| Type: | Package |
| Title: | Structural Equation Modeling for the Social Relations Model |
| Version: | 0.4-26 |
| Date: | 2022-11-03 10:20:31 |
| Author: | Steffen Nestler [aut], Alexander Robitzsch [aut, cre], Oliver Luedtke [aut] |
| Maintainer: | Alexander Robitzsch <robitzsch@ipn.uni-kiel.de> |
| Description: | Provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; <doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979, <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>; Nestler, 2018, <doi:10.3102/1076998617741106>) and least squares estimation is supported (Bond & Malloy, 2018, <doi:10.1016/B978-0-12-811967-9.00014-X>). |
| Depends: | R (≥ 3.1) |
| Imports: | Rcpp, stats, utils |
| Enhances: | amen, TripleR |
| LinkingTo: | Rcpp, RcppArmadillo |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/alexanderrobitzsch/srm,https://sites.google.com/site/alexanderrobitzsch2/software |
| NeedsCompilation: | yes |
| Packaged: | 2022-11-03 09:21:57 UTC; sunpn563 |
| Repository: | CRAN |
| Date/Publication: | 2022-11-03 10:00:02 UTC |
Structural Equation Modeling for the Social Relations Model
Description
Provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; <doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979, <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>; Nestler, 2018, <doi:10.3102/1076998617741106>) and least squares estimation is supported (Bond & Malloy, 2018, <doi:10.1016/B978-0-12-811967-9.00014-X>).
Author(s)
Steffen Nestler [aut], Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>
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
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 forsocial interaction data.Journal of Personality and Social Psychology, 37(10), 1742-1757. doi:10.1037/0022-3514.37.10.1742
See Also
See also theR packagesamen andTripleR for estimating thesocial relations model.
Hallmark and Kenny Round Robin Data
Description
Data from Kenny et al. (1994)
Usage
data(HallmarkKenny)Format
A data frame with 802 measurements of 30 round-robin groups on the following 7round-robin variables (taken on unnumbered 7-point rating scales with higher numbersindicating a higher value of the trait):
calm: rating of dimension calm-anxioussociable rating of dimension sociable-withdrawnliking rating of dimension like-do not likecareful rating of dimension careful-carelessrelaxed rating of dimension relaxed-tensetalkative rating of dimension talkative-quietresponsible rating of dimension responsible-undependable
The data frame also contains participants gender (actor.sex;1 = F,2 = M) and their age in years (actor.age).Note that the data was assessed in two conditions: odd round robin group numbers indicategroups in which participants rated all traits for a person at a time whereas even numbersrefer to groups in which participants rated all the people for each trait.
Source
http://davidakenny.net/srm/srmdata.htm
References
Kenny, D. A., Albright, L., Malloy, T. E., & Kashy, D. A. (1994). Consensus ininterpersonal perception: Acquaintance and the big five.Psychological Bulletin, 116(2), 245-258.doi:10.1037/0033-2909.116.2.245
Zero Acquaintance Round Robin Data from Kenny
Description
Data from Albright et al. (1988) Study 2
Usage
data(Kenzer)Format
A data frame with 124 measurements from 7 round-robin groups on the following 5 round-robinvariables (taken on unnumbered 7-point rating scales with higher numbers indicating ahigher value of the trait):
sociable: rating of dimension sociableirritable: rating of dimension good-naturedresponsible: rating of dimension responsibleanxious: rating of dimension calmintellectual: rating of dimension intellectual
The data frame also contains the gender (actor.sex;1 = F,2 = M) of the participants and their self-ratings on the five assessed traits(actor.sociable and so on).
Source
http://davidakenny.net/srm/srmdata.htm
References
Albright, L., Kenny, D. A., & Malloy, T. E. (1988). Consensus in personality judgmentsat zero acquaintance.Journal of Personality and Social Psychology, 55(3),387-395. doi:10.1037/0022-3514.55.3.387
Zero Acquaintance Round Robin Data from Malloy
Description
Data from Albright et al. (1988) Study 1
Usage
data(Malzer)Format
A data frame with 216 measurements from 12 round-robin groups on the following 5 round-robinvariables (assessed on numbered 7-point rating scales with higher numbers indicating ahigher value of the trait with the exception for good and calm):
sociable: rating of dimension sociableirritable: rating of dimension good-naturedresponsible: rating of dimension responsibleanxious: rating of dimension calmintellectual: rating of dimension intellectual
The data frame also contains the gender (actor.sex;1 = F,2 = M) of the participants and their self-ratings on the five assessed traits(actor.sociable and so on).
Source
http://davidakenny.net/srm/srmdata.htm
References
Albright, L., Kenny, D. A., & Malloy, T. E. (1988). Consensus in personality judgmentsat zero acquaintance.Journal of Personality and Social Psychology, 55(3),387-395. doi:10.1037/0022-3514.55.3.387
Round Robin Data Reported in Warner et al.
Description
Data from Warner et al. (1979)
Usage
data(Warner)Format
A data frame with 56 measurements of a single round-robin group on a single round-robinvariable that was measured at three consecutive time points. The variable reflects theproportion of time an actor spent when speaking to a partner.
prop.T1: proportion of time spent in the first interactionprop.T2: proportion of time spent in the second interactionprop.T3: proportion of time spent in the third interaction
Source
See Table 7 (p. 1752) of the Warner et al. (1979).
References
Warner, R. M., Kenny, D. A., & Soto, M. (1979). A new round robin analysis of variance forsocial interaction data.Journal of Personality and Social Psychology, 37(10),1742-1757. doi:10.1037/0022-3514.37.10.1742
Zero Acquaintance Round Robin Data From Albirght, Kenny, and Malloy
Description
Data from Study 3 of Albright et al. (1988)
Usage
data(Zero)Format
A data frame with 636 measurements of 36 round robin groups on the following 15 round-robinvariables (taken on 7-point rating scales with higher values indicating more of thetrait):
sociable: rating of dimension sociable-reclusivegood: rating of dimension good-natured-irritableresponsible: rating of dimension responsible-undependablecalm: rating of dimension calm-anxiousintellectual: rating of dimension intellectual-unintellectualimaginative: rating of dimension imaginative-unimaginativetalkative: rating of dimension talkative-silentfussy: rating of dimension fussy-carelesscomposed: rating of dimension composed-excitablecooperative: rating of dimension cooperative-negativisticphysically_attractive: rating of dimension physically attractive-unattractiveformal_dress: rating of dimension formal dress-casual dressneatly_dressed: rating of dimension neatly dressed-sloppy dressathletic: rating of dimension athletic-not athleticyoung: rating of dimension young-old
The data frame also contains the gender (actor.sex;1 = F,2 = M) of the participants and their self-ratings on the five assessed traits(actor.sociable and so on).
Source
http://davidakenny.net/srm/srmdata.htm
References
Albright, L., Kenny, D. A., & Malloy, T. E. (1988). Consensus in personality judgmentsat zero acquaintance.Journal of Personality and Social Psychology, 55(3),387-395. doi:10.1037/0022-3514.55.3.387
Dataset Back et al. (2011)
Description
Dataset used in Back, Schmukle and Egloff (2011).
Usage
data(data.back)Format
The dataset
data.backis a round-robin desiogn with 54 units andhas the following structure'data.frame': 2862 obs. of 8 variables:$ Group : num 1 1 1 1 1 1 1 1 1 1 ...$ Actor : int 1 1 1 1 1 1 1 1 1 1 ...$ Partner: int 2 3 4 5 6 7 8 9 10 11 ...$ Dyad : int 1 2 3 4 5 6 7 8 9 10 ...$ y : int 3 3 2 2 4 3 3 2 3 3 ...$ sex : int 1 1 1 1 1 1 1 1 1 1 ...$ age : int 22 22 22 22 22 22 22 22 22 22 ...$ n : num -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 ...
Source
References
Back, M. D., Schmukle, S. C., & Egloff, B. (2011). A closer look at first sight: Socialrelations lens model analysis of personality and interpersonal attraction at zeroacquaintance.European Journal of Personality, 25(3), 225-238.doi:10.1002/per.790
Dataset Bond and Malloy (2018)
Description
This is the illustration dataset of Bond and Malloy (2018) for a bivariatesocial relations model. The round robin design contains 16 persons andsome missing values for one person.
Usage
data(data.bm1)data(data.bm2)Format
The dataset
data.bm1contains all ratings in a wideformat. The two outcomes are arranged one below the other.'data.frame': 32 obs. of 16 variables:$ a: int NA 12 13 14 15 15 14 14 13 13 ...$ b: int 10 NA 10 18 7 15 14 8 12 12 ...$ c: int 13 12 NA 14 13 14 13 13 11 12 ...[...]$ p: int 11 13 14 14 9 8 17 13 11 12 ...The dataset
data.bm2is a subdataset ofdata.bm1which contains observations 9 to 16.
Source
http://thomasemalloy.org/arbsrm-the-general-social-relations-model/
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
Example Datasets for thesrm Package
Description
Some simulated example datasets for thesrm package.
Usage
data(data.srm01)Format
The dataset
data.srm01contains three variables, 10 round robingroups with 10 members each.'data.frame': 900 obs. of 7 variables:$ Group : num 1 1 1 1 1 1 1 1 1 1 ...$ dyad : num 1 2 3 4 5 6 7 8 9 10 ...$ Actor : num 1 1 1 1 1 1 1 1 1 2 ...$ Partner: num 2 3 4 5 6 7 8 9 10 3 ...$ Wert1 : num -0.15 -0.95 0.82 1.15 -1.79 1.17 1.79 -0.57 -0.46 1.19 ...$ Wert2 : num -0.77 0.17 0.42 0.16 -0.44 0.89 1.67 -1.9 -0.74 2.67 ...$ Wert3 : num -0.49 0.08 -0.12 1.16 -2.78 -0.74 2.66 -1.28 -0.45 1.93 ...
Structural Equation Model for the Social Relations Model
Description
Provides an estimation routine for a multiple group structural equation modelfor 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 tolavaan language, see Examples. |
data | Data frame containing round robin identifier variables and variables in theround 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: |
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 whetherRcpp package should be used |
shortcut | Logical indicating whether shortcuts for round robin groups withsame structure should be used |
use_woodbury | Logical indicating whether matrix inversion shouldbe simplified by Woodbury identity |
object | Object of class |
file | Optional file name for summary output |
digits | Number of digits after decimal in summary output |
layout | Different layouts ( |
... | 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 interactiondata.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 forsocial interaction data.Journal of Personality and Social Psychology, 37(10),1742-1757. doi:10.1037/0022-3514.37.10.1742
See Also
See alsoTripleR andamen packages for alternative estimationroutines for the SRM.
Examples
############################################################################## EXAMPLE 1: Univariate SRM#############################################################################data(data.srm01, package="srm")dat <- data.srm01#-- define modelmf <- '%PersonF1@A =~ 1*Wert1@AF1@P =~ 1*Wert1@PWert1@A ~~ 0*Wert1@A + 0*Wert1@PWert1@P ~~ 0*Wert1@P%DyadF1@AP =~ 1*Wert1@APF1@PA =~ 1*Wert1@PAWert1@AP ~~ 0*Wert1@AP + 0*Wert1@PAWert1@PA ~~ 0*Wert1@PA'#-- estimate modelmod1 <- 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 modelmf <- '%PersonF1@A =~ 1*Wert1@AF1@P =~ 1*Wert1@PF2@A =~ 1*Wert2@AF2@P =~ 1*Wert2@PWert1@A ~~ 0*Wert1@A + 0*Wert1@PWert1@P ~~ 0*Wert1@PWert2@A ~~ 0*Wert2@A + 0*Wert2@PWert2@P ~~ 0*Wert2@P%DyadF1@AP =~ 1*Wert1@APF1@PA =~ 1*Wert1@PAF2@AP =~ 1*Wert2@APF2@PA =~ 1*Wert2@PAWert1@AP ~~ 0*Wert1@AP + 0*Wert1@PAWert1@PA ~~ 0*Wert1@PAWert2@AP ~~ 0*Wert2@AP + 0*Wert2@PAWert2@PA ~~ 0*Wert2@PA'#-- estimate modelmod1 <- 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 modelmf <- '# definition of factor for persons and dyad%Personf1@A=~Wert1@A+Wert2@A+Wert3@Af1@P=~Wert1@P+Wert2@P+Wert3@P%Dyadf1@AP=~Wert1@AP+Wert2@AP+Wert3@AP# define some constraintsWert1@AP ~~ 0*Wert1@PAWert3@AP ~~ 0*Wert3@PA'#-- estimate modelmod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", conv_par=1e-4)summary(mod1)coef(mod1)#- use stats::nlminb() optimizermod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", optimizer="nlminb", conv_par=1e-4)summary(mod1)Least Squares Estimation of the Social Relations Model (Bond & Malloy, 2018)
Description
Provides least squares estimation of the bivariate social relations modelwith missing completely at random data (Bond & Malloy, 2018a). The code isbasically taken from Bond and Malloy (2018b) and rewritten forreasons 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 (seeexample 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). Seehttp://thomasemalloy.org/arbsrm-the-general-social-relations-model/ andhttp://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 theTripleR package.
Examples
############################################################################## EXAMPLE 1: Bond and Malloy (2018) illustration dataset#############################################################################data(data.bm2, package="srm")dat <- data.bm2#- estimationmod1 <- srm::srm_arbsrm(dat)mod1$par_summarycoef(mod1)summary(mod1)#-- estimation with original Bond and Malloy codemod1a <- srm::srm_arbsrm(dat, use_srm=FALSE)summary(mod1a)