LinearRegression: Fast Linear Regression
In WeakCha/BIOSTAT625_HW4: Linear Regression

Description Usage Arguments Value Author(s) Examples

View source: R/LinearRegression.R

This function will do a linear regression like a machine learning taste, which means it will split the dataset into training set (with the ratio training_part) and test set and return the predicted result and the RMSE of the test set.

1	LinearRegression(data, training_part = 0.8, features = NULL, target, seed = 200)

`data`	`data` is a `data.frame`.
`training_part`	`training_part` is the ratio for training data.
`features`	`features` is a vector with the names of the features you want to use as predictors.
`target`	`target` is a string with the name of the target. We only allow uni-label predicting in this package.
`seed`	`seed` is the seed you want to specify, this ensures the reproducibility of the data.

`pred`	The prediction for the test set the algorithm automatically splitted
`RMSE`	The RMSE between the prediction and true target result for the test set

Li (Richard) Liu

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (data, training_part = 0.8, features = NULL, target,
    seed = 200)
{
    set.seed(seed)
    res = dataPreprocess(data, features, target, training_part)
    X_train = res$X_train
    X_test = res$X_test
    Y_train = res$Y_train
    Y_test = res$Y_test
    Y_train = as.matrix(Y_train)
    Y_test = as.matrix(Y_test)
    res = fastRidgeRegression(X_train, Y_train, X_test, Y_test,
        training_part)
    beta = res$beta
    pred = res$pred
    RMSE = res$RMSE
    return(list(pred = pred, RMSE = RMSE))
  }