cleanerR (version 0.1.1)

GenerateCandidates: GenerateCandidates Asks for a dataframe and some parameters and returns all possible combinations of collums for prediction that satisfy a given error in input in a list the first element of the list are the combinations while the second is its measure of error,to get the best parameters call BestVector

Description

GenerateCandidates Asks for a dataframe and some parameters and returns all possible combinations of collums for prediction that satisfy a given error in input in a list the first element of the list are the combinations while the second is its measure of error,to get the best parameters call BestVector

Usage

GenerateCandidates(df, goal, maxi, repetitions, trigger = 1)

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

Examples

# NOT RUN {
#The GenerateCandidates function generates all sets of maximum length maxi.
#Maxi is a measure of error.
#This measure of error is related to the repetitions parameter.
#This parameter should range from 0 (rejects anything less to 100 percent accuracy)
#To number of rows of the dataframe to accept all.
#Lets generate a dataset
e=sample(1:5,1e4,replace=TRUE)
e1=sample(1:5,1e4,replace=TRUE)
e2=sample(1:5,1e4,replace=TRUE)
e=data.frame(e,e1,e2,paste(LETTERS[e],LETTERS[e1]),paste(LETTERS[e],LETTERS[e1],LETTERS[e2])   )
names(e)=c("random1","random2","random3","2randoms","3randoms")
#We can then generate all candidates to predict the 5 collumn
#We shall determine the reject part to 80 percent of the dataframe length
z=GenerateCandidates(df=e, goal=5, maxi=4, repetitions=0.8*nrow(e), trigger = 1)
#We can see z is a list
#z[[1]] is another list that contains all sets that satisfy our request
#z[[2]] is a measure of error, the smaller the more accurate
#Lets then order z[[1]] by z[[2]]
m=z[[1]][order(z[[2]])]
print(m)
#We can see then that m[[1]] holds the best set for prediction, while m[[length(m)]] the worst
#To prove it we can do the following
##cat("The best set to predict",names(e)[5],"is ",names(e)[m[[1]]],"\n"   )
##cat("Its expected accuracy is",MeanAccuracy(e,m[[1]],5),"\n"  )
##cat("The worst set to predict",names(e)[5],"is ",names(e)[m[[length(m)]]],"\n"   )
##cat("Its expected accuracy is",MeanAccuracy(e,m[[length(m)]],5),"\n"  )
# }