--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MatrixEQTL/man/modelLINEAR_CROSS.Rd	Fri Mar 12 08:20:57 2021 +0000
@@ -0,0 +1,76 @@
+\name{modelLINEAR_CROSS}
+\alias{modelLINEAR_CROSS}
+\docType{data}
+\title{
+	Constant for \code{\link{Matrix_eQTL_engine}}.
+}
+\description{
+	Set parameter \code{useModel = modelLINEAR_CROSS} in the call of \code{\link{Matrix_eQTL_main}} to indicate that Matrix eQTL should include the interaction of SNP and last covariate in the model and test for its significance.
+}
+\examples{
+library('MatrixEQTL')	
+
+# Number of columns (samples)
+n = 25;
+
+# Number of covariates
+nc = 10;
+
+# Generate the standard deviation of the noise
+noise.std = 0.1 + rnorm(n)^2;
+
+# Generate the covariates
+cvrt.mat = 2 + matrix(rnorm(n*nc), ncol = nc);
+
+# Generate the vectors with single genotype and expression variables
+snps.mat = cvrt.mat \%*\% rnorm(nc) + rnorm(n);
+gene.mat = cvrt.mat \%*\% rnorm(nc) + rnorm(n) * noise.std + 
+           1 + 0.5 * snps.mat + snps.mat * cvrt.mat[,nc];
+
+# Create 3 SlicedData objects for the analysis
+snps1 = SlicedData$new( matrix( snps.mat, nrow = 1 ) );
+gene1 = SlicedData$new( matrix( gene.mat, nrow = 1 ) );
+cvrt1 = SlicedData$new( t(cvrt.mat) );
+
+# name of temporary output file
+filename = tempfile();
+
+# Call the main analysis function
+me = Matrix_eQTL_main(
+  snps = snps1, 
+  gene = gene1, 
+  cvrt = cvrt1, 
+  output_file_name = filename, 
+  pvOutputThreshold = 1, 
+  useModel = modelLINEAR_CROSS, 
+  errorCovariance = diag(noise.std^2), 
+  verbose = TRUE,
+  pvalue.hist = FALSE );
+# remove the output file
+unlink( filename );
+
+# Pull Matrix eQTL results - t-statistic and p-value
+beta = me$all$eqtls$beta;
+tstat = me$all$eqtls$statistic;
+pvalue = me$all$eqtls$pvalue;
+rez = c(beta = beta, tstat = tstat, pvalue = pvalue)
+# And compare to those from the linear regression in R
+{
+  cat('\n\n Matrix eQTL: \n'); 
+  print(rez);
+  cat('\n R summary(lm()) output: \n')
+  lmodel = lm( gene.mat ~ snps.mat + cvrt.mat + snps.mat*cvrt.mat[,nc], 
+               weights = 1/noise.std^2 );
+  lmout = tail(summary( lmodel )$coefficients,1)[,c(1,3,4)];
+  print( tail(lmout) );
+}
+
+# Results from Matrix eQTL and 'lm' must agree
+stopifnot(all.equal(lmout, rez, check.attributes=FALSE))
+}
+\references{
+	The package website: \url{http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/}
+}
+\seealso{
+	See \code{\link{Matrix_eQTL_engine}} for reference and sample code.
+}