# learningRlab" In LearningRlab: Statistical Learning Functions

```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:

• arithmetic mean:
```explain.mean(data)
```
• geometric mean:
```explain.geometricMean(data)
```
• mode:
```explain.mode(data)
```
• median:
```explain.median(data)
```
• standard deviation:
```explain.standardDeviation(data)
```
• average absolute deviation:
```explain.averageDeviation(data)
```
• variance:
```explain.variance(data)
```
• quartile:
```explain.quartile(data)
```
• percentile:
```explain.percentile(data)
```
• absolute frecuency:
```explain.absolute_frecuency(data,10)
```
• relative frecuency:
```explain.relative_frecuency(data,8)
```
• absolute acumulated frecuency:
```explain.absolute_acum_frecuency(data,10)
```
• relative acumulated frecuency:
```explain.relative_acum_frecuency(data,8)
```
• covariance:
```explain.covariance(data,data2)
```
• harmonic mean:
```explain.harmonicMean(data)
```
• pearson correlaction:
```explain.pearson(data,data2)
```
• coefficient of variation:
```explain.cv(data)
```
• Laplace rule:
```explain.laplace(data,data2)
```
• binomial distribution:
```explain.binomial(n,x,p)
```
• poisson distribution:
```explain.poisson(k,lam)
```
• normal distribution:
```explain.normal(nor)
```
• tstudent distribution:
```explain.tstudent(xt,ut,st,nt)
```
• chisquared distribution:
```explain.chisquared(data,data2)
```
• fisher distribution:
```explain.fisher(data,data2)
```

## User Interactive Functions:

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

• interactive.mean()
• interactive.geometricMean()
• interactive.mode()
• interactive.median()
• interactive.standardDeviation()
• interactive.averageDeviation()
• interactive.variance()
• interactive.quartile()
• interactive.percentile()
• interactive.absolute_frecuency()
• interactive.relative_frecuency()
• interactive.absolute_acum_frecuency()
• interactive.relative_acum_frecuency()
• interactive.covariance()
• interactive.harmonicMean()
• interactive.pearson()
• interactive.cv()
• interactive.laplace()
• interactive.binomial()
• interactive.poisson()
• interactive.normal()
• interactive.tstudent()
• interactive.chisquared()
• interactive.fisher()

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LearningRlab documentation built on June 18, 2022, 1:06 a.m.