Example R package

09_DevelopingDataProducts/RPackages/example.R

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+#' Building a Model with Top Ten Features
+#' 
+#' 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 'lm' object representing the linear model with the top 10 predictors
+#' @author Roger Peng
+#' @details
+#' This function runs a univariate regression of y on each predictor in x and
+#' calculates the p-value indicating the significance of the association. The
+#' final set of 10 predictors is the taken from the 10 smallest p-values.
+#' @seealso \code{lm}
+#' @import stats
+#' @export
+
+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)
+        x10 <- x[, ord]
+        fit <- lm(y ~ x10)
+        coef(fit)
+}
+
+#' Prediction with Top Ten Features
+#' 
+#' This function takes a set 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 observations
+#' @param b a vector of coefficients obtained from the \code{topten} function
+#' @return a numeric vector containing the predicted values
+
+predict10 <- function(X, b) {
+        X <- cbind(1, X)
+        drop(X %*% b)
+}