Package 'leem'

Description

Compute the sample range

Usage

amplitude(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  amplitude(grouped = FALSE)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  amplitude()

Description

Help in building the plot of the probability function of any discrete variable

Usage

apf(x, p, main = NULL, xlab = NULL, ylab = NULL)

Arguments

Details

Consider the $X$ distribution:

where $p_X(x)$ and $x$ are probability function and values of $X$. See Example 1.

Value

The output is plot of distribution function. See Example 1.

Examples

# Example 1
x <- 1:5
p <- c(0.23, 0.27,0.30, 0.12, 0.08)
apf(x, p)

Description

Class method leem for generic barplot

Usage

## S3 method for class 'leem'
barplot(
  height,
  freq = "a",
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  barborder = "gray",
  posx1 = 0,
  posx2 = 0,
  xang = 0,
  labels = NULL,
  ...
)

Arguments

Examples

library(graphics)
# Example 1 - Simple example
library(leem)
rep(1:5, 5:1) |>
  new_leem() |>
  barplot()
# Example 2 - Color bars
rep(1:5, 5:1) |>
  new_leem() |>
  barplot(barcol = heat.colors(5))
# Example 3 - Ordered data
library(leem)
school <- rep(c("high", "university", "basic"), 3:5)
sample(school, 30, TRUE) |>
 new_leem() |>
 tabfreq(ordered = c("basic", "high", "university")) |>
 barplot(xang = 15, posx2 = -0.2)
# Example 4 - Coerced to histogram
rnorm(100, 10, 2) |>
  new_leem(variable = 2) |>
  barplot(barcol = heat.colors(10))

Description

Produce box-and-whisker plot(s) of leem class object and computes the necessary values for the development of the plot.

Usage

## S3 method for class 'leem'
boxplot(
  x,
  type = "rawdata",
  details = FALSE,
  horizontal = FALSE,
  coef = 1.5,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  col = rgb(0, 175, 239, maxColorValue = 255),
  ...
)

Arguments

Examples

library(leem)
# Example 1
x <- rnorm(30, 100, 2) |>
  new_leem(variable = 2) |>
  tabfreq()
boxplot(x, details = TRUE)
# Example 2
boxplot(x, type = "classes")

Description

Help in building the plot of the cumulative distribution function of any discrete variable

Usage

cdfd(x, fda, main = NULL, xlab = NULL, ylab = NULL)

Arguments

Details

Consider the $X$ distribution:

where $p_X(x)$ and $x$ are probability function and values of $X$. Consider also the $X$ distribution function:

$F_X(x) = \left\{\begin{array}{ll} 0, & \textrm{if } x < 1;\\ 0.23, & \textrm{if } 1 \leq x < 2;\\ 0.50, & \textrm{if } 2 \leq x < 3;\\ 0.80, & \textrm{if } 3 \leq x < 4;\\ 0.92, & \textrm{if } 4 \leq x < 5;\\ 1.00 & \textrm{if } x \geq 5.\\ \end{array}\right.$

This way, the cdfd function needs to consider only the vectors x <- 1:5 and fda <- c(0.23, 0.50, 0.80, 0.92, 1), that is, only the equality conditions for $x$. See Example 1.

Value

The output is plot of distribution function. See Example 1.

Examples

# Example 1
x <- 1:5
fda <- c(0.23, 0.5, 0.8, 0.92, 1)
cdfd(x, fda)

Description

Compute the sample coeffient of variation

Usage

cv(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  cv()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  cv(grouped = FALSE)

Description

Density, distribution function, quantile function and random generation for the normal distribution with parameters: location and scale

Usage

dgumbel(x, location, scale)

pgumbel(q, location, scale, lower.tail = TRUE)

qgumbel(p, location = 0, scale = 1, lower.tail = TRUE)

Arguments

Details

The CDF of Gumbel distribution is:

$F(x;\mu ,\beta )=e^{-e^{-(x-\mu )/\beta }}, \quad \mu \in \mathbf{R}, \beta > 0,$

where $\mu$ is location parameter (location) and $\beta$ is scale parameter (scale). The PDF of Gumbel distribution is:

$\frac{1}{\beta }e^{-(z+e^{-z})},$

where $z={\frac {x-\mu }{\beta }}$. The quantile is:

$\mu -\beta \ln(-\ln(p)), \quad 0 < p < 1.$

Examples

# PDF
dgumbel(1, 0, 1)
# CDF
pgumbel(1, 0, 1)
# Quantile
qgumbel(0.2, 0, 1)

Description

Class method leem for generic hist

Usage

## S3 method for class 'leem'
hist(
  x,
  freq = "a",
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  barborder = "gray",
  ...
)

Arguments

Examples

# Example 1
library(leem)
rnorm(36, 100, 50) |> new_leem(variable = "continuous") |> tabfreq() |> hist()

# Example 2
library(leem)
school <- rep(c("high", "university", "basic"), 3:5)
sample(school, 30, TRUE) |>
 new_leem() |>
 tabfreq(ordered = c("basic", "high", "university"))

Description

Generic function that allows inserting measures of position in plots

Usage

insert(x, ...)

Arguments

Examples

# Example 1
library(leem)
set.seed(10)
rnorm(36, 100, 50) |>
 new_leem(variable = "continuous") |>
 tabfreq() |>
 hist() |>
 insert(
  lcol = "black",
  tcol = "purple",
  acol = "brown",
  parrow = 0.6,
  larrow = 0.6,
  ptext = 0.4,
  side = "left",
  lwd = 2,
  lwdarrow = 4
 )

Description

Method of insert function

Usage

## S3 method for class 'leem'
insert(
  x,
  type = "mean",
  lty = 1,
  lcol = "black",
  tcol = lcol,
  acol = lcol,
  parrow = 0.5,
  larrow = 0.6,
  ptext = 0.06,
  side = "right",
  lwd = 2,
  lwdarrow = lwd,
  ...
)

Arguments

Value

No return value. This function adds elements to an existing plot.

Examples

# Example 1
library(leem)
set.seed(10)
rnorm(36, 100, 50) |>
 new_leem(variable = "continuous") |>
 tabfreq() |>
 hist() |>
 insert(
  lcol = "black",
  tcol = "purple",
  acol = "brown",
  parrow = 0.6,
  larrow = 0.6,
  ptext = 0.4,
  side = "left",
  lwd = 2,
  lwdarrow = 4
 )

Description

leem A Graphical User Interface (GUI) for the leem package

Usage

leem(gui = TRUE)

Arguments

Value

leem presents GUI with various problems for the teaching of statistics and mathematics. The idea is to use this package to learn these subjects without necessarily programming in R

Examples

# Loading package
library(leem)
if (interactive()) {
  leem(gui = FALSE)
}

Description

Compute the sample mean absolute deviation

Usage

madev(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  madev(grouped = FALSE)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  madev()

Description

Arithmetic mean Class method leem for the generic mean function

Usage

## S3 method for class 'leem'
mean(
  x,
  trim = 0,
  na.rm = FALSE,
  rounding = 2,
  grouped = TRUE,
  details = FALSE,
  ...
)

Arguments

Examples

# Example 1
set.seed(10)
x <- rnorm(36, 100, 50)
y <- rbinom(36, 10, 0.8)
y |> new_leem(variable = "discrete") |> tabfreq() |> mean()
x |> new_leem(variable = "continuous") |> tabfreq() |> mean()

Description

Compute the sample median absolute deviation

Usage

medev(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  medev(grouped = FALSE)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  medev()

Description

Class method leem for the generic median function

Usage

## S3 method for class 'leem'
median(x, na.rm = FALSE, rounding = 2, grouped = TRUE, details = FALSE, ...)

Arguments

Examples

library(leem)
library(stats)
# Examples
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> median()

Description

Compute the sample mode.

Usage

mfreq(x, na.rm = FALSE, rounding = 2, grouped = TRUE, details = FALSE)

Arguments

Examples

library(leem)
# set.seed(10)
x <- rnorm(36, 100, 50)
set.seed(10)
y <- rbinom(36, 10, 0.8)
w <- rep(letters[1:4], 1:4)
(tab1 <- y |> new_leem(variable = "discrete") |> tabfreq())
(tab2 <- x |> new_leem(variable = "continuous") |> tabfreq())
(tab3 <- w |> new_leem(variable = "discrete") |> tabfreq())
y |> new_leem(variable = "discrete") |> tabfreq() |> mfreq()
x |> new_leem(variable = "continuous") |> tabfreq() |> mfreq()
w |> new_leem(variable = "discrete") |> tabfreq() |> mfreq()

Description

Compute all measures of position

Usage

mpos(
  x,
  trim = 0,
  na.rm = FALSE,
  rounding = 2,
  grouped = TRUE,
  details = FALSE,
  ...
)

Arguments

Details

The measures of position are: average, median and mode.

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  mpos()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  mpos(grouped = FALSE)

Description

Compute the sample mean standard error

Usage

mstde(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  mstde(rounding = 4)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  mstde(grouped = FALSE)

Description

Function that assists other functions of leem package

Usage

new_leem(x, variable = "discrete")

Arguments

Value

The variable argument also allows using variable = 1 for categorical variable and variable = 2 for continuous variable.

Examples

# Example 1
library(leem)
x <- rbinom(36, 10, 0.6)
new_leem(x, variable = 1)

# Example 2 (Pipe operator)
rnorm(36, 100, 4) |> new_leem(variable = 2)

Description

Generic function that plots the culmulative frequency curve.

Usage

ogive(x, ...)

## S3 method for class 'leem'
ogive(
  x,
  freq = "a",
  decreasing = FALSE,
  both = FALSE,
  bars = FALSE,
  histogram = FALSE,
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  histcol = barcol,
  barborder = "gray",
  histborder = barborder,
  type = "b",
  lpcol = "black",
  lwd = 2,
  pch = 19,
  lty = 2,
  ...
)

Arguments

Value

Ogive plot.

Examples

library(leem)
# Example 1 - Both ogives
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> ogive(both = TRUE)

# Example 2 - Insert barplot
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> ogive(both = TRUE, bars = TRUE)
# Example 3 - Insert histogram
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> ogive(both = TRUE, histogram = TRUE)

Description

P Compute the cumulative distribution function for multiple distributions

Usage

P(
  q,
  dist = "normal",
  lower.tail = TRUE,
  rounding = 5,
  porcentage = FALSE,
  gui = "plot",
  main = NULL,
  ...
)

Arguments

Details

The argument that can have length 2, when we use the functions that give us the probability regions, given by: %<X<%, %<=X<%, %<X<=%, %<=X<=%, %>X>%, %>X=>%, %>X=>% and %>=X=>%. The additional arguments represent the parameters of the distributions, that is:

• If dist = "normal" (Default); the additional arguments are: mean ($\mu$) and sd ($\sigma$). The PDF is given by:

  $\displaystyle{\frac {1}{\sqrt {2\pi \sigma ^{2}}}}e^{-{\frac {(x-\mu )^{2}}{2\sigma ^{2}}}}, \quad \mu \in \mathbb{R},~\sigma^2 > 0;$

• If dist = "t-student"; the additional argument is: df ($\nu$). The PDF is given by:

  $\displaystyle{\frac {\Gamma \left({\frac {\ \nu +1\ }{2}}\right)}{{\sqrt {\pi \ \nu \ }}\ \Gamma \left({\frac {\nu }{\ 2\ }}\right)}}\left(\ 1+{\frac {~x^{2}\ }{\nu }}\ \right)^{-{\frac {\ \nu +1\ }{2}}}, \quad \nu > 1;$

• If dist = "chisq"; the additional argument is: df ($\nu$). The PDF is given by:

  $\displaystyle{\frac {1}{2^{k/2}\Gamma (k/2)}}\;x^{k/2-1}e^{-x/2}, \quad \nu > 0;$

Value

P returns the probability and its graphical representation. The result can be given as a percentage or not.

Examples

# Loading package
library(leem)
# Example 1 - Student's t distribution
## Not run: 
P(q = 2, dist = "t-student", df = 10)
P(q = 2, dist = "t-student", df = 10, gui = 'rstudio')
P(q = 2, dist = "t-student", df = 10, gui = 'tcltk')
P(-1 %<X<% 1, dist = "t-student", df = 10)

## End(Not run)
# Example 2 - Normal distribution
P(-2,  dist = "normal", mean = 3, sd = 2,
  main = expression(f(x) == (1 / sqrt(n * sigma^2)) *
  exp(-1/2 * (x - mu)^2/sigma^2)))

Description

Draw a pie chart.

Usage

piechart(
  x,
  labels = NULL,
  col = heat.colors(5, 1),
  border = FALSE,
  main = NULL,
  ...
)

Arguments

Examples

library(leem)
# Example 1
school <- rep(c("high", "university", "basic"), 3:5)
x <- sample(school, 30, TRUE) |>
  new_leem() |>
  tabfreq(ordered = c("basic", "high", "university"))
# Example 2
x <- rbinom(36, 10, 0.6)
x <- new_leem(x, variable = "discrete")
x <- tabfreq(x)
piechart(x)

Description

Generic function that plots the frequency polygon curve.

Usage

polyfreq(x, ...)

Arguments

Examples

# Example 1
library(leem)
rnorm(36, 100, 50) |> new_leem(variable = "continuous") |> tabfreq() |> polyfreq()

Description

Plot the frequency polygon curve.

Usage

## S3 method for class 'leem'
polyfreq(
  x,
  freq = "a",
  type = "b",
  bars = TRUE,
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  barborder = "gray",
  lpcol = "black",
  lwd = 2,
  pch = 19,
  lty = 2,
  ...
)

Arguments

Examples

# Example 1
library(leem)
rnorm(36, 100, 50) |> new_leem(variable = "continuous") |> tabfreq() |> polyfreq()

Description

Using a graphical visualization, it is possible to understand the probabilities involved in a normal distribution.

Usage

probnormal(
  a = 1,
  b = 2,
  col = "lightblue",
  mean = 0,
  sd = 1,
  type = 1,
  rounding = 4,
  zang = 0,
  xang = 0
)

Arguments

Details

• ⁠type = 1,2⁠: a and b must be greater than mean;

• ⁠type = 3,4⁠: a and b must be less than mean;

• ⁠type = 5,6⁠: a and b can be any real value.

Examples

## Not run: 
probnormal(type = 2)
probnormal(-1, 0, type = 3)
probnormal(-1, 0, type = 4)
probnormal(-1, 0, type = 5)
probnormal(-1, 2, type = 5)
probnormal(1, 2, type = 5)
probnormal(1, 2, type = 6)

## End(Not run)

Description

Graphically it is possible to observe some properties of the normal distribution

Usage

propofnormal(col = "lightblue2", type = 1)

Arguments

Description

Q Quantile function for multiple distributions.

Usage

Q(
  p,
  dist = "normal",
  lower.tail = TRUE,
  two.sided = FALSE,
  rounding = 2,
  gui = "plot",
  mfrow = c(1, 2),
  type = "both",
  ...
)

Arguments

Details

The expression of quantile function is given by:

$Q(p)=\inf {x\in \mathbb{R}: p \le F(x)},$

where p is the first argument of Q() and x its return value;

Value

Q returns the quantile and its graphical representation for a given distribution. The output is a vector.

Examples

# Attaching package
library(leem)
## Not run: 
Q(p = 0.8, dist = "normal", mean = 200, sd=30)

## End(Not run)

Description

These binary operators return a vector of length 2, describing the desired probability region.

Usage

a %>x>% b

a %>X>% b

a %<X<% b

a %<x<% b

a %>=X>=% b

a %>=x>=% b

a %<=X<=% b

a %<=x<=% b

a %>=X>% b

a %>=x>% b

a %>X>=% b

a %>x>=% b

a %<=X<% b

a %<=x<% b

a %<X<=% b

a %<x<=% b

Arguments

Value

A vector of lenght 2.

Examples

# Example 1 - Discrete
2L %>x>% 5L
2L %>X>% 5L
2L %<X<% 5L
2L %<x<% 5L
2L %>=X>=% 5L
2L %>=x>=% 5L
2L %<=X<=% 5L
2L %<=x<=% 5L
2L %>=X>% 5L
2L %>=x>% 5L
2L %>x>=% 5L
2L %>X>=% 5L
2L %<=X<% 5L
2L %<=x<% 5L
2L %<X<=% 5L
2L %<x<=% 5L

# Example 2 - Continuous
2 %>x>% 5
2 %>X>% 5
2 %<X<% 5
2 %<x<% 5
2 %>=X>=% 5
2 %>=x>=% 5
2 %<=X<=% 5
2 %<=x<=% 5
2 %>=X>% 5
2 %>=x>% 5
2 %>x>=% 5
2 %>X>=% 5
2 %<=X<% 5
2 %<=x<% 5
2 %<X<=% 5
2 %<x<=% 5

Description

Compute the sample standard deviation

Usage

sdev(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  sdev()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  sdev(grouped = FALSE)

Description

Detailing of a box plot, showing the main information contained in this type of graph

Usage

showboxplot(horizontal = FALSE, col = rgb(0, 175, 239, maxColorValue = 255))

Arguments

Examples

library(leem)
# Example 1
showboxplot()

Description

Graphic presentation of properties for distribution function

Usage

showcdf(variable = "discrete", prop = NULL)

Arguments

Details

• prop = 1: $\lim_{x\rightarrow\infty}F_X(x)=1$ and $\lim_{x\rightarrow -\infty}F_X(x)=0$;

• prop = 2: $F_X(x)\leq F_X(y), ~ x\leq y~\forall x,y \in \mathbb{R}$;

• prop = 3: $\lim_{x_n\downarrow x}F_X(x_n)\downarrow F_X(x)$.

Examples

library(leem)
# Example 1
showcdf()

Description

Detailing the confiance indice plot, showing the main information contained in this type of graph.

Usage

showci(dist = "normal", ci = "two.sided", main = NULL)

Arguments

Examples

library(leem)
# Example 1
showci()

Description

showkur Interpretation of kutosis

Usage

showkur()

Value

showkur returns a plot with the kurtosis characteristics.

Examples

# Loading package
library(leem)
## Not run: 
showkur()

## End(Not run)

Description

showpar Function that exemplifies the interpretation of location and scale parameters

Usage

showpar(gui = "rstudio")

Arguments

Details

The result of the showpar() call will interactively present a plot of the normal distribution showing the behavior of the location and scale parameters via RStudio. For showpar(gui = "tcltk") the result will be displayed in a tcltk interface.

Value

showpar returns an interactive plot.

Examples

# Loading package
library(leem)
## Not run: 
showpar()

## End(Not run)

Description

showskew Interpretation of asymmetry based on frequency distributions

Usage

showskew(mpos = FALSE)

Arguments

Examples

# Loading package
library(leem)
## Not run: 
showskew()

## End(Not run)

Description

Detailing of the Ztable, showing the main information contained in this type of table.

Usage

showtabnormal(z)

Arguments

Examples

library(leem)
# Example 1
showtabnormal(1)

Description

Compute the skewness

Usage

skewness(
  x,
  type = "pearson",
  rounding = 2,
  na.rm = FALSE,
  details = FALSE,
  grouped = TRUE
)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  skewness()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  skewness(grouped = TRUE)

Description

Stick chart for discrete data

Usage

stickchart(
  x,
  freq = "a",
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  posx1 = 0,
  posx2 = 0,
  xang = 0,
  labels = NULL,
  lcol = "black",
  pcol = lcol,
  pty = 19,
  pwd = 3,
  lty = 1,
  lwd = 2,
  ...
)

Arguments

Value

The result of stickchart() is x object.

Examples

library(leem)
# Example 1
rbinom(30, 10, 0.4) |>
  new_leem() |>
  tabfreq() |>
  stickchart()
# Example 2
school <- rep(c("high", "university", "basic"), 3:5)
sample(school, 30, TRUE) |>
  new_leem() |>
  tabfreq(ordered = c("basic", "high", "university")) |>
  stickchart(xang = 15, posx2 = -0.5)

Description

Generic function that allows you to tabulate continuous and categorical data (quantitative or qualitative) in frequency distribution. Depending on the nature of the data, they can be grouped into class ranges or not.

Usage

tabfreq(data, ...)

Arguments

Value

The result of tabfreq() is a list. This list has two elements: table and statistics. The first is the data frequency table, and the second represents some useful statistics for methods of leem class.

Examples

# Example 1
library(leem)
x <- rbinom(36, 10, 0.6)
x <- new_leem(x, variable = "discrete")
tabfreq(x)

# Example 2 (Pipe operator)
rnorm(36, 100, 4) |>
  new_leem(variable = "continuous") |> tabfreq()

# Example 3
x <- rbinom(36, 10, 0.6)
# Constructor (object of leem class)
x <- new_leem(x, variable = "discrete")
tab <- tabfreq(x)
# Details
tab$table
tab$statistics

# Example 3 - ordered categories ("d","a", "b", "c")
w <- rep(letters[1:4], 1:4)
w |> new_leem(variable = "discrete") |> tabfreq(ordered = c("d","a", "b", "c"))

Description

Allows you to tabulate continuous and categorical data (quantitative or qualitative) in frequency distribution. Depending on the nature of the data, they can be grouped into class ranges or not.

Usage

## S3 method for class 'leem'
tabfreq(
  data,
  k = NULL,
  na.rm = FALSE,
  ordered = NULL,
  namereduction = TRUE,
  ...
)

Arguments

Value

The result of tabfreq() is a list. This list has two elements: table and statistics. The first is the data frequency table, and the second represents some useful statistics for methods of leem class.

Examples

# Example 1
library(leem)
x <- rbinom(36, 10, 0.6)
x <- new_leem(x, variable = "discrete")
tabfreq(x)

# Example 2 (Pipe operator)
rnorm(36, 100, 4) |>
  new_leem(variable = "continuous") |> tabfreq()

# Example 3
x <- rbinom(36, 10, 0.6)
# Constructor (object of leem class)
x <- new_leem(x, variable = "discrete")
tab <- tabfreq(x)
# Details
tab$table
tab$statistics

# Example 3 - ordered categories ("d","a", "b", "c")
w <- rep(letters[1:4], 1:4)
w |> new_leem(variable = "discrete") |> tabfreq(ordered = c("d","a", "b", "c"))

Description

Performs hypothesis testing for various parameters of one or more populations

Usage

th(
  x,
  y = NULL,
  test = "ztest",
  h0,
  prop = FALSE,
  delta = 0,
  p,
  pa,
  alternative = c("two.sided", "L", "less", "greater", "G"),
  alpha = 0.05,
  exact = TRUE,
  correct = FALSE,
  paired = FALSE,
  plot = FALSE,
  ...
)

Arguments

Examples

# Null hypothesis
nullhyp <- h0 <- 90
# Simulation
set.seed(10)
data <- rnorm(30, 100, 10)
# Test of hypothesis
th(data, h0 = h0, sd = 10, plot = TRUE)

Description

Compute the sample variance

Usage

variance(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  variance()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  variance(grouped = FALSE)