Least Squares Estimation of the Social Relations Model (Bond & Malloy, 2018)

Provides least squares estimation of the bivariate social relations model with missing completely at random data (Bond & Malloy, 2018a). The code is basically taken from Bond and Malloy (2018b) and rewritten for reasons of computation time reduction.

srm_arbsrm(data, serror = TRUE, use_srm = TRUE)

# S3 method for srm_arbsrm
coef(object, ...)
# S3 method for srm_arbsrm
summary(object, digits=3, file=NULL, ...)

Arguments

data	Rectangular dataset currently containing only one round robin group. Bivariate observations are stacked one below the other (see example dataset data.bm1).
serror	Logical indicating whether standard errors should be calculated.
use_srm	Logical indicating whether the rewritten code (TRUE) or the original code of Bond and Malloy (2018b) should be used.
object	Object of class srm_arbsrm
file	Optional file name for summary output
digits	Number of digits after decimal in summary output
...	Further arguments to be passed

Value

List containing entries

par_summary

Parameter summary table

est

Estimated parameters (as in Bond & Malloy, 2018b)

se

Estimated standard errors (as in Bond & Malloy, 2018b)

References

Bond, C. F., & Malloy, T. E. (2018a). Social relations analysis of dyadic data structure: The general case. In T. E. Malloy. Social relations modeling of behavior in dyads and groups (Ch. 14). Academic Press. doi: 10.1016/B978-0-12-811967-9.00014-X

Bond, C. F., & Malloy, T. E. (2018b). ARBSRM - The general social relations model. http://thomasemalloy.org/arbsrm-the-general-social-relations-model/.

Note

If you use this function, please also cite Bond and Malloy (2018a).

See also

Without missing data, ANOVA estimation can be conducted with the TripleR package.

Examples

#############################################################################
# EXAMPLE 1: Bond and Malloy (2018) illustration dataset
#############################################################################

data(data.bm2, package="srm")
dat <- data.bm2

#- estimation
mod1 <- srm::srm_arbsrm(dat)
mod1$par_summary
coef(mod1)
summary(mod1)

# \donttest{
#-- estimation with original Bond and Malloy code
mod1a <- srm::srm_arbsrm(dat, use_srm=FALSE)
summary(mod1a)
# }