learningRlab"

library(LearningRlab)
library(graphics)
knitr::opts_chunk$set(
  comment = "#>", 
  collapse = TRUE
)

There are three families of fuctions in LearningRlab:

  1. Main functions: these functions return the result of performing the process represented with the function.

  2. Explained fuctions: these funcions returns the process itself to get the result, with the result.

  3. User Interactive Functions: these functions maintain an interactive contact with the user to guide him in the resolution of the represented function.

Main Functions:

To explain the use of each function, we present a dataset to work with them:

data <- c(1,1,2,3,4,7,8,8,8,10,10,11,12,15,20,22,25)
plot(data); 
data2 <- c(1,1,4,5,5,5,7,8,10,10,10,11,20,22,22,24,25)
plot(data2);

#Binomial variables
n = 3
x = 2
p = 0.7

#Poisson variables
lam = 2
k = 3

#Normal variables
nor = 0.1

#T-Student variables
xt = 290 
ut = 310
st = 50
nt = 16

The arithmetic mean calculus function:

mean_(data)

The geometric mean calculus function:

geometricMean_(data)

The mode calculus function:

mode_(data)

The median calculus function:

median_(data)

The standard deviation calculus function:

standardDeviation_(data)

The average absolute deviation calculus function:

averageDeviation_(data)

The variance calculus function:

variance_(data)

The quartiles calculus function:

quartile_(data)

The percentile calculus function:

percentile_(data,0.3)

The absolute frecuency calculus function:

frecuency_abs(data,1)

The relative frecuency calculus function:

frecuency_relative(data,20)

The absolute acumulated frecuency calculus function:

frecuency_absolute_acum(data,1)

The relative acumulated frecuency calculus function:

frecuency_relative_acum(data,20)

The covariance calculus function:

covariance_(data, data2)

The harmonic mean calculus funtion:

harmonicMean_(data)

The pearson correlaction calculus funtion:

pearson_(data,data2)

The coefficient of variation calculus funtion:

cv_(data)

The Laplace rule calculus funtion:

laplace_(data,data2)

The binomial distribution calculus funtion:

binomial_(n,x,p)

The poisson distribution calculus funtion:

poisson_(k,lam)

The normal distribution calculus funtion:

normal_(nor)

The tstudent distribution calculus funtion:

tstudent_(xt,ut,st,nt)

The chisquared distribution calculus funtion:

chisquared_(data,data2)

The fisher distribution calculus funtion:

fisher_(data,data2)

Explained Functions:

For each main function, there are an explained function to see the calculus process:

explain.mean(data)
explain.geometricMean(data)
explain.mode(data)
explain.median(data)
explain.standardDeviation(data)
explain.averageDeviation(data)
explain.variance(data)
explain.quartile(data)
explain.percentile(data)
explain.absolute_frecuency(data,10)
explain.relative_frecuency(data,8)
explain.absolute_acum_frecuency(data,10)
explain.relative_acum_frecuency(data,8)
explain.covariance(data,data2)
explain.harmonicMean(data)
explain.pearson(data,data2)
explain.cv(data)
explain.laplace(data,data2)
explain.binomial(n,x,p)
explain.poisson(k,lam)
explain.normal(nor)
explain.tstudent(xt,ut,st,nt)
explain.chisquared(data,data2)
explain.fisher(data,data2)

User Interactive Functions:

These functions are designed for the user to practice with them, and they are the following:



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LearningRlab documentation built on Feb. 9, 2021, 1:05 a.m.