#' Building a Model with Top Ten Features
#' This function develops a prediction algorithm based on the top the features 
#' in 'x' that are most predictive of 'y
#' @param x a n x p matrix of n observations and p predictors
#' @param y a vector of length n representing the response
#' @return a vector of coefficients from the final fitted model with top 10 features
#' @author Piegiorgio Palla
#' @details 
#' This function runs a univariate regression model of y on each predictor in x 
#' and calculates ...
#' @seealso \code{lm}
#' @export
#' @importFrom stats lm

topten <- function(x, y){
  p <- ncol(x)
  if (p < 10)
    stop("There are less than 10 features")
  pvalues <- numeric(p)
  for (i in seq_len(p)){
    fit <- lm(y ~ x[,i])
    summ <- summary(fit)
    pvalues[i] <- summ$coefficients[2,4]
  ord <- order(pvalues)
  ord <- ord[1:10]
  x10 <- x[, ord]
  fit <- lm(y ~ x10)

#' Prediction with Top Ten Features
#' This function takes a set of coefficients produced by the \code{topten}
#' function and makes a prediction for each of the values provided in the input 'X' matrix
#' @param X a n x 10 containing n new observations
#' @param b a vector of coefficients obtained from the \code{topten} function
#' @return a numeric vector containing the predicted values
#' @export

predict10 <- function(X, b){
  X <- cbind(1, X)

pjpalla/topten documentation built on May 25, 2019, 8:19 a.m.