srm_arbsrm {srm} | R Documentation |
Least Squares Estimation of the Social Relations Model (Bond & Malloy, 2018)
Description
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.
Usage
srm_arbsrm(data, serror = TRUE, use_srm = TRUE)
## S3 method for class 'srm_arbsrm'
coef(object, ...)
## S3 method for class '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 |
serror |
Logical indicating whether standard errors should be calculated. |
use_srm |
Logical indicating whether the rewritten code ( |
object |
Object of class |
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) |
Note
If you use this function, please also cite Bond and Malloy (2018a).
Author(s)
Rewritten code of Bond and Malloy (2018b). See http://thomasemalloy.org/arbsrm-the-general-social-relations-model/ and http://thomasemalloy.org/wp-content/uploads/2017/09/arbcodeR.pdf.
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/.
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)
#-- estimation with original Bond and Malloy code
mod1a <- srm::srm_arbsrm(dat, use_srm=FALSE)
summary(mod1a)