Nothing
#####################
## Mosaic Displays ##
#####################
#########################
## Hair Eye Color Data ##
#########################
data(HairEyeColor)
## Basic Mosaic Display ##
HairEye <- margin.table(HairEyeColor, c(1,2))
mosaic(HairEye, main = "Basic Mosaic Display of Hair Eye Color data")
## Hair Mosaic Display with Pearson residuals ##
Hair <- margin.table(HairEyeColor,1)
Hair
mHair <- as.table(rep(mean(margin.table(HairEyeColor, 1)), 4))
names(mHair) <- names(Hair)
mHair
## Pearson residuals from Equiprobability model ##
resid <- (Hair - mHair) / sqrt(mHair)
resid
## First Step in a Mosaic Display ##
mosaic(Hair, residuals = resid, main = "Hair Color Proportions")
## Hair Eye Mosais Display with Pearson residuals ##
mosaic(HairEye, main = " Hair Eye Color with Pearson residuals")
## Show Pearson Residuals ##
(HairEye - loglin(HairEye, c(1, 2), fit = TRUE)$fit) /
sqrt(loglin(HairEye, c(1, 2), fit = TRUE)$fit)
###################
## UKSoccer Data ##
###################
data(UKSoccer)
## UKSoccer Mosaic Display ##
mosaic(UKSoccer, main = "UK Soccer Scores")
###############################
## Repeat Victimization Data ##
###############################
data(RepVict)
## mosaic(RepVict[-c(4, 7), -c(4, 7)], main = "Repeat Victimization Data")
##################
## 3-Way Tables ##
##################
## Hair Eye Sex Mosais Display with Pearson residuals ##
mosaic(HairEyeColor, main = "Hair Eye Color Sex" )
mosaic(HairEyeColor, expected = ~ Hair * Eye + Sex,
main = "Model: (Hair Eye) (Sex)" )
mosaic(HairEyeColor, expected = ~ Hair * Sex + Eye*Sex,
main = "Model: (Hair Sex) (Eye Sex)")
####################
## Premarital Sex ##
####################
data(PreSex)
## Mosaic display for Gender and Premarital Sexual Expirience ##
## (Gender Pre) ##
mosaic(margin.table(PreSex, c(3, 4)), legend = FALSE,
main = "Gender and Premarital Sex")
## (Gender Pre)(Extra) ##
mosaic(margin.table(PreSex,c(2,3,4)), legend = FALSE,
expected = ~ Gender * PremaritalSex + ExtramaritalSex ,
main = "(PreMaritalSex Gender) (Sex)")
## (Gender Pre Extra)(Marital) ##
mosaic(PreSex,
expected = ~ Gender * PremaritalSex * ExtramaritalSex + MaritalStatus,
legend = FALSE,
main = "(PreMarital ExtraMarital) (MaritalStatus)")
## (GPE)(PEM) ##
mosaic(PreSex,
expected = ~ Gender * PremaritalSex * ExtramaritalSex
+ MaritalStatus * PremaritalSex * ExtramaritalSex,
legend = FALSE,
main = "(G P E) (P E M)")
############################
## Employment Status Data ##
############################
data(Employment)
## Employment Status ##
# mosaic(Employment,
# expected = ~ LayoffCause * EmploymentLength + EmploymentStatus,
# main = "(Layoff Employment) + (EmployStatus)")
# mosaic(Employment,
# expected = ~ LayoffCause * EmploymentLength +
# LayoffCause * EmploymentStatus,
# main = "(Layoff EmpL) (Layoff EmplS)")
# ## Closure ##
# mosaic(Employment[,,1], main = "Layoff : Closure")
# ## Replaced ##
# mosaic(Employment[,,2], main = "Layoff : Replaced")
#####################
## Mosaic Matrices ##
#####################
data(UCBAdmissions)
pairs(PreSex)
pairs(UCBAdmissions)
pairs(UCBAdmissions, type = "conditional")
pairs(UCBAdmissions, type = "pairwise", gp = shading_max)
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