library(LearningRlab) library(graphics) knitr::opts_chunk$set( comment = "#>", collapse = TRUE )
There are three families of fuctions in LearningRlab:
Main functions: these functions return the result of performing the process represented with the function.
Explained fuctions: these funcions returns the process itself to get the result, with the result.
User Interactive Functions: these functions maintain an interactive contact with the user to guide him in the resolution of the represented function.
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)
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)
These functions are designed for the user to practice with them, and they are the following:
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