cleanerR (version 0.1.1)

BestVector: BestVector Asks for a dataframe and some parameters and returns the best combination of collums to predict the missing value

Description

BestVector Asks for a dataframe and some parameters and returns the best combination of collums to predict the missing value

Usage

BestVector(df, goal, maxi, repetitions, trigger = 1, ratio = 0.99)

Arguments

df

A dataframe with the missing values you wish to fill

goal

The collum with the missing values you wish to fill

maxi

What will be the length of possible combinations you will test example if 2 they will test up to all possible pairs of collums

repetitions

Measure of error, the bigger the less likely you will get the right prediction

trigger

When you pair all possible combination of tuples a percentage of them will show only once, trigger rejects the set if this percentage is higher than this value

ratio

Rejects collumns that the ratio of unique values to total values is higher than this value, primary keys have ratio equal to 1

Examples

# NOT RUN {
#The Best Vector Function shall do the following
#Take a dataframe and a goal collumn to predict
#Tests every combination of vectors limited by a parameter length
#Returns the best set to predict the goal
#Then to run some experiments first lets build a dataframe
e=sample(1:2,1e2,replace=TRUE)
e1=sample(1:2,1e2,replace=TRUE)
e2=sample(1:2,1e2,replace=TRUE)
e=data.frame(e,e1,e2,paste(LETTERS[e],LETTERS[e1]),paste(LETTERS[e],LETTERS[e1],LETTERS[e2])   )
#We can easily see that to predict the last collumn you need the first three.
#Lets Check what the function will find
z=BestVector(e,5,3,nrow(e),1)
print(z)
#Lets now check what is the best set if we use only 2 collumns maximum
z1=BestVector(e,5,2,nrow(e),1)
print(z1)
#We could also predict which collumn is best to predict the fourth one
z2=BestVector(e,4,2,nrow(e),1)
print(z2)
#We could also take a look at the dataset iris.
#Since this dataset does not repeat lines we must use trigger=0
#To predict Species
z3=BestVector(iris,5,2,nrow(iris),0)
print(names(iris))[z3]
#We can check the accuracy of these predictions with the accuracy functions
print(MeanAccuracy(iris,z3,5))
print(MeanAccuracy(e,z2,4))
print(MeanAccuracy(e,z1,5))
print(MeanAccuracy(iris,z,5))
# }