R/topten.R

In jmcimula/lmr: Building a Prediction Model from Top 10 Features

#'Building a Model with Top Ten Feautures
#'
#'This function develops a prediction algorithm based on the top 10 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 model with top 10 features
#'@author Jean Marie Cimula
#'@details
#'This function runs a univariate regression of y on each predictor in x and
#'calculates a p-value indicating the significance of the association. The
#'final set of 10 predictors is taken  from 10 smallest p-values.
#'@seealso \code{lm}
#'@export
#'@importFrom stats lm


topten  <- function(x,y) {

          p <- ncol(x)
          if(p < 10)
                  stop("there are less than 10 predictors")
          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)
          coef(fit)
}

#' 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 matrix 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)
          drop(X %*% b)
}