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stats2 hold-and-fit feature

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John Kerl 2015-10-20 23:25:46 -04:00
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commit 029fa1d8a7
2 changed files with 22 additions and 6 deletions
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14
doc/data/linreg-example.txt
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@ -1,14 +1,22 @@
# Prepare input data:
mlr filter '($x<.5 && $y<.5) || ($x>.5 && $y>.5)' data/medium > data/medium-squares
# Do a linear regression and examine coefficients:
mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares
x_y_pca_m=1.014419
x_y_pca_b=0.000308
x_y_pca_quality=0.861354
# Set x_y_pca_m and x_y_pca_b as shell variables
# Option 1 to apply the regression coefficients and produce a linear fit:
# Set x_y_pca_m and x_y_pca_b as shell variables:
eval $(mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares)
# In addition to x and y, make a new yfit which is the line fit. Plot using your favorite tool.
# In addition to x and y, make a new yfit which is the line fit, then plot
# using your favorite tool:
mlr --onidx put '$yfit='$x_y_pca_m'*$x+'$x_y_pca_b then cut -x -f a,b,i data/medium-squares \
| pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
# Option 2 to apply the regression coefficients and produce a linear fit: use --fit option
mlr --onidx stats2 -a linreg-pca --fit -f x,y then cut -f a,b,i data/medium-squares \
| pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1

14
doc/reference.html
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@ -2094,19 +2094,27 @@ distributed on the unit interval. Here we remove half the data and fit a line to
<div class="pokipanel">
<pre>
# Prepare input data:
mlr filter '($x&lt;.5 &amp;&amp; $y&lt;.5) || ($x&gt;.5 &amp;&amp; $y&gt;.5)' data/medium &gt; data/medium-squares
# Do a linear regression and examine coefficients:
mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares
x_y_pca_m=1.014419
x_y_pca_b=0.000308
x_y_pca_quality=0.861354
# Set x_y_pca_m and x_y_pca_b as shell variables
# Option 1 to apply the regression coefficients and produce a linear fit:
# Set x_y_pca_m and x_y_pca_b as shell variables:
eval $(mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares)
# In addition to x and y, make a new yfit which is the line fit. Plot using your favorite tool.
# In addition to x and y, make a new yfit which is the line fit, then plot
# using your favorite tool:
mlr --onidx put '$yfit='$x_y_pca_m'*$x+'$x_y_pca_b then cut -x -f a,b,i data/medium-squares \
| pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
# Option 2 to apply the regression coefficients and produce a linear fit: use --fit option
mlr --onidx stats2 -a linreg-pca --fit -f x,y then cut -f a,b,i data/medium-squares \
| pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
</pre>
</div>
<p/>