BestVector: 'BestVector' Asks for a dataframe and some parameters and...

In cleanerR: How to Handle your Missing Data

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

#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))

cleanerR documentation built on May 2, 2019, 5:51 a.m.