From 789708430268cbf8c121cced209c75b2b3016773 Mon Sep 17 00:00:00 2001 From: daslu Date: Mon, 16 Dec 2024 12:58:55 +0200 Subject: [PATCH] rerendered docs --- docs/index.html | 9 +- docs/search.json | 14 ++- docs/tablemath_book.reference.html | 154 ++++++++++++++--------------- 3 files changed, 97 insertions(+), 80 deletions(-) diff --git a/docs/index.html b/docs/index.html index 4843c0b..2643ef8 100644 --- a/docs/index.html +++ b/docs/index.html @@ -165,6 +165,7 @@

Table of contents

@@ -232,7 +233,7 @@

General info

License -EPLv2.0 +EPLv1.0 Status @@ -249,6 +250,12 @@

General info

+
+

License

+

Copyright © 2024 Scicloj

+

EPLv1.0 is just the default for projects generated by clj-new: you are not required to open source this project, nor are you required to use EPLv1.0! Feel free to remove or change the LICENSE file and remove or update this section of the README.md file!

+

Distributed under the Eclipse Public License version 1.0.

+

Chapters in this book

In any case, the result of the spec is turned into a sequence of named columns, which is conctenated to the columns from the previous specs. Some default naming mechanisms are invoked if column names are missing.

-

Columns of strings and keywords that have at most 20 distinct values are one-hot-encoded by default.

+

Columns of strings and keywords that have at most 20 distinct values are one-hot-encoded by default.

Eventually, the sequence of all resulting columns is returned.

Examples

@@ -602,7 +602,7 @@
Linear relationship plotly/layer-point)
+ [{"y":[-1.2539819293858387,-0.6058112611141196,3.3822436726084515,5.364141657551802,7.5777294393565375,8.149666852389464,9.810254085683946,11.599306407676211,13.386413619048158],"r":null,"name":"","fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[0,1,2,3,4,5,6,7,8],"text":null}], {"width":500,"height":400,"margin":{"t":25},"automargin":false,"plot_bgcolor":"rgb(235,235,235)","xaxis":{"gridcolor":"rgb(255,255,255)","title":"x"},"yaxis":{"gridcolor":"rgb(255,255,255)","title":"y"},"title":null}, {});

Note how the coefficients fit the way we generated the data:

(-> linear-toydata
@@ -614,24 +614,24 @@ 
Linear relationship
 
Residuals:
 
-|      :min |       :q1 |   :median |      :q3 |     :max |
-|-----------+-----------+-----------+----------+----------|
-| -0.846927 | -0.585569 | -0.103833 | 0.429761 | 1.543147 |
+|      :min |       :q1 |   :median |     :q3 |     :max |
+|-----------+-----------+-----------+---------+----------|
+| -1.443772 | -0.350436 | -0.244968 | 0.76477 | 1.198845 |
 
 Coefficients:
 
-|     :name | :estimate |  :stderr |  :t-value | :p-value |  :confidence-interval |
-|-----------+-----------+----------+-----------+----------+-----------------------|
-| Intercept | -2.026896 | 0.480301 | -4.220058 | 0.003936 | [-3.162626 -0.891166] |
-|        :x |  2.029809 | 0.100883 | 20.120369 |      0.0 |    [1.791258 2.26836] |
+|     :name | :estimate |  :stderr |  :t-value | :p-value | :confidence-interval |
+|-----------+-----------+----------+-----------+----------+----------------------|
+| Intercept | -1.009014 | 0.523742 | -1.926548 | 0.095406 | [-2.247467 0.229439] |
+|        :x |  1.846975 | 0.110008 | 16.789489 |   1.0E-6 |  [1.586848 2.107102] |
 
-F-statistic: 404.82925567183804 on degrees of freedom: {:residual 7, :model 1, :intercept 1}
-p-value: 1.8756698494382107E-7
+F-statistic: 281.8869508632346 on degrees of freedom: {:residual 7, :model 1, :intercept 1}
+p-value: 6.506718930321398E-7
 
-R2: 0.9830026645664584
-Adjusted R2: 0.9805744737902381
-Residual standard error: 0.7814385951869863 on 7 degrees of freedom
-AIC: 24.839927049107413
+R2: 0.975769068214805
+Adjusted R2: 0.9723075065312058
+Residual standard error: 0.8521167114773845 on 7 degrees of freedom
+AIC: 26.398491770973536
@@ -655,7 +655,7 @@
Cubic relationship
plotly/layer-point)
+ [{"y":[51.36735776750876,48.795530180439464,32.29345490684168,9.646323307078069,-13.248996661761945,-27.24347715033698,-32.00916895682476,-17.85190644242819,19.84573681298417],"r":null,"name":"","fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[0,1,2,3,4,5,6,7,8],"text":null}], {"width":500,"height":400,"margin":{"t":25},"automargin":false,"plot_bgcolor":"rgb(235,235,235)","xaxis":{"gridcolor":"rgb(255,255,255)","title":"x"},"yaxis":{"gridcolor":"rgb(255,255,255)","title":"y"},"title":null}, {});

Note how the coefficients fit the way we generated the data:

(-> cubic-toydata
@@ -667,26 +667,26 @@ 
Cubic relationship

 

 
Residuals:
 
-|      :min |       :q1 |   :median |      :q3 |    :max |
-|-----------+-----------+-----------+----------+---------|
-| -1.204506 | -0.671053 | -0.009447 | 0.634496 | 0.89975 |
+|      :min |       :q1 |  :median |      :q3 |     :max |
+|-----------+-----------+----------+----------+----------|
+| -1.200883 | -0.416095 | 0.089125 | 0.557187 | 0.843251 |
 
 Coefficients:
 
 |     :name | :estimate |  :stderr |   :t-value | :p-value |  :confidence-interval |
 |-----------+-----------+----------+------------+----------+-----------------------|
-| Intercept | 51.069977 | 0.870195 |  58.687958 |      0.0 | [48.833069 53.306885] |
-|        :x |  3.426066 | 1.004331 |   3.411292 | 0.019017 |   [0.844351 6.007781] |
-|       :x2 | -8.679892 | 0.303233 | -28.624455 |   1.0E-6 | [-9.459379 -7.900406] |
-|       :x3 |  0.968896 | 0.024873 |  38.953938 |      0.0 |   [0.904959 1.032834] |
+| Intercept | 51.854441 | 0.761092 |   68.13167 |      0.0 | [49.897993 53.810889] |
+|        :x |  4.161807 |  0.87841 |   4.737889 |  0.00516 |   [1.903784 6.419831] |
+|       :x2 | -9.051775 | 0.265215 | -34.130006 |      0.0 |  [-9.73353 -8.370019] |
+|       :x3 |  1.004353 | 0.021754 |  46.167917 |      0.0 |   [0.948432 1.060275] |
 
-F-statistic: 2994.6276756171496 on degrees of freedom: {:residual 5, :model 3, :intercept 1}
-p-value: 1.4862932218306923E-8
+F-statistic: 4069.899285168728 on degrees of freedom: {:residual 5, :model 3, :intercept 1}
+p-value: 6.905335636631094E-9
 
-R2: 0.9994437573628376
-Adjusted R2: 0.9991100117805403
-Residual standard error: 0.939128021321511 on 5 degrees of freedom
-AIC: 29.120351133198678
+R2: 0.99959065708713
+Adjusted R2: 0.999345051339408
+Residual standard error: 0.8213817632386143 on 5 degrees of freedom
+AIC: 26.709002578008814
@@ -722,7 +722,7 @@
Categorical relat :=y :traffic}))
+ [{"y":[61.65261545138374,60.99172481480467,62.49159778297785],"r":null,"name":":Mon","marker":{"color":"#1B9E77","size":10},"fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[0,7,14],"text":null},{"y":[62.03352974227212,64.8264911424553,64.45937457182094],"r":null,"name":":Tue","marker":{"color":"#D95F02","size":10},"fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[1,8,15],"text":null},{"y":[64.06864407006681,60.99999943139974,61.44873727008392],"r":null,"name":":Wed","marker":{"color":"#7570B3","size":10},"fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[2,9,16],"text":null},{"y":[64.1066542654662,62.52729647911608,60.18244959248523],"r":null,"name":":Thu","marker":{"color":"#E7298A","size":10},"fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[3,10,17],"text":null},{"y":[64.23575456216837,64.46407656616557],"r":null,"name":":Fri","marker":{"color":"#66A61E","size":10},"fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[4,11],"text":null},{"y":[54.215580163849005,52.75269147083546],"r":null,"name":":Sat","marker":{"color":"#E6AB02","size":10},"fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[5,12],"text":null},{"y":[52.805450498116514,52.14812879605265],"r":null,"name":":Sun","marker":{"color":"#A6761D","size":10},"fill":null,"mode":"markers","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[6,13],"text":null},{"y":[61.65261545138374,62.03352974227212,64.06864407006681,64.1066542654662,64.23575456216837,54.215580163849005,52.805450498116514,60.99172481480467,64.8264911424553,60.99999943139974,62.52729647911608,64.46407656616557,52.75269147083546,52.14812879605265,62.49159778297785,64.45937457182094,61.44873727008392,60.18244959248523],"r":null,"name":"","fill":null,"mode":"lines","width":null,"type":"scatter","theta":null,"z":null,"lon":null,"lat":null,"x":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],"text":null}], {"width":500,"height":400,"margin":{"t":25},"automargin":false,"plot_bgcolor":"rgb(235,235,235)","xaxis":{"gridcolor":"rgb(255,255,255)","title":"t"},"yaxis":{"gridcolor":"rgb(255,255,255)","title":"traffic"},"title":null}, {});

A model with all days except for one, dropping one category to avoid multicolinearity (note we begin with Thursday due to the order of appearance):

(-> categorical-toydata
@@ -734,29 +734,29 @@ 
Categorical relat
 

 
Residuals:
 
-|      :min |       :q1 |   :median |      :q3 |     :max |
-|-----------+-----------+-----------+----------+----------|
-| -1.638849 | -1.158227 | -0.192556 | 0.797888 | 2.719674 |
+|      :min |       :q1 |  :median |      :q3 |     :max |
+|-----------+-----------+----------+----------+----------|
+| -2.089684 | -0.725653 | 0.027399 | 0.743488 | 1.896184 |
 
 Coefficients:
 
 |             :name | :estimate |  :stderr |  :t-value | :p-value |   :confidence-interval |
 |-------------------+-----------+----------+-----------+----------+------------------------|
-|         Intercept | 61.661512 | 0.988273 | 62.393227 |      0.0 |  [59.486338 63.836685] |
-| :day-of-week=:Tue |  0.491172 | 1.397628 |  0.351433 | 0.731903 |   [-2.584987 3.567332] |
-| :day-of-week=:Wed |  0.046784 | 1.397628 |  0.033474 | 0.973896 |   [-3.029375 3.122944] |
-| :day-of-week=:Thu |  1.800642 | 1.397628 |  1.288355 | 0.224062 |   [-1.275518 4.876801] |
-| :day-of-week=:Fri |   -0.4912 | 1.562596 | -0.314349 | 0.759138 |    [-3.93045 2.948051] |
-| :day-of-week=:Sat | -8.016678 | 1.562596 | -5.130358 |  3.28E-4 | [-11.455929 -4.577427] |
-| :day-of-week=:Sun | -7.656106 | 1.562596 | -4.899607 |  4.72E-4 | [-11.095357 -4.216855] |
+|         Intercept | 61.711979 | 0.785093 | 78.604691 |      0.0 |  [59.984002 63.439957] |
+| :day-of-week=:Tue |  2.061152 | 1.110289 |  1.856411 | 0.090358 |   [-0.382577 4.504882] |
+| :day-of-week=:Wed |  0.460481 | 1.110289 |   0.41474 | 0.686306 |    [-1.983249 2.90421] |
+| :day-of-week=:Thu |  0.560154 | 1.110289 |  0.504512 | 0.623855 |   [-1.883575 3.003884] |
+| :day-of-week=:Fri |  2.637936 | 1.241341 |   2.12507 | 0.057066 |   [-0.094236 5.370109] |
+| :day-of-week=:Sat | -8.227844 | 1.241341 | -6.628191 |   3.7E-5 | [-10.960016 -5.495671] |
+| :day-of-week=:Sun |  -9.23519 | 1.241341 | -7.439689 |   1.3E-5 | [-11.967362 -6.503017] |
 
-F-statistic: 12.577222778150933 on degrees of freedom: {:residual 11, :model 6, :intercept 1}
-p-value: 2.3080916758566605E-4
+F-statistic: 28.03879101493394 on degrees of freedom: {:residual 11, :model 6, :intercept 1}
+p-value: 4.726292776258134E-6
 
-R2: 0.8727784466366092
-Adjusted R2: 0.8033848720747596
-Residual standard error: 1.7117382259124394 on 11 degrees of freedom
-AIC: 77.56754744532842
+R2: 0.9386272863637274
+Adjusted R2: 0.9051512607439424
+Residual standard error: 1.359820669918769 on 11 degrees of freedom
+AIC: 69.28191236879977
 

 

 
A model with all days except for one, dropping one category to avoid multicolinearity, and speciftying the order of encoded values:

@@ -771,29 +771,29 @@ 
Categorical relat
 

 
Residuals:
 
-|      :min |       :q1 |   :median |      :q3 |     :max |
-|-----------+-----------+-----------+----------+----------|
-| -1.638849 | -1.158227 | -0.192556 | 0.797888 | 2.719674 |
+|      :min |       :q1 |  :median |      :q3 |     :max |
+|-----------+-----------+----------+----------+----------|
+| -2.089684 | -0.725653 | 0.027399 | 0.743488 | 1.896184 |
 
 Coefficients:
 
 |             :name | :estimate |  :stderr |  :t-value | :p-value |   :confidence-interval |
 |-------------------+-----------+----------+-----------+----------+------------------------|
-|         Intercept | 61.661512 | 0.988273 | 62.393227 |      0.0 |  [59.486338 63.836685] |
-| :day-of-week=:Tue |  0.491172 | 1.397628 |  0.351433 | 0.731903 |   [-2.584987 3.567332] |
-| :day-of-week=:Wed |  0.046784 | 1.397628 |  0.033474 | 0.973896 |   [-3.029375 3.122944] |
-| :day-of-week=:Thu |  1.800642 | 1.397628 |  1.288355 | 0.224062 |   [-1.275518 4.876801] |
-| :day-of-week=:Fri |   -0.4912 | 1.562596 | -0.314349 | 0.759138 |    [-3.93045 2.948051] |
-| :day-of-week=:Sat | -8.016678 | 1.562596 | -5.130358 |  3.28E-4 | [-11.455929 -4.577427] |
-| :day-of-week=:Sun | -7.656106 | 1.562596 | -4.899607 |  4.72E-4 | [-11.095357 -4.216855] |
+|         Intercept | 61.711979 | 0.785093 | 78.604691 |      0.0 |  [59.984002 63.439957] |
+| :day-of-week=:Tue |  2.061152 | 1.110289 |  1.856411 | 0.090358 |   [-0.382577 4.504882] |
+| :day-of-week=:Wed |  0.460481 | 1.110289 |   0.41474 | 0.686306 |    [-1.983249 2.90421] |
+| :day-of-week=:Thu |  0.560154 | 1.110289 |  0.504512 | 0.623855 |   [-1.883575 3.003884] |
+| :day-of-week=:Fri |  2.637936 | 1.241341 |   2.12507 | 0.057066 |   [-0.094236 5.370109] |
+| :day-of-week=:Sat | -8.227844 | 1.241341 | -6.628191 |   3.7E-5 | [-10.960016 -5.495671] |
+| :day-of-week=:Sun |  -9.23519 | 1.241341 | -7.439689 |   1.3E-5 | [-11.967362 -6.503017] |
 
-F-statistic: 12.577222778150933 on degrees of freedom: {:residual 11, :model 6, :intercept 1}
-p-value: 2.3080916758566605E-4
+F-statistic: 28.03879101493394 on degrees of freedom: {:residual 11, :model 6, :intercept 1}
+p-value: 4.726292776258134E-6
 
-R2: 0.8727784466366092
-Adjusted R2: 0.8033848720747596
-Residual standard error: 1.7117382259124394 on 11 degrees of freedom
-AIC: 77.56754744532842
+R2: 0.9386272863637274
+Adjusted R2: 0.9051512607439424
+Residual standard error: 1.359820669918769 on 11 degrees of freedom
+AIC: 69.28191236879977
 

 

 
A model with all days and no intercept, dropping the intercept to avoid multicolinearity and have an easier interpretation of the coefficients:

@@ -810,29 +810,29 @@ 
Categorical relat
 

 
Residuals:
 
-|      :min |       :q1 |   :median |      :q3 |     :max |
-|-----------+-----------+-----------+----------+----------|
-| -1.638849 | -1.158227 | -0.192556 | 0.797888 | 2.719674 |
+|      :min |       :q1 |  :median |      :q3 |     :max |
+|-----------+-----------+----------+----------+----------|
+| -2.089684 | -0.725653 | 0.027399 | 0.743488 | 1.896184 |
 
 Coefficients:
 
 |             :name | :estimate |  :stderr |  :t-value | :p-value |  :confidence-interval |
 |-------------------+-----------+----------+-----------+----------+-----------------------|
-| :day-of-week=:Mon | 61.661512 | 0.988273 | 62.393227 |      0.0 | [59.486338 63.836685] |
-| :day-of-week=:Tue | 62.152684 | 0.988273 | 62.890227 |      0.0 | [59.977511 64.327857] |
-| :day-of-week=:Wed | 61.708296 | 0.988273 | 62.440566 |      0.0 | [59.533123 63.883469] |
-| :day-of-week=:Thu | 63.462153 | 0.988273 | 64.215236 |      0.0 |  [61.28698 65.637326] |
-| :day-of-week=:Fri | 61.170312 | 1.210382 | 50.538034 |      0.0 |  [58.50628 63.834344] |
-| :day-of-week=:Sat | 53.644834 | 1.210382 | 44.320592 |      0.0 | [50.980802 56.308866] |
-| :day-of-week=:Sun | 54.005406 | 1.210382 | 44.618491 |      0.0 | [51.341373 56.669438] |
+| :day-of-week=:Mon | 61.711979 | 0.785093 | 78.604691 |      0.0 | [59.984002 63.439957] |
+| :day-of-week=:Tue | 63.773132 | 0.785093 | 81.230053 |      0.0 | [62.045154 65.501109] |
+| :day-of-week=:Wed |  62.17246 | 0.785093 | 79.191222 |      0.0 | [60.444483 63.900438] |
+| :day-of-week=:Thu | 62.272133 | 0.785093 | 79.318179 |      0.0 | [60.544156 64.000111] |
+| :day-of-week=:Fri | 64.349916 | 0.961538 | 66.923915 |      0.0 | [62.233584 66.466247] |
+| :day-of-week=:Sat | 53.484136 | 0.961538 | 55.623504 |      0.0 | [51.367804 55.600468] |
+| :day-of-week=:Sun |  52.47679 | 0.961538 | 54.575864 |      0.0 | [50.360458 54.593121] |
 
-F-statistic: 3197.104811102357 on degrees of freedom: {:residual 11, :model 7, :intercept 0}
+F-statistic: 5127.2787830847365 on degrees of freedom: {:residual 11, :model 7, :intercept 0}
 p-value: 0.0
 
-R2: 0.9995087253433349
-Adjusted R2: 0.9991960960163662
-Residual standard error: 1.7117382259124396 on 11 degrees of freedom
-AIC: 77.56754744532843
+R2: 0.9996936099697633
+Adjusted R2: 0.9994986344959763
+Residual standard error: 1.359820669918769 on 11 degrees of freedom
+AIC: 69.28191236879977