This package is unmaintained. Its reliability is not guaranteed.

Tools for resampling data to assess model fits

Depending on the level of granularity, you can use several functions for resampling data:

• splitrandom(df::DataFrame, proportion::Real): Use this to split df into two randomly chosen pieces. If proportion == 0.75, the first piece will contain ~75% of the data and the second piece will ~25% of the data.
• resample(df::DataFrame, n::Integer): Use this to generate a new data set of size n that is resampled with replacement from the rows of df.
• jackknife(df::DataFrame, statistic::Function): Use this to run the jackknife. The Jackknife moves through the data, removing one row at a time and then applying the function statistic to the remaining data. The results of all calls to statistic are stored in a vector that is returned to the caller.
• bootstrap(df::DataFrame, statistic::Function, n::Integer, proportion::Real): Use this to run the nonparametric bootstrap. The bootstrap resamples the data n times with each resampled data set containing proportion of the data. The function statistic is called on each resampled data set. The results of all calls to statistic are stored in a vector that is returned to the caller.
• crossvalidate(df::DataFrame, train::Function, test::Function, n::Integer, proportion::Real): Use this function to fit a model using the train function on n resampled data sets and then test the fitted model using the test function on those same data sets. Each time, the training data set will contain proportion of df and 1 - proportion will be held out as a test data set.
• kfold_crossvalidate(df::DataFrame, train::Function, test::Function, k::Integer): Use this function to fit a model using the train function on k data sets and then test the fitted model using the test function on those same data sets. Each time, the training data set will contain the majority of the data with one of k folds removed.

Using splitrandom:

using DataFrames, Resampling

df = DataFrame()
df["A"] = 1:100

df1, df2 = splitrandom(df, 0.75)

Using resample:

using DataFrames, Resampling

df = DataFrame()
df["A"] = 1:100

new_df = resample(df, 100)

Using jackknife:

using DataFrames, Resampling

df = DataFrame()
df["A"] = 1:100

resampled_means = jackknife(df, df -> mean(df["A"]))
se_hat = std(resampled_means)

Using bootstrap:

using DataFrames, Resampling

df = DataFrame()
df["A"] = 1:100

resampled_means = bootstrap(df, df -> mean(df["A"]), 1_000, 0.90)
se_hat = std(resampled_means)

Using crossvalidate:

using DataFrames, Resampling

df = DataFrame()
df["A"] = 1:100

function train(df)
    mean(df["A"])
end

function test(df, m)
    sqrt(mean((df["A"] - m).^2))
end

n_reps = 100
training_results, test_results = crossvalidate(df, train, test, n_reps, 0.75)

Using kfold_crossvalidate:

using DataFrames, Resampling

df = DataFrame()
df["A"] = 1:100

function train(df)
    mean(df["A"])
end

function test(df, m)
    sqrt(mean((df["A"] - m).^2))
end

k = 10
training_results, test_results = kfold_crossvalidate(df, train, test, k)