Example 1: Using SEAGLE with .txt Input Files

This tutorial demonstrates how to use the SEAGLE package when the user inputs \({\bf y}\), \({\bf X}\), \({\bf E}\), and \({\bf G}\) from .txt files. We’ll begin by loading the SEAGLE package.

#> Loading required package: Matrix
#> Loading required package: CompQuadForm

If you have your own files ready to read in for \({\bf y}\), \({\bf X}\), \({\bf E}\), and \({\bf G}\), you can read them into R using the read.csv() command.

As an example, we’ve included y.txt, X.txt, E.txt, and G.txt files in the extdata folder of this package. The following code loads those files into R so we can use them in this tutorial.

y_loc <- system.file("extdata", "y.txt", package = "SEAGLE")
y <- as.numeric(unlist(read.csv(y_loc)))

X_loc <- system.file("extdata", "X.txt", package = "SEAGLE")
X <- as.matrix(read.csv(X_loc))

E_loc <- system.file("extdata", "E.txt", package = "SEAGLE")
E <- as.numeric(unlist(read.csv(E_loc)))

G_loc <- system.file("extdata", "G.txt", package = "SEAGLE")
G <- as.matrix(read.csv(G_loc))

Now we can input \({\bf y}\), \({\bf X}\), \({\bf E}\), and \({\bf G}\) into the prep.SEAGLE function. The intercept = 1 parameter indicates that the first column of \({\bf X}\) is the all ones vector for the intercept.

This preparation procedure formats the input data for the SEAGLE function by checking the dimensions of the input data. It also pre-computes a QR decomposition for \(\widetilde{\bf X} = \begin{pmatrix} {\bf 1}_{n} & {\bf X} & {\bf E} \end{pmatrix}\), where \({\bf 1}_{n}\) denotes the all ones vector of length \(n\).

objSEAGLE <- prep.SEAGLE(y=as.matrix(y), X=X, intercept=1, E=E, G=G)

Finally, we’ll input the prepared data into the SEAGLE function to compute the score-like test statistic \(T\) and its corresponding p-value. The init.tau and init.sigma parameters are the initial values for \(\tau\) and \(\sigma\) employed in the REML EM algorithm.

res <- SEAGLE(objSEAGLE, init.tau=0.5, init.sigma=0.5)
#> [1] 246.1886
#> [1] 0.8441451

The score-like test statistic \(T\) for the G\(\times\)E effect and its corresponding p-value can be found in res$T and res$pv, respectively.