# Plot function for MultOrdRS Plot function for a `MultOrdRS` object. Plots show coefficients of the explanatory variables, both with repect to location and response styles. The coefficient pairs are displayed as stars, where the rays represent (1-alpha) confidence intervals. ```r ## S3 method for class 'MultOrdRS' plot(x, alpha = 0.05, CIfactor = 0.9, xlab = expression(exp(gamma)), ylab = expression(exp(alpha)), xlim = range(c(1,betaX.KI)), ylim = range(c(1,betaXRS.KI)), ...) ``` ## Arguments - `x`: `MultOrdRS` object - `alpha`: Specifies the confidence level 1-alpha of the confidence interval. - `CIfactor`: Argument that helps to control the appearance (the width) of the stars that represent the confidence intervals of both parameters (location and response style) corresponding to one covariate. - `xlab`: Label for x-axis - `ylab`: Label for y-axis - `xlim`: Limits for x-axis - `ylim`: Limits for y-axis - `...`: Further plot arguments. ## Returns No return value, called for side effects ## Examples ```r 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) ## 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") ``` ## References Schauberger, Gunther and Tutz, Gerhard (2021): Multivariate Ordinal Random Effects Models Including Subject and Group Specific Response Style Effects, **Statistical Modelling**, tools:::Rd_expr_doi("10.1177/1471082X20978034") ## See Also `multordRS`, `ctrl.multordRS` ## Author(s) Gunther Schauberger gunther.schauberger@tum.de [https://orcid.org/0000-0002-0392-1580](https://orcid.org/0000-0002-0392-1580)

plot.MultOrdRS function