r6qualitytools (version 1.0.1)

Distr: Distr-class: Class `Distr`

Description

R6 Class for Distribution Objects

Public fields

x

Numeric vector of data values.

name

Character string representing the name of the distribution.

parameters

List of parameters for the distribution.

sd

Numeric value representing the standard deviation of the distribution.

n

Numeric value representing the sample size.

loglik

Numeric value representing the log-likelihood.

Methods

Public methods

• Distr$new()

• Distr$plot()

• Distr$clone()

---

Method new()

Initialize the fiels of the `Distribution` object

Usage

Distr$new(x, name, parameters, sd, n, loglik)

Arguments

x

Numeric vector of data values.

name

Character string representing the name of the distribution.

parameters

List of parameters for the distribution.

sd

Numeric value representing the standard deviation of the distribution.

n

Numeric value representing the sample size.

loglik

Numeric value representing the log-likelihood.

---

Method plot()

Plot the distribution with histogram and fitted density curve.

Usage

Distr$plot(
  main = NULL,
  xlab = NULL,
  xlim = NULL,
  xlim.t = TRUE,
  ylab = NULL,
  line.col = "red",
  fill.col = "lightblue",
  border.col = "black",
  box = TRUE,
  line.width = 1
)

Arguments

main

Character string for the main title of the plot. Defaults to the name of the distribution.

xlab

Character string for the x-axis label. Defaults to "x".

xlim

Numeric vector specifying the x-axis limits.

xlim.t

Logical value specifyind to change the xlim default.

ylab

Character string for the y-axis label. Defaults to "Density".

line.col

Character string for the color of the plot line. Default is "red".

fill.col

Character string for the color of the fill histogram plot line. Default is "lightblue".

border.col

Character string for the color of the border of the fill histogram plot line. Default is "black".

box

Logical value indicating whether to draw a box with the parameters in the plot. Default is TRUE.

line.width

Numeric value specifying the width of the plot line. Default is 1.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Distr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

distribution, FitDistr, DistrCollection

Examples

# Normal
set.seed(123)
data1 <- rnorm(100, mean = 5, sd = 2)
parameters1 <- list(mean = 5, sd = 2)
distr1 <- Distr$new(x = data1, name = "normal", parameters = parameters1,
                    sd = 2, n = 100, loglik = -120)
distr1$plot()

# Log-normal
data2 <- rlnorm(100, meanlog = 1, sdlog = 0.5)
parameters2 <- list(meanlog = 1, sdlog = 0.5)
distr2 <- Distr$new(x = data2, name = "log-normal", parameters = parameters2,
                    sd = 0.5, n = 100, loglik = -150)
distr2$plot()

# Geometric
data3 <- rgeom(100, prob = 0.3)
parameters3 <- list(prob = 0.3)
distr3 <- Distr$new(x = data3, name = "geometric", parameters = parameters3,
                    sd = sqrt((1 - 0.3) / (0.3^2)), n = 100, loglik = -80)
distr3$plot()

# Exponential
data4 <- rexp(100, rate = 0.2)
parameters4 <- list(rate = 0.2)
distr4 <- Distr$new(x = data4, name = "exponential", parameters = parameters4,
                    sd = 1 / 0.2, n = 100, loglik = -110)
distr4$plot()

# Poisson
data5 <- rpois(100, lambda = 3)
parameters5 <- list(lambda = 3)
distr5 <- Distr$new(x = data5, name = "poisson", parameters = parameters5,
                    sd = sqrt(3), n = 100, loglik = -150)
distr5$plot()

# Chi-square
data6 <- rchisq(100, df = 5)
parameters6 <- list(df = 5)
distr6 <- Distr$new(x = data6, name = "chi-squared", parameters = parameters6,
                    sd = sqrt(2 * 5), n = 100, loglik = -130)
distr6$plot()

# Logistic
data7 <- rlogis(100, location = 0, scale = 1)
parameters7 <- list(location = 0, scale = 1)
distr7 <- Distr$new(x = data7, name = "logistic", parameters = parameters7,
                    sd = 1 * sqrt(pi^2 / 3), n = 100, loglik = -140)
distr7$plot()

# Gamma
data8 <- rgamma(100, shape = 2, rate = 0.5)
parameters8 <- list(shape = 2, rate = 0.5)
distr8 <- Distr$new(x = data8, name = "gamma", parameters = parameters8,
                    sd = sqrt(2 / (0.5^2)), n = 100, loglik = -120)
distr8$plot()

# f
data9 <- rf(100, df1 = 5, df2 = 10)
parameters9 <- list(df1 = 5, df2 = 10)
df1 <- 5
df2 <- 10
distr9 <- Distr$new(x = data9, name = "f", parameters = parameters9,
                    sd = sqrt(((df2^2 * (df1 + df2 - 2)) /
                               (df1 * (df2 - 2)^2 * (df2 - 4)))),
                    n = 100, loglik = -150)
distr9$plot()

# t
data10 <- rt(100, df = 10)
parameters10 <- list(df = 10)
distr10 <- Distr$new(x = data10, name = "t", parameters = parameters10,
                     sd = sqrt(10 / (10 - 2)), n = 100, loglik = -120)
distr10$plot()

# negative binomial
data11 <- rnbinom(100, size = 5, prob = 0.3)
parameters11 <- list(size = 5, prob = 0.3)
distr11 <- Distr$new(x = data11, name = "negative binomial",
                     parameters = parameters11,
                     sd = sqrt(5 * (1 - 0.3) / (0.3^2)),
                     n = 100, loglik = -130)
distr11$plot()