added (prev_data ~ number) check to diff;

added ($x ~ number) check to lin_reg;
added gaussian expressions gaussian;

1 files changed, 38 insertions, 35 deletions

diff --git a/lin_reg.awk b/lin_reg.awk
index 52e55d3..0213c7f 100644
--- a/lin_reg.awk
+++ b/lin_reg.awk
@@ -17,7 +17,7 @@ NR == 1 {
         ($n ~ number) ? header[n] = "col" n : header[n] = $n
 }
 
-NF > 0 {
+NF {
     if (NF > nf_max)
         nf_max = NF
 
@@ -25,6 +25,7 @@ NF > 0 {
     for (y=1; y<=nf_max; y++) {
         if ($y == header[n])
             continue
+
         if ($y ~ number) {
 
             ### mean
@@ -42,45 +43,47 @@ NF > 0 {
 
             # x = row, y = col, trendline: y = A + Bx
             for (x=1; x<=nf_max; x++) {
-                count[x,y] += 1
-                sum_xy[x,y] += $x*$y
-                sum_delta_xy[x,y] += delta0[x]*delta1[y]
+                if ($x ~ number) {
+                    count[x,y] += 1
+                    sum_xy[x,y] += $x*$y
+                    sum_delta_xy[x,y] += delta0[x]*delta1[y]
 
-                # covariance
-                #(count[x,y] > 1) ? cov[x,y] = sum_delta_xy[x,y]/(count[x,y] - 1) : cov[x,y] = ""
+                    # covariance
+                    #(count[x,y] > 1) ? cov[x,y] = sum_delta_xy[x,y]/(count[x,y] - 1) : cov[x,y] = ""
 
-                # correlation
-                r_den[x,y] = sqrt(sum_delta2[x]*sum_delta2[y])
-                (r_den[x,y]) ? r[x,y] = sum_delta_xy[x,y]/r_den[x,y] : r[x,y] = 1
+                    # correlation
+                    r_den[x,y] = sqrt(sum_delta2[x]*sum_delta2[y])
+                    (r_den[x,y]) ? r[x,y] = sum_delta_xy[x,y]/r_den[x,y] : r[x,y] = 1
 
-                ab_den[x,y] = (count[x,y]*sum2[x] - sum[x]*sum[x])
-                if (ab_den[x,y]) {
-                    a[x,y] = (sum[y]*sum2[x] - sum[x]*sum_xy[x,y])/ab_den[x,y]
-                    b[x,y] = (count[x,y]*sum_xy[x,y] - sum[x]*sum[y])/ab_den[x,y]
-                }
-                else {
-                    a[x,y] = 0
-                    b[x,y] = 1
-                }
+                    ab_den[x,y] = (count[x,y]*sum2[x] - sum[x]*sum[x])
+                    if (ab_den[x,y]) {
+                        a[x,y] = (sum[y]*sum2[x] - sum[x]*sum_xy[x,y])/ab_den[x,y]
+                        b[x,y] = (count[x,y]*sum_xy[x,y] - sum[x]*sum[y])/ab_den[x,y]
+                    }
+                    else {
+                        a[x,y] = 0
+                        b[x,y] = 1
+                    }
+
+                    # error estimate
+                    err_den[x,y] = count[x,y]*(count[x,y] - 2)
+                    if (count[x,y] > 2) {
+                        err[x,y] = $y - (a[x,y] + b[x,y]*$x)
+                        sum_err2[x,y] += err[x,y]*err[x,y]
+                    }
+                    b_err_den[x,y] = (count[x,y] - 2)*sum_delta2[x]
+                    if (b_err_den[x,y])
+                        b_err[x,y] = sqrt(sum_err2[x,y]/b_err_den[x,y])
+                    a_err_den[x,y] = count[x,y]*b_err_den[x,y]
+                    if (a_err_den[x,y])
+                        a_err[x,y] = sqrt(sum2[x]/count[x,y])*b_err[x,y]
 
-                # error estimate
-                err_den[x,y] = count[x,y]*(count[x,y] - 2)
-                if (count[x,y] > 2) {
-                    err[x,y] = $y - (a[x,y] + b[x,y]*$x)
-                    sum_err2[x,y] += err[x,y]*err[x,y]
+                    # weighted mean, from HP-20S manual, pg 60
+                    xw[x,y] = sum_xy[x,y]/sum[y]
+                    yw[x,y] = b[x,y]*xw[x,y] + a[x,y]
+                    xw_dist[x,y] = (xw[x,y] - mean[x])
+                    yw_dist[x,y] = b[x,y]*(xw[x,y] - mean[x])
                 }
-                b_err_den[x,y] = (count[x,y] - 2)*sum_delta2[x]
-                if (b_err_den[x,y])
-                    b_err[x,y] = sqrt(sum_err2[x,y]/b_err_den[x,y])
-                a_err_den[x,y] = count[x,y]*b_err_den[x,y]
-                if (a_err_den[x,y])
-                    a_err[x,y] = sqrt(sum2[x]/count[x,y])*b_err[x,y]
-
-                # weighted mean, from HP-20S manual, pg 60
-                xw[x,y] = sum_xy[x,y]/sum[y]
-                yw[x,y] = b[x,y]*xw[x,y] + a[x,y]
-                xw_dist[x,y] = (xw[x,y] - mean[x])
-                yw_dist[x,y] = b[x,y]*(xw[x,y] - mean[x])
             }
         }
         else