multordRS: Model Multivariate Ordinal Responses Including Response...
In MultOrdRS: Model Multivariate Ordinal Responses Including Response Styles

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/MultOrdRS.R

A model for multivariate ordinal responses. The response is modelled using a mixed model approach that is also capable of the inclusion of response style effects of the respondents.

multordRS(
  formula,
  data = NULL,
  control = ctrl.multordRS(),
  se = TRUE,
  model = c("acat", "cumulative")
)

`formula`	Formula containing the (multivariate) ordinal response on the left side and the explanatory variables on the right side.
`data`	Data frame containing the ordinal responses as well as the explanatory variables from the `formula`.
`control`	Control argument for `multord()` function. For details see `ctrl.multordRS`.
`se`	Should standard errors be calculated for the regression coefficients? Default is `TRUE`.
`model`	Specifies, which type of model is used, either the (multivariate) adjacent categories model (`model = "acat"`) or the (multivariate) cumulative model (`model = "cumulative"`).

`beta.thresh`	Matrix containing all threshold parameters for the respective model.
`beta.shift`	Vector containing all shift parameters. Only relevant if `model = "acat"` and `thresholds.acat = "shift"`.
`beta.X`	Vector containing parameter estimates for the location effects of the explanatory variables.
`beta.XRS`	Vector containing parameter estimates for the response style effects of the explanatory variables.
`Sigma`	Estimate of the variance (or covariance matrix) of the random effects. The estimate is a matrix if person-specific random response style effects are considered in the model (i.e. if `RS = TRUE`).
`Y`	Matrix containing the explanatory variables.
`X`	Data frame containing the multivariate ordinal response, one row per obeservation, one column per response variable.
`se.thresh`	Matrix containing all standard errors of the threshold parameters for the respective model.
`se.shift`	Vector containing all standard errors of the shift parameters. Only relevant if `model = "acat"` and `thresholds.acat = "shift"`.
`se.X`	Vector containing standard errors of the parameter estimates for the location effects of the explanatory variables.
`se.XRS`	Vector containing standard errors of the parameter estimates for the response style effects of the explanatory variables.
`coef.vec`	Complete vector of all parameter estimates (for internal use).
`se.vec`	Complete vector of all standard errors (for internal use).
`design.values`	Some values of the design matrix (for internal use).
`loglik`	(Marginal) Log Likelihood
`call`	Function call
`df`	Degrees of freedom
`control`	Control argument from function call

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, https://journals.sagepub.com/doi/10.1177/1471082X20978034

ctrl.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, cores = 2))
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) 
## 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")