Visualization of Categorical Response Models

Description

The package EffectStars2 provides a more up-to-date implementation of effect stars!

The package provides functions that visualize categorical regression models.
Included models are the multinomial logit model, the sequential logit model and the cumulative logit model.
The exponentials of the effects of the predictors are plotted as star plots showing the strengths of the effects.
In addition p-values for the effect of predictors are given.
Various data sets and examples are provided.
The plots should in general be exported to file formats like pdf, ps or png to recieve the optimal display. Plotting in R devices may not provide the optimal results.

For further details see star.nominal, star.sequential and star.cumulative.

Author(s)

Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/

References

Tutz, G. and Schauberger, G. (2012): 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

star.nominal, star.sequential, star.cumulative

British Election Panel Study

Description

These data are drawn from the 1997-2001 British Election Panel Study (BEPS).

Usage

data(BEPS)

Format

A data frame with 1525 observations on the following 10 variables.

Europe

An 11-point scale that measures respondents' attitudes toward European integration. High scores represent eurosceptic sentiment

Leader_Cons

Assessment of the Conservative leader Hague, 1 to 5

Leader_Labour

Assessment of the Labour leader Blair, 1 to 5

Leader_Liberals

Assessment of the Liberals leader Kennedy, 1 to 5

Vote

Party Choice with levels Conservative, Labour and Liberal Democrat

Age

Age in years

Gender

Gender with levels female and male

Political_Knowledge

Knowledge of parties' positions on European integration, 0 to 3

National_Economy

Assessment of current national economic conditions, 1 to 5

Household

Assessment of current household economic conditions, 1 to 5

Source

R package carData: BEPS

References

British Election Panel Study (BEPS)
J. Fox and R. Andersen (2006): Effect displays for multinomial and proportional-odds logit models. Sociological Methodology 36, 225–255

Examples

## Not run: 
data(BEPS)

BEPS$Europe<-scale(BEPS$Europe)
BEPS$Age<-scale(BEPS$Age)
BEPS$Leader_Labour<-BEPS$Leader_Labour-BEPS$Leader_Cons
BEPS$Leader<-BEPS$Leader_Labour
BEPS$Leader_Liberals<-BEPS$Leader_Liberals-BEPS$Leader_Cons

star.nominal(Vote ~ Age + Household + National_Economy + Household + Leader + 
Europe + Political_Knowledge + Gender, data = BEPS, 
xij = list(Leader~Leader_Labour+Leader_Liberals), catstar = FALSE, symmetric = FALSE)

## End(Not run)

Party Identification

Description

Subset of the 1996 American National Election Study.

Usage

data(election)

Format

A data frame with 944 observations on the following 6 variables.

TVnews

Days in the past week spent watching news on TV

PID

Party identification with levels Democrat, Independent and Republican

Income

Income

Education

Educational level with levels low (no college) and high (at least college)

Age

Age in years

Population

Population of respondent's location in 1000s of people

Source

R package faraway: nes96

Examples

## Not run: 
data(PID)
PID$TVnews <- scale(PID$TVnews)
PID$Income <- scale(PID$Income)
PID$Age <- scale(PID$Age)
PID$Population <- scale(PID$Population)

star.nominal(PID ~ TVnews + Income + Population + Age + Education, data = PID)

## End(Not run)
 

Alligator Food

Description

The data describe the food choice of alligators, they originate from a study of the Florida Game and Fresh Water Commission.

Usage

data(alligator)

Format

A data frame with 219 observations on the following 4 variables.

Food

Food type with levels bird, fish, invert, other and rep

Size

Size of the alligator with levels <2.3 and >2.3

Gender

Gender with levels female and male

Lake

Name of the lake with levels George, Hancock, Oklawaha and Trafford

Source

http://www.stat.ufl.edu/~aa/cda/sas/sas.html

References

Agresti (2002): Categorical Data Analysis, Wiley.

Examples

## Not run: 
data(alligator)
star.nominal(Food ~ Size + Lake + Gender, data = alligator, nlines = 2)

## End(Not run)

Coffee Brands

Description

The data frame is part of a long-term panel about the choice of coffee brands in 2111 households. The explanatory variables either refer to the household as a whole or to the head of the household.

Usage

data(coffee)

Format

A data frame with 2111 observations on the following 8 variables.

Education

Educational level with levels no Highschool and Highschool

PriceSensitivity

Price sensitivity with levels not sensitive and sensitive

Income

Income with levels < 2499 and >= 2500

SocialLevel

Social level with levels high and low

Age

Age with levels < 49 and >= 50

Brand

Coffee Brand with levels Jacobs, JacobsSpecial, Aldi, AldiSpecial, Eduscho, EduschoSpecial, Tchibo, TchiboSpecial and Others

Amount

Amount of packs with levels 1 and >= 2

Persons

Number of persons in household

References

Gesellschaft für Konsumforschung (GfK)

Examples

## Not run: 
data(coffee)

star.nominal(Brand ~ Amount + Age + SocialLevel + Income + Persons +
                 PriceSensitivity + Education, coffee, cex.cat = 0.5, cex.labels = 0.8)

## End(Not run)

Election Data

Description

The data set contains data from the German Longitudinal Election Study. The Response Categories refer to the five dominant parties in Germany. The explanatory variables refer to the declarations of single voters.

Usage

data(election)

Format

A data frame with 816 observations on the following 30 variables.

Age

Standardized age of the voter

AgeOrig

Unstandardized age of the voter

Partychoice

Party Choice with levels CDU, SPD, FDP, Greens and Left Party

Gender

Gender with levels female and male

West

Regional provenance (West-Germany or East-Germany) with levels east and west

Union

Member of a Union with levels no member and member

Highschool

Educational level with levels no highschool and highschool

Unemployment

Unemployment with levels not unemployed and unemployed

Pol.Interest

Political Interest with levels very interested and less interested

Democracy

Satisfaction with the functioning of democracy with levels satisfied and not satisfied

Religion

Religion with levels evangelical, catholic and other religion

Social_CDU

Difference in attitude towards the socioeconomic dimension of politics between respondent and CDU

Social_SPD

Difference in attitude towards the socioeconomic dimension of politics between respondent and SPD

Social_FDP

Difference in attitude towards the socioeconomic dimension of politics between respondent and FDP

Social_Greens

Difference in attitude towards the socioeconomic dimension of politics between respondent and the Greens

Social_Left

Difference in attitude towards the socioeconomic dimension of politics between respondent and the Left party

Immigration_CDU

Difference in attitude towards immigration of foreigners between respondent and CDU

Immigration_SPD

Difference in attitude towards immigration of foreigners between respondent and SPD

Immigration_FDP

Difference in attitude towards immigration of foreigners between respondent and FDP

Immigration_Greens

Difference in attitude towards immigration of foreigners between respondent and the Greens

Immigration_Left

Difference in attitude towards immigration of foreigners between respondent and the Left party

Nuclear_CDU

Difference in attitude towards nuclear energy between respondent and CDU

Nuclear_SPD

Difference in attitude towards nuclear energy between respondent and SPD

Nuclear_FDP

Difference in attitude towards nuclear energy between respondent and FDP

Nuclear_Greens

Difference in attitude towards nuclear energy between respondent and the Greens

Nuclear_Left

Difference in attitude towards nuclear energy between respondent and the Left party

Left_Right_CDU

Difference in attitude towards the positioning on a political left-right scale between respondent and CDU

Left_Right_SPD

Difference in attitude towards the positioning on a political left-right scale between respondent and SPD

Left_Right_FDP

Difference in attitude towards the positioning on a political left-right scale between respondent and FDP

Left_Right_Greens

Difference in attitude towards the positioning on a political left-right scale between respondent and the Greens

Left_Right_Left

Difference in attitude towards the positioning on a political left-right scale between respondent and the Left party

References

German Longitudinal Election Study (GLES)

Examples

## Not run: 
data(election)

# simple multinomial logit model
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election)

# Use effect coding for the categorical predictor religion
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election,
                 pred.coding = "effect")                 

# Use reference category "FDP" instead of symmetric side constraints
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election,
                 refLevel = 3, symmetric = FALSE)
                 
# Use category-specific covariates, subtract values for reference 
# category CDU
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

star.nominal(Partychoice ~ Social + Immigration + Nuclear + Left_Right + Age + 
Religion + Democracy + Pol.Interest + Unemployment + Highschool + Union + West + 
Gender, data = election, 
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),
symmetric = FALSE)

## End(Not run)

Insolvency data

Description

The data set originates from the Munich founder study. The data were collected on business founders who registered their new companies at the local chambers of commerce in Munich and surrounding administrative districts. The focus was on survival of firms measured in 7 categories, the first six represent failure in intervals of six months, the last category represents survival time beyond 36 months.

Usage

data(insolvency)

Format

A data frame with 1224 observations on the following 16 variables.

Insolvency

Survival of firms in ordered categories with levels 1 < 2 < 3 < 4 < 5 < 6 < 7

Sector

Economic Sector with levels industry, commerce and service industry

Legal

Legal form with levels small trade, one man business, GmBH and GbR, KG, OHG

Location

Location with levels residential area and business area

New_Foundation

New Foundation or take-over with levels new foundation and take-over

Pecuniary_Reward

Pecuniary reward with levels main and additional

Seed_Capital

Seed capital with levels < 25000 and > 25000

Equity_Capital

Equity capital with levels no and yes

Debt_Capital

Debt capital with levels no and yes

Market

Market with levels local and national

Clientele

Clientele with levels wide spread and small

Degree

Educational level with levels no A-levels and A-Levels

Gender

Gender with levels female and male

Experience

Professional experience with levels < 10 years and > 10 years

Employees

Number of employees with levels 0 or 1 and > 2

Age

Age of the founder at formation of the company

Source

Münchner Gründer Studie

References

Brüderl, J. and Preisendörfer, P. and Ziegler, R. (1996): Der Erfolg neugegründeter Betriebe: eine empirische Studie zu den Chancen und Risiken von Unternehmensgründungen, Duncker & Humblot.

Examples

## Not run: 
data(insolvency)


star.sequential(Insolvency ~ Sector + Legal + Pecuniary_Reward + Seed_Capital
+ Debt_Capital + Employees, insolvency, test.glob = FALSE, globcircle = TRUE, dist.x = 1.3)

star.cumulative(Insolvency ~ Sector + Employees, insolvency, select = 2:4)

## End(Not run)

Chilean Plebiscite

Description

The data origin from a survey refering to the plebiscite in Chile 1988. The chilean people had to decide, wether Augusto Pinochet would remain president for another ten years (voting yes) or if there would be presidential elections in 1989 (voting no).

Usage

data(plebiscite)

Format

A data frame with 2431 observations on the following 7 variables.

Gender

Gender with levels female and male

Education

Educational level with levels low and high

SantiagoCity

Respondent from Santiago City with levels no and yes

Income

Monthly Income in Pesos

Population

Population size of respondent's community

Age

Age in years

Vote

Response with levels Abstention, No, Undecided and Yes

Source

R package carData: Chile

References

Personal communication from FLACSO/Chile.
Fox, J. (2008): Applied Regression Analysis and Generalized Linear Models, Second Edition.

Examples

## Not run: 
data(plebiscite)
plebiscite$Population <- scale(plebiscite$Population)
plebiscite$Age <- scale(plebiscite$Age)
plebiscite$Income <- scale(plebiscite$Income)

star.nominal(Vote ~ SantiagoCity + Population + Gender + Age + Education + 
Income, data = plebiscite)

## End(Not run)

Effect stars for cumulative logit models

Description

The package EffectStars2 provides a more up-to-date implementation of effect stars!

The function computes and visualizes cumulative logit models. The computation is done with help of the package VGAM. The visualization is based on the function stars from the package graphics.

Usage

star.cumulative(formula, data, global = NULL, test.rel = TRUE, test.glob = FALSE, 
    partial = FALSE, globcircle = FALSE, maxit = 100, scale = TRUE, 
    nlines = NULL, select = NULL, dist.x = 1, dist.y = 1, dist.cov = 1, 
    dist.cat = 1, xpd = TRUE, main = "", col.fill = "gray90", 
    col.circle = "black", lwd.circle = 1, lty.circle = "longdash", 
    col.global = "black", lwd.global = 1, lty.global = "dotdash", cex.labels = 1, 
    cex.cat = 0.8, xlim = NULL, ylim = NULL)

Arguments

Details

The underlying models are fitted with the function vglm from the package VGAM. The family argument for vglm is cumulative(parallel=FALSE).

The stars show the exponentials of the estimated coefficients. In cumulative logit models the exponential coefficients can be interpreted as odds. More precisely, the exponential e^{\gamma_{rj}}, r=1,\ldots,k-1 represents the multiplicative effect of the covariate j on the cumulative odds \frac{P(Y\leq r|x)}{P(Y>r|x)} if x_j increases by one unit.

In addition to the stars, we plot a cirlce that refers to the case where the coefficients of the corresponding star are zero. Therefore, the radii of these circles are always exp(0)=1. If scale=TRUE, the stars are scaled so that they all have the same maximal ray length. In this case, the actual appearances of the circles differ, but they still refer to the no-effects case where all the coefficients are zero. Now the circles can be used to compare different stars based on their respective circles radii. The p-values beneath the covariate labels, which are given out if test.rel=TRUE, correspond to the distance between the circle and the star as a whole. They refer to a likelihood ratio test if all the coefficients from one covariate are zero (i.e. the variable is left out completely) and thus would lie exactly upon the cirlce.
The form of the circles can be modified by col.circle, lwd.circle and lty.circle.

By setting globcircle=TRUE, an addictional circle can be drawn. The radii now correspond to a model, where the respective covariate is not included category-specific but globally. Therefore, the distance between this circle and the star as a whole corresponds to the p-value p-global that is given if test.glob=TRUE.

Please note:

Regular fitting of cumulative logit models may fail because of the restrictions in the parameter space that have to be considered. If partial=TRUE, (sub)models with only one category-specific covariate, so-called partial proportional odds models, are fitted. Then at least estimates for every coefficient should be available. If partial=TRUE, the resulting effects of these (sub)models are plotted. It should be noted that in this case no coherent model is visualized. Also the p-values refer to the various submodels. For partial=TRUE, the p-values p-rel and p-global refer to tests of the corresponding partial proportial odds models against the proportional odds model.

It is strongly recommended to standardize metric covariates, display of effect stars can benefit greatly as in general differences between the coefficients are increased.

Value

P-values are only available if the corresponding option is set TRUE.

Author(s)

Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/

References

Tutz, G. and Schauberger, G. (2012): 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

star.sequential, star.nominal

Examples

## Not run: 
data(insolvency)

star.cumulative(Insolvency ~ Sector + Employees, insolvency, select = 2:4)

## End(Not run)

Effect stars for multinomial logit models

Description

The package EffectStars2 provides a more up-to-date implementation of effect stars!

The function computes and visualizes multinomial logit models. The computation is done with help of the package VGAM. The visualization is based on the function stars from the package graphics.

Usage

star.nominal(formula, data, xij = NULL, conf.int = FALSE, symmetric = TRUE, 
    pred.coding = "reference", printpvalues = TRUE, test.rel = TRUE, refLevel = 1, 
    maxit = 100, scale = TRUE, nlines = NULL, select = NULL, catstar = TRUE, 
    dist.x = 1, dist.y = 1, dist.cov = 1, dist.cat = 1, xpd = TRUE, main = "", 
    lwd.stars = 1, col.fill = "gray90", col.circle = "black", lwd.circle = 1, 
    lty.circle = "longdash", lty.conf = "dotted", cex.labels = 1, cex.cat = 0.8, 
    xlim = NULL, ylim = NULL)

Arguments

Details

The underlying models are fitted with the function vglm from the package VGAM. The family argument for vglm is multinomial(parallel=FALSE).

The stars show the exponentials of the estimated coefficients. In multinomial logit models the exponential coefficients can be interpreted as odds. More precisely, for the model with symmetric side constraints, the exponential e^{\gamma_{rj}}, r=1,\ldots,k represents the multiplicative effect of the covariate j on the odds \frac{P(Y=r|x)}{GM(x)} if x_j increases by one unit and GM(x) is the median response. For the model with reference category k, the exponential e^{\gamma_{rj}}, r=1,\ldots,k-1 represents the multiplicative effect of the covariate j on the odds \frac{P(Y=r|x)}{P(Y=k|x)} if x_j increases by one unit.

In addition to the stars, we plot a cirlce that refers to the case where the coefficients of the corresponding star are zero. Therefore, the radii of these circles are always exp(0)=1. If scale=TRUE, the stars are scaled so that they all have the same maximal ray length. In this case, the actual appearances of the circles differ, but they still refer to the no-effects case where all the coefficients are zero. Now the circles can be used to compare different stars based on their respective circles radii. The distances between the rays of a star and the cirlce correspond to the p-values that are printed beneath the category levels if printpvalues=TRUE. The closer a star ray lies to the no–effects circle, the more the p-value is increased.
The p-values beneath the covariate labels, which are given if test.rel=TRUE, correspond to the distance between the circle and the star as a whole. They refer to a likelihood ratio test if all the coefficients from one covariate are zero (i.e. the variable is left out completely) and thus would lie exactly upon the cirlce.
The appearance of the circles can be modified by col.circle, lwd.circle and lty.circle.

The argument xij is important because it has to be used to include category-specific covariates. If its default xij=NULL is kept, an ordinary multinomial logit model without category-specific covariates is fitted. If category-specific covariates are to be included, attention has to be paid to the exact usage of xij. Our xij argument is identical to the xij argument used in the embedded vglm function. For details see also vglm.control. The data are thought to be present in a wide format, i.e. a category-specific covariate consists of k columns. Before calling star.nominal, the values for the reference category (defined by refLevel) have to be subtracted from the values of the further categories. Additionally, the resulting variable for the first response category (but not the reference category) has to be duplicated. This duplicate should be denoted by an appropriate name for the category-specific variable, independent from the different response categories. It will be used as an assignment variable for the corresponding coefficient of the covariate and has to be included in to the formula. For every category-specific covariate, a formula has to be specified in the xij argument. On the left hand side of that formula, the assignment variable has to be placed. On the right hand side, the variables containing the differences from the values for the reference category are written. So the left hand side of the formula contains k-1 terms. The order of these terms has to be chosen according to the order of the response categories, ignoring the reference category. Examples for effect stars for models with category-specific covariates are recieved by typing vignette("election") or vignette("plebiscite").

It is strongly recommended to standardize metric covariates, display of effect stars can benefit greatly as in general differences between the coefficients are increased.

Value

P-values are only available if the corresponding option is set TRUE.
catspec and catspecse are only available if xij is specified.

Author(s)

Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/

References

Tutz, G. and Schauberger, G. (2012): 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

star.sequential, star.cumulative

Examples

## Not run: 
data(election)

# simple multinomial logit model
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election)

# Use effect coding for the categorical predictor religion
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election,
                 pred.coding = "effect")                 

# Use reference category "FDP" instead of symmetric side constraints
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election,
                 refLevel = 3, symmetric = FALSE)
                 
# Use category-specific covariates, subtract values for reference 
# category CDU
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

star.nominal(Partychoice ~ Social + Immigration + Nuclear + Left_Right + Age + 
Religion + Democracy + Pol.Interest + Unemployment + Highschool + Union + West + 
Gender, data = election, 
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),
symmetric = FALSE)

## End(Not run)

Effect stars for sequential logit models

Description

The package EffectStars2 provides a more up-to-date implementation of effect stars!

The function computes and visualizes sequential logit models. The computation is done with help of the package VGAM. The visualization is based on the function stars from the package graphics.

Usage

star.sequential(formula, data, global = NULL, test.rel = TRUE, test.glob = FALSE,
    globcircle = FALSE, maxit = 100, scale = TRUE, nlines = NULL, select = NULL, 
    dist.x = 1, dist.y = 1, dist.cov = 1, dist.cat = 1, xpd = TRUE, main = "", 
    col.fill = "gray90", col.circle = "black", lwd.circle = 1, 
    lty.circle = "longdash", col.global = "black", lwd.global = 1, 
    lty.global = "dotdash", cex.labels = 1, cex.cat = 0.8, xlim = NULL, 
    ylim = NULL)

Arguments

Details

The underlying models are fitted with the function vglm from the package VGAM. The family argument for vglm is sratio(parallel=FALSE).

The stars show the exponentials of the estimated coefficients. In sequential logit models the exponential coefficients can be interpreted as odds. More precisely, the exponential e^{\gamma_{rj}}, r=1,\ldots,k-1 represents the multiplicative effect of the covariate j on the continuation ratio odds \frac{P(Y=r|x)}{P(Y>r|x)} if x_j increases by one unit.

In addition to the stars, we plot a cirlce that refers to the case where the coefficients of the corresponding star are zero. Therefore, the radii of these circles are always exp(0)=1. If scale=TRUE, the stars are scaled so that they all have the same maximal ray length. In this case, the actual appearances of the circles differ, but they still refer to the no-effects case where all the coefficients are zero. Now the circles can be used to compare different stars based on their respective circles radii. The p-values beneath the covariate labels, which are given out if test.rel=TRUE, correspond to the distance between the circle and the star as a whole. They refer to a likelihood ratio test if all the coefficients from one covariate are zero (i.e. the variable is left out completely) and thus would lie exactly upon the cirlce.
The appearance of the circles can be modified by col.circle, lwd.circle and lty.circle.

By setting globcircle=TRUE, an addictional circle can be drawn. The radii now correspond to a model, where the respective covariate is not included category-specific but globally. Therefore, the distance between this circle and the star as a whole corresponds to the p-value p-global that is given if test.glob=TRUE.

It is strongly recommended to standardize metric covariates, display of effect stars can benefit greatly as in general differences between the coefficients are increased.

Value

P-values are only available if the corresponding option is set TRUE.

Author(s)

Gunther Schauberger
gunther.schauberger@tum.de
https://www.sg.tum.de/epidemiologie/team/schauberger/

References

Tutz, G. and Schauberger, G. (2012): 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

star.nominal, star.cumulative

Examples

## Not run: 
data(insolvency)

star.sequential(Insolvency ~ Sector + Legal + Pecuniary_Reward + Seed_Capital
+ Debt_Capital + Employees, insolvency, test.glob = FALSE, globcircle = TRUE, dist.x = 1.3)

## End(Not run)

Canadian Women's Labour-Force Participation

Description

The data are from a 1977 survey of the Canadian population.

Usage

data(womenlabour)

Format

A data frame with 263 observations on the following 4 variables.

Participation

Labour force participation with levels fulltime, not.work and parttime

IncomeHusband

Husband's income in 1000 $

Children

Presence od children in household with levels absent and present

Region

Region with levels Atlantic, BC, Ontario, Prairie and Quebec

Source

R package carData: Womenlf

References

Social Change in Canada Project. York Institute for Social Research.
Fox, J. (2008): Applied Regression Analysis and Generalized Linear Models, Second Edition.

Examples

## Not run: 
data(womenlabour)
womenlabour$IncomeHusband <- scale(womenlabour$IncomeHusband)

star.nominal(Participation ~ IncomeHusband + Children + Region, womenlabour)

## End(Not run)