--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/execute_dwt_cor_aVa_perClass.R	Mon Jul 06 20:28:54 2020 -0400
@@ -0,0 +1,178 @@
+##################################################################################
+## code to do all correlation tests of form: motif(a) vs. motif(a)
+## add code to create null bands by permuting the original data series
+## generate plots and table matrix of correlation coefficients including p-values
+##################################################################################
+library("wavethresh");
+library("waveslim");
+
+options(echo = FALSE)
+
+## normalize data
+norm <- function(data) {
+    v <- (data - mean(data)) / sd(data);
+    if (sum(is.na(v)) >= 1) {
+        v <- data;
+    }
+    return(v);
+}
+
+dwt_cor <- function(data_short, names_short, data_long, names_long, test, pdf, table, filter = 4, bc = "symmetric", method = "kendall", wf = "haar", boundary = "reflection") {
+    print(test);
+    print(pdf);
+    print(table);
+
+    pdf(file = pdf);
+    final_pvalue <- NULL;
+    title <- NULL;
+
+    short_levels <- wavethresh::wd(data_short[, 1], filter.number = filter, bc = bc)$nlevels;
+    title <- c("motif");
+    for (i in 1:short_levels) {
+        title <- c(title, paste(i, "cor", sep = "_"), paste(i, "pval", sep = "_"));
+    }
+    print(title);
+
+    ## normalize the raw data
+    data_short <- apply(data_short, 2, norm);
+    data_long <- apply(data_long, 2, norm);
+
+    for (i in seq_len(length(names_short))) {
+        ## Kendall Tau
+        ## DWT wavelet correlation function
+        ## include significance to compare
+        wave1_dwt <- NULL;
+        wave2_dwt <- NULL;
+        tau_dwt <- NULL;
+        out <- NULL;
+
+        print(names_short[i]);
+        print(names_long[i]);
+
+        ## need exit if not comparing motif(a) vs motif(a)
+        if (names_short[i] != names_long[i]) {
+            stop(paste("motif", names_short[i], "is not the same as", names_long[i], sep = " "));
+        }
+        else {
+            wave1_dwt <- waveslim::dwt(data_short[, i], wf = wf, short_levels, boundary = boundary);
+            wave2_dwt <- waveslim::dwt(data_long[, i], wf = wf, short_levels, boundary = boundary);
+            tau_dwt <- vector(length = short_levels)
+
+            ## perform cor test on wavelet coefficients per scale
+            for (level in 1:short_levels) {
+                w1_level <- NULL;
+                w2_level <- NULL;
+                w1_level <- (wave1_dwt[[level]]);
+                w2_level <- (wave2_dwt[[level]]);
+                tau_dwt[level] <- cor.test(w1_level, w2_level, method = method)$estimate;
+            }
+
+            ## CI bands by permutation of time series
+            feature1 <- NULL;
+            feature2 <- NULL;
+            feature1 <- data_short[, i];
+            feature2 <- data_long[, i];
+            null <- NULL;
+            results <- NULL;
+            med <- NULL;
+            cor_25 <- NULL;
+            cor_975 <- NULL;
+
+            for (k in 1:1000) {
+                nk_1 <- NULL;
+                nk_2 <- NULL;
+                null_levels <- NULL;
+                cor <- NULL;
+                null_wave1 <- NULL;
+                null_wave2 <- NULL;
+
+                nk_1 <- sample(feature1, length(feature1), replace = FALSE);
+                nk_2 <- sample(feature2, length(feature2), replace = FALSE);
+                null_levels <- wavethresh::wd(nk_1, filter.number = filter, bc = bc)$nlevels;
+                cor <- vector(length = null_levels);
+                null_wave1 <- waveslim::dwt(nk_1, wf = wf, short_levels, boundary = boundary);
+                null_wave2 <- waveslim::dwt(nk_2, wf = wf, short_levels, boundary = boundary);
+
+                for (level in 1:null_levels) {
+                    null_level1 <- NULL;
+                    null_level2 <- NULL;
+                    null_level1 <- (null_wave1[[level]]);
+                    null_level2 <- (null_wave2[[level]]);
+                    cor[level] <- cor.test(null_level1, null_level2, method = method)$estimate;
+                }
+                null <- rbind(null, cor);
+            }
+
+            null <- apply(null, 2, sort, na.last = TRUE);
+            print(paste("NAs", length(which(is.na(null))), sep = " "));
+            cor_25 <- null[25, ];
+            cor_975 <- null[975, ];
+            med <- (apply(null, 2, median, na.rm = TRUE));
+
+            ## plot
+            results <- cbind(tau_dwt, cor_25, cor_975);
+            matplot(results, type = "b", pch = "*", lty = 1, col = c(1, 2, 2), ylim = c(-1, 1), xlab = "Wavelet Scale", ylab = "Wavelet Correlation Kendall's Tau", main = (paste(test, names_short[i], sep = " ")), cex.main = 0.75);
+            abline(h = 0);
+
+            ## get pvalues by comparison to null distribution
+            ### modify pval calculation for error type II of T test ####
+            out <- (names_short[i]);
+            for (m in seq_len(length(tau_dwt))) {
+                print(paste("scale", m, sep = " "));
+                print(paste("tau", tau_dwt[m], sep = " "));
+                print(paste("med", med[m], sep = " "));
+                out <- c(out, format(tau_dwt[m], digits = 3));
+                pv <- NULL;
+                if (is.na(tau_dwt[m])) {
+                    pv <- "NA";
+                }
+                else {
+                    if (tau_dwt[m] >= med[m]) {
+                        ## R tail test
+                        print(paste("R"));
+                        ### per sv ok to use inequality not strict
+                        pv <- (length(which(null[, m] >= tau_dwt[m]))) / (length(na.exclude(null[, m])));
+                        if (tau_dwt[m] == med[m]) {
+                            print("tau == med");
+                            print(summary(null[, m]));
+                        }
+                }
+                    else if (tau_dwt[m] < med[m]) {
+                        ## L tail test
+                        print(paste("L"));
+                        pv <- (length(which(null[, m] <= tau_dwt[m]))) / (length(na.exclude(null[, m])));
+                    }
+                }
+                out <- c(out, pv);
+                print(paste("pval", pv, sep = " "));
+            }
+            final_pvalue <- rbind(final_pvalue, out);
+            print(out);
+        }
+    }
+    colnames(final_pvalue) <- title;
+    write.table(final_pvalue, file = table, sep = "\\t", quote = FALSE, row.names = FALSE)
+    dev.off();
+}
+## execute
+## read in data
+args <- commandArgs(trailingOnly = TRUE)
+
+input_data1 <- NULL;
+input_data2 <- NULL;
+input_data_short1 <- NULL;
+input_data_short2 <- NULL;
+input_data_names_short1 <- NULL;
+input_data_names_short2 <- NULL;
+
+input_data1 <- read.delim(args[1]);
+input_data_short1 <- input_data1[, +c(seq_len(ncol(input_data1)))];
+input_data_names_short1 <- colnames(input_data_short1);
+
+input_data2 <- read.delim(args[2]);
+input_data_short2 <- input_data2[, +c(seq_len(ncol(input_data2)))];
+input_data_names_short2 <- colnames(input_data_short2);
+
+## cor test for motif(a) in input_data1 vs motif(a) in input_data2
+dwt_cor(input_data_short1, input_data_names_short1, input_data_short2, input_data_names_short2, test = "cor_aVa", pdf = args[3], table = args[4]);
+print("done with the correlation test");