Package 'interpretCI' reference manual

Title:	Estimate the Confidence Interval and Interpret Step by Step
Description:	Estimate confidence intervals for mean, proportion, mean difference for unpaired and paired samples and proportion difference. Plot the confidence intervals. Generate documents explaining the statistical result step by step.
Authors:	Keon-Woong Moon [aut, cre]
Maintainer:	Keon-Woong Moon <[email protected]>
License:	GPL-3
Version:	0.1.2
Built:	2024-10-29 04:48:09 UTC
Source:	https://github.com/cardiomoon/interpretci

Demographic data of 857 patients with ACS

Description

A dataset containing demographic data and laboratory data of 857 patients with acute coronary syndrome(ACS).

Usage

acs
acs

Format

An object of class data.frame with 857 rows and 17 columns.

Examples

interpretCI::acs
interpretCI::acs

Draw normal distribution curve

Description

Draw normal distribution curve

Usage

draw_n(mean = 0, sd = 1, z = NULL, p = 0.05, alternative = "two.sided")
draw_n(mean = 0, sd = 1, z = NULL, p = 0.05, alternative = "two.sided")

Arguments

`mean`	vector of means
`sd`	vector of standard deviations
`z`	vector of quantiles
`p`	vector of probabilities
`alternative`	a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

Value

A ggplot

Examples

draw_n()
draw_n(alternative="less")
draw_n(alternative="greater")
draw_n(z=-1.75)
draw_n(z=-1.75,alternative="greater")
draw_n(z=-1.75,alternative="less")
draw_n()
draw_n(alternative="less")
draw_n(alternative="greater")
draw_n(z=-1.75)
draw_n(z=-1.75,alternative="greater")
draw_n(z=-1.75,alternative="less")

Draw t distribution curve

Description

Draw t distribution curve

Usage

draw_t(DF = 50, t = NULL, p = 0.05, alternative = "two.sided")
draw_t(DF = 50, t = NULL, p = 0.05, alternative = "two.sided")

Arguments

`DF`	numeric degree of freedom
`t`	numeric t value
`p`	numeric p value
`alternative`	a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

Value

A ggplot

Examples

draw_t(DF=30)
draw_t(DF=20,t=2.5)
draw_t(DF=49,t=1.77)
draw_t(DF=49,p=0.005)
draw_t(DF=19,t=-0.894,alternative="less")
draw_t(DF=146,t=0.67,alternative="greater")
draw_t(DF=30)
draw_t(DF=20,t=2.5)
draw_t(DF=49,t=1.77)
draw_t(DF=49,p=0.005)
draw_t(DF=19,t=-0.894,alternative="less")
draw_t(DF=146,t=0.67,alternative="greater")

Draw chi-squared distribution curve

Description

Draw chi-squared distribution curve

Usage

draw_x2(q = NULL, p = 0.05, df = 2)
draw_x2(q = NULL, p = 0.05, df = 2)

Arguments

`q`	vector of quantiles
`p`	vector of probabilities
`df`	degrees of freedom (non-negative, but can be non-integer).

Value

A ggplot

Examples

draw_x2(df=1)
draw_x2(q=0.78)
draw_x2(df=1)
draw_x2(q=0.78)

Convert numeric to string with uppercase first letter

Description

Convert numeric to string with uppercase first letter

Usage

English(x, digits = 2)
English(x, digits = 2)

Arguments

`x`	A numeric
`digits`	integer indicating the number of decimal places

Value

A string

Examples

English(40)
English(13.1)
English(40)
English(13.1)

Convert numeric to string

Description

Convert numeric to string

Usage

english2(x, digits = 2)
english2(x, digits = 2)

Arguments

`x`	A numeric
`digits`	integer indicating the number of decimal places

Value

A character string

Examples

english2(45)
english2(12.34)
english2(45)
english2(12.34)

Draw estimation plot1

Description

Draw estimation plot1

Usage

estimationPlot1(x, palette = NULL)
estimationPlot1(x, palette = NULL)

Arguments

`x`	An object of class meanCI
`palette`	The name of color palette from RColorBrewer package or NULL

Value

A ggplot

Examples

x=meanCI(iris,Species,Sepal.Length)
estimationPlot1(x)
x=meanCI(iris,Species,Sepal.Length)
estimationPlot1(x)

Interpret an object of meanCI

Description

Interpret an object of meanCI. Render appropriate rmarkdown file to html file and show RStudio viewer or browser.

Usage

interpret(x, viewer = "rstudio")
interpret(x, viewer = "rstudio")

Arguments

`x`	An object of class "meanCI"
`viewer`	Character One of c("rstudio","browser")

Value

No return value, called for side effect

Examples

x=meanCI(mtcars$mpg)
x=meanCI(mtcars,mpg,mu=23)
x=meanCI(n=150,m=115,s=10,alpha=0.01)
x=meanCI(n=50,m=295,s=20,mu=300)
x= meanCI(n=20,m=108,s=10,mu=110,alpha=0.01,alternative="less")
x=meanCI(n1=500,n2=1000,m1=20,s1=3,m2=15,s2=2,alpha=0.01)
x=meanCI(n1=15,n2=20,m1=1000,s1=100,m2=950,s2=90,alpha=0.1)
x=meanCI(n1=30,n2=25,m1=78,s1=10,m2=85,s2=15,mu=0,alpha=0.10)
x=meanCI(n1=100,n2=100,m1=200,s1=40,m2=190,s2=20,mu=7,alpha=0.05,alternative="greater")
x1=c(95,89,76,92,91,53,67,88,75,85,90,85,87,85,85,68,81,84,71,46,75,80)
y1=c(90,85,73,90,90,53,68,90,78,89,95,83,83,83,82,65,79,83,60,47,77,83)
x=meanCI(x=x1,y=y1,paired=TRUE,alpha=0.1,mu=0)
x=propCI(n=1600,p=0.4,alpha=0.01)
x=propCI(n=100,p=0.73,P=0.8,alpha=0.01)
x=propCI(n=100,p=0.73,P=0.8,alpha=0.05,alternative="greater")
x=propCI(n1=100,n2=200,p1=0.38,p2=0.51,alpha=0.01)
x=propCI(n1=150,n2=100,p1=0.71,p2=0.63,P=0,alternative="greater")
## Not run: 
interpret(x)
interpret(x,"browser")

## End(Not run)
x=meanCI(mtcars$mpg)
x=meanCI(mtcars,mpg,mu=23)
x=meanCI(n=150,m=115,s=10,alpha=0.01)
x=meanCI(n=50,m=295,s=20,mu=300)
x= meanCI(n=20,m=108,s=10,mu=110,alpha=0.01,alternative="less")
x=meanCI(n1=500,n2=1000,m1=20,s1=3,m2=15,s2=2,alpha=0.01)
x=meanCI(n1=15,n2=20,m1=1000,s1=100,m2=950,s2=90,alpha=0.1)
x=meanCI(n1=30,n2=25,m1=78,s1=10,m2=85,s2=15,mu=0,alpha=0.10)
x=meanCI(n1=100,n2=100,m1=200,s1=40,m2=190,s2=20,mu=7,alpha=0.05,alternative="greater")
x1=c(95,89,76,92,91,53,67,88,75,85,90,85,87,85,85,68,81,84,71,46,75,80)
y1=c(90,85,73,90,90,53,68,90,78,89,95,83,83,83,82,65,79,83,60,47,77,83)
x=meanCI(x=x1,y=y1,paired=TRUE,alpha=0.1,mu=0)
x=propCI(n=1600,p=0.4,alpha=0.01)
x=propCI(n=100,p=0.73,P=0.8,alpha=0.01)
x=propCI(n=100,p=0.73,P=0.8,alpha=0.05,alternative="greater")
x=propCI(n1=100,n2=200,p1=0.38,p2=0.51,alpha=0.01)
x=propCI(n1=150,n2=100,p1=0.71,p2=0.63,P=0,alternative="greater")
## Not run: 
interpret(x)
interpret(x,"browser")

## End(Not run)

Decide whether a vector can be treated as a numeric variable

Description

Decide whether a vector can be treated as a numeric variable

Usage

is.mynumeric(x, maxy.lev = 5)
is.mynumeric(x, maxy.lev = 5)

Arguments

`x`	A vector
`maxy.lev`	An integer indicating the maximum number of unique values of a numeric variable be treated as a categorical variable

Value

logical

Examples

x=1:5
is.mynumeric(x)
x=1:13
is.mynumeric(x)
x=1:5
is.mynumeric(x)
x=1:13
is.mynumeric(x)

Whether the arg is provided in function call

Description

Whether the arg is provided in function call

Usage

isProvided(x, seek = "mu")
isProvided(x, seek = "mu")

Arguments

`x`	An object of class "meanCI" or function call or character string
`seek`	character. Default="mu"

Value

logical

Examples

x=meanCI(mtcars,am,mpg)
isProvided(x)
x=meanCI(mtcars,am,mpg)
isProvided(x)

Calculate confidence intervals of mean or difference between means

Description

Calculate confidence intervals of mean or difference between means

Usage

meanCI(x, ...)
meanCI(x, ...)

Arguments

`x`	An object of class data.frame or vector
`...`	Further arguments

Value

An object of class "meanCI" which is a list containing at least the following components:

data: A tibble containing raw data or a list of numeric vector
result: A data.frame consist of summary statistics
call: the matched call
attr(*,"measure"): character. One of c("mean","unpaired","paired")

Examples

meanCI(mtcars$mpg)
meanCI(n=150,m=115,s=10,alpha=0.01)
meanCI(n=50,m=295,s=20,mu=300)
meanCI(n=20,m=108,s=10,mu=110,alpha=0.01,alternative="less")
meanCI(mtcars,am,mpg)
meanCI(n1=15,n2=20,m1=1000,s1=100,m2=950,s2=90,alpha=0.1)
meanCI(n1=500,n2=1000,m1=20,s1=3,m2=15,s2=2,alpha=0.01)
meanCI(n1=30,n2=25,m1=78,s1=10,m2=85,s2=15,alpha=0.10)
meanCI(n1=100,n2=100,m1=200,s1=40,m2=190,s2=20,mu=7,alpha=0.05,alternative="greater")
x=c(3.04,2.92,2.86,1.71,3.60,3.49,3.30,2.28,3.11,2.88,2.82,2.13,2.11,3.03,3.27,2.60,3.13)
y=c(2.56,3.47,2.65,2.77,3.26,3.00,2.70,3.20,3.39,3.00,3.19,2.58,2.98)
meanCI(x=x,y=y)
x=c(95,89,76,92,91,53,67,88,75,85,90,85,87,85,85,68,81,84,71,46,75,80)
y=c(90,85,73,90,90,53,68,90,78,89,95,83,83,83,82,65,79,83,60,47,77,83)
meanCI(x=x,y=y,paired=TRUE,alpha=0.1)
meanCI(10:30,1:15)
meanCI(acs,sex,age)
meanCI(iris$Sepal.Width,iris$Sepal.Length)
meanCI(iris$Sepal.Width,iris$Sepal.Length,paired=TRUE)
meanCI(mtcars$mpg)
meanCI(n=150,m=115,s=10,alpha=0.01)
meanCI(n=50,m=295,s=20,mu=300)
meanCI(n=20,m=108,s=10,mu=110,alpha=0.01,alternative="less")
meanCI(mtcars,am,mpg)
meanCI(n1=15,n2=20,m1=1000,s1=100,m2=950,s2=90,alpha=0.1)
meanCI(n1=500,n2=1000,m1=20,s1=3,m2=15,s2=2,alpha=0.01)
meanCI(n1=30,n2=25,m1=78,s1=10,m2=85,s2=15,alpha=0.10)
meanCI(n1=100,n2=100,m1=200,s1=40,m2=190,s2=20,mu=7,alpha=0.05,alternative="greater")
x=c(3.04,2.92,2.86,1.71,3.60,3.49,3.30,2.28,3.11,2.88,2.82,2.13,2.11,3.03,3.27,2.60,3.13)
y=c(2.56,3.47,2.65,2.77,3.26,3.00,2.70,3.20,3.39,3.00,3.19,2.58,2.98)
meanCI(x=x,y=y)
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)
meanCI(10:30,1:15)
meanCI(acs,sex,age)
meanCI(iris$Sepal.Width,iris$Sepal.Length)
meanCI(iris$Sepal.Width,iris$Sepal.Length,paired=TRUE)

Calculate confidence intervals of mean or difference between means in a data.frame

Description

Calculate confidence intervals of mean or difference between means in a data.frame

Usage

## S3 method for class 'data.frame'
meanCI(x, ...)

meanCI_sub(data = data, x, y, group, paired = FALSE, idx = NULL, ...)
## S3 method for class 'data.frame'
meanCI(x, ...)

meanCI_sub(data = data, x, y, group, paired = FALSE, idx = NULL, ...)

Arguments

`x`	Name of a categorical or numeric column. If !missing(y), name of continuous variable
`...`	Further arguments to be passed to meanCI
`data`	A data.frame
`y`	Name of a numeric column
`group`	Name of categorical column
`paired`	logical
`idx`	A vector containing factors or strings in the x columns. These must be quoted (ie. surrounded by quotation marks). The first element will be the control group, so all differences will be computed for every other group and this first group.

Value

An object of class "meanCI" which is a list containing at least the following components:

data: A tibble containing raw data or a list of numeric vector
result: A data.frame consists of summary statistics
call: the matched call
attr(*,"measure"): character. One of c("mean","unpaired","paired")

Methods (by generic)

meanCI: S3 method for data.frame

Examples

meanCI(acs,age)
meanCI(acs,sex,age)
meanCI(acs,Dx,age)
acs %>% select(age) %>% meanCI()
acs %>% select(sex,age) %>% meanCI()
meanCI(iris,Species,Sepal.Length)
meanCI(iris,Sepal.Width,Sepal.Length,paired=TRUE)
meanCI(iris,Sepal.Length,Sepal.Width)
iris %>% select(starts_with("Petal")) %>% meanCI(paired=TRUE)
iris %>% meanCI(paired=TRUE)
meanCI(acs,sex,age,Dx,mu=10)
acs %>% select(sex,TC,TG,HDLC) %>% meanCI(group=sex)
acs %>% select(sex,TC,TG,HDLC) %>% meanCI(sex)
iris %>% select(Species,starts_with("Sepal")) %>% meanCI(Species)
iris %>% select(Species,starts_with("Sepal")) %>% meanCI(group=Species)
meanCI(acs,age)
meanCI(acs,sex,age)
meanCI(acs,Dx,age)
acs %>% select(age) %>% meanCI()
acs %>% select(sex,age) %>% meanCI()
meanCI(iris,Species,Sepal.Length)
meanCI(iris,Sepal.Width,Sepal.Length,paired=TRUE)
meanCI(iris,Sepal.Length,Sepal.Width)
iris %>% select(starts_with("Petal")) %>% meanCI(paired=TRUE)
iris %>% meanCI(paired=TRUE)
meanCI(acs,sex,age,Dx,mu=10)
acs %>% select(sex,TC,TG,HDLC) %>% meanCI(group=sex)
acs %>% select(sex,TC,TG,HDLC) %>% meanCI(sex)
iris %>% select(Species,starts_with("Sepal")) %>% meanCI(Species)
iris %>% select(Species,starts_with("Sepal")) %>% meanCI(group=Species)

Calculate confidence intervals of mean or difference between means

Description

Calculate confidence intervals of mean or difference between means

Usage

## Default S3 method:
meanCI(x, ...)

meanCI2(
  x,
  y,
  n,
  m,
  s,
  n1,
  n2,
  m1,
  m2,
  s1,
  s2,
  mu = 0,
  paired = FALSE,
  var.equal = FALSE,
  alpha = 0.05,
  digits = 2,
  alternative = "two.sided"
)
## Default S3 method:
meanCI(x, ...)

meanCI2(
  x,
  y,
  n,
  m,
  s,
  n1,
  n2,
  m1,
  m2,
  s1,
  s2,
  mu = 0,
  paired = FALSE,
  var.equal = FALSE,
  alpha = 0.05,
  digits = 2,
  alternative = "two.sided"
)

Arguments

`x`	A vector
`...`	Further arguments to be passed to meanCI2
`y`	A vector
`n`, `n1`, `n2`	integer sample(s) size
`m`, `m1`, `m2`	Numeric mean value of sample(s)
`s`, `s1`, `s2`	Numeric standard deviation of sample(s)
`mu`	numeric hypothesized true value of mean or mean difference
`paired`	logical If true, difference between paired sample calculated
`var.equal`	logical If true, pooled standard deviation is used
`alpha`	Numeric Confidence level
`digits`	integer indicating the number of decimal places
`alternative`	A character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

Value

An object of class "meanCI" which is a list containing at least the following components:

data: A tibble containing raw data or a list of numeric vector
result: A data.frame containing of summary statistics
call: the matched call
attr(*,"measure"): character. One of c("mean","unpaired","paired")

Methods (by generic)

meanCI: Default S3 method

Prepare data to plot from an object of class meanCI

Description

Prepare data to plot from an object of class meanCI

Usage

meanCI2df(x)
meanCI2df(x)

Arguments

`x`	An object of class meanCI

Value

A data.frame

Examples

x=meanCI(acs,sex,age)
meanCI2df(x)
x=meanCI(acs,sex,age)
meanCI2df(x)

Extract data from an object of class meanCI

Description

Extract data from an object of class meanCI

Usage

meanCI2df1(x)
meanCI2df1(x)

Arguments

`x`	An object of class meanCI

Value

A data.frame summarizing mean and confidence interval

Examples

x=meanCI(acs,sex,age)
meanCI2df1(x)
x=meanCI(acs,sex,age)
meanCI2df1(x)

Draw meanCI plot for data with single vector

Description

Draw meanCI plot for data with single vector

Usage

meanCIplot1(x)
meanCIplot1(x)

Arguments

`x`	An object of class "meanCI" with attr(x,"measure")=="mean"

Value

A ggplot

Examples

x=meanCI(mtcars,mpg)
meanCIplot1(x)
x=meanCI(mtcars,mpg)
meanCIplot1(x)

Draw a pair plot with an object of class meanCI

Description

Draw a pair plot with an object of class meanCI

Usage

pairPlot(x, palette = NULL)
pairPlot(x, palette = NULL)

Arguments

`x`	An object of class "meanCI" with attr(x,"measure")=="paired"
`palette`	The name of color palette from RColorBrewer package or NULL

Value

A ggplot

Examples

x=meanCI(iris,paired=TRUE)
pairPlot(x)
x=meanCI(iris,Petal.Width, Petal.Length,paired=TRUE)
pairPlot(x)
x=meanCI(iris,paired=TRUE)
pairPlot(x)
x=meanCI(iris,Petal.Width, Petal.Length,paired=TRUE)
pairPlot(x)

Draw a pair plot

Description

Draw a pair plot

Usage

pairPlot1(data, ref = NULL, palette = NULL)
pairPlot1(data, ref = NULL, palette = NULL)

Arguments

`data`	a data.frame
`ref`	Numeric or NULL
`palette`	The name of color palette from RColorBrewer package or NULL

Value

A ggplot

Examples

x=meanCI(mtcars,paired=TRUE)
pairPlot1(x$data)
pairPlot1(x$data,ref=c(1,4,6))
pairPlot1(x$data,ref=c(1,3))
x=meanCI(mtcars,paired=TRUE)
pairPlot1(x$data)
pairPlot1(x$data,ref=c(1,4,6))
pairPlot1(x$data,ref=c(1,3))

Extract hexadecimal colors from a color palette

Description

Extract hexadecimal colors from a color palette

Usage

palette2colors(name, reverse = FALSE)
palette2colors(name, reverse = FALSE)

Arguments

`name`	The name of color palette from RColorBrewer package
`reverse`	Whether or not reverse the order of colors

Value

hexadecimal colors

Examples

palette2colors("Reds")
palette2colors("Reds")

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, ...)
## 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()

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()

S3 method "print" for class "meanCI"

Description

S3 method "print" for class "meanCI"

Usage

## S3 method for class 'meanCI'
print(x, ...)
## S3 method for class 'meanCI'
print(x, ...)

Arguments

`x`	An object of class "meanCI"
`...`	Further arguments

Value

No return value, called for side effect

Calculate confidence intervals of proportion or difference between proportions

Description

Calculate confidence intervals of proportion or difference between proportions

Usage

propCI(
  x,
  y,
  n,
  p,
  n1,
  n2,
  p1,
  p2,
  P = 0,
  alpha = 0.05,
  digits = 2,
  alternative = "two.sided"
)
propCI(
  x,
  y,
  n,
  p,
  n1,
  n2,
  p1,
  p2,
  P = 0,
  alpha = 0.05,
  digits = 2,
  alternative = "two.sided"
)

Arguments

`x`	A vector
`y`	A vector
`n`, `n1`, `n2`	integer sample size
`p`, `p1`, `p2`, `P`	Numeric proportion
`alpha`	numeric confidence level
`digits`	integer indicating the number of decimal places
`alternative`	A character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

Value

A list containing at least the following components:

data: A tibble containing raw data or a list of numeric vector
result: A data.frame consists of summary statistics
call: the matched call
attr(*,"measure"): character. One of c("prop","propdiff")

#'@examples propCI(acs$sex) propCI(acs$sex,acs$DM) propCI(n=1600,p=0.4,alpha=0.01) propCI(n=100,p=0.73,P=0.8,alpha=0.01) propCI(n1=400,n2=300,p1=0.4,p2=0.3,alpha=0.1) propCI(n1=100,n2=200,p1=0.38,p2=0.51,alpha=0.01) propCI(n1=100,n2=200,p1=0.38,p2=0.51,alpha=0.01,alternative="less")

Calculate confidence intervals of proportion or difference between proportions in a data.frame

Description

Calculate confidence intervals of proportion or difference between proportions in a data.frame

Usage

propCI_sub(data, x, y = NULL)
propCI_sub(data, x, y = NULL)

Arguments

`data`	A data.frame
`x`	Character Name of a categorical column
`y`	Character Optional. Name of another categorical column

Value

A list containing at least the following components:

data: A tibble containing raw data or a list of numeric vector
result: A data.frame consists of summary statistics
call: the matched call
attr(*,"measure"): character. One of c("prop","propdiff")

Examples

propCI_sub(acs,"sex")
propCI_sub(acs,"sex","HBP")
propCI_sub(acs,"sex")
propCI_sub(acs,"sex","HBP")

Show html file in RStudio viewer or browser

Description

Show html file in RStudio viewer or browser

Usage

rstudio_viewer(file_name, file_path = NULL, viewer = "rstudio")
rstudio_viewer(file_name, file_path = NULL, viewer = "rstudio")

Arguments

`file_name`	character file name
`file_path`	character file path
`viewer`	Character One of c("rstudio","browser")

Value

No return value, called for side effect

Show t-value table

Description

Show t-value table

Usage

show_t_table(DF = 20, t = NULL, p = 0.05, alternative = "two.sided")
show_t_table(DF = 20, t = NULL, p = 0.05, alternative = "two.sided")

Arguments

`DF`	Numeric degree of freedom
`t`	Numeric vector of quantile
`p`	Numeric probability
`alternative`	Character One of c("two.sided","greater","less")

Value

An object of class "flextable"

Examples

show_t_table()
show_t_table(t=1.4)
show_t_table(DF=10)
show_t_table()
show_t_table(t=1.4)
show_t_table(DF=10)

Show chisquare table

Description

Show chisquare table

Usage

show_x2_table(DF = 1, x2 = NULL, p = 0.05)
show_x2_table(DF = 1, x2 = NULL, p = 0.05)

Arguments

`DF`	Numeric degree of freedom
`x2`	Numeric vector of chi-square value
`p`	Numeric probability

Value

An object of class "flextable"

Examples

show_x2_table(DF=2,x2=1.5)
show_x2_table(p=0.05)
show_x2_table(DF=2,x2=1.5)
show_x2_table(p=0.05)

Show z-value table

Description

Show z-value table

Usage

show_z_table(z = NULL, p = 0.05, alternative = "two.sided")
show_z_table(z = NULL, p = 0.05, alternative = "two.sided")

Arguments

`z`	Numeric vector of quantile
`p`	Numeric probability
`alternative`	Character One of c("two.sided","greater","less")

Value

An object of class "flextable"

Examples

show_z_table()
show_z_table(z=1.4)
show_z_table(z=-1.39234)
show_z_table(p=0.160)
show_z_table()
show_z_table(z=1.4)
show_z_table(z=-1.39234)
show_z_table(p=0.160)

Draw textbox

Description

Draw textbox

Usage

textBox(
  string,
  color = "black",
  lcolor = "red",
  bg = "cornsilk",
  lwd = 1,
  width = 10,
  bold = FALSE,
  italic = FALSE,
  fontsize = 11,
  space = 1.5,
  fontname
)
textBox(
  string,
  color = "black",
  lcolor = "red",
  bg = "cornsilk",
  lwd = 1,
  width = 10,
  bold = FALSE,
  italic = FALSE,
  fontsize = 11,
  space = 1.5,
  fontname
)

Arguments

`string`	string
`color`	font color
`lcolor`	line color
`bg`	background color
`lwd`	numeric line width
`width`	numeric box width
`bold`, `italic`	logical
`fontsize`	numeric font size
`space`	space between lines
`fontname`	name of font

Value

A flextable

Examples

string="Good Morning!"
textBox(string,italic=TRUE)
string="Good Morning!"
textBox(string,italic=TRUE)

Package 'interpretCI'

Help Index

Demographic data of 857 patients with ACS

Description

Usage

Format

Examples

Draw normal distribution curve

Description

Usage

Arguments

Value

Examples

Draw t distribution curve

Description

Usage

Arguments

Value

Examples

Draw chi-squared distribution curve

Description

Usage

Arguments

Value

Examples

Convert numeric to string with uppercase first letter

Description

Usage

Arguments

Value

Examples

Convert numeric to string

Description

Usage

Arguments

Value

Examples

Draw estimation plot1

Description

Usage

Arguments

Value

Examples

Interpret an object of meanCI

Description

Usage

Arguments

Value

Examples

Decide whether a vector can be treated as a numeric variable

Description

Usage

Arguments

Value

Examples

Whether the arg is provided in function call

Description

Usage

Arguments

Value

Examples

Calculate confidence intervals of mean or difference between means

Description

Usage

Arguments

Value

Examples

Calculate confidence intervals of mean or difference between means in a data.frame

Description

Usage

Arguments

Value

Methods (by generic)

Examples

Calculate confidence intervals of mean or difference between means

Description

Usage

Arguments

Value

Methods (by generic)