = "cfa", sat.config.mod, tabletype = "measurement",
  writeTo = tempdir(), fileName = "APIM_indist"
)

A Function That Fits and Compares Competing Dyadic Uni-construct Models

Description

This function takes the outputted object from scrapeVarCross() along with the corresponding dataset and automatically tests competing uni-construct dyadic models for the latent variable under consideration. It inspects four possible model variants:

Bifactor (scripted via dySEM::scriptBifac)
Hierarchical (scripted via dySEM::scriptHier)
Correlated Factors (scripted via dySEM::scriptCor)
Unidimensional (scripted via dySEM::scriptUni)

Usage

outputUniConstructComp(
  dvn,
  dat,
  indexes = c("df", "chisq", "cfi", "rmsea", "bic", "GenTEFI"),
  ...,
  gtTab = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross().

dat

Input data frame containing the dataset for model estimation.

indexes

Input character vector specifying which index(es) to return. Default is c("df", "chisq", "cfi", "rmsea", "bic", "GenTEFI"). Note:

Valid entries include "GenTEFI"—the Generalized Total Entropy Fit Index (see Golino et al., 2024)—and those from lavaan::fitMeasures().
If "chisq" is entered, chi-squared difference tests are automatically performed via lavaan::lavTestLRT(), and the resulting p-values are added to the output.

...

Additional arguments to be passed to lavaan::cfa(), allowing users to customize model estimation settings. By default, the models are fit with maximum-likelihood estimation (estimator = "ml") and missing data are handled via listwise deletion (missing = "listwise"), as per lavaan::cfa()'s default behaviour.

gtTab

A logical input indicating whether to generate the requested index(es) for each fitted model (requested via the indexes argument) in gt::gt() table object format (TRUE). Users can also apply the writeTo argument if they wish to export the gt::gt() table object.

writeTo

A character vector string specifying a directory path to where the gt::gt() table object should be saved. If set to ".", the file will be written to the current working directory. The default is NULL, and examples use a temporary directory created by tempdir(). writeTo is only relevant if gtTab = TRUE.

fileName

A character string specifying a desired base name for the output gt::gt() file. The resulting base name will automatically be appended with a .rtf file extension. fileName is only relevant if gtTab = TRUE and writeTo is specified.

Details

If "chisq" is included in indexes, the specific form of the applied chi-squared difference test (e.g., standard vs. robust) is determined automatically by lavaan::lavTestLRT(), based on the model estimation method used.
If gtTab = TRUE and writeTo is specified, then output will simultaneously be saved as a .rtf file to the user's specified directory.
If output file is successfully saved, a confirmation message will be printed to the console.
If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

A list containing up to two components:

Indexes: A tibble::tibble() if gtTab = FALSE (default), or gt::gt() object if gtTab = TRUE, with the desired index(es) for each fitted model (requested via the indexes argument).
GenTEFI: A tibble::tibble() of the GenTEFI (if "GenTEFI" is included in the indexes argument).

Examples

dvn <- scrapeVarCross(
commitmentM,
x_order = "sip",
x_stem = "sat.g",
x_delim1 = "",
x_delim2 = "_",
distinguish_1 = "f",
distinguish_2 = "m"
)

# Quick example for CRAN checks
outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  missing = "listwise"
)


# More comprehensive examples (slower due to FIML estimation)
outputUniConstructComp(
  dvn,
  commitmentM,
  missing = "fiml"
)

outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  missing = "fiml"
)

outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  estimator = "ml",
  missing = "fiml"
)

outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  missing = "fiml",
  gtTab = TRUE,
  writeTo = tempdir(),
  fileName = "uni-construct-dyad-models"
)

Ratings on items from the Positive-Negative Relationship Quality Scale (PNRQ; Rogge et al., 2017) from 219 (M)ixed-sex couples

Description

A data set containing ratings on items (4 each) assessing romantic (1) satisfaction and (2) dissatisfaction for each member of a mixed-sex dyad . Positive-Negative Relationship Quality Scale (Rogge et al., 2017). Data are from Prine et al. (Under Review). Variable names follow a stem-item-partner ("sip") order, with a delimiter ("_") between the item number and distinguishing partner character.

Usage

data(pnrqM)

Format

A data frame with 219 rows and 16 variables. Participants responded–on a six-point scale (1 = "Not at all true", 6 = "Completely true")–to the prompt, "My relationship is...":

sat.pnrq1_w: Enjoyable for partner w
sat.pnrq2_w: Pleasant for partner w
sat.pnrq3_w: Strong for partner w
sat.pnrq4_w: Alive for partner w
dsat.pnrq1_w: Miserable for partner w
dsat.pnrq2_w: Bad for partner w
dsat.pnrq3_w: Empty for partner w
dsat.pnrq4_w: Lifeless for partner w
sat.pnrq1_m: Enjoyable for partner m
sat.pnrq2_m: Pleasant for partner m
sat.pnrq3_m: Strong for partner m
sat.pnrq4_m: Alive for partner m
dsat.pnrq1_m: Miserable for partner m
dsat.pnrq2_m: Bad for partner m
dsat.pnrq3_m: Empty for partner m
dsat.pnrq4_m: Lifeless for partner m

References

Prine, M., Sakaluk, J. K., Camanto, O. J., & Quinn-Nilas, C. (Under Review).

Ratings on items from the Perceived Relationship Quality Components (PRQC) Inventory from 118 (Q)ueer couples

Description

A data set containing ratings on items (3 each) assessing romantic: (1) satisfaction, (2) commitment, (3) intimacy, (4) trust, (5) passion, and (6) love for each member of a dyad in which one or more members identify as LGBTQ+. Perceived Relationship Quality Components (PRQC) Inventory (Fletcher, Simpson, & Thomas, 2000). Data are from Sakaluk, Fisher, and Kilshaw (2021). Variable names follow a stem-partner-item ("spi") order, with a delimiter (".") between the stem and distinguishing partner character, and another delimiter ("_") between the distinguishing partner character and item number.

Usage

data(prqcQ)

Format

A data frame with 118 rows and 36 variables:

prqc.1_1: Satisfaction item 1 for partner 1
prqc.1_2: Satisfaction item 2 for partner 1
prqc.1_3: Satisfaction item 3 for partner 1
prqc.1_4: Commitment item 1 for partner 1
prqc.1_5: Commitment item 2 for partner 1
prqc.1_6: Commitment item 3 for partner 1
prqc.1_7: Intimacy item 1 for partner 1
prqc.1_8: Intimacy item 2 for partner 1
prqc.1_9: Intimacy item 3 for partner 1
prqc.1_10: Trust item 1 for partner 1
prqc.1_11: Trust item 2 for partner 1
prqc.1_12: Trust item 3 for partner 1
prqc.1_13: Passion item 1 for partner 1
prqc.1_14: Passion item 2 for partner 1
prqc.1_15: Passion item 3 for partner 1
prqc.1_16: Love item 1 for partner 1
prqc.1_17: Love item 2 for partner 1
prqc.1_18: Love item 3 for partner 1
prqc.2_1: Satisfaction item 1 for partner 2
prqc.2_2: Satisfaction item 2 for partner 2
prqc.2_3: Satisfaction item 3 for partner 2
prqc.2_4: Commitment item 1 for partner 2
prqc.2_5: Commitment item 2 for partner 2
prqc.2_6: Commitment item 3 for partner 2
prqc.2_7: Intimacy item 1 for partner 2
prqc.2_8: Intimacy item 2 for partner 2
prqc.2_9: Intimacy item 3 for partner 2
prqc.2_10: Trust item 1 for partner 2
prqc.2_11: Trust item 2 for partner 2
prqc.2_12: Trust item 3 for partner 2
prqc.2_13: Passion item 1 for partner 2
prqc.2_14: Passion item 2 for partner 2
prqc.2_15: Passion item 3 for partner 2
prqc.2_16: Love item 1 for partner 2
prqc.2_17: Love item 2 for partner 2
prqc.2_18: Love item 3 for partner 2

References

A Variable Name-Scraping and Indexing Function for cross-sectional data

Description

This function scrapes the names of indicator variables in a wide-format data set used for dyadic analyses of two latent variables (LV; X and Y), and indexes which indicators correspond to which partner, for which LV. It is used primarily to guide the syntax-writing of the other dySEM functions.

Usage

scrapeVarCross(
  dat,
  x_order = "spi",
  x_stem,
  x_delim1 = NULL,
  x_delim2 = NULL,
  x_item_num = "\\d+",
  distinguish_1 = "1",
  distinguish_2 = "2",
  y_order = NULL,
  y_stem = NULL,
  y_delim1 = NULL,
  y_delim2 = NULL,
  y_item_num = "\\d+",
  var_list = NULL,
  var_list_order = NULL,
  var_list_item_num = "\\d+",
  covs_order = NULL,
  covs_stem = NULL,
  covs_delim1 = NULL,
  covs_delim2 = NULL,
  verbose = TRUE
)

Arguments

dat

input data frame of indicators of a particular LV

x_order

input character for order of (S)tem, (P)artner number, and (I)tem number when creating variable names. Defaults to "spi" (Qualtrics-friendly), but can alternatively take "sip" or "psi"

x_stem

input character stem of indicator variables for LV X

x_delim1

optional character to separate stem from partner number (spi) or item number (sip)

x_delim2

optional character to separate stem/partner number (spi) or stem/item number (sip) from from final element of variable name

x_item_num

defaults to scrape all items that match the stem with any digits that follow. Will be updated to allow particular range of values, to make more sub-scale friendly.

distinguish_1

input character used as the identifier for the first partner

distinguish_2

input character used as the identifier for the first partner

y_order

optional character for order of (S)tem, (P)artner number, and (I)tem number when creating variable names. Defaults to "spi" (Qualtrics-friendly), but can alternatively take "sip" or "psi". This and other Y-arguments only necessary if there is a latent Y variable to model

y_stem

optional input character stem of indicator variables for LV X

y_delim1

optional character to separate stem from partner number (spi) or item number (sip)

y_delim2

optional character to separate stem/partner number (spi) or stem/item number (sip) from from final element of variable name

y_item_num

defaults to scrape all items that match the stem with any digits that follow. Will be updated to allow particular range of values, to make more sub-scale friendly.

var_list

optional named list of indicator variable information, if more than one LV is to be scripted (e.g., a dyadic CFA with multiple sub-scales from the same measure). If supplied, this list must contain the following elements: "stem" (a vector of stems), "delim1" (a vector of delimiting characters), and"delim2" (a vector of subsequently delimiting characters). Optionally may include numeric vectors "min_num" and "max_num" if indicators for different LVs share the same stem and must be separated by range of item numbers within a measure.

var_list_order

optional character for order of (S)tem, (P)artner number, and (I)tem number for any of the indicator variables of a multi-LV model (i.e., this functionality assumes the same ordering of elements throughout)

var_list_item_num

optional character for item number of any of the indicator variables of a multi-LV model

covs_order

optional character for order of (S)tem, (P)artner number, and (I)tem number for any covariate(s). Defaults to NULL. This and other covariate arguments only necessary if there are covariates to be scripted in your model(s).

covs_stem

optional input character stem(s) of indicator variables for covariate(s). Can accept a single stem (e.g., "anx"), or a vector of stems (e.g., c("anx", "dep")). Defaults to NULL.

covs_delim1

optional character to separate stem from partner number (spi) or item number (sip) for covariate(s). Defaults to NULL.

covs_delim2

optional character to separate stem/partner number (spi) or stem/item number (sip) from

verbose

logical indicating whether to print a summary of scraped variables to the console. Defaults to TRUE.

Value

a list, referred in short-hand as a "dvn" (dyad variable names list) containing variable names for p1, p2, # of items per LV, characters distinguishing partners, and total number of indicators

Examples

dvnx <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2"
)
dvnxy <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2",
  y_order = "spi", y_stem = "com", y_delim1 = ".", y_delim2 = "_"
)

A Function That Writes, Saves, and Exports Syntax for Fitting Latent Actor-Partner Interdependence Models (APIMs)

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan() syntax for specifying Actor-Partner Interdependence Models (APIMs). Users can also invoke configural, loading, and/or intercept invariant measurement models, and particular types of structural comparisons.

Usage

scriptAPIM(
  dvn,
  scaleset = "FF",
  lvxname,
  lvyname,
  constr_dy_x_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_x_struct = c("variances", "means"),
  constr_dy_y_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_y_struct = c("variances", "means"),
  constr_dy_xy_struct = c("actors", "partners"),
  includeMeanStruct = FALSE,
  model = lifecycle::deprecated(),
  equate = lifecycle::deprecated(),
  est_k = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross

scaleset

input character to specify how to set the scale of the latent variable(s). Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable)

lvxname

input character to (arbitrarily) name LV X in lavaan syntax

lvyname

input character to (arbitrarily) name LV Y in lavaan syntax

constr_dy_x_meas

input character vector detailing which measurement model parameters to constrain across dyad members for latent X. Default is c("loadings", "intercepts", "residuals"), but user can specify any combination of "loadings", "intercepts", and "residuals", or "none" to specify an otherwise unconstrained dyadic configural invariance model

constr_dy_x_struct

input character vector detailing which structural model parameters to constrain across dyad members for latent X. Default is c("variances", "means"), but user can specify any combination of "variances" and "means", or "none".

constr_dy_y_meas

constr_dy_y_struct

constr_dy_xy_struct

input character vector detailing which structural model parameters to constrain for modeling the predictive association(s) between partners' latent x and y. Default is c("actors", "partners"), but users can also specify "all", "actors_zero", "partners_zero", or "none".

includeMeanStruct

input logical for whether the user wants to include the mean structure in the model. Defaults FALSE, to support subsequent calculation of dynamic fit indexes (see Details)

model

Deprecated input character used to specify which level of invariance is modeled. Users should rely upon constr_dy_x_meas/constr_dy_y_meas and constr_dy_x_struct/constr_dy_y_struct instead, for making constraints to the measurement and/or structural portions of the model for latent x and y.

equate

Deprecated input character to specify which type of structural parameters are constrained to equivalency between partners. Users should rely upon constr_dy_xy_struct for making constraints to the structural portion of the model for associative relationship between latent x and y.

est_k

input logical for whether Kenny & Ledermann's (2010) k parameter should be calculated to characterize the dyadic pattern in the APIM. Defaults FALSE, and requires at least a loading-invariant model to be specified, otherwise a warning is returned.

writeTo

A character string specifying a directory path to where a .txt file of the resulting lavaan script should be written. If set to “.”, the .txt file will be written to the current working directory. The default is NULL, and examples use a temporary directory created by tempdir().

fileName

A character string specifying a desired base name for the .txt output file. The default is NULL. The specified name will be automatically appended with the .txt file extension. If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

character object of lavaan script that can be passed immediately to lavaan functions. Users will receive message if structural comparisons are specified when the recommended level of invariance is not also specified. If user supplies dvn with containing X or Y variables, they are alerted to respecify the dvn object.

Examples

dvn <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2",
  y_order = "spi", y_stem = "com", y_delim1 = ".", y_delim2 = "_"
)
apim.script.indist <- scriptAPIM(dvn,
  lvxname = "Sat", lvyname = "Com", est_k = TRUE,
  writeTo = tempdir(),
  fileName = "latAPIM_indist"
)

A Function That Writes, Saves, and Exports Syntax for Fitting Bifactor Dyadic (BiDy) models

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan() syntax for specifying dyadic configural, loading, and intercept invariant BiDy CFA (BiDy-C) or SEM (BiDy-S) Model. Currently only uses fixed-factor scale-setting

Usage

scriptBiDy(
  dvn,
  type = "CFA",
  lvxname,
  lvyname,
  constr_dy_x_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_x_struct = c("variances", "means"),
  constr_dy_y_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_y_struct = c("variances", "means"),
  constr_dy_xy_struct = c("actors"),
  model = lifecycle::deprecated(),
  equate = lifecycle::deprecated(),
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross

type

input character to specify whether to script a BiDy-CFA ("CFA", default) or BiDy-SEM ("SEM") model

lvxname

input character to (arbitrarily) name LV X in lavaan syntax

lvyname

input character to (arbitrarily) name LV Y in lavaan syntax

constr_dy_x_meas

constr_dy_x_struct

constr_dy_y_meas

constr_dy_y_struct

constr_dy_xy_struct

input character vector detailing which structural model parameters to constrain for modeling the predictive association(s) between partners' latent x and y. Default is c("actors"), but users can also specify "dyadic_zero" or "none".

model

equate

writeTo

fileName

Value

character object of lavaan script that can be passed immediately to lavaan functions

Examples

dvn <- scrapeVarCross(DRES, x_order = "sip", x_stem = "sexsat",
x_delim2=".", distinguish_1="1", distinguish_2="2")

sexsat.bidyc.script <- scriptBiDy(dvn, lvxname = "SexSat", type = "CFA",
writeTo = tempdir(),
fileName = "BiDy_C")

dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2",
y_order="spi", y_stem="com", y_delim1 = ".", y_delim2="_")

comsat.bidys.config.script <- scriptBiDy(dvn, lvxname = "Sat",
lvyname = "Com", type = "SEM",
writeTo = tempdir(),
fileName = "BiDy_S")

A Function That Writes, Saves, and Exports Syntax for Fitting Bifactor Dyadic Models

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan syntax for specifying dyadic configural, loading, intercept, and residual invariant bifactor models.

Usage

scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "X",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = c("variances", "means"),
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross()

scaleset

Input character to specify how to set the scale of the latent variable. Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable)

lvname

Input character to (arbitrarily) name the latent variable in lavaan syntax

constr_dy_meas

Input character vector detailing which measurement model parameters to constrain across dyad members.

constr_dy_struct

Input character vector detailing which structural model parameters to constrain across dyad members. Default is c("variances", "means")(in combination with defaults for constr_dy_meas, an indistinguishable correlated dyadic factors model), but user can specify any combination of "variances" and "means", or "none".

writeTo

A character string specifying a directory path to where a .txt file of the resulting lavaan script should be written. If set to ⁠“.”⁠, the .txt file will be written to the current working directory. The default is NULL, and examples use a temporary directory created by tempdir().

fileName

A character string specifying a desired base name for the .txt output file. The default is NULL. The specified name will be automatically appended with the .txt file extension. If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Details

By default, many ⁠dySEM::⁠ functions (including scriptBifac()) default to a fixed-factor method of scale-setting, whereby the latent variance of a given factor is constrained to 1 for both partners in the configurally invariant model, and then one of these variances is freely estimated in subsequent models of the invariance testing sequence. We have selected this default for two reasons: (1) the selection of a marker-variable is usually arbitrary, yet can have a large influence on the estimation and testing of of structural parameters (see https://stats.stackexchange.com/questions/402133/in-cfa-does-it-matter-which-factor-loading-is-set-to-1/402732#402732); and (2) the selection of a non-invariant marker-variable can have disastrous down-stream consequences for the identification of non-invariant measurement parameters, following a the rejection of an omnibus invariance constraint set (see Lee, Preacher, & Little, 2011).

Value

Character object of lavaan script that can be passed immediately to lavaan functions.

Examples

dvn <- scrapeVarCross(
  dat = commitmentQ,
  x_order = "spi",
  x_stem = "sat.g",
  x_delim1 = ".",
  x_delim2 = "_",
  distinguish_1 = "1",
  distinguish_2 = "2"
)

sat.indist.script <- scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "Sat"
)

sat.lvars.script <- scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "loadings",
  constr_dy_struct = "variances"
)

sat.resids.script <- scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dBiFac_residual"
)

sat.ints.script <- scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dBiFac_intercept"
)

sat.loads.script <- scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dBiFac_loading"
)

sat.config.script <- scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "none",
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dBiFac_configural"
)

sat.source.script <- scriptBifac(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "loadings_source",
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dBiFac_source"
)

A Function That Writes, Saves, and Exports Syntax for Fitting Latent Dyadic Confirmatory Factor Analysis (CFA) Models with Multiple Factors

Description

This function takes the outputted object from scrapeVarCross() when the var_list argument has been used, and automatically writes, returns, and exports (.txt) lavaan() syntax for specifying dyadic configural, loading, and intercept invariant measurement models for either a group of latent variables (e.g., different sub-scales from a self-report measures).

Usage

scriptCFA(
  dvn,
  scaleset = "FF",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = c("variances", "means"),
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross(); scrapeVarCross must have been run with the var_list argument

scaleset

input character to specify how to set the scale of the latent variable(s). Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable)

constr_dy_meas

input character vector detailing which measurement model parameters to constrain across dyad members. Default is c("loadings", "intercepts", "residuals")(in combination with defaults for constr_dy_struct, an indistinguishable dyadic CFA), but user can specify any combination of "loadings", "intercepts", and "residuals", or "none" to specify an otherwise unconstrained dyadic configural invariance model

constr_dy_struct

input character vector detailing which structural model parameters to constrain across dyad members. Default is c("variances", "means")(in combination with defaults for constr_dy_meas, an indistinguishable dyadic CFA), but user can specify any combination of "variances" and "means", or "none".

writeTo

fileName

Details

By default, many dySEM:: functions (including scriptCFA() default to a fixed-factor method of scale-setting, whereby the latent variance of a given factor is constrained to 1 for both partners in the configurally invariant #model, and then one of these variances is freely estimated in subsequent #models of the invariance testing sequence. We have selected this default for two reasons: (1) the selection of a marker-variable is usually arbitrary, yet can have a large influence on the estimation and testing of of structural parameters (see https://stats.stackexchange.com/questions/402133/in-cfa-does-it-matter-which-factor-loading-is-set-to-1/402732#402732); and (2) the selection of a non-invariant marker-variable can have disastrous down-stream consequences for the identification of non-invariant measurement parameters, following a the rejection of an omnibus #invariance constraint set (see Lee, Preacher, & Little, 2011).

Value

character object of lavaan script that can be passed immediately to lavaan functions

Examples


# When different factor use distinct stems:
imsList <- list(
  lvnames = c("Sat", "Q_Alt", "Invest", "Comm"),
  stem = c("sat.g", "qalt.g", "invest.g", "com"),
  delim1 = c("", "", "", ""),
  delim2 = c("_", "_", "_", "_")
)

dvnIMS <- scrapeVarCross(imsM,
  var_list = imsList,
  var_list_order = "sip",
  distinguish_1 = "f",
  distinguish_2 = "m"
)

script.ims.config <- scriptCFA(dvnIMS,
  scaleset = "FF",
  constr_dy_meas = "none",
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "ims_config"
)

script.ims.load <- scriptCFA(dvnIMS,
  scaleset = "FF",
  constr_dy_meas = c("loadings"),
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "ims_load"
)

script.ims.int <- scriptCFA(dvnIMS,
  scaleset = "FF",
  constr_dy_meas = c("loadings", "intercepts"),
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "ims_int"
)

script.ims.res <- scriptCFA(dvnIMS,
  scaleset = "FF",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "ims_res"
)

script.ims.indist <- scriptCFA(dvnIMS,
  scaleset = "FF",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = c("variances", "means"), writeTo = tempdir(), fileName = "ims_indist"
)

# When different factor use the same stem and muse be detected through numeric position

prqcList <- list(
  lvnames = c("Sat", "Comm", "Intim", "Trust", "Pass", "Love"),
  stem = c("prqc", "prqc", "prqc", "prqc", "prqc", "prqc"),
  delim1 = c(".", ".", ".", ".", ".", "."),
  delim2 = c("_", "_", "_", "_", "_", "_"),
  min_num = c(1, 4, 7, 10, 13, 16),
  max_num = c(3, 6, 9, 12, 15, 18)
)

dvnPRQC <- scrapeVarCross(prqcQ,
  var_list = prqcList,
  var_list_order = "spi",
  distinguish_1 = "1",
  distinguish_2 = "2"
)

script.prqc.config <- scriptCFA(dvnPRQC,
  scaleset = "FF",
  constr_dy_meas = "none",
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "prqc_config"
)

script.prqc.load <- scriptCFA(dvnPRQC,
  scaleset = "FF",
  constr_dy_meas = c("loadings"),
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "prqc_load"
)

script.prqc.int <- scriptCFA(dvnPRQC,
  scaleset = "FF",
  constr_dy_meas = c("loadings", "intercepts"),
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "prqc_int"
)

script.prqc.res <- scriptCFA(dvnPRQC,
  scaleset = "FF",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none", writeTo = tempdir(), fileName = "prqc_res"
)

script.prqc.indist <- scriptCFA(dvnPRQC,
  scaleset = "FF",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = c("variances", "means"), writeTo = tempdir(), fileName = "prqc_indist"
)

A Function That Writes, Saves, and Exports Syntax for Fitting Latent Common Fate Models (CFMs)

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan() syntax for specifying Common Fate Models (CFMs). Users can also invoke configural, loading, and/or intercept invariant measurement models, and particular types of structural comparisons.

Usage

scriptCFM(
  dvn,
  scaleset = "FF",
  lvxname,
  lvyname,
  constr_dy_x_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_x_struct = c("variances", "means"),
  constr_dy_y_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_y_struct = c("variances", "means"),
  constr_dy_xy_struct = "none",
  model = lifecycle::deprecated(),
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross

scaleset

input character to specify how to set the scale of the latent variable(s). Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable)

lvxname

input character to (arbitrarily) name LV X in lavaan syntax

lvyname

input character to (arbitrarily) name LV Y in lavaan syntax

constr_dy_x_meas

constr_dy_x_struct

constr_dy_y_meas

constr_dy_y_struct

constr_dy_xy_struct

input character vector detailing which structural model parameters to constrain for modeling the predictive association(s) between partners' latent x and y. Defaults to "none". Options include "p1_zero" or "p2_zero" (to constrain within-person latent residual covariances between X and Y to zero), or "covar_zero" (to constrain both within-person latent residual correlations to zero), and/or "dyadic_zero" (to constrain the dyadic effect to zero).

model

writeTo

fileName

Value

Examples

dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2",
y_order="spi", y_stem="com", y_delim1 = ".", y_delim2="_")
cfm.script.indist <-  scriptCFM(dvn, lvxname = "Sat", lvyname = "Com",
writeTo = tempdir(),
fileName = "CFM_indist")

A Function That Writes, Saves, and Exports Syntax for Fitting Correlated Dyadic Factor Models

Description

Usage

scriptCor(
  dvn,
  scaleset = "FF",
  lvname = "X",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = c("variances", "means"),
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross()

scaleset

Input character to specify how to set the scale of the latent variable. Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable)

lvname

Input character to (arbitrarily) name the latent variable in lavaan syntax

constr_dy_meas

Input character vector detailing which measurement model parameters to constrain across dyad members.

constr_dy_struct

writeTo

fileName

Details

By default, many ⁠dySEM::⁠ functions (including scriptCor()) default to a fixed-factor method of scale-setting, whereby the latent variance of a given factor is constrained to 1 for both partners in the configurally invariant model, and then one of these variances is freely estimated in subsequent models of the invariance testing sequence. We have selected this default for two reasons: (1) the selection of a marker-variable is usually arbitrary, yet can have a large influence on the estimation and testing of of structural parameters (see https://stats.stackexchange.com/questions/402133/in-cfa-does-it-matter-which-factor-loading-is-set-to-1/402732#402732); and (2) the selection of a non-invariant marker-variable can have disastrous down-stream consequences for the identification of non-invariant measurement parameters, following a the rejection of an omnibus invariance constraint set (see Lee, Preacher, & Little, 2011).

Value

Character object of lavaan script that can be passed immediately to lavaan functions.

Examples

dvn <- scrapeVarCross(
  dat = commitmentQ,
  x_order = "spi",
  x_stem = "sat.g",
  x_delim1 = ".",
  x_delim2 = "_",
  distinguish_1 = "1",
  distinguish_2 = "2"
)

sat.indist.script <- scriptCor(
  dvn,
  scaleset = "FF",
  lvname = "Sat"
)

sat.lvars.script <- scriptCor(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "loadings",
  constr_dy_struct = "variances"
)

sat.resids.script <- scriptCor(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dCor_residual"
)

sat.ints.script <- scriptCor(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dCor_intercept"
)

sat.loads.script <- scriptCor(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dCor_loading"
)

sat.config.script <- scriptCor(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "none",
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dCor_configural"
)

A Function That Writes, Saves, and Exports Syntax for Fitting Dyadic Exploratory Factor Analysis (DEFA) Models

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan() syntax for specifying a dyadic EFA model of a given number of exploratory factors.

Usage

scriptDyEFA(
  dvn,
  nFactor = 1,
  constr_dy_meas = "none",
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross

nFactor

numeric argument for number of exploratory factors to extract. Defaults to 1. Note that higher values may cause estimation problems as solution becomes over-factored and/or in the presence of insufficient data.

constr_dy_meas

input character vector detailing which measurement model parameters to constrain across dyad members. Default is "none" but user can specify "loadings" and/or "residuals", to fit an exploratory model with loadings and/or residuals constrained across partners

writeTo

fileName

Value

character object of lavaan script that can be passed immediately to lavaan functions

Examples

dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")

sat.defa1.script <- scriptDyEFA(dvn, nFactor = 1,
writeTo = tempdir(), fileName = "DEFA_1fac")

A Function That Writes, Saves, and Exports Syntax for Fitting Hierarchical Dyadic Factor Models

Description

Usage

scriptHier(
  dvn,
  scaleset = "FF",
  lvname = "X",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = c("variances", "means"),
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross()

scaleset

Input character to specify how to set the scale of the latent variable. Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable)

lvname

Input character to (arbitrarily) name the latent variable in lavaan syntax

constr_dy_meas

Input character vector detailing which measurement model parameters to constrain across dyad members.

constr_dy_struct

writeTo

fileName

Details

By default, many ⁠dySEM::⁠ functions (including scriptHier()) default to a fixed-factor method of scale-setting, whereby the latent variance of a given factor is constrained to 1 for both partners in the configurally invariant model, and then one of these variances is freely estimated in subsequent models of the invariance testing sequence. We have selected this default for two reasons: (1) the selection of a marker-variable is usually arbitrary, yet can have a large influence on the estimation and testing of of structural parameters (see https://stats.stackexchange.com/questions/402133/in-cfa-does-it-matter-which-factor-loading-is-set-to-1/402732#402732); and (2) the selection of a non-invariant marker-variable can have disastrous down-stream consequences for the identification of non-invariant measurement parameters, following a the rejection of an omnibus invariance constraint set (see Lee, Preacher, & Little, 2011).

Value

Character object of lavaan script that can be passed immediately to lavaan functions.

Examples

dvn <- scrapeVarCross(
  dat = commitmentQ,
  x_order = "spi",
  x_stem = "sat.g",
  x_delim1 = ".",
  x_delim2="_",
  distinguish_1="1",
  distinguish_2="2"
  )

sat.indist.script <- scriptHier(
  dvn,
  scaleset = "FF",
  lvname = "Sat"
  )

sat.lvars.script <- scriptHier(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "loadings",
  constr_dy_struct = "variances"
  )

sat.resids.script <- scriptHier(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dHier_residual"
  )

sat.ints.script <- scriptHier(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dHier_intercept"
  )

sat.loads.script <- scriptHier(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dHier_loading"
  )

sat.config.script <- scriptHier(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "none",
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dHier_configural"
  )

A Function That Writes, Saves, and Exports Syntax for Fitting the I-NULL model for indistinguishable dyads

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan() syntax for the I-NULL model described in Olsen & Kenny (2006)

Usage

scriptINULL(
  dvn,
  lvxname = "X",
  lvyname = NULL,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross

lvxname

input character to (arbitrarily) name X LV in lavaan syntax

lvyname

(optional) input character to (arbitrarily) name Y LV in lavaan syntax

writeTo

fileName

Value

character object of lavaan script that can be passed immediately to lavaan functions

Examples

dvn <- scrapeVarCross(dat = DRES, x_order = "sip", x_stem = "PRQC", x_delim1 = "_",
x_delim2=".", x_item_num="\\d+", distinguish_1="1", distinguish_2="2")
qual.inull.script <- scriptINULL(dvn, lvxname = "Qual",
writeTo = tempdir(),
fileName = "I-NULL_script")

A Function That Writes, Saves, and Exports Syntax for Fitting the I-SAT model for indistinguishable dyads

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan() syntax for the I-SAT model described in Olsen & Kenny (2006)

Usage

scriptISAT(dvn, lvxname = "X", lvyname = NULL, writeTo = NULL, fileName = NULL)

Arguments

dvn

input dvn list from scrapeVarCross

lvxname

input character to (arbitrarily) name X LV in lavaan syntax

lvyname

(optional) input character to (arbitrarily) name X LV in lavaan syntax

writeTo

fileName

Value

character object of lavaan script that can be passed immediately to lavaan functions

Examples

dvn <- scrapeVarCross(dat = DRES, x_order = "sip", x_stem = "PRQC", x_delim1 = "_",
x_delim2=".", x_item_num="\\d+", distinguish_1="1", distinguish_2="2")

qual.isat.script <- scriptISAT(dvn, lvxname = "Qual",
writeTo = tempdir(),
fileName = "I-SAT_script")

A Function That Writes, Saves, and Exports Syntax for Fitting Latent Mutual influence Model

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan() syntax for specifying Mutual Influence Models (MIMs). Users can also invoke configural, loading, and/or intercept invariant measurement models, and particular types of structural comparisons.

Usage

scriptMIM(
  dvn,
  scaleset = "FF",
  lvxname,
  lvyname,
  constr_dy_x_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_x_struct = c("variances", "means"),
  constr_dy_y_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_y_struct = c("variances", "means"),
  constr_dy_xy_struct = c("actors", "partners"),
  model = lifecycle::deprecated(),
  equate = lifecycle::deprecated(),
  est_k = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

input dvn list from scrapeVarCross

scaleset

input character to specify how to set the scale of the latent variable(s). Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable)

lvxname

input character to (arbitrarily) name LV X in lavaan syntax

lvyname

input character to (arbitrarily) name LV Y in lavaan syntax

constr_dy_x_meas

constr_dy_x_struct

constr_dy_y_meas

constr_dy_y_struct

constr_dy_xy_struct

model

equate

est_k

writeTo

fileName

Value

Examples

dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2",
y_order="spi", y_stem="com", y_delim1 = ".", y_delim2="_")

mim.script.indist <- scriptMIM(dvn, lvxname = "Sat", lvyname = "Com", est_k = TRUE,
writeTo = tempdir(),
fileName = "MIM_indist")

A Function That Writes, Saves, and Exports Syntax for Fitting Observed Actor-Partner Interdependence Models

Description

A Function That Writes, Saves, and Exports Syntax for Fitting Observed Actor-Partner Interdependence Models

Usage

scriptObsAPIM(
  X1 = NULL,
  Y1 = NULL,
  X2 = NULL,
  Y2 = NULL,
  equate = "none",
  k = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

X1

character of vector name containing X variable/composite for partner 1

Y1

character of vector name containing Y variable/composite for partner 1

X2

character of vector name containing X variable/composite for partner 2

Y2

character of vector name containing Y variable/composite for partner 2

equate

character of what parameter(s) to constrain ("actor", "partner", "all"); default is "none" (all freely estimated)

k

input logical for whether Kenny & Ledermann's (2010) k parameter should be calculated to characterize the dyadic pattern in the APIM. Default to FALSE

writeTo

fileName

Value

character object of lavaan script that can be passed immediately to lavaan functions.

Examples


obsAPIMScript <- scriptObsAPIM (X1 = "SexSatA", Y1 = "RelSatA",
X2 = "SexSatB", Y2 = "RelSatB",
equate = "none",
writeTo = tempdir(),
fileName = "obsAPIM_script")

A Function That Writes, Saves, and Exports Syntax for Fitting Unidimensional Dyadic Factor Models

Description

This function takes the outputted object from scrapeVarCross() and automatically writes, returns, and exports (.txt) lavaan syntax for specifying dyadic configural, loading, intercept, and residual invariant one-factor models.

Usage

scriptUni(
  dvn,
  scaleset = "FF",
  lvname = "X",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none",
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross().

scaleset

Input character to specify how to set the scale of the latent variable. Default is "FF" (fixed-factor; see Details for rationale), but user can specify "MV" (Marker Variable).

lvname

Input character to (arbitrarily) name the latent variable in lavaan syntax.

constr_dy_meas

Input character vector detailing which measurement model parameters to constrain across dyad members.

constr_dy_struct

Input character vector detailing which structural model parameters to constrain across dyad members. Note: Within the context of scriptUni(), constr_dy_struct is irrelevant, as the unidimensional dyadic factor model assumes a single latent variable shared by both partners, leaving no structural parameters to constrain across the modeled dyad members. For consistency with other scripter functions, constr_dy_struct is included as an argument, but defaults to "none".

writeTo

fileName

Details

Users do not need to modify constr_dy_struct when using scriptUni().
By default, many ⁠dySEM::⁠ functions (including scriptUni()) default to a fixed-factor method of scale-setting, whereby the latent variance of a given factor is constrained to 1 for both partners in the configurally invariant model, and then one of these variances is freely estimated in subsequent models of the invariance testing sequence. We have selected this default for two reasons: (1) the selection of a marker-variable is usually arbitrary, yet can have a large influence on the estimation and testing of of structural parameters (see https://stats.stackexchange.com/questions/402133/in-cfa-does-it-matter-which-factor-loading-is-set-to-1/402732#402732); and (2) the selection of a non-invariant marker-variable can have disastrous down-stream consequences for the identification of non-invariant measurement parameters, following a the rejection of an omnibus invariance constraint set (see Lee, Preacher, & Little, 2011).

Value

Character object of lavaan script that can be passed immediately to lavaan functions.

Examples


dvn <- scrapeVarCross(
  commitmentQ,
  x_order = "spi",
  x_stem = "sat.g",
  x_delim1 = ".",
  x_delim2 = "_",
  distinguish_1 = "1",
  distinguish_2 = "2"
)

sat.resids.script <- scriptUni(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dUni_residual"
)

sat.ints.script <- scriptUni(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings", "intercepts"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dUni_intercept"
)

sat.loads.script <- scriptUni(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = c("loadings"),
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dUni_loading"
)

sat.config.script <- scriptUni(
  dvn,
  scaleset = "FF",
  lvname = "Sat",
  constr_dy_meas = "none",
  constr_dy_struct = "none",
  writeTo = tempdir(),
  fileName = "dUni_configural"
)

Pipe operator

Description

Usage

Value

Relationship quality and sexual satisfaction of 121 couples

Description

Usage

Format

References

Ratings of relational satisfaction and commitment from 282 (M)ixed-sex couples

Description

Usage

Format

References

Ratings of relational satisfaction and commitment from 282 (Q)ueer couples

Description

Usage

Format

References

A function that performs a score test for relaxing each invariance equality constraint between partners in a given dyadic SEM model.

Description

Usage

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Value

Examples

A Function Calculates Omega Total Coefficients from a Dyadic CFA

Description

Usage

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Value

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Examples

Calculates dmacs difference in expected indicator scores for between dyad members

Description

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Value

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Examples

A Function that Computes Corrected Fit Indexes According to the ISAT and INULL Models of Olsen & Kenny (2006)

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Examples

Compare model fit of nested dyadic invariance models in order from most parsimonious (residual) to least parsimonious (configural)

Description

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Value

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Ratings on the full Investment Model Scale (IMS) from 282 (M)ixed-sex couples

Description

Usage

Format

References

Evaluate Invariance Equality Constraints in a specified Dyadic Invariance Model

Description

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Compare Model Fit of Nested Dyadic Invariance Models

Description

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A Function That Exports Tables and/or SEM Diagrams based on dySEM models

Description

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Value

Examples

A function That Exports SEM diagrams based on dySEM models

Description