--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MatrixEQTL/demo/d.ANOVA5.r	Fri Mar 12 08:20:57 2021 +0000
@@ -0,0 +1,56 @@
+library("MatrixEQTL");
+			
+anova.groups = 5;
+options(MatrixEQTL.ANOVA.categories = anova.groups);
+
+# Number of columns (samples)
+n = 100;
+# 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 = floor(runif(n, min = 0, max = anova.groups));
+gene.mat = 1 + (snps.mat==1) + cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std;
+
+# 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$new( t(cvrt.mat) );
+
+# Produce no output files
+filename = NULL; # tempfile()
+
+# Call the main analysis function
+me = Matrix_eQTL_main(
+  snps = snps1, 
+  gene = gene1, 
+  cvrt = cvrt1, 
+  output_file_name = filename, 
+  pvOutputThreshold = 1, 
+  useModel = modelANOVA, 
+  errorCovariance = diag(noise.std^2), 
+  verbose = TRUE,
+  pvalue.hist = FALSE );
+
+# Pull Matrix eQTL results - t-statistic and p-value
+
+fstat = me$all$eqtls$statistic;
+pvalue = me$all$eqtls$pvalue;
+rez = c( Fstat = fstat, pvalue = pvalue);
+# And compare to those from ANOVA in R
+{
+  cat("\n\n Matrix eQTL: \n"); 
+  print(rez);
+  cat("\n R anova(lm()) output: \n");
+	lmdl = lm( gene.mat ~ cvrt.mat + factor(snps.mat), 
+						 weights = 1/noise.std^2 );
+	lmout = anova(lmdl)[2, c("F value","Pr(>F)")];
+  print( lmout );
+}
+
+# Results from Matrix eQTL and "lm" must agree
+stopifnot(all.equal(lmout, rez, check.attributes=FALSE));