plot.meanCI {interpretCI}

S3 method for an object of class "meanCI"

Description

S3 method for an object of class "meanCI"

Usage

## S3 method for class 'meanCI'
plot(x, ref = "control", side = NULL, palette = NULL, ...)

Arguments

x

an object of class "meanCI"

ref

string One of c("test","control").

side

logical or NULL If true draw side by side plot

palette

The name of color palette from RColorBrewer package or NULL

...

Further arguments to be passed

Value

A ggplot or an object of class "plotCI" containing at least the following components: '

p1

A ggplot

p2

A ggplot

side

logical

Examples

meanCI(mtcars,mpg) %>% plot()
meanCI(mtcars,am,mpg) %>% plot()
meanCI(iris,Sepal.Width) %>% plot()
meanCI(iris,Sepal.Width,Sepal.Length) %>% plot()

meanCI(iris,Sepal.Width,Sepal.Length,paired=TRUE) %>% plot(palette="Dark2")
meanCI(iris,Sepal.Width,Sepal.Length) %>% plot()
meanCI(iris,Species,Sepal.Width) %>% plot(side=TRUE)
meanCI(iris,Species,Sepal.Width,mu=0.5,alternative="less") %>% plot(ref="test")
meanCI(acs,age) %>% plot()
meanCI(acs,sex,age) %>% plot()
meanCI(acs,smoking,age) %>% plot(palette="Set1")
meanCI(acs,Dx,age) %>% plot()
meanCI(acs,Dx,age,sex,mu=0) %>% plot(palette="Dark2")
x=c(95,89,76,92,91,53,67,88,75,85,90,85,87,85,85,68,81,84,71,46,75,80)
y=c(90,85,73,90,90,53,68,90,78,89,95,83,83,83,82,65,79,83,60,47,77,83)
meanCI(x=x,y=y,paired=TRUE,alpha=0.1) %>% plot()
meanCI(10:30,1:15) %>% plot()
iris %>% meanCI() %>% plot(side=TRUE)
meanCI(n=150,m=115,s=10,alpha=0.01) %>% plot()
meanCI(n1=30,n2=25,m1=78,s1=10,m2=85,s2=15,alpha=0.10) %>% plot()
data(anscombe2,package="PairedData")
meanCI(anscombe2,idx=list(c("X1","Y1"),c("X4","Y4"),c("X3","Y3"),c("X2","Y2")),
paired=TRUE,mu=0) %>% plot()
x=meanCI(anscombe2,idx=list(c("X1","X2","X3","X4"),c("Y1","Y2","Y3","Y4")),paired=TRUE,mu=0)
plot(x)
longdf=tidyr::pivot_longer(anscombe2,cols=X1:Y4)
x=meanCI(longdf,name,value,idx=list(c("X1","X2","X3","X4"),c("Y1","Y2","Y3","Y4")),paired=TRUE,mu=0)
plot(x)
acs %>% select(sex,TC,TG,HDLC) %>% meanCI(group=sex) %>% plot()
acs %>% select(sex,TC,TG,HDLC) %>% meanCI(sex) %>% plot()