ctrl.multordRS | R Documentation |
Control function for multordRS, a model for multivariate ordinal responses including response styles
ctrl.multordRS(
RS = TRUE,
thresholds.acat = c("full", "shift", "minimal"),
XforRS = TRUE,
opt.method = c("L-BFGS-B", "nlminb"),
Q = 10,
cores = 5,
lambda = 0.01
)
RS |
Logical value indicating whether response style should be modelled. |
thresholds.acat |
Type of parametrization used for thresholds: |
XforRS |
Logical value indicating whether also covariate effects on the
response style should be considered. Only relevant if |
opt.method |
Specifies optimization algorithm used by |
Q |
Number of nodes to be used (per dimension) in Gauss-Hermite-Quadrature. If |
cores |
Number of cores to be used in parallelized computation. |
lambda |
Tuning parameter for internal ridge penalty. It is supposed to be set to a small value to stabilize estimates. |
Returns list of control parameters used in multordRS
.
Gunther Schauberger
gunther.schauberger@tum.de
https://orcid.org/0000-0002-0392-1580
Schauberger, Gunther and Tutz, Gerhard (2021): Multivariate Ordinal Random Effects Models Including Subject and Group Specific Response Style Effects, Statistical Modelling, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1471082X20978034")}
multordRS
MultOrdRS-package
plot.MultOrdRS
data(tenseness)
## create a small subset of the data to speed up calculations
set.seed(1860)
tenseness <- tenseness[sample(1:nrow(tenseness), 300),]
## scale all metric variables to get comparable parameter estimates
tenseness$Age <- scale(tenseness$Age)
tenseness$Income <- scale(tenseness$Income)
## two formulas, one without and one with explanatory variables (gender and age)
f.tense0 <- as.formula(paste("cbind(",paste(names(tenseness)[1:4],collapse=","),") ~ 1"))
f.tense1 <- as.formula(paste("cbind(",paste(names(tenseness)[1:4],collapse=","),") ~ Gender + Age"))
####
## Adjacent Categories Models
####
## Multivariate adjacent categories model, without response style,
## without explanatory variables
m.tense0 <- multordRS(f.tense0, data = tenseness, control = ctrl.multordRS(RS = FALSE))
m.tense0
## Multivariate adjacent categories model, with response style as a random effect,
## without explanatory variables
m.tense1 <- multordRS(f.tense0, data = tenseness)
m.tense1
## Multivariate adjacent categories model, with response style as a random effect,
## without explanatory variables for response style BUT for location
m.tense2 <- multordRS(f.tense1, data = tenseness, control = ctrl.multordRS(XforRS = FALSE))
m.tense2
## Multivariate adjacent categories model, with response style as a random effect, with
## explanatory variables for location AND response style
m.tense3 <- multordRS(f.tense1, data = tenseness)
m.tense3
plot(m.tense3)
####
## Cumulative Models
####
## Multivariate cumulative model, without response style, without explanatory variables
m.tense0.cumul <- multordRS(f.tense0, data = tenseness,
control = ctrl.multordRS(RS = FALSE), model = "cumulative")
m.tense0.cumul
## Multivariate cumulative model, with response style as a random effect,
## without explanatory variables
m.tense1.cumul <- multordRS(f.tense0, data = tenseness, model = "cumulative")
m.tense1.cumul
## Multivariate cumulative model, with response style as a random effect,
## without explanatory variables for response style BUT for location
m.tense2.cumul <- multordRS(f.tense1, data = tenseness,
control = ctrl.multordRS(XforRS = FALSE), model = "cumulative")
m.tense2.cumul
## Multivariate cumulative model, with response style as a random effect, with
## explanatory variables for location AND response style
m.tense3.cumul <- multordRS(f.tense1, data = tenseness, model = "cumulative")
m.tense3.cumul
plot(m.tense3.cumul)
################################################################
## Examples from Schauberger and Tutz (2020) on
## Data from the German Longitudinal Election Study (GLES) 2017
#################################################################
####
## Source: German Longitudinal Election Study 2017
## Rossteutscher et al. 2017, https://doi.org/10.4232/1.12927
####
## load GLES data
data(GLES17)
## scale data
GLES17[,7:11] <- scale(GLES17[,7:11])
## define formula
f.GLES <- as.formula(cbind(RefugeeCrisis, ClimateChange, Terrorism,
Globalization, Turkey, NuclearEnergy) ~
Age + Gender + Unemployment + EastWest + Abitur)
## fit adjacent categories model without and with response style parameters
m.GLES0 <- multordRS(f.GLES, data = GLES17, control = ctrl.multordRS(RS = FALSE, cores = 6))
m.GLES <- multordRS(f.GLES, data = GLES17, control = ctrl.multordRS(cores = 6))
m.GLES0
m.GLES
plot(m.GLES, main = "Adjacent categories model")
## fit cumulative model without and with response style parameters (takes pretty long!!!)
m.GLES20 <- multordRS(f.GLES, data = GLES17, model="cumul",
control = ctrl.multordRS(opt.method = "nlminb", cores = 6, RS = FALSE))
m.GLES2 <- multordRS(f.GLES, data = GLES17, model="cumul",
control = ctrl.multordRS(opt.method = "nlminb", cores = 6))
m.GLES20
m.GLES2
plot(m.GLES2, main = "Cumulative model")
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