1d:T7a4,# Graphical F Plot for Significance in Regression This function analyzes regression data graphically. It allows visualization of the usual F-test for significance of regression. ```r GFplot(X, y, plotIt=TRUE, sortTrt=FALSE, type="hist", includeIntercept=TRUE, labels=FALSE) ``` ## Arguments - `X`: The design matrix. - `y`: A numeric vector containing the response. - `plotIt`: Logical: if TRUE, a graph is drawn. - `sortTrt`: Logical: if TRUE, an attempt is made at sorting the predictor effects in descending order. - `type`: "QQ" or "hist" - `includeIntercept`: Logical: if TRUE, the intercept effect is plotted; otherwise, it is omitted from the plot. - `labels`: logical: if TRUE, names of predictor variables are used as labels; otherwise, the design matrix column numbers are used as labels ## Returns A QQ-plot or a histogram and rugplot, or a list if plotIt=FALSE ## Author(s) W. John Braun ## Source Braun, W.J. 2013. Regression Analysis and the QR Decomposition. Preprint. ## Examples ```r # Example 1 X <- p4.18[,-4] y <- p4.18[,4] GFplot(X, y, type="hist", includeIntercept=FALSE) title("Evidence of Regression in the Jojoba Oil Data") # Example 2 set.seed(4571) Z <- matrix(rnorm(400), ncol=10) A <- matrix(rnorm(81), ncol=9) simdata <- data.frame(Z[,1], crossprod(t(Z[,-1]),A)) names(simdata) <- c("y", paste("x", 1:9, sep="")) GFplot(simdata[,-1], simdata[,1], type="hist", includeIntercept=FALSE) title("Evidence of Regression in Simulated Data Set") # Example 3 GFplot(table.b1[,-1], table.b1[,1], type="hist", includeIntercept=FALSE) title("Evidence of Regression in NFL Data Set") # An example where stepwise AIC selects the complement # of the set of variables that are actually in the true model: X <- pathoeg[,-10] y <- pathoeg[,10] par(mfrow=c(2,2)) GFplot(X, y) GFplot(X, y, sortTrt=TRUE) GFplot(X, y, type="QQ") GFplot(X, y, sortTrt=TRUE, type="QQ") X <- table.b1[,-1] # NFL data y <- table.b1[,1] GFplot(X, y) ```

GFplot function