Package 'leem'

Title:	Laboratory of Teaching to Statistics and Mathematics
Description:	An educational package for the teaching of statistics and mathematics in primary and higher education. The objective is to assist in teaching/learning for both student study planning and teacher teaching strategies. The leem package will try to bring, in a simple and at the same time in-depth, knowledge of statistics and mathematics to everyone who wants to study these areas of knowledge. The main function of the package is 'leem' function.
Authors:	Ben Deivide [aut, cre] , Alexandre Celestino [ctb] , Juliane Nassarala [ctb]
Maintainer:	Ben Deivide <[email protected]>
License:	GPL (>= 2)
Version:	0.2.0.9000
Built:	2024-11-07 21:13:45 UTC
Source:	https://github.com/bendeivide/leem

Range

Description

Compute the sample range

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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()
# 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()

Plot of probability function of any discrete variable

Description

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

Usage

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

Arguments

`x`	numeric vector of values of $X$ . See Details.
`p`	numeric vector of $p_X(x)$ . See Details.
`main`	main title for the plot.
`xlab`	a label for the x axis.
`ylab`	a label for the y axis.

Details

Consider the $X$ distribution:

$p_X(x)$ :	0.23	0.27	0.30	0.12	0.08
$x$ :	1	2	3	4	5

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

Plot of cumulative distribution function of any discrete variable

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

Arguments

`x`	numeric vector of values of $X$ . See Details.
`fda`	numeric vector of $F_X(x)$ . See Details.
`main`	main title for the plot.
`xlab`	a label for the x axis.
`ylab`	a label for the y axis.

Details

Consider the $X$ distribution:

$p_X(x)$ :	0.23	0.27	0.30	0.12	0.08
$x$ :	1	2	3	4	5

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

Coefficient of variation

Description

Compute the sample coeffient of variation

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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)

# 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)

Insert measures of position in plot

Description

Generic function that allows inserting measures of position in plots

Usage

insert(dados, ...)

## Leem S3 method:
insert(x, type = "black", lcol, tcol = lcol, acol = lcol, parrow = 0.5,
       larrow = 0.2, ptext = 0.6, side = "right", lwd = 2, lwdarrow = lwd)

## Default S3 method:
insert(x)
insert(dados, ...)

## Leem S3 method:
insert(x, type = "black", lcol, tcol = lcol, acol = lcol, parrow = 0.5,
       larrow = 0.2, ptext = 0.6, side = "right", lwd = 2, lwdarrow = lwd)

## Default S3 method:
insert(x)

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`type`	Type of measure of position. The default is `type = "mean"`. Other options: `"median"`, `"mode"` or `"all"`.
`lcol`	Vertical line color type. The default is `lpcol = "black"`. This argument must be the same length as the `type` argument.
`tcol`	Text color type. The default is `tcol = lcol`.
`acol`	Arrow color type. The default is `acol = lcol`.
`parrow`	Text and arrow height. The default is `parrow = 0.5`. This argument must be the same length as the `type` argument.
`larrow`	Text and arrow length. The default is `larrow = 0.6`.
`ptext`	Distance between lines of text. The default is `ptext = 0.06`.
`side`	Side to insert the text. The default is `side = "right"`. This argument must be the same length as the `type` argument.
`lwd`	numeric argument. The vertical line width. The default is `lwd = 2`.
`lwdarrow`	numeric argument. The arrow width. The default is `lwdarrow = lwd`.

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)
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
 )
# 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
 )

Mean absolute deviation

Description

Compute the sample mean absolute deviation

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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()
# 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()

Median absolute deviation

Description

Compute the sample median absolute deviation

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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()
# 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()

Mode value

Description

Compute the sample mode.

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).
`details`	Logical object. Details of data (default `FALSE`).
`...`	further arguments passed to or from other methods.

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()

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()

Measures of position

Description

Compute all measures of position

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function. Complex vectors are allowed for `trim = 0`, only.
`trim`	The fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).
`details`	Logical object. Details of data (default `FALSE`).
`...`	further arguments passed to or from other methods.

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)
# 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)

Mean standard error

Description

Compute the sample mean standard error

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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)
# 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)

Ogives Graph

Description

Generic function that plots the culmulative frequency curve.

Usage

ogive(x, ...)

## Leem S3 method:
ogive(x, 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)
ogive(x, ...)

## Leem S3 method:
ogive(x, 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

`x`	R object (list) of class leem. Use `new_leem()` function.
`...`	further arguments passed to or from other methods.
`freq`	Character argument. Type of frequency with options: `"a"` (absolute and default), `"r"` relative and `"p"` percentage.
`decreasing`	Logical argument. Default is `FALSE`. If `decreasing = FALSE`, it represents the "ogive larger than", if `decreasing = TRUE`, it represents the "ogive less than".
`both`	Logical argument. Default is `FALSE`. If `both = TRUE`, both o will be plotted. If `both = FALSE` otherside.
`bars`	Logical argument. Default is `FALSE`. If `bars = TRUE`, the bars of the accumulated frequency will be inserted to plot, according to the `decreasing` argument. If `bars = FALSE` otherside.
`histogram`	Logical argument. Default is `FALSE`. If `histogram = TRUE`, the histogram will be inserted to plot.
`bg`	Logical argument. Default is `TRUE`, it displays the background, and `bg = FALSE` otherwise.
`main`	Insert the plot title. The default is `NULL`.
`xlab`	Insert the title of the x-axis graphic label. The default is `NULL`.
`ylab`	Insert the title of the y-axis graphic label. The default is `NULL`.
`grids`	Insert grids to plot. The default is `grid(col = "white")`.
`bgcol`	Insert the background color. This argument is only valid when `bg = TRUE`. The default is `bgcol="gray"`.
`bgborder`	Insert the background border color. This argument is only valid when `bg = TRUE`. The default is bgborder = NA.
`barcol`	Insert the barplot color. The default is `barcol = "yellow"`. This argument is only valid when `bars = TRUE`.
`histcol`	Insert the histogram color. The default is `histcol = barcol`. This argument is only valid when `histogram = TRUE`.
`barborder`	Insert the barplot border color. This argument is only valid when `bars = TRUE`. The default is barborder = "gray".
`histborder`	Insert the histogram border color. This argument is only valid when `histogram = TRUE`. The default is histborder = barborder.
`type`	Type of plot. The default is `type = "b"`, i.e., line and points. See `graphical parameter` for details.
`lpcol`	Type of line color. The default is `lpcol = "black"`.
`lwd`	numeric argument. The line width. The default is `lwd = 2`.
`pch`	Type of point. The default is `pch = 19`.
`lty`	Type of line. The default is `lty = 2`.

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, hist = TRUE)
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, hist = TRUE)

Cumulative distribution function

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

Arguments

`q`	quantile. The `q` argument can have length 1 or 2. See Details.
`dist`	distribution to use. The default is `'normal'`. Options: `'normal'`, `'t-student'`, `'gumbel'`, `'binomial'`, `'poisson'`, and ....
`lower.tail`	logical; if `TRUE` (default), probabilities are $P[X \leq x]$ otherwise, $P[X > x]$ . This argument is valid only if `q` has length 1.
`rounding`	numerical; it represents the number of decimals for calculating the probability.
`porcentage`	logical; if `FALSE` (default), the result in decimal. Otherwise, probability is given in percentage.
`gui`	default is `'plot'`; it graphically displays the result of the probability. Others options are: `'none'`, `'rstudio'` or `'tcltk'`.
`...`	additional arguments according to the chosen distribution.

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:

dist = "t-student": nu argument ( $\nu$ ) represents the degrees of freedom parameter. The PDF is

$\frac{\Gamma\left$ \frac{\nu + 1}{2} \right$}{\sqrt{\nu \pi}}\left$1 + \frac{x^2}{\nu}\right$$

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 - t-Student 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)))
# Loading package
library(leem)
# Example 1 - t-Student 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)))

Pie Chart

Description

Draw a pie chart.

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`labels`	One or more expressions or character strings giving names for the slices
`col`	Character vector. Default `col = heat.colors(5)`.
`border`	Logical argument (default `FALSE`).
`main`	Title name.
`...`	further arguments passed to or from other methods.

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)
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)

Frequency polygon Graph

Description

Generic function that plots the frequency polygon curve.

Usage

polyfreq(x, ...)

## Leem S3 method:
polyfreq.leem(x, 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)
polyfreq(x, ...)

## Leem S3 method:
polyfreq.leem(x, 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

`x`	R object (list) of class leem. Use `new_leem()` function.
`...`	further arguments passed to or from other methods.
`freq`	Character argument. Type of frequency with options: `"a"` (absolute and default), `"r"` relative and `"p"` percentage.
`type`	Type of plot. The default is `type = "b"`, i.e., line and points. See `graphical parameter` for details.
`bars`	Logical argument. Default is `FALSE`. If `bars = TRUE`, the histogram will be inserted to plot.
`bg`	Logical argument. Default is `TRUE`, it displays the background, and `bg = FALSE` otherwise.
`main`	Insert the plot title. The default is `NULL`.
`xlab`	Insert the title of the x-axis graphic label. The default is `NULL`.
`ylab`	Insert the title of the y-axis graphic label. The default is `NULL`.
`grids`	Insert grids to plot. The default is `grid(col = "white")`.
`bgcol`	Insert the background color. This argument is only valid when `bg = TRUE`. The default is `bgcol="gray"`.
`bgborder`	Insert the background border color. This argument is only valid when `bg = TRUE`. The default is bgborder = NA.
`barcol`	Insert the barplot color. The default is `barcol = "yellow"`. This argument is only valid when `bars = TRUE`.
`histcol`	Insert the histogram color. The default is `histcol = barcol`. This argument is only valid when `histogram = TRUE`.
`barborder`	Insert the barplot border color. This argument is only valid when `bars = TRUE`. The default is barborder = "gray".
`histborder`	Insert the histogram border color. This argument is only valid when `histogram = TRUE`. The default is histborder = barborder.
`lpcol`	Type of line color. The default is `lpcol = "black"`.
`lwd`	numeric argument. The line width. The default is `lwd = 2`.
`pch`	Type of point. The default is `pch = 19`.
`lty`	Type of line. The default is `lty = 2`.

Examples

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

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

Draw a pie chart.

Description

Draw a pie chart.

Usage

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

Arguments

`col`	Character vector. Default `col = heat.colors(5)`.
`x`	R object (list) of class leem. Use `new_leem()` function.
`labels`	One or more expressions or character strings giving names for the slices
`border`	Logical argument (default `FALSE`).
`main`	Title name.
`...`	further arguments passed to or from other methods.

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)
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)

Quantile distribution function.

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

Arguments

`p`	probability. The `p` argument need have length 1 and value lower then 1.
`dist`	distribution to use. The default is `'normal'`. Options: `'normal'`, `'t-student'`, `'gumbel'`, `'binomial'`, `'poisson'`, and ....
`lower.tail`	logical; if `TRUE` (default), the quantile function is computed; otherwise, the complement of the quantile function (survival function) will be computed. The `lower.tail` argument will only be valid for two-`sided = FALSE`.
`two.sided`	logical. if `TRUE` (default), the calculation of the quantile function and survival will be presented; otherwise the `Q()` function will be based according to the `lower.tail` argument.
`rounding`	numerical; it represents the number of decimals for calculating the probability.
`gui`	default is `'plot'`; it graphically displays the result of the probability. Others options are: `"plot"` and `"rstudio"` and `"tcltk"`.
`mfrow`	numerical vector. Considering the arguments `two.sided = TRUE` and `type = "both"`, the default will be to present two graphs (based on CDF and PDF) horizontally for the quantile function, that is, `mfrow = c(1, 2)` (default).
`type`	character argument. The default is `"both"`; the output will display two plots (based on CDF and PDF) to present the result of Q(). The other options are: `"cdf"` and `"pdf"`.
`...`	additional parameters according to the chosen distribution.

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

## End(Not run)

Standard Deviation

Description

Compute the sample standard deviation

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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)
# 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)

Undertanding a box plot

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

Arguments

`horizontal`	Logical argument indicating if the boxplots should be horizontal; default `FALSE` means vertical boxes.
`col`	Character vector. Default `col = rgb(0, 175, 239, maxColorValue = 255)`.

Examples

library(leem)
# Example 1
showboxplot()

library(leem)
# Example 1
showboxplot()

Distribution Function Properties

Description

Graphic presentation of properties for distribution function

Usage

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

Arguments

`variable`	Variabe type. Defaults `variable = "discrete"`. The options are: `discrete` or "`1`" and `continuous` or "`2`".
`prop`	Properties for distribution function. See Details.

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()
library(leem)
# Example 1
showcdf()

Understanding the Confiance Indice

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

Arguments

`dist`	Parameter to indicate the distribution of the graphic, fixed for now.
`ci`	Parameter to indicate the region of the confiance indice.
`main`	Parameter to indicate the title of the graphic.

Examples

library(leem)
# Example 1
showci()

library(leem)
# Example 1
showci()

Plot of interpretation about Kurtosis

Description

showkur Interpretation of kutosis

Usage

showkur()
showkur()

Value

showkur returns a plot with the kurtosis characteristics.

Examples

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

## End(Not run)

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

## End(Not run)

Interpretation of location and scale parameters

Description

showpar Function that exemplifies the interpretation of location and scale parameters

Usage

showpar(gui = "rstudio")
showpar(gui = "rstudio")

Arguments

gui

character argument. The options are: "rstudio" (default) and "tcltk".

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)

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

## End(Not run)

Plot of interpretation about skewsness

Description

showskew Interpretation of asymmetry based on frequency distributions

Usage

showskew(mpos = FALSE)
showskew(mpos = FALSE)

Arguments

measures

shows the measures of position or not (default FALSE).

Value

showskew returns a plot with the skewsness characteristics.

Examples

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

## End(Not run)

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

## End(Not run)

Understanding the Ztable.

Description

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

Usage

showtabnormal(z)
showtabnormal(z)

Arguments

`z`	Parameter for lacate the z value on the table; default `NULL` means error if not insert a number.

Examples

library(leem)
# Example 1
showtabnormal(zvalue)
library(leem)
# Example 1
showtabnormal(zvalue)

Skewness value

Description

Compute the skewness

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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)

# 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)

Stick chart

Description

Stick chart for discrete data

Usage

stickchartunction(x, ...)
stickchartunction(x, ...)

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`freq`	Character argument. Type of frequency with options: `"a"` (absolute and default), `"r"` relative and `"p"` percentage.
`bg`	Logical argument. Default is `TRUE`, it displays the background, and `bg = FALSE` otherwise.
`main`	Insert the plot title. The default is `NULL`.
`xlab`	Insert the title of the x-axis graphic label. The default is `NULL`.
`ylab`	Insert the title of the y-axis graphic label. The default is `NULL`.
`grids`	Insert grids to plot. The default is `grid(col = "white")`.
`bgcol`	Insert the background color. This argument is only valid when `bg = TRUE`. The default is `bgcol="gray"`.
`bgborder`	Insert the background border color. This argument is only valid when `bg = TRUE`. The default is bgborder = NA.
`posx1`	Numeric argument.Distance of the labels (horizontal) in relation to the x axis.
`posx2`	Numeric argument.Distance of the labels (vertical) in relation to the x axis.
`xang`	Numeric argument.Angle of the labels in relation to the x axis
`labels`	Character argument. Labels name vector.
`lcol`	Line color. The default is `lcol = "black"`.
`pcol`	Point color. The default is `pcol = lcol`.
`pty`	Point type. The default is `pty = 19`.
`pwd`	Point width. The default is `pwd = 3`.
`lty`	Line type. The default is `lty = 2`.
`lwd`	Line width. The default is `lwd = 2`.
`...`	further arguments passed to or from other methods.

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)
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)

Frequency distribution table

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(dados, ...)

## Leem S3 method:
tabfreq(data, k = NULL, na.rm = FALSE, ordered = NULL, namereduction = TRUE, ...)

## Default S3 method:
tabfreq(data)
tabfreq(dados, ...)

## Leem S3 method:
tabfreq(data, k = NULL, na.rm = FALSE, ordered = NULL, namereduction = TRUE, ...)

## Default S3 method:
tabfreq(data)

Arguments

`data`	R object (data structure vector) of class leem. Use `new_leem()` function.
`k`	Number of classes. Default is `NULL`.
`na.rm`	a logical evaluating to TRUE or FALSE indicating whether NA values should be stripped before the computation proceeds.
`ordered`	Ordered vector of the same length and elements of data object. Default is `NULL`.
`namereduction`	Logical argument. If `TRUE` (default), the group names are reduzed the 10 characters. If `FALSE`, otherwise.

Value

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"))
# 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"))

Test of hypothesis

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,
  ...
)
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

`x`	R object. See in details.
`y`	an optional (non-empty) numeric vector of data values.
`test`	character value. The options are: `"ttest"`, `"ztest"`, `"ptest"`, `"chitest"`, `"ftest"`, `"anova"`, `"friedman"`, `"kruskal"`, `"mann whitney"`.
`h0`	numeric value. The hypothesized parameter.
`prop`	a logical indicating whether you want to use the proportion test of not. Default is `prop=FALSE`.
`alternative`	a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.
`alpha`	significance level of the test
`exact`	a logical indicating whether you want to use the exact test or not. Default is `exact=TRUE`.
`correct`	a logical indicating whether Yates' continuity correction should be applied where possible. This argument must be used when `exact = FALSE`.
`paired`	a logical indicating whether you want a paired t-test. Valid only for `test="ttest"`.
`plot`	a logical indicating whether you want a graph indicating the regions of rejection or not of the null hypothesis, as well as the test decision.

Variance value

Description

Compute the sample variance

Usage

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

Arguments

`x`	R object (list) of class leem. Use `new_leem()` function.
`rounding`	Numerical object. Rounds the values in its first argument to the specified number of decimal places (default `2`).
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`details`	Logical object. Details of data (default `FALSE`).
`grouped`	Logical object. Determines whether the measure of position result will be based on grouped data or not (default `TRUE`).

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)

# 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)

Package 'leem'

Help Index

Range

Description

Usage

Arguments

Examples

Plot of probability function of any discrete variable

Description

Usage

Arguments

Details

Value

Examples

Plot of cumulative distribution function of any discrete variable

Description

Usage

Arguments

Details

Value

Examples

Coefficient of variation

Description

Usage

Arguments

Examples

Insert measures of position in plot

Description

Usage

Arguments

Value

Examples

Mean absolute deviation

Description

Usage

Arguments

Examples

Median absolute deviation

Description

Usage

Arguments

Examples

Mode value

Description

Usage

Arguments

Examples

Measures of position

Description

Usage

Arguments

Details

Examples

Mean standard error

Description

Usage

Arguments

Examples

Ogives Graph

Description

Usage

Arguments

Examples

Cumulative distribution function

Description

Usage

Arguments

Details

Value

Examples

Pie Chart

Description

Usage

Arguments

Examples

Frequency polygon Graph

Description

Usage

Arguments

Examples