Nothing
#####################
## Fourfold tables ##
#####################
### Berkeley Admission Data ###
###############################
data(UCBAdmissions)
## unstratified
### no margin is standardized
x <- margin.table(UCBAdmissions, 2:1)
fourfold(x, std = "i", extended = FALSE)
### std. for gender
fourfold(x, margin = 1, extended = FALSE)
### std. for both
fourfold(x, extended = FALSE)
## stratified
fourfold(UCBAdmissions, extended = FALSE)
fourfold(UCBAdmissions) ## extended plots
## using cotabplot
cotabplot(UCBAdmissions, panel = function(x, condlevels, ...)
fourfold(co_table(x, names(condlevels))[[paste(condlevels, collapse = ".")]],
newpage = F, return_grob = FALSE, ...)
)
### Coal Miners Lung Data ###
#############################
data(CoalMiners)
## Fourfold display, both margins equated
fourfold(CoalMiners, mfcol = c(3,3))
## Log Odds Ratio Plot
data(CoalMiners, package = "vcd")
lor_CM <- loddsratio(CoalMiners)
plot(lor_CM)
lor_CM_df <- as.data.frame(lor_CM)
# fit linear models using WLS
age <- seq(20, 60, by = 5)
lmod <- lm(LOR ~ age, weights = 1 / ASE^2, data = lor_CM_df)
grid.lines(age, fitted(lmod), gp = gpar(col = "blue"))
qmod <- lm(LOR ~ poly(age, 2), weights = 1 / ASE^2, data = lor_CM_df)
grid.lines(age, fitted(qmod), gp = gpar(col = "red"))
## Fourfold display, strata equated
fourfold(CoalMiners, std = "ind.max", mfcol = c(3,3))
####################
## Sieve Diagrams ##
####################
### Hair Eye Color ###
######################
data(HairEyeColor)
## aggregate over `sex':
(tab <- margin.table(HairEyeColor, 1:2))
## plot expected values:
sieve(t(tab), sievetype = "expected", shade = TRUE)
## plot sieve diagram:
sieve(t(tab), shade = TRUE)
### Visual Acuity ###
#####################
data(VisualAcuity)
attach(VisualAcuity)
sieve(Freq ~ right + left,
data = VisualAcuity,
subset = gender == "female",
main = "Unaided distant vision data",
labeling_args = list(set_varnames = c(left = "Left Eye Grade",
right = "Right Eye Grade")),
shade = TRUE
)
detach(VisualAcuity)
### Berkeley Admission ###
##########################
## -> Larger tables: e.g., Cross factors
### Cross Gender and Admission
data(UCBAdmissions)
(tab <- xtabs(Freq ~ Dept + I(Gender : Admit), data = UCBAdmissions))
sieve(tab,
labeling_args = list(set_varnames = c("I(Gender:Admit)" = "Gender:Admission",
Dept = "Department")),
main = "Berkeley Admissions Data",
shade = TRUE
)
## or use extended sieve plots:
sieve(UCBAdmissions, shade = TRUE)
######################
## Association Plot ##
######################
### Hair Eye Color ###
######################
data(HairEyeColor)
assoc(margin.table(HairEyeColor, 1:2),
labeling_args = list(set_varnames = c(Hair = "Hair Color", Eye = "Eye Color")),
main = "Association Plot")
####################
## Agreement Plot ##
####################
### Sexual Fun ###
##################
data(SexualFun)
## Kappa statistics
Kappa(SexualFun)
## Agreement Chart
agreementplot(t(SexualFun), weights = 1)
## Partial Agreement Chart and B-Statistics
(agreementplot(t(SexualFun),
xlab = "Husband's Rating",
ylab = "Wife's Rating",
main = "Husband's and Wife's Sexual Fun")
)
### MS Diagnosis data ###
#########################
data(MSPatients)
## use e.g., X11(width = 12), or expand graphics device
agreementplot(t(MSPatients[,,1]), main = "Winnipeg Patients")
agreementplot(t(MSPatients[,,2]), main = "New Orleans Patients")
##################
## Ternary Plot ##
##################
### sample data ###
###################
(x <- rbind(c(A=10,B=10,C=80),
c(40,30,30),
c(20,60,20)
)
)
ternaryplot(x,
cex = 2,
col = c("black", "blue", "red"),
coordinates = TRUE
)
### Arthritis Treatment Data ###
################################
data(Arthritis)
## Build table by crossing Treatment and Sex
(tab <- as.table(xtabs(~ I(Sex:Treatment) + Improved, data = Arthritis)))
## Mark groups
col <- c("red", "red", "blue", "blue")
pch <- c(1, 19, 1, 19)
## plot
ternaryplot(
tab,
col = col,
pch = pch,
cex = 2,
bg = "lightgray",
grid_color = "white",
labels_color = "white",
main = "Arthritits Treatment Data"
)
## legend
grid_legend(0.8, 0.7, pch, col, rownames(tab), title = "GROUP")
### Baseball Hitters Data ###
#############################
data(Hitters)
attach(Hitters)
colors <- c("black","red","green","blue","red","black","blue")
pch <- substr(levels(Positions), 1, 1)
ternaryplot(
Hitters[,2:4],
pch = as.character(Positions),
col = colors[as.numeric(Positions)],
main = "Baseball Hitters Data"
)
grid_legend(0.8, 0.9, pch, colors, levels(Positions), title = "POSITION(S)")
detach(Hitters)
### Lifeboats on the Titanic ###
################################
data(Lifeboats)
attach(Lifeboats)
ternaryplot(
Lifeboats[,4:6],
pch = ifelse(side=="Port", 1, 19),
col = ifelse(side=="Port", "red", "blue"),
id = ifelse(men/total > 0.1, as.character(boat), NA),
dimnames_position = "edge",
dimnames = c("Men of Crew", "Men passengers", "Women and Children"),
main = "Lifeboats on the Titanic"
)
grid_legend(0.8, 0.9, c(1, 19), c("red", "blue"), c("Port", "Starboard"), title = "SIDE")
## Load against time for Port/Starboard boats
plot(launch, total,
pch = ifelse(side == "Port", 1, 19),
col = ifelse(side == "Port", "red", "darkblue"),
xlab = "Launch Time",
ylab = "Total loaded",
main = "Lifeboats on the Titanic"
)
legend(as.POSIXct("1912-04-15 01:48:00"), 70,
legend = c("SIDE","Port","Starboard"),
pch = c(NA, 1, 19),
col = c(NA, "red", "darkblue")
)
text(as.POSIXct(launch),
total,
labels = as.character(boat),
pos = 3,
offset = 0.3
)
abline(lm(total ~ as.POSIXct(launch),
subset = side == "Port"),
col = "red")
abline(lm(total ~ as.POSIXct(launch),
subset = side == "Starboard"),
col = "darkblue")
detach(Lifeboats)
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