srm: Structural Equation Model for the Social Relations Model

In alexanderrobitzsch/srm: Structural Equation Modeling for the Social Relations Model

Structural Equation Model for the Social Relations Model

Description

Provides an estimation routine for a multiple group structural equation model for the social relations model (SRM; Kenny & La Voie, 1984; Warner, Kenny, & Soto, 1979). The model is estimated by maximum likelihood (Gill & Swartz, 2001; Nestler, 2018).

Usage

srm(model.syntax = NULL, data = NULL, group.var = NULL, rrgroup_name = NULL,
  person_names = c("Actor", "Partner"), fixed.groups = FALSE, var_positive = -1,
  optimizer = "srm", maxiter = 300, conv_dev = 1e-08, conv_par = 1e-06,
  do_line_search = TRUE, line_search_iter_max = 6, verbose = TRUE, use_rcpp = TRUE,
  shortcut = TRUE, use_woodbury = TRUE)

## S3 method for class 'srm'
coef(object, ...)
## S3 method for class 'srm'
vcov(object, ...)
## S3 method for class 'srm'
summary(object, digits=3, file=NULL, layout=1, ...)
## S3 method for class 'srm'
logLik(object, ...)

Arguments

model.syntax	Syntax similar to lavaan language, see Examples.
data	Data frame containing round robin identifier variables and variables in the round robin design
group.var	Name of grouping variable
rrgroup_name	Name of variable indicating round robin group
person_names	Names for identifier variables for actors and partners
fixed.groups	Logical indicating whether groups should be handled with fixed effects
var_positive	Nonnegative value if variances are constrained to be positive
optimizer	Optimizer to be used: "srm" for internal optimization using Fisher scoring and "nlminb" for L-FBGS optimization.
maxiter	Maximum number of iterations
conv_dev	Convergence criterion for change relative deviance
conv_par	Convergence criterion for change in parameters
do_line_search	Logical indicating whether line search should be performed
line_search_iter_max	Number of iterations during line search algorithm
verbose	Logical indicating whether convergence progress should be displayed
use_rcpp	Logical indicating whether Rcpp package should be used
shortcut	Logical indicating whether shortcuts for round robin groups with same structure should be used
use_woodbury	Logical indicating whether matrix inversion should be simplified by Woodbury identity
object	Object of class srm
file	Optional file name for summary output
digits	Number of digits after decimal in summary output
layout	Different layouts (1 or 2) for layout of summary
...	Further arguments to be passed

Value

List with following entries (selection)

parm.table	Parameter table with estimated values
coef	Vector of parameter estimates
vcov	Covariance matrix of parameter estimates
parm_list	List of model matrices
sigma	Model implied covariance matrices
...	Further values

References

Gill, P. S., & Swartz, T. B. (2001). Statistical analyses for round robin interaction data. Canadian Journal of Statistics, 29(2), 321-331. doi: 10.2307/3316080

Kenny, D. A., & La Voie, L. J. (1984). The social relations model. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 18, pp. 142-182). Orlando, FL: Academic. doi: 10.1016/S0065-2601(08)60144-6

Nestler, S. (2018). Likelihood estimation of the multivariate social relations model. Journal of Educational and Behavioral Statistics, 43(4), 387-406. doi: 10.3102/1076998617741106

Warner, R. M., Kenny, D. A., & Soto, M. (1979). A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology, 37(10), 1742-1757. doi: 10.1037/0022-3514.37.10.1742

See Also

See also TripleR and amen packages for alternative estimation routines for the SRM.

Examples

#############################################################################
# EXAMPLE 1: Univariate SRM
#############################################################################

data(data.srm01, package="srm")
dat <- data.srm01

#-- define model
mf <- '
%Person
F1@A =~ 1*Wert1@A
F1@P =~ 1*Wert1@P
Wert1@A ~~ 0*Wert1@A + 0*Wert1@P
Wert1@P ~~ 0*Wert1@P

%Dyad
F1@AP =~ 1*Wert1@AP
F1@PA =~ 1*Wert1@PA
Wert1@AP ~~ 0*Wert1@AP + 0*Wert1@PA
Wert1@PA ~~ 0*Wert1@PA
'

#-- estimate model
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", conv_par=1e-4, maxiter=20)
summary(mod1)
round(coef(mod1),3)


#############################################################################
# EXAMPLE 2: Bivariate SRM
#############################################################################

data(data.srm01, package="srm")
dat <- data.srm01

#-- define model
mf <- '
%Person
F1@A =~ 1*Wert1@A
F1@P =~ 1*Wert1@P
F2@A =~ 1*Wert2@A
F2@P =~ 1*Wert2@P
Wert1@A ~~ 0*Wert1@A + 0*Wert1@P
Wert1@P ~~ 0*Wert1@P
Wert2@A ~~ 0*Wert2@A + 0*Wert2@P
Wert2@P ~~ 0*Wert2@P

%Dyad
F1@AP =~ 1*Wert1@AP
F1@PA =~ 1*Wert1@PA
F2@AP =~ 1*Wert2@AP
F2@PA =~ 1*Wert2@PA
Wert1@AP ~~ 0*Wert1@AP + 0*Wert1@PA
Wert1@PA ~~ 0*Wert1@PA
Wert2@AP ~~ 0*Wert2@AP + 0*Wert2@PA
Wert2@PA ~~ 0*Wert2@PA
'

#-- estimate model
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", conv_par=1e-4, maxiter=20)
summary(mod1)

#############################################################################
# EXAMPLE 3: One-factor model
#############################################################################

data(data.srm01, package="srm")
dat <- data.srm01

#-- define model
mf <- '
# definition of factor for persons and dyad
%Person
f1@A=~Wert1@A+Wert2@A+Wert3@A
f1@P=~Wert1@P+Wert2@P+Wert3@P

%Dyad
f1@AP=~Wert1@AP+Wert2@AP+Wert3@AP

# define some constraints
Wert1@AP ~~ 0*Wert1@PA
Wert3@AP ~~ 0*Wert3@PA
'
#-- estimate model
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", conv_par=1e-4)
summary(mod1)
coef(mod1)

#- use stats::nlminb() optimizer
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", optimizer="nlminb", conv_par=1e-4)
summary(mod1)

alexanderrobitzsch/srm documentation built on Nov. 5, 2022, 4:47 p.m.