# Plot effect stars for vglm objects. Plots effect stars for `vglm`-objects. In particular, the method works for multinomial logit models created by family `multinomial` and for models with ordinal response like `sratio`, `cratio`, `cumulative` or `acat`. For more details on plotting effect stars see `effectstars`. ```r ## S3 method for class 'vglm' effectstars(x, p.values = FALSE, symmetric = TRUE, plot.parallel = FALSE, ...) ``` ## Arguments - `x`: A `vglm`-object. - `p.values`: Should the p-values of the single coefficients be included in the labels? Default is `FALSE`. - `symmetric`: Should the parameters be transformed to parameters with symmetric (sum-to-zero) side constraints instead of using reference levels. Default is `TRUE` for `multinomial`-models. If the `multinomial`-model contains object-specific covariates (`xij` argument from `vglm.control`) symmetric side constraints are not possible. In ordinal response models, no side constraints are needed and the option is obsolete. - `plot.parallel`: Should parallel parameters (equal over all response categories) be represented by effect stars. Default is `FALSE`. - `...`: further arguments for generic function `effectstars`. ## Examples ```r ## Not run: ############################################ ### Examples for multinomial logit model ############################################ ### German election data data(election) library(VGAM) m_elect <- vglm(Partychoice ~ Gender + West + Age + Union + Highschool + Unemployment + Pol.Interest + Democracy + Religion, family = multinomial(), data = election) effectstars(m_elect) # include p.values effectstars(m_elect, p.values = TRUE) ### German election data with category-specific covariates data(election) election[,13:16] <- election[,13:16] - election[,12] election[,18:21] <- election[,18:21] - election[,17] election[,23:26] <- election[,23:26] - election[,22] election[,28:31] <- election[,28:31] - election[,27] election$Social <- election$Social_SPD election$Immigration <- election$Immigration_SPD election$Nuclear <- election$Nuclear_SPD election$Left_Right <- election$Left_Right_SPD m.all <- vglm(Partychoice ~ Social + Immigration + Nuclear + Left_Right + Age + Religion + Democracy + Pol.Interest + Unemployment + Highschool + Union + West + Gender, data = election, family = multinomial(parallel = TRUE~-1 + Social + Immigration + Nuclear + Left_Right, refLevel = 1), xij = list(Social ~ Social_SPD + Social_FDP + Social_Greens + Social_Left, Immigration ~ Immigration_SPD + Immigration_FDP + Immigration_Greens + Immigration_Left, Nuclear ~ Nuclear_SPD + Nuclear_FDP + Nuclear_Greens + Nuclear_Left, Left_Right ~ Left_Right_SPD + Left_Right_FDP + Left_Right_Greens + Left_Right_Left), form2 = ~Social + Immigration + Nuclear + Left_Right + Age + Religion + Democracy + Pol.Interest + Unemployment + Highschool + Union + West + Gender + Social_SPD + Social_FDP + Social_Greens + Social_Left + Immigration_SPD + Immigration_FDP + Immigration_Greens + Immigration_Left + Nuclear_SPD + Nuclear_FDP + Nuclear_Greens + Nuclear_Left + Left_Right_SPD + Left_Right_FDP + Left_Right_Greens + Left_Right_Left ) effectstars(m.all, symmetric = FALSE, p.values = TRUE) summary(m.all) ### Chilean plebiscite data data(plebiscite) m_chile <- vglm(Vote ~ ., family = multinomial(), data = plebiscite) effectstars(m_chile) # choose fixed circle sizes and use reference category instead of symmetric side constraints effectstars(m_chile, symmetric = FALSE, fixed = TRUE) ############################################ ### Examples for ordinal data ############################################ ### Munich insolvency data data(insolvency) insolvency$Age <- scale(insolvency$Age) my_formula <- Insolvency ~ Age + Gender m_acat <- vglm(my_formula, data = insolvency,family = acat()) m_cratio <- vglm(my_formula, data = insolvency,family = cratio()) m_sratio <- vglm(my_formula, data = insolvency,family = sratio()) m_cumulative <- vglm(my_formula, data = insolvency,family = cumulative()) summary(m_acat) effectstars(m_acat, p.values = TRUE) summary(m_cratio) effectstars(m_cratio, p.values = TRUE) summary(m_sratio) effectstars(m_sratio, p.values = TRUE) summary(m_cumulative) effectstars(m_cumulative, p.values = TRUE) ## End(Not run) ``` ## References Tutz, G. and Schauberger, G. (2013): **Visualization of Categorical Response Models - from Data Glyphs to Parameter Glyphs**, Journal of Computational and Graphical Statistics 22(1), 156--177. Gerhard Tutz (2012): **Regression for Categorical Data**, Cambridge University Press ## See Also `effectstars` `effectstars.DIFlasso` ## Author(s) Gunther Schauberger gunther.schauberger@tum.de [https://www.sg.tum.de/epidemiologie/team/schauberger/](https://www.sg.tum.de/epidemiologie/team/schauberger/)

effectstars.vglm function