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<div class="frontmatter">
<div class="title"><h1>doby: Groupwise computations and miscellaneous utilities</h1></div>
<div class="author"><h2>Søren Højsgaard</h2></div>
</div>
<div class="body">
<p>The \doby{} package contains a variety of utility functions. This
working document describes some of these functions. The package
originally grew out of a need to calculate groupwise summary
statistics (much in the spirit of \code{PROC SUMMARY} of the
\proglang{SAS} system), but today the package contains many different
utilities.</p>
<pre><code class="language-r">library(doBy)
</code></pre>
<p>\section{Data used for illustration}
\label{sec:co2data}</p>
<p>The description of the \code{doBy} package is based on the \code{mtcars}
dataset.</p>
<pre><code class="language-r">head(mtcars)
#&gt;                    mpg cyl disp  hp drat   wt qsec vs am gear carb
#&gt; Mazda RX4         21.0   6  160 110 3.90 2.62 16.5  0  1    4    4
#&gt; Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0  0  1    4    4
#&gt; Datsun 710        22.8   4  108  93 3.85 2.32 18.6  1  1    4    1
#&gt; Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4  1  0    3    1
#&gt; Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0  0    3    2
#&gt; Valiant           18.1   6  225 105 2.76 3.46 20.2  1  0    3    1
tail(mtcars)
#&gt;                 mpg cyl  disp  hp drat   wt qsec vs am gear carb
#&gt; Porsche 914-2  26.0   4 120.3  91 4.43 2.14 16.7  0  1    5    2
#&gt; Lotus Europa   30.4   4  95.1 113 3.77 1.51 16.9  1  1    5    2
#&gt; Ford Pantera L 15.8   8 351.0 264 4.22 3.17 14.5  0  1    5    4
#&gt; Ferrari Dino   19.7   6 145.0 175 3.62 2.77 15.5  0  1    5    6
#&gt; Maserati Bora  15.0   8 301.0 335 3.54 3.57 14.6  0  1    5    8
#&gt; Volvo 142E     21.4   4 121.0 109 4.11 2.78 18.6  1  1    4    2
</code></pre>
<h1 id="chp:groupwise-computations">Groupwise computations</h1>
<h2 id="sec:summaryby-and-summary-by">summaryBy()  and summary_by()</h2>
<p>\label{sec:summaryBy}</p>
<p>The \summaryby{} function is used for calculating quantities like
``the mean and variance of numerical variables \(x\) and \(y\) for
each combination of two factors \(A\) and \(B\)’’.
Notice: A functionality similar to \summaryby\ is provided by
\code{aggregate()} from base \R.</p>
<pre><code class="language-r">myfun1 &lt;- function(x){
    c(m=mean(x), s=sd(x))
}
summaryBy(cbind(mpg, cyl, lh=log(hp)) ~ vs, 
          data=mtcars, FUN=myfun1)
#&gt;   vs mpg.m mpg.s cyl.m cyl.s lh.m  lh.s
#&gt; 1  0  16.6  3.86  7.44 1.149 5.20 0.330
#&gt; 2  1  24.6  5.38  4.57 0.938 4.48 0.289
</code></pre>
<p>A simpler call is</p>
<pre><code class="language-r">summaryBy(mpg ~ vs, data=mtcars, FUN=mean)
#&gt;   vs mpg.mean
#&gt; 1  0     16.6
#&gt; 2  1     24.6
</code></pre>
<p>Instead of formula we may specify a list containing the left hand side
and the right hand side of a formula\footnote{This is a feature of
\summaryby\ and it does not work with \code{aggregate}.} but that is
possible only for variables already in the dataframe:</p>
<pre><code class="language-r">summaryBy(list(c(&quot;mpg&quot;, &quot;cyl&quot;), &quot;vs&quot;), 
          data=mtcars, FUN=myfun1)
#&gt;   vs mpg.m mpg.s cyl.m cyl.s
#&gt; 1  0  16.6  3.86  7.44 1.149
#&gt; 2  1  24.6  5.38  4.57 0.938
</code></pre>
<p>Inspired by the \pkg{dplyr} package, there is a \verb|summary_by| function which
does the samme as \summaryby{} but with the data argument being the first so
that one may write</p>
<pre><code class="language-r">mtcars |&gt; summary_by(cbind(mpg, cyl, lh=log(hp)) ~ vs,
                      FUN=myfun1)
#&gt;   vs mpg.m mpg.s cyl.m cyl.s lh.m  lh.s
#&gt; 1  0  16.6  3.86  7.44 1.149 5.20 0.330
#&gt; 2  1  24.6  5.38  4.57 0.938 4.48 0.289
</code></pre>
<h2 id="sec:orderby-and-order-by">orderBy() and order_by()</h2>
<p>Ordering (or sorting) a data frame is possible with the \code{orderBy}
function. For example, we order the rows according to \code{gear} and \code{carb} (within \code{gear}):</p>
<pre><code class="language-r">x1 &lt;- orderBy(~ gear + carb, data=mtcars)
head(x1, 4)
#&gt;                    mpg cyl disp  hp drat   wt qsec vs am gear carb
#&gt; Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4  1  0    3    1
#&gt; Valiant           18.1   6  225 105 2.76 3.46 20.2  1  0    3    1
#&gt; Toyota Corona     21.5   4  120  97 3.70 2.46 20.0  1  0    3    1
#&gt; Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0  0    3    2
tail(x1, 4)
#&gt;                 mpg cyl  disp  hp drat   wt qsec vs am gear carb
#&gt; Lotus Europa   30.4   4  95.1 113 3.77 1.51 16.9  1  1    5    2
#&gt; Ford Pantera L 15.8   8 351.0 264 4.22 3.17 14.5  0  1    5    4
#&gt; Ferrari Dino   19.7   6 145.0 175 3.62 2.77 15.5  0  1    5    6
#&gt; Maserati Bora  15.0   8 301.0 335 3.54 3.57 14.6  0  1    5    8
</code></pre>
<p>If we want the ordering to be by decreasing values of one of the
variables, we can do</p>
<pre><code class="language-r">x2 &lt;- orderBy(~ -gear + carb, data=mtcars)
</code></pre>
<p>Alternative forms are:</p>
<pre><code class="language-r">x3 &lt;- orderBy(c(&quot;gear&quot;, &quot;carb&quot;), data=mtcars)
x4 &lt;- orderBy(c(&quot;-gear&quot;, &quot;carb&quot;), data=mtcars)
x5 &lt;- mtcars |&gt; order_by(c(&quot;gear&quot;, &quot;carb&quot;))
x6 &lt;- mtcars |&gt; order_by(~ -gear + carb)
</code></pre>
<h2 id="sec:splitby-and-split-by">splitBy() and split_by()</h2>
<p>Suppose we want to split the \code{airquality} data into a list of dataframes, e.g.\ one
dataframe for each month. This can be achieved by:</p>
<pre><code class="language-r">x &lt;- splitBy(~ Month, data=airquality)
x &lt;- splitBy(~ vs, data=mtcars)
lapply(x, head, 4)
#&gt; $`0`
#&gt;                    mpg cyl disp  hp drat   wt qsec am gear carb
#&gt; Mazda RX4         21.0   6  160 110 3.90 2.62 16.5  1    4    4
#&gt; Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0  1    4    4
#&gt; Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0    3    2
#&gt; Duster 360        14.3   8  360 245 3.21 3.57 15.8  0    3    4
#&gt; 
#&gt; $`1`
#&gt;                 mpg cyl disp  hp drat   wt qsec am gear carb
#&gt; Datsun 710     22.8   4  108  93 3.85 2.32 18.6  1    4    1
#&gt; Hornet 4 Drive 21.4   6  258 110 3.08 3.21 19.4  0    3    1
#&gt; Valiant        18.1   6  225 105 2.76 3.46 20.2  0    3    1
#&gt; Merc 240D      24.4   4  147  62 3.69 3.19 20.0  0    4    2
attributes(x)
#&gt; $names
#&gt; [1] &quot;0&quot; &quot;1&quot;
#&gt; 
#&gt; $groupid
#&gt;            vs
#&gt; Mazda RX4   0
#&gt; Datsun 710  1
#&gt; 
#&gt; $idxvec
#&gt; $idxvec$`0`
#&gt;  [1]  1  2  5  7 12 13 14 15 16 17 22 23 24 25 27 29 30 31
#&gt; 
#&gt; $idxvec$`1`
#&gt;  [1]  3  4  6  8  9 10 11 18 19 20 21 26 28 32
#&gt; 
#&gt; 
#&gt; $grps
#&gt;           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#&gt;                 &quot;0&quot;                 &quot;0&quot;                 &quot;1&quot;                 &quot;1&quot; 
#&gt;   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#&gt;                 &quot;0&quot;                 &quot;1&quot;                 &quot;0&quot;                 &quot;1&quot; 
#&gt;            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#&gt;                 &quot;1&quot;                 &quot;1&quot;                 &quot;1&quot;                 &quot;0&quot; 
#&gt;          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#&gt;                 &quot;0&quot;                 &quot;0&quot;                 &quot;0&quot;                 &quot;0&quot; 
#&gt;   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#&gt;                 &quot;0&quot;                 &quot;1&quot;                 &quot;1&quot;                 &quot;1&quot; 
#&gt;       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#&gt;                 &quot;1&quot;                 &quot;0&quot;                 &quot;0&quot;                 &quot;0&quot; 
#&gt;    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#&gt;                 &quot;0&quot;                 &quot;1&quot;                 &quot;0&quot;                 &quot;1&quot; 
#&gt;      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#&gt;                 &quot;0&quot;                 &quot;0&quot;                 &quot;0&quot;                 &quot;1&quot; 
#&gt; 
#&gt; $class
#&gt; [1] &quot;splitByData&quot; &quot;list&quot;
</code></pre>
<p>Alternative forms are:</p>
<pre><code class="language-r">splitBy(&quot;vs&quot;, data=mtcars)
#&gt;            listentry vs
#&gt; Mazda RX4          0  0
#&gt; Datsun 710         1  1
mtcars |&gt; split_by(~ vs)
#&gt;            listentry vs
#&gt; Mazda RX4          0  0
#&gt; Datsun 710         1  1
</code></pre>
<h2 id="sec:subsetby-and-subset-by">subsetBy() and subset_by()</h2>
<p>Suppose we want to select those rows within each month for which the the
wind speed is larger than the mean wind speed (within the month). This
is achieved by:</p>
<pre><code class="language-r">x &lt;- subsetBy(~am, subset=mpg &gt; mean(mpg), data=mtcars)
head(x)
#&gt;                      mpg cyl disp  hp drat   wt qsec vs gear carb
#&gt; 0.Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4  1    3    1
#&gt; 0.Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0    3    2
#&gt; 0.Valiant           18.1   6  225 105 2.76 3.46 20.2  1    3    1
#&gt; 0.Merc 240D         24.4   4  147  62 3.69 3.19 20.0  1    4    2
#&gt; 0.Merc 230          22.8   4  141  95 3.92 3.15 22.9  1    4    2
#&gt; 0.Merc 280          19.2   6  168 123 3.92 3.44 18.3  1    4    4
</code></pre>
<p>Note that the statement \code{Wind &gt; mean(Wind)} is evaluated within
each month.</p>
<p>Alternative forms are</p>
<pre><code class="language-r">x &lt;- subsetBy(&quot;am&quot;, subset=mpg &gt; mean(mpg), data=mtcars)
x &lt;- mtcars  |&gt; subset_by(&quot;vs&quot;, subset=mpg &gt; mean(mpg))
x &lt;- mtcars  |&gt; subset_by(~vs, subset=mpg &gt; mean(mpg))
</code></pre>
<h2 id="sec:transformby-and-transform-by">transformBy() and transform_by()</h2>
<p>The \code{transformBy} function is analogous to the \code{transform}
function except that it works within groups. For example:</p>
<pre><code class="language-r">head(x)
#&gt;                      mpg cyl disp  hp drat   wt qsec am gear carb
#&gt; 0.Mazda RX4         21.0   6  160 110 3.90 2.62 16.5  1    4    4
#&gt; 0.Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0  1    4    4
#&gt; 0.Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0    3    2
#&gt; 0.Merc 450SL        17.3   8  276 180 3.07 3.73 17.6  0    3    3
#&gt; 0.Pontiac Firebird  19.2   8  400 175 3.08 3.85 17.1  0    3    2
#&gt; 0.Porsche 914-2     26.0   4  120  91 4.43 2.14 16.7  1    5    2
x &lt;- transformBy(~vs, data=mtcars, 
                 min.mpg=min(mpg), max.mpg=max(mpg))
head(x)
#&gt;    mpg cyl disp  hp drat   wt qsec am gear carb min.mpg max.mpg
#&gt; 1 21.0   6  160 110 3.90 2.62 16.5  1    4    4    10.4      26
#&gt; 2 21.0   6  160 110 3.90 2.88 17.0  1    4    4    10.4      26
#&gt; 3 18.7   8  360 175 3.15 3.44 17.0  0    3    2    10.4      26
#&gt; 4 14.3   8  360 245 3.21 3.57 15.8  0    3    4    10.4      26
#&gt; 5 16.4   8  276 180 3.07 4.07 17.4  0    3    3    10.4      26
#&gt; 6 17.3   8  276 180 3.07 3.73 17.6  0    3    3    10.4      26
</code></pre>
<p>Alternative forms:</p>
<pre><code class="language-r">x &lt;- transformBy(&quot;vs&quot;, data=mtcars, 
                 min.mpg=min(mpg), max.mpg=max(mpg))
x &lt;- mtcars |&gt; transform_by(&quot;vs&quot;,
                             min.mpg=min(mpg), max.mpg=max(mpg))
</code></pre>
<h2 id="sec:lapplyby-and-lapply-by">lapplyBy() and lapply_by()</h2>
<p>This \code{lapplyBy} function is a wrapper for first splitting data
into a list according to the formula (using splitBy) and then applying
a function to each element of the list (using lapply).</p>
<pre><code class="language-r">lapplyBy(~vs, data=mtcars,
         FUN=function(d) lm(mpg~cyl, data=d)  |&gt; summary()  |&gt; coef())
#&gt; $`0`
#&gt;             Estimate Std. Error t value Pr(&gt;|t|)
#&gt; (Intercept)    36.93       3.69   10.01 2.73e-08
#&gt; cyl            -2.73       0.49   -5.56 4.27e-05
#&gt; 
#&gt; $`1`
#&gt;             Estimate Std. Error t value Pr(&gt;|t|)
#&gt; (Intercept)     41.9       5.78    7.26  0.00001
#&gt; cyl             -3.8       1.24   -3.07  0.00978
</code></pre>
<h1 id="chp:miscellaneous-utilities">Miscellaneous utilities</h1>
<h2 id="sec:firstobs-and-lastobs">firstobs() and lastobs()</h2>
<p>To obtain the indices of the first/last occurences of an item in a
vector do:</p>
<pre><code class="language-r">x &lt;- c(1, 1, 1, 2, 2, 2, 1, 1, 1, 3)
firstobs(x)
#&gt; [1]  1  4 10
lastobs(x)
#&gt; [1]  6  9 10
</code></pre>
<p>The same can be done on variables in a data frame, e.g.</p>
<pre><code class="language-r">firstobs(~vs, data=mtcars)
#&gt; [1] 1 3
lastobs(~vs, data=mtcars)
#&gt; [1] 31 32
</code></pre>
<p>\subsection{The \code{which.maxn()} and \code{which.minn()} functions}
\label{sec:whichmaxn}</p>
<p>The location of the \(n\) largest / smallest entries in a numeric vector
can be obtained with</p>
<pre><code class="language-r">x &lt;- c(1:4, 0:5, 11, NA, NA)
which.maxn(x, 3)
#&gt; [1] 11 10  4
which.minn(x, 5)
#&gt; [1] 5 1 6 2 7
</code></pre>
<h2 id="sec:subsequences-subseq">Subsequences - subSeq()</h2>
<p>Find (sub) sequences in a vector:</p>
<pre><code class="language-r">x &lt;- c(1, 1, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 1)
subSeq(x)
#&gt;   first last slength midpoint value
#&gt; 1     1    2       2        2     1
#&gt; 2     3    5       3        4     2
#&gt; 3     6    7       2        7     1
#&gt; 4     8   11       4       10     3
#&gt; 5    12   14       3       13     1
subSeq(x, item=1)
#&gt;   first last slength midpoint value
#&gt; 1     1    2       2        2     1
#&gt; 2     6    7       2        7     1
#&gt; 3    12   14       3       13     1
subSeq(letters[x])
#&gt;   first last slength midpoint value
#&gt; 1     1    2       2        2     a
#&gt; 2     3    5       3        4     b
#&gt; 3     6    7       2        7     a
#&gt; 4     8   11       4       10     c
#&gt; 5    12   14       3       13     a
subSeq(letters[x], item=&quot;a&quot;)
#&gt;   first last slength midpoint value
#&gt; 1     1    2       2        2     a
#&gt; 2     6    7       2        7     a
#&gt; 3    12   14       3       13     a
</code></pre>
<h2 id="sec:recoding-values-of-a-vector-recodevar">Recoding values of a vector - recodeVar()</h2>
<pre><code class="language-r">x &lt;- c(&quot;dec&quot;, &quot;jan&quot;, &quot;feb&quot;, &quot;mar&quot;, &quot;apr&quot;, &quot;may&quot;)
src1 &lt;- list(c(&quot;dec&quot;, &quot;jan&quot;, &quot;feb&quot;), c(&quot;mar&quot;, &quot;apr&quot;, &quot;may&quot;))
tgt1 &lt;- list(&quot;winter&quot;, &quot;spring&quot;)
recodeVar(x, src=src1, tgt=tgt1)
#&gt; [1] &quot;winter&quot; &quot;winter&quot; &quot;winter&quot; &quot;spring&quot; &quot;spring&quot; &quot;spring&quot;
</code></pre>
<h2 id="sec:renaming-columns-of-a-dataframe-or-matrix-renamecol">Renaming columns of a dataframe or matrix - renameCol()</h2>
<pre><code class="language-r">head(renameCol(mtcars, c(&quot;vs&quot;, &quot;mpg&quot;), c(&quot;vs_&quot;, &quot;mpg_&quot;)))
#&gt;                   mpg_ cyl disp  hp drat   wt qsec vs_ am gear carb
#&gt; Mazda RX4         21.0   6  160 110 3.90 2.62 16.5   0  1    4    4
#&gt; Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0   0  1    4    4
#&gt; Datsun 710        22.8   4  108  93 3.85 2.32 18.6   1  1    4    1
#&gt; Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4   1  0    3    1
#&gt; Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0   0  0    3    2
#&gt; Valiant           18.1   6  225 105 2.76 3.46 20.2   1  0    3    1
</code></pre>
<h2 id="sec:time-since-an-event-timesinceevent">Time since an event - timeSinceEvent()</h2>
<p>Consider the vector</p>
<pre><code class="language-r">yvar &lt;- c(0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0)
</code></pre>
<p>Imagine that “1” indicates an event of some kind which takes place
at a certain time point. By default time points are assumed
equidistant but for illustration we define time time variable</p>
<pre><code class="language-r">tvar &lt;- seq_along(yvar) + c(0.1, 0.2)
</code></pre>
<p>Now we find time since event as</p>
<pre><code class="language-r">tse &lt;- timeSinceEvent(yvar, tvar)
#&gt; Warning: 'timeSinceEvent' is deprecated.
#&gt; Use 'time_since_event' instead.
#&gt; See help(&quot;Deprecated&quot;)
tse
#&gt;    yvar tvar abs.tse sign.tse ewin run tae  tbe
#&gt; 1     0  1.1     3.1     -3.1    1  NA  NA -3.1
#&gt; 2     0  2.2     2.0     -2.0    1  NA  NA -2.0
#&gt; 3     0  3.1     1.1     -1.1    1  NA  NA -1.1
#&gt; 4     1  4.2     0.0      0.0    1   1 0.0  0.0
#&gt; 5     0  5.1     0.9      0.9    1   1 0.9 -5.1
#&gt; 6     0  6.2     2.0      2.0    1   1 2.0 -4.0
#&gt; 7     0  7.1     2.9      2.9    1   1 2.9 -3.1
#&gt; 8     0  8.2     2.0     -2.0    2   1 4.0 -2.0
#&gt; 9     0  9.1     1.1     -1.1    2   1 4.9 -1.1
#&gt; 10    1 10.2     0.0      0.0    2   2 0.0  0.0
#&gt; 11    0 11.1     0.9      0.9    2   2 0.9 -3.1
#&gt; 12    0 12.2     2.0      2.0    2   2 2.0 -2.0
#&gt; 13    0 13.1     1.1     -1.1    3   2 2.9 -1.1
#&gt; 14    1 14.2     0.0      0.0    3   3 0.0  0.0
#&gt; 15    1 15.1     0.0      0.0    4   4 0.0  0.0
#&gt; 16    0 16.2     1.1      1.1    4   4 1.1   NA
#&gt; 17    0 17.1     2.0      2.0    4   4 2.0   NA
#&gt; 18    0 18.2     3.1      3.1    4   4 3.1   NA
#&gt; 19    0 19.1     4.0      4.0    4   4 4.0   NA
#&gt; 20    0 20.2     5.1      5.1    4   4 5.1   NA
</code></pre>
<p>The output reads as follows:
\begin{itemize}
\item \verb"abs.tse": Absolute time since (nearest) event.
\item \verb"sign.tse": Signed time since (nearest) event.
\item \verb"ewin": Event window: Gives a symmetric window around each event.
\item \verb"run": The value of \verb"run" is set to \(1\) when the first
event occurs and is increased by \(1\) at each subsequent event.
\item \verb”tae”: Time after event.
\item \verb”tbe”: Time before event.
\end{itemize}cYMR;`</p>
<pre><code class="language-r">plot(sign.tse ~ tvar, data=tse, type=&quot;b&quot;)
grid()
rug(tse$tvar[tse$yvar == 1], col=&quot;blue&quot;,lwd=4)
points(scale(tse$run), col=tse$run, lwd=2)
lines(abs.tse + .2 ~ tvar, data=tse, type=&quot;b&quot;,col=3)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-27" /></p>
<pre><code class="language-r">plot(tae ~ tvar, data=tse, ylim=c(-6,6), type=&quot;b&quot;)
grid()
lines(tbe ~ tvar, data=tse, type=&quot;b&quot;, col=&quot;red&quot;)
rug(tse$tvar[tse$yvar==1], col=&quot;blue&quot;, lwd=4)
lines(run ~ tvar, data=tse, col=&quot;cyan&quot;, lwd=2)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-28" /></p>
<pre><code class="language-r">plot(ewin ~ tvar, data=tse, ylim=c(1, 4))
rug(tse$tvar[tse$yvar==1], col=&quot;blue&quot;, lwd=4)
grid()
lines(run ~ tvar, data=tse, col=&quot;red&quot;)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-29" /></p>
<p>We may now f
ind times for which time since an event is at most 1 as</p>
<pre><code class="language-r">tse$tvar[tse$abs &lt;= 1]
#&gt; [1]  4.2  5.1 10.2 11.1 14.2 15.1
</code></pre>
<h2 id="sec:example-using-subseq-and-timesinceevent">Example: Using subSeq() and timeSinceEvent()</h2>
<p>Consider the \verb|lynx| data:</p>
<pre><code class="language-r">lynx &lt;- as.numeric(lynx)
tvar &lt;- 1821:1934
plot(tvar, lynx, type=&quot;l&quot;)
</code></pre>
<p><img 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IVeo5F5YaBOPB4P/OUVDcQrwwDxSsmB1A0AYALxyjCARy85vqkbZs6UTqeT9pAAQFnIN17pcDgaGxv9XrTZbCIPx1mt1ujoq8cJ1o0kOJ1Or7Iz83RiY2OlPSQAUBbyjVeuXbv266+/9nvx4MGDeXl5xA6MA77uJEwSkQRfdxiEXj4w/VzBu1cE8o1XFhYWFhb63xxmzJhRUVFB9WABOQMz7+VDVFSU3W73Pu8CcgbilWGAgkVWxMTEQKdi8QnqlzJdo0HoFQHEKwElARW9JAQtd6Kjo+12u/gHAwgA4pVh8KtloFOutIDQywf4LBQExCv5wVQxsOaUVIC4yAf4LBSEfOOVMsHvoZXxiEHopQLERT7AZ6Eg5BuvlAN2u92vQS5c3NICf3/5AJ+FgqBukrvLNy1YUlzjQQgh67nVr08c2qdb9+uHTXr1h9MW2vvGJnBlcFifWlpAXMQnVFgePgsFQV3oXaUr5vx3S5UboeZt/7h16pqU8a99+vm/pvU8/o+Rjy43y7znrG+jGwZYn1p8fMfDQVzEJ1TryqioKPgslIJ4rUedh1euT/nb8g8e66JDCA24Prqkx5c/198t63hloB0PQiMt8EQlPqHaRMN3QUGIJ/Sa5JapcS1a/P4IqI2O1rld8q/o/YQeJuyIT2ALBAkPJgIBoVcB9IPsOp374Dujrrtx9PNbGw//64XlFW7kNu6Y99dZKzuPvjmR+u6xCLRu4OIWGT/fAKwz8QGhVwHUK3rDTR+eqZpdfvbs2bNnzww+15zoQsh5bMPmmKlLv5zSWuYTpoJaN1DRi4mfyoC4iA8IvQoQw7rRxaa17ZHWtke/ob+/MuSV74eIsGNsgqZu4OIWExB6yQGhVwEyL6klBlI3kgNCLzkg9CoAhJ4NyNFLjt+cNRAX8QGhVwEg9GwEevRQ0YsMVPSSwyL00L1SKYDQswEVveQECj38/UUGKnoVAELPBsQrJQcqeskBoVcBIPRsgHUjOX4qA39/8QGhVwEg9GyAdSM5UNFLDgi9CgChZwPilZJjt9tB6KUFhF4FgNCzAU3NJMfhcEC8UlpA6FUACD0b0NRMcsC6kRwQehUAQs8GtECQHD+V0el0Tqf/SvMAVUDoVQAIPRtB45VQ0YtJKJUBRAOEXgWA0LMB8UrJAaGXHBB6FQBCz4bVaoWKXloCVSbo+qXy59NPPy0uLpb6KIQQailBnU7ncrnEPx5AACD0bLhcLp1O5/sKVPQi4xevVC7nzp0zGo1SH4UQ/PrKAUoEhJ4fer0eBgPFxC9eqVyampqam5ulPgohgHumAkDoeePxyHylW1WhGpUBoQckBISeNwr1iBWKalQGhB6QEBB6NqB4l5xAlVHohwJCD0gICD0gawJVRqHDJCD0gISA0LMR1KVRaEWpUAJTNwoNPlksFhB6QCqCxGOp4G66fOLIiTJzg00Tk5yVX3BN+1S5XzqB2UpAfAJTN8w8nbi4OKkOSRhNTU0Wi0XqoxACCL0KEEHoneWrZj/05Me7bVkd26QnGNzNtRVnzzXm3vXawo8e65Ug34HNwP4HDDAYKyaBs3UUOiFTuRV9qAlT6Pc5U1APyR/qQu8xLnn6yV0Dvji+amhO9O8vuhtOLX/23slv9tr7dj/ZlgqB/Q8A8QksJw0Gg8PhkOp4BONyuRQq9CwVPXPTjY2NFfmQAL5Q9+idpQdPdJv0xJA/VB4hpE3oNHb62JhjJXUytrsDW1cyKFRoFErgtEyFDsa2aNFCoULPMjM2KirKbreLfDyAAKgLvb7rgF7H57+69HDVH9eDp/nSjs/mLHX36Z0sYxcklNBDuxsxUUdF7/F4YmNjFSr0YSt6kY8HEAB160aTetcHi84++bfhuVPsqVlpCQaPtc5obM648eE3i57rKdZYsBBCCT2T+khISBD/kCIQdQi9xWJp2bIlCD0gFSIorTbtpmf/t3+WxXz+bJm50aGNSclu3z4nUbbe/O8Etq5kgItbTALjlQoV+pSUFLPZLPWBCAGEXgWIlaN3N9eaTSazyWg0GisrqxoU8EVlqejBuhGNwHilEoW+qakpLi5OoTMwQOhVAMQrQxIqXgkXt5iow7phhF6hwVwQehUA8cqQQEUvB9Qk9FIfhUBA6FUAxCtDEipHDxe3mKhG6JUbNnc4HKEmTMF3QSlAvDIkoawbqOjFJFBllCj0FotFHI/ebrcT34vb7dZqgwsFRI2VAnWh16Te9cGi8VXvDs9NSslp36lL5/y2mUmp3R5ckf2yzOOVUNHLAdVU9OJ49HPmzNm0aRPtvXiB74JSkG+88ueff162bJnfizt37szOzqZ1pH/GYrGA0EuO2+32a6Wi1+sVKvQ6nY6lbwwRLBbL2bNn6W3fj+joaJgZqwhE617JxCvNDTZNjE2bmJaeGK57ZZ8+fVJSUvxebGhoEO1LzlLRw+OqhBgMBsW1QGhqasrKymrRooXVao2Pj6e3I7vdfu7cOXrb9wOKHqUg33hlSkpKnz59/F7MyMioqKigfMBXYZ8ZK84xAIEo17ph2t3QFvrKykp62/cjOjpaodN9Iw2IV4aEZWZsY2Oj+McDMChd6KnuyGaznT9/nuoufImOjq6trRVtd4BgIF4ZEovFEjQSBxW9tBgMBsX5wsy1FBsbS3vtEbvdXlVVRXabLGPIYN0oBYhXhqS2tjY5OTnwdbi4xSQwLKhEj76xsTEuLi4mJkaEij4lJUW0J074LigFiFeGhEXoYTBWQpRo3TA5ehGsG7vd3rlz5wsXLlDdixcQeqUg33il5Fit1ujo6MDXwboRk0DfQIlCL6ZH37t373PnznXv3p3qjhhA6JWCWN0rkTY2rX2PPv3697/+mpijX/9wRLlXB1zc0qJcoRdh7RGmohctYRkVFQXfBUVAv6L3NJUfKbnc/IfT6ty3YM7S7p3zLIlJ7Xp2SZd9Ze8PWDfSokSht9ls0dHRIlT0bre7Y8eOe/bsoboXL1D0KAURhN649vnR09Zb0zu0TmJmOFqM5pod0yYvjen/0saFE9JlPB4bFOh1Iy1KFHpmSLlFixa0UzcIoXbt2olW0ZOyMffv319fX3/zzTcH/anH46mrqws6YAZwhL51o23/6A97f3z+hqy2d7275uDx48cPL/pL7tC3dx8/fuhL+ap8qBA9gipGapQo9AziLBubmppaXV1Namsul8uvBYUvpIqeAwcO/Prrr6F+euzYsWeffRZ/L5GMKB59VJvRb65d88+kL+4e+tfFR+tlnJ33UlNTE9iAgQEGY0XD4/GoYzCWQYR4JQPBBpYszegRuTOqqalhmfFeWVmpuJkTckO0wVhdxuDnVmz68NqNU4bO2FAre61neVSEil40gqqMEpuaMYjg0TOkpKTU1NQQ2RS70JM6o9raWhahN5lMIPSYBAi9/dd5L357vNFn7NS08+Ppb20i8XfWpFz3+Fdblr713JuP9ZX5KgyhQvQIBmNFJKjKKHHCFPNcIo51gxBq164dqUYIYSt6It+F2tpalhY9JpNJobd2+RAg9Pq81o0fje4z5qWVZ5qdpp3zH76h1/0/xPfoSGzUNq7LbVMfu61zVPh3SkldXV1iYmLQH4F1IxqhhF5Z9Z23NbFoFX27du1INStmF3qtVkvEJqqrq2MZV6iqqoJvHCYB+q1te+e/t9w8+f9mTuzX/jGPu8NDH28+PLZzvFwHTWlRU1MTqqLXaDQiLBUEILVU9N4FY8UU+lOnThHZFLvQI0LjATU1NYmJiUGHZBBCRqMRKnpMgnj0rqrib+d/utkx4IHHxneqWLdk+W9lkWdUsFg3gGiEEnplfe3FF/q2bduSSliGFXoiNDU15eXlhSrqwbrBJ9Cj3/hE37/8kPbcxv0/vfPyx1t2fzH87Oyb+v1zR4T9nevr65OSkkL9FCp6cVCHdeNdGVycHD0S0aNHrL0tuePxeLKyskKNx9bW1rpcLvy9RDIB1o2++4wNB/M7XvVqdC37PrZg5x079zaJFs+RByzxSkTo4gbCYrfbo6L8R3PAugmFdzAgPj6eVANLcSp6hFBWVlZlZWWPHj2C/hS+cZgEevTZnToij62husnuLVsNnXvkhpwzoU7YrRuo6MVBZdYN7dSN330xlOXNC9GEPjMzU7TF4yKQwEK9ed+/huUkZ3bs1uMP+szcEGGD3nV1dSzWDdQX4uBwOAKX0lac0HtXsKF95DabzSv0GRkZRqMRf5siCD2zi1DWDdMmiN7et2/fHgmLZAV69Ns+m2edtstUXVnxB+c+G0PxTy1HamtrWawbQBwcDkegdaPX6xVn3VBdJ9aL3W73aiIpmz6s0EdFRWEOmTA2aSihNxqN6enp9J6hv/rqq/3791PauHwIEHpXQ4OnwzX5cVIcjIywWCxBVwYHxEQ034AqXuuGNr7FL6ngTdiPAH/ggSmqGI8+8Kdmszk9PZ3eM3Rtbe2lS5cobVw+BAh9zPDpD11896/z1h86e8n4O6a6CHNuwqDRaNxut9RHoX7sdrs6hN67+DDV0R1fj160ih5f6Jk5K+np6SaTKfCnJpMpLS0NZ/vs1NXVXbx4kd72ZULAYKx9y5cLDp+0Hp649tU/Xoy544vSBYUR5t6wwEyOhZKfNk6nUwVCb7FYMjIymH9THd3xE/rly5fjbzPoMIkv+CueM1FmnU4X1JEzm81Uhb6+vj4SKvqAjzC6cEG5eYEUh6IgmHY3IPS0UY11463oqeI7GJuYmFhfX4+/TdEq+lA/NZlMnTt3xtk+OzabLRIq+gDrxrbq6YGF095c9PPxK0pKNhAlaHzbF2h3Iw6qsW68Hj1t68br0eOPkXq3yf5dIOLRM0IfFxfX1NTk91PGo8fZPjsJCQl1dXX0ti8TAoQ+6qYnXrsnt/Kn2aPyc7oNnzx7/sr9FdYIS42zz5ZC0KlYLIKmbpDS5jH4pm6oRoZ8B2OjoqKIRDmbm5tDrcDDgN/A0vt1Czoey6RucLYPoCBCr0nMv+WBF/77/a7zlw5+9VR/za63bm+f0WHQ+Flzv91RZlHS90s47P0PEJ1Oxc3NzaWlpWS3qXRC+QbKmsfgzdEjynOmfB+ASC3bbbPZ2IUev6+Dd85K0DlTjEev1WppxB+YC0xxMzMEEKKzgc1csml5UdGSpd+tPWm4ZsjIW65tcfSzB/v0fOQnUwRofW1tLbvQ07Budu3aNW/ePLLbVDqq8ei91g3VLgi+1g0p8WJZU5OBoHUTNErf2NgYHx9Pyonyg7nHsLTZweTf//43jc0KIEDonfv/e0//tpmdb39pRVmr4c8vPXLp/O4f5r/6jzcWbtzxdsd1P+1X+Z0PIYmsm4qKCnE6XikIdXj0jY2NXqGnupqg72AsqYee5uZm9tBBixYtCFo3gYLLnAilopu5x+Tk5NAYj62rq/vll1+Ib1YYAakbTXznu99YPf+m7mn+3qim5YSvDoxLlPmKISQI26OYhtBXVlYGjkRFOA6HQ5w5pVTxTd3Qtm6IdwsQuaLft2+f308ZoadU0XuFnkbCsrKyMjMzk/hmheEr9E5LfZPDk9HvtgyEmuvqfD48jSEuMVavjW1JMc8qI8JaNzQ8ehD6QNRh3bhcLm8Unap141vRE9xmWKG/cuUKzi68Dw2BHn19fT1zp6dU0TPWTevWrUktyOWLXIXetnp658k/BJWvmDsXnf/yjoiZMFVXV5eTk8PyBlJLZfpiNBpJtZZVDWFn6ygOqi3p7XY7uygLgEtFT+qMAlM33tlS9Dz6xMTEnJycbdu2Ed+4yWSST17I51sUfceXFTVfSnco8oG9dSWiMxhbWVkJkU0/QqkMs5qjsrI3DLQHY9mvWwGIYN34bsrvnuEN0VMdjG3dujUl6yYrK4v4ZoURYeuJcEOSwVir1arTRVjX/3CEagemrAaWvjck/KFLFmhYNyIMxrLgDdHTG4xNSUkJ1WYHE1kJvSjPxbaL24o+L1q3+1iZucGmiUnOyu89ZOxDU+/oniTPkkySwVggEIvFElToma+9Eu172taN4gZj/W4ker3e95P1uh9UK4tbJw4AACAASURBVHqtVktjqcLKykpvjyPJoS/0rtOf3Dn0HdvoqeMfvK1teoLB3VxbUbpvw5xbF21ZuPXfw1JkqPVchD7UQsbCYC5uWBjTD9+pRr4oaIaL3yRexQ3G0hZ6v+9aRkaGyWTyjpCZTKbrr78eUY5XIjpmoFwHY+ngPLz409ppa7a90NV3V2MfmDp0et9P1tUPm0DYUyRBqELSC3GP3mg0ZmRkXL58meA2VUAo60ZBQu8nlC1atKC3npESK3o/oWei9F6hF8ejRwilpaURb6rT0NAgn3AwdY9eY4jS2y0Wf0PV47DakF6v0CEC4vFKr52nrC4utFFBRe93r6Kdo/dbMxZ/myIIve94mN+cKW8zeoPBQEnomdsMjfFYrVZG8ka9otd1mzwz/6bR/UvuHTu4oG1agsFjraso3b9x2Zr6Ccs+SKC9e0GE/YYQr+grKioyMjKYcS3ofuwlVINfBQm939OhmNaNTqdzuVyYI/wul4tdsDDPqKamxjcplJmZ6Zuw9Ao9qR5tftTV1SUkJCCEmMmxvXr1IrVluaXC6Hv02twJ3+zvsaro2/V71hebGx3amJTs/J6TFu4aN7C1UpP5xAdjGTuPadMKQu8l1MLQzJCd+McjAL+KXrReN+h3u4P25UTcuvntt9+8//W2L6VU0XvnshGv6Kuqqlq2bElwg5iIkrrRphQUPlFQKMau8OES56Bh3dxwww2M0Ldq1YrgllWJwWBQSrxSZKH3reijo6PxhT5sWYr5dFVbW+sriH7WjXfvlDx6Lzk5OUePHiW4QVmNxCKIVwYSNnKDKFg33ooeJsdyQUHWjZhC7/cARFsciVBbW9uhQwfvf32F3uPxeF0jStaNl9atW5Pta8bEKwhuEBOIV/oTdlosolDRM5dFfHw8tLvhgiIkjMFvPBl/hVUW/Jp9KuKv5B0OZUhOTvZ69NXV1d4f0T6XrKwsspm3iBN6xcUrw06LRRQqesbRi42NBaHngoJmxvr2KEb0K3rfhIwihN7v66bRaAYNGrRkyZIJEyb4aiUNj973YYuxuQhuvKKiolOnTgQ3iAnEK/3hWNGTFXrmKTXomplAIGDdBMXPo1eE0Ac6pa+99tqHH35oMpl8m4LRsG78HibIJpuNRmNkefSKi1dy8eiJWzfMoBMIvS8s0UAFCb3FYmECggzKEnoRZnUEtgSPjY19++23Z82aVVhY6P3T0ajo/XadmJjY0NDApC3xibzBWKHxSrvdHqh6NpuN9sXHpaIn26a4vr6eubzi4+NhMNZLc3Nz0BA9oha2o0FTU1ObNm28/6V65G632zfzji/0NKbactnFzTff/M033yxevHjixInMKzSeTvy+6cx4bNeuXYlsXFY9ipGc45Xr1q37+uuv/V48ePCg79eGBrW1tWHNNbIVpffmHxcXV1NTQ2qzSifUbCmkqIo+sIuDaPNo8MUx7LRYBqYpGNneq++99163bt1mzpzJ/JeSdZOYmOj9L7POFCmh5/inEw35xisLCwsLC/1vDjNmzKC0jK8XP+dOBHyFHqwbL6H6HyBFCX3Yvkn0EE3oGT+KbF+XpKSkVatWdezYkfkvpYre95tOaeVYmQDxSn9qampEFvqKigqm0U1cXBysD+6lqakplHYoaMKUX+pGTPBtorDN6BmY1h3ChJ7l+ca3IQGNW7vfaFzr1q13795NZMtOp1NuPbQhXukPF48eEX0ANxqNTLs+mDDli8ViCaUyyqro/Z5LROtbhx8ZtFqtXDx6wU32Wdw5PyhV9Lm5ud7/EozSe1v0yAeIV/rDJUdPFqjogxKqRzFSlNAHPpeI5tHjV/QcW+wJXmSK+3dNhIo+MTGxvr6eyJZltbYUA8Qr/WlsbBS5i7T3sgCP3hcWj16v1ysodRMhHr2A7XOJMjOI4NEnJCSQep6W1dpSDPKNV0oFx/6iBB/AvZeF0q2bDz74YMqUKaTWp2YR+qioKKXcEZubm/20kpnWyzRNpIr8rRuONimiI/T19fW+qZv4+PiGhgYiW5Zb/wMk53ilVHB8sib4AO5VNEVX9JWVlS+88MLNN9/cs2dPIhtkqYUV1AIBBVwqMTExzc3NpCbmsCDaYKzgaSXSWjd+sQuCF1VFRUXv3r2JbIoUYpjkntrD3//ntdmvfrzxfLPxl7cfGDHoxpETnlt8uF6WiylJuMaTgqznQObOnXvdddcZjUZSG2Sv6JVi3QReTlT7mvmCX9H7Nc8JheBls6St6Om5aiaTKfIqevuhOSNHFKXdNapN8Ywx/9fCFTNo5qy7405/O2fMZM3u7yZlyWo81m9uIQuMKOOHqIjPNJGE2tranTt3Tp482Xd5IEzUkaMPhFIXhEA7SBEePcfpozSEnt4iUHLrf4DEiFceX7FCN2PFj8931tl/m1XwkObLdx8ZYEBoROrpHp//0jDpL7KKVzY2NnJ8pmb6muELvd/NX1bLj3Fn3rx506ZNS0xMLCkpIbVN1Vg3fgjOqLATuBoXEeuGi9AzZpSA7VdXV3fu3JnLO8W5tZP69kVivBLZmm1R0VEahJCh06iHHxrWgbm3aKOiNE6HW2buDfeuRqQaWFZUVMjt5s8Xq9W6du3acePGZWRkkLVulB6v9Jtkz0CpovfraIaUUNFzn4UuTncjUratd4VC+UBd6PUFhaPM70+a/t43e8wthz/73KgMjct8dN1nM57/sc3wQXJbYoq70JPqaybDpzy+LFiwYNKkSXq9PjMzUxyPXilCHzR9QUnoAyt6fKHnWNHjWDccB2OJ97pxu92BlinZZoWygn5F32LgnI1LHky/ePBU9dXbZcP2T95cYR+/+Ku/dpCVQY8Qamho4BiiJ9WpOFALJBwNFoDb7S4qKpo8eTJCKD09HYTel6AtDCkNxvotL4WUMBgb2KM4FMTNOm/LWF8SEhLwE5Zya2fGIMbzhSH7xkdevfGP/yffNW/bXSLsVwC8Knoi1k1lZaVvTw/GwMVc0FlMampqsrOzmSub7IiZCrpXilnRB/b7jYqKwrxERbBuSM26ELDrQNeImTOFaa/LMESPxIlXKgheHj2Rit7Po1dclJ775Ea+qMCjDxqzEzy9iB2bzUbDo+fYAkGY0BPJrQkj6MMEkTlT8jRjQej/RENDQ+DoWVBILTJZWlravn17738Vt2xsTU1Namqq9796vZ6UBKugog/6naeUugla0WP+lThW9IJTNxIStEAhsvKPPOMVIPR/grtHT2rcxu/WEh8fryyh9wuWpKenm81mIltWwVKCQWN2lCr6oKkbmVs3EhI0EEXEo6+qqpLV2lIMIPR/wq/9BQtE4pWBRqHiKvrq6mrf4ATB4A1LqFkpQi9mRR80dYP5V+KeulFcWCVo4IdIRe/3jZAJIPR/QmSP/siRIz169PB9RekefUZGBsHJsaEgaBBRJWgvF/DoGehNTOVC0HFgIhU998yomIDQ/wmRJ0wdPXq0e/fuvq8ozrrxu6wJzpliiZkqpaIPqmUwYYqBZQUxEQg1GItf0dOLJ+AAQv8nuDejJ/K4WlJSEljRK6tTsV9lRHZybCiUIvRBEZw6Z4dSvJJLm2Jhg7Hcx8No0NDQQK+iB6GXO7xy9Phf1xMnTnTp0sX3FdrWjcfjIbt9v8ua7OTYUChC6ENN+FJQRe9wOPy2GRStVitglh/3hBsNglb0IPSRAnehJ1LRBz4a0xb6TZs2vf766wQ3SMmjD6xPfVGE0IeaOCOaRy9Mf0Uj6NxU0Qjq0ROxblhmdEsICP2f4LjSAiJR0QcVAtpCf+TIEVLL6DD4CX16errJZMLfLPuy0Xq93uVy4e+FKqGakouWo8eH6n2Ce1HlheDxBK27iVT08gSEXiD4OfqjR4/6GfSI/vrgJSUlZLfvN6pBqtZmmRarFEItHEqpc1ZgRS9zuEeZGQwGA8F2N4FpVESoopcnIPQCwXdaQwk91Uvt2LFjiriU5fn8y4ugHc2QiB49PlTjj9zXfmAQYVkxqOgjBe5XNn5dFhi5QZStG4/H43A4aMc3ibQOp7fMm2iE6nlCSbCClqhyhq9HL0JLevxOhbItUEDo/wR3ExC/Ljt9+nTHjh39XqSaoy8vL8/Pz6c9W51IFwTZfmG4E6qiR3S8bxoVPVX4evTiLBSM+dFI2I+THRD6P+A1nIU5pObxeDweT+AyNFRbIJSUlBQUFBBUmaB/MSIJS/bBWEVgNBpDNbeiYYnQGIzljgAV5huvJLj2CEvXTMyPprq62rfHn3wAof8DXiUGpnVTVlaWm5sb+DpV66akpKR79+4EVaampiYwukAkYamCwdiqqqqWLVsG/RGNip74YGzgauMsCHjAFWDdkBJ6lnsM5kcjzxA9AqH3hZfQY1o3gc0PGIgvmeYLI/QENxj0siYyOVYFFX3QxeroQbyi57VSkoCvg4TWDb0If9DSRw6A0P8BX6HHqeiDRm5oc/bs2Xbt2hHcYND+TUSEXgUePUttSMm6IVvRiyD0fK0bUkLPsmvMNQvl2dEMgdD7wkvodTodzgURNHJDFabBllZL8hMPOo+clEevaOtG/AxMqD0K9iI49ihmEKGiJ2jdsFT0mFF6qOgVAN8HOhw77/z5823atBH86zh71Ol0pL4wVD169opezpP7EWvkBomYusFxAnmtXSxA6Jubm3ndC8lW9KG+6cyysYK3DBW9AuDeuhITprgO9fxOScJKSkq6deuGiCY46+vrA4VenHilhK3MucAu9GQneTIEFXqcZtq8HkpiY2P5zrjm+wmKJvT19fWCtyzbeCXXUXVc3E2XTxw5UWZusGlikrPyC65pnyrNosAs8J2TLVhrbDab+Aa01yxiuiwQecCsrq4OzA4RWRVE6akb9hWimcgW2aoiqC7jTDLi3vcJUevr4AtZ6ybUNx2zDPJbQlk+iCD0zvJVsx968uPdtqyObdITDO7m2oqz5xpz73pt4UeP9UqQUV3W2NgYNPJIHPavEPMUzP07xpGSkpJ7770XEe2yELgUIim4VPSklih6++23e/fuPWLECPxNeTGbzSwVPdMRj6zQh6roBQs9x2b0DJRacvpCtqJv27Zt0B9hVvRBR63kAHWh9xiXPP3krgFfHF81NMd71bgbTi1/9t7Jb/ba+3Y/+RT2ovVNZVcx5imYuNCfP3+eubgJ9k0LulQe+n3JC5zRyLCDsUw6ItS0F16cOXMmaPcxHCorK3v27BnqpzTq36A5ehxx5Ju64XtGfG/SBFsgsFf0mB59hA7GOksPnug26YkhOb5fem1Cp7HTx8YcK6mT04iagL6pwmAv2GnMmXK73VqtlvlqEdx+qMsa36YPm6Mn+CB/4cIF/EEFP4xGI0tFT6NTscfjCYxU4Qg91dQNr7sIgzgTpjCF3uVyiTl5gjvUhV7fdUCv4/NfXXq46o/rzdN8acdnc5a6+/ROlpFzw1voNRqN2+0WsCP2gp2G0J89e7Z9+/bEtx9K6DMzMzGDN2E9evkLPctTggiONgNmRU8vdSPg6VmcCVNqbWBJ3brRpN71waKzT/5teO4Ue2pWWoLBY60zGpszbnz4zaLneoo1FswJvn1Tma+rgGFV9oqeRlNsb+SG7PZD1WX4c6bCGsSkvvYej8dutxNZLMUXs9mclpYW6qdE1qHkAs5fyWaz8aroefmBAp6eRUvdlJeXE9mLrBBBabVpNz37v/2zLObzZ8vMjQ5tTEp2+/Y5ifLx5n+Hb5WBI/Qsv0Vj7ZGjR4/26dOH+TfVvmkMRCbHsnu4mDMYvZjN5u7duxOv6NnnqVKybgJfxLRuuA8X8z0jAQvGGgwGUt8LSh69yE0veCFWjt7dXGs2mcwmo9ForKysapDjgp98c/SC292E9eiJV/SlpaWdOnVi/k21EzID/uTYsJMJSA3NlZWVde3alfYfxA8a1k3Q+6KYFT2v70JDQwPf0BFOgsgPlqF+HOtG2lVw2YF45R+4XC7u7foQxtc1rEdPvKKvrq5u1aoV8+/Y2NiLFy8S2Wyoojs5Obmuro7ILkJBas7RhQsX8vLy9uzZg78pLy6Xiz0OFB0dLY5Hj5mjpyf0fOesIKKjMizBXJyKXrY9ihHEK3EQ/ADOHq+kMRjrW2uQquhZblfx8fG0R7RIfe0vXLjQsWNHsrORzWaz97YaFEqrCQYiZrySb0XPt/gVYYUphNcCQbbZSgTxShzoWTc0nARvCUNq+yyXNWbDEC6QEvqysrK8vLyEhASCf3P2yA2C1I3Ug7Es4NQoES30CopX8i3rBGcnxPfofU+N1PZZ2npgVvRBI+F+kBX6tLQ0gsEbk8nEErlBdAZjgyLmzFh1CD1OGRTRQq9JveuDReOr3h2em5SS075Tl875bTOTUrs9uCL7ZVnFK7mIix84Hr3IqRtfR5LU9kNNi0XY7hCXZvSkhP7KlSupqalpaWkEgzdhO0OIVtHj2B20c/QCPHoiozLsgSitVitscgxi/UZIjnzjlevXr//+++/9Xty5cydLrygcBKx0IbguE7mi95utJ4J1w7RAELxlLp8FkdZp6PdbIFmhD6tixHP0oRZBVZNHT6qiF5Ds5EhNTU2XLl1obBkf0bpXMvFKc4NNE2PTJqalJ4brXjlw4MD8/Hy/F9944w1Ko3wColE41o2Yg7F+jZZI3UjoPahyWUeQSH1nsViYkB+R1speGhoa8vLyWN5AvKIPVaVGRUUJbtHFS+j5PmAJUFtSQk8vBCln60a+8cr4+PjApG1SUhKlJnkCSgylDMb6XX+kDOKamhoWJxqnrySXHsVErBsmW4kQSktLKykpwdyaFy4VPVmhD9VCTrSKni/CUjdEnuHoVfQRLfRKiVcKuPJiYmKElUvsOXriE5r8rj+mwS+RzQY+chGBY0VPVujJVvTs1xLxwViWil6cHD1fJOx1E3bXgjtgy9mjh3jlVUSu6CW0bkjBPuSIcy8Rv6Inbt2oo6Knt+ytgCExUjn6sN90wWkFOVf0EK+8ijChpzEYSzxGRun6Y18eE6cXjWipm7KyMmYdXeKDsezXkpgePc61RHYpeT8ELCVIxLoJa6wJDgfLeUV76F55FWHWDY14JXHoCT3LZpk5U8L2K5p14xV6skmnsBU98ZmxlISeHgIe+Aimbti/6SJM9xMf+cYrRUbAEI3gr6vdbieyNBJHAs10Ih59TU0NiyPE9AwRJvSiVfSXL1/OysrC3EggjY2N7JWdIqwbuUFqMLa+vp5lTRik0pb04pTUtsrDvxWXR3cb1K9H0tU9uk0HN5YmDx7QltZwD08aGxtzcnJ4/Qq9jiVkW6/U19f7KTLzncG82TgcDpaJJziTY7msXW4wGPC1ktJ6QMx6XixvIJ6jD1U6EOz4GBatVkt1fSXMyRlewi47IUKnJvGh36bYY147o3+P4U++8+ajN7TpMv7Lk8x159w/76GX1lbLZjBWzBy9yAR6LJTa6fiC0wVQnMFYv2alIvxNvJCym73Y7fZQFb1gceRbbXAfwxRWZBCs6MNaNwKEHnORZNpQF3r3haJ3fug2/9CR7TtKzm562PTiA+8clOOzpADrhupEdoJFfWDqi0aXBT9whD6s9YFITJi6dOlSdna2979kgzdhIfvQFnRlcEThjsIC93EOYYsz48zM8IXLYKyAS7empka2PYqRCELvulxu6jpkcIYWIRTf5+8LX0xdMGv+SfmtOyJm6iYssbGxBJ8VKFX07F88HKFnj58y4Nd3Fy5cYEZiGcgGb8JCSrYY5ODRc5//IUzoSRG2pBMs9LLNViIx4pUdr8k/9OV/tlTYEUJI227qvH9Gv/+X59ZdssnGtUEIiZujD/slJzvuH3hq+EIfdjUuHKNTnNQN07fS+19SDSydTiev5WuIQDx1I6DHH3d9pDc3lQuUrBv2tLHk0O9emT7hX+92W39f+5Z93zzkREjf4ZHFX9917NFr7yuqkZPUixmvDAvZ4aDAaX74Qh+2fsG5V3Hx6PGbmlGq6CUpV1k8emFCL6D/AXehl3bJvbBlhLBLN9IreoSi8u//vPii8fSKx7voEUJI0+qm2evOnD3w84rXRqbKZsIUe1uCoOh0OpfLxXdHXEaiaCd58WPjYbP5ONaNOE3NAit6UkIvfrnK4tGLJvS0PXqChHUdhVX0ES70CCGEDImZOak+JYehVZdBIwe2l0m2koGsbRoKLncUHJXkAn5FT1XoxUndlJWV5ebmev9LajBWknKVuHWjbqFnByp6NUM2BcECe/8DBoJTNoJGm/H7prEsL8WAcwpc7oWYw4xOp7Ompsb3dqJ06yao0AtuRCGg0Q13fZS/0Au4dMOuNiMtIPRiw0XoCXr0QRVZ5hU9F3cLs6KfOXPmAw884PsKqcFYjipGKhXOEMqjFwyv5aUYlJK6CQtYNwABuGQHCVo3Qa8/mQs9F3AGY+fPn48Qevzxx31fJHXAHBfJIzuSH8qjFwzVwVgJUzfs6wgyCI5XRnTqRhGIGYnj4ksQHIwNJfSY279y5UrLli1Z3kB7PFnwYOzPP/+8evXquXPnEj8kBo7lKtlp1TQqelV69Fxuw8IetmRe0cunfaSUiHnliWzdUKrowwo97Ype2LexvLz85ZdfXrNmDb37ekNDg+8YbyjIzrbjUqjyQq3xSnrfdJkLPVT0CGF8/ALWjFeHdRNW6AVbKxwX9xEm9MXFxePHjw81jEykMwRHFVOldUPbo9fpdJiZWo7GmgDwuwRSBYQeIYwrT8DXlUsPXoJCH9Q6xFc0s9ncqlUr9vcIi6tSHcw0Go0sLWqJBG8k8eiJWzcC1hHkXj0IVlv81j30hF6ccLZgQOgRwhB6AV0QuHyFCMYrQ6VuMG8kXJRFWGI1bHCTQZjQm81mlgXNiQRvJPHoZVLRc7xow/bPCAV+6x5K1g29+wcpQOgRisiKXrSWvHyprq7m0gVQmNCbTKaMjIxQP4WK3gvVil5w23r8TCrHT4fvF9C7+LBsAaFHSFyh5+LR007d0Ou7iQ/HQS3BQs9S0ROZHMu9oifr0UueoxfWEYQXolX0KSkpNTU13DfrN8tahoDQI4Rn3fA1u7nUSgSf64M6IRqNhu8Ysi8cZUXY15KqdcM+hkykoufoS5Bdnoz44pTyXEZDTKGvrq7mvtmLFy+C0CuAy5cv+65BwR1K1g1BQs3jwBk7qqqqCjsSi4Q2WqiuruYy8URY/cg+YYKIRx92HUGGmJgYIgvjMbDosrAuCFxCwIFwvKgEX3uiWTepqal8K3rfTqgyBIQeIYTKy8uF3ZAFeCBcrBuCNDQ0CBv4YoG70FPt6028PZGYa4+INmFKWBVss9n4evQc4XgjDAqRip6L0AuwbsCjVwAXL15s3bq1gF8UlroRUCvhELSAwlFJjuOlwkYaOFo3iH9hGHYMMCMjg0i7Gy4QnzAVyrqJjo4W8OggIHWDuF1UXNaJDIXBYJCndXP58uXMzEyM46IOCD1CGM035G/d0IB7RS+rtTerqqpYRmKRKEvpeomOjiYo9CwLQgkTRwGpG8TNWsFpdCNajp6vdeO30LwMAaFHCKO8pZSjRwjp9XqqGQYcu/PKlStctFiwdcOxoucLl0lemHD/wpMdjGUhOjpagNALq+i53NpxPgV5pm5wzCjRkPvxiYDFYhHsYtOr6PHnNCHWsUec6jVsaezdhawqepPJxDItlsFgMOCMkXLv4kJ2MJalUhEmjgLilYjb8DtO5Bx//Ugui5chhFJTU7lbNxUVFcKiHGICQo/KysqEGfRI0JAax8FYIlF6lkw6zpypsI1uGISdAr2OV1yEPjs7u7KyUvAuuPsS9BYc9kOw0FOq6HECKvgVPUd4VfTyH4lFIPQII3KDBA2pcVy7h8jkWEpCX1VVxUXohZ0Cx6ZmAuAo9JcvXxa8C+53KbLWDctfTEyh5/IMhyOL+B49R3hV9PKfLYVA6BFG5AYJ+rpyFDIi7W5YEiw41lB1dTXHip5Uxx4icBlDxhR67jPvyMYriVs3jY2NAiIDXG7t5eXlOEIvTkXPa0fl5eWCBUQ0QOixSgwBD+Acy1UiHj3LgsU4Hn1TUxOXhByRUyAIe6MbBvyKnrt1Q9CjZ0GYOLLEeFjg8phYWVkZ9rkqFPjxShoPi/KfLYVEWnjEdnFb0edF63YfKzM32DQxyVn5vYeMfWjqHd2T5NDZE9O6EVDRc3kbkXKY3bqhrcIJCQl83SGn00mvqTdH66a4uFjwLiSp6NlnZggQR2G+DeJs1glWW0zrhlJfB0V49PSF3nX6kzuHvmMbPXX8g7e1TU8wuJtrK0r3bZhz66ItC7f+e1iK5Fp/8eLFnJwcYb9Lb0hNzh49RwScAtVleqqrq8NuXDTrhuCEKfaxcQEV/YULF4SVqGE/ccwum5gVPa9xfmZNIS6PNTJfW4qButA7Dy/+tHbamm0vdPXd1dgHpg6d3veTdfXDJlCJTPPBZrMJnqpKLw1NqqLv3Llz0B8JFnruK+kIyNGzeE34cLEjsrKycFI39fX1HIs7giUCcaE/f/68MKGPi4tjH8PEtLOjoqJwqh9eXeOTkpLq6urkvN43L6h79BpDlN5usfh3VfI4rDak18thiADHtpN5RV9XVxdKN4V1HEOc+x8g+hU93x6cHFcoxLHOuVf0+P25vNAQ+rZt2wo4krAXFabLgWnd8JqUyzF4o5SJ7tQrel23yTPzbxrdv+TesYML2qYlGDzWuorS/RuXramfsOwDaVYI9gFzHibfit7tdnOcOUnKuglVksTFxV28eFHANjn2P0CCToFXRW8wGJxOJ8eVlYgvnx0UnPn9gmFPuwqzbsaMGSPgSGgLvZjWDccovSIMeiSGR6/NnfDN/h6rir5dv2d9sbnRoY1Jyc7vOWnhrnEDW0vf8BozA8t3SM1isXAc5pLthCmOs6WQoC4O3FtXot/nSXKUb/ZFBH1h7k/CJkvTm+3FwpUrV1huvSJX9OwX7YULFwYNGiRgkvZtjQAAIABJREFUywyY8Upey05w7GuGE+UQE1FSN9qUgsInCgrF2BVfcEL0iP+QGvfWlaQ8euI5eu7WDeLfRIh760rE80Gee4uV7OzsioqK/Px8jlv2hdKSpOxUV1d37Ngx1E+jo6P53tErKiqE9WIMOyqDKYuY1g0vj55jXzMQeh9kHK/EfPLSarW8ilbuzeiJWDcslangHD3V1mDV1dXc77u8ltQwGo1hQ/QMTPBGmNDzkhJS/fTZzTQBdoewED3i8JiI+QCNObBhNBq5f9m5WzdDhgwRfEiiEenxyvLy8oKCAtF2Z7FYOFb0wlo/BhJqBFJwRX/lypWePXviHVRIeA3G8vrac7ducBKWgj0fHMgOxgprZ8YQtjrBXM8E07oxGo19+/bl+OaUlJSzZ8+GfZsi+h8giFfiP3nxCu2IXNGzIIJHLwB6Qm8ymTp06MDlndnZ2SdOnOC4WT94dawlNUuT7GCs4GwlQig2NpZ9yAqznS/mYGxlZSV3S4qjdYPp/YpGpMcrL1++nJWVJdruuHv0RPrRs5gDgoWeY0czBr7N0EOtcBsUvhU9x5n3jEfPcbM4kLJu2DtSiCn0Go2G5aTwTT9Mj76yspKjfYc4WzfipLnwifR4Jf6ce15fV17rCGIKAfslKHiqF8eOZgyMAcX9/XwHY7lLGK/B2EuXLnHcLA7MXRBfJtifDPjeawVPi2VguWjxk4hRUVE4sxwsFgv3VQy55OjpdVoljnzjlU1NTUaj0e/Furo6XnNk2BE86OQLr09azOkV7CE5wRdoXV0d9/FGxoDiLvS8boSUBmPT09Orqqo4bhYHZrYd7XqQ7wDm+fPnCwupJORwlhxhYGZOkDoedpKTk+vq6tjfw33gR3LkG6/ctm3bihUr/F7cu3cvwUV4RVhbzg8xVwYvLS1ld6UFPzFwv0lQ7VTMq6LnPpWJb5LKC9+FQ5lJGJgTrJxOJ/uK53wXB8es6FnA7/KIU9HzXfCPy2VQWlravn17YccjMvKNV44aNWrUqFF+L86YMYOgf0pkVhtzQbB/2bxwT93gc/r0aZZ4NaLTstUPwY0WuMCroidliLPAN0RPpFNxWCeN7wAmvVGrsrKym266CWcLOB49l96lfCkuLu7Tpw/ZbVIiouOVREbMmTlTHL2/5uZm7rONmEd7wXG0M2fO3HzzzSxvEKB9fMsiUiHRoBBsF+MHMxDHt6EVX6En0hEv7Pw1vhU9wqsAmJMKWs0Q8egFp25oCP3+/fsp2VzEieh4JZGKnpFjjkJvsVi431oYg1uw0Id9rkxKSuI1wQfxTMUgniFRjuuZeOEu9LxG4dDvUXq+Qs/3j0mkI17Y1kO8qmCcED0Dk+YKuhHuXZJCgXNrF/CkwjwMsQyinDlzRlivCPGJ6HglkenLvNrd8PLoMdvdNDU1sReYubm5ZWVlvLbJ97vKS+j59vXm/rU3mUy8Bs2EBW/4NrohIvRhpzXwqoJxspUMVOd/4FT0vEL0DOxR+sbGxsTEREjdXEXO8UoiQs+r3Q33CVMI7zvDZWAwNze3vLy8R48e3DfLd7ZUfHx8bW0txzfz6miGfm9qxuWdfB/bhU2OFeDRE6no5Sb0QUdl8J8V0O+LgQj7XaPRyHcOPOPghQpr7du3r3fv3sIORnzkG68UAZzlK73wclr5VvSCDW4uUba8vDy+FT2vjmYIoYSEhPLyco5v5tsymrspwfdBJDs7e+/evdzfzyCVR9+tWzeWN/BKqly4cAHTiwhVnUjeKsBoNA4bNozXr7BH6fft26eUkVgk53glbWw2m8FgwM/R86rLeOXocZZ1PX36dNi2XLm5uevWreO1Wb6BVF6pG74DANxTN9xD9Aw5OTk//fQT9/cz8PXoo6Oj5ebR44foQ120kvdtr6ys5OvRs0+O3bt377hx47CPSyQk70EgGbt3777++uvxtyPPir60tJQ9W4kQysvL415uM7C3Pg+El/vEdx1BXh69CNaNJIOxYc00XgOYgjvRewn1iePPlsLEaDTyndzE3pJeKQ2KGSJX6Ldv337jjTfib0eeg7FnzpwJ28NLgJwJsG6436tYFj4MCnf3ma91w7HPiR+NjY0yjFey95/xQ8CIpR8s1o20Qi+g2QnLYCzfa1Vy6Au989C/hrXPzgpCm3H/V0l9FktIfvvttwEDBuBvh15FjxNC51LRcx/M9MKroxnieQp8Uzf0BmORoEkGfFM3fFetCQrfPxo7+M1bQpl1J0+e7NSpE86WxYflfq+skVgkhkevv/aZNZszJ934vGHO+hf7+u5PE5eZJlE2yeVyWSwWnNVivfD16HkJPZeO2EHh2B2MKfe4f7erqqp4Pf/y9eh5PS4YDAaOTzwXL17MycnhvmWEkE6n4z7hmUESj57vFDYWiARj4uLigs5dv3TpUnZ2NubGBSOsDz5LRV9cXHzddddhH5d4iDIYG9V23ORRc9fmdurcGatRJDkOHjx4zTXXENkUr4qel3YI9ui5f/kzMjKMRiP3p3W+9SOv8WS+qRvuk/sdDgffx/aMjIzKykpetwdJUjcEIdK5Jah1w7cLEHGMRqOAfB2LR79v374HHngA+7jEQySPPnrUJ7v/PVgmKo8Q2rZtG2bbDS9EHsCDIjhHz32YiO94rMvl4lU/MnUxxzdTmjAlrMVgp06dTp06xetXjEYjL18LfzCWbAOfQ4cOXXvttZgbCXrRnjt3rl27dphbxkHY2AOLdVNRUSHmOhb4iDUYq9EZdDKaQrZt2zac1eh94fV15WWAChb6sO3MvPCaHIu5DlxYBKRuuMQrjx07xp40D0p+fj4vobdarR6Ph5f1gd/UjOyQ4MGDB/EXiQy6oI2wj4AgwnQ51CN1dXW1yF1v8YnE1I3H4zGZTLyC1SzwegDnVYIJtm7OnDnDXei5V/QlJSVdu3YVcDwccbvdvDxxjlOBTpw40aVLF74H07lz55MnT3J//44dO/r3789rF/jWDd8QFDtHjx7lNU06KEGjYsI+AoLwnUjBzt69e5U1EosiU+jJXna84pW8EFzRc4ncMPCybg4dOiSg4ouKiqL098nLyzt//nzYtx07dkzA/YmvdbN58+ahQ4fy2gX+lUN2/d6mpib8VXGCjsoI+wiCIqwlPX5s1Je9e/cqayQWRabQb9u2jUiCnoGeRx8bG2uxWAT8Ineh52XdCPNwu3fvXlJSwve3uNCuXbtz586FfdvJkycF3NcTExPr6+u5v3/nzp1807r4Hj1Boa+oqCCSiglanZw/f56UR9+6dWsB/ebICv3GjRtJGb+iEYlC/+uvv5IaiUV8vq58Vy7kNdXFF+45xbS0tCtXrnDcbElJiQCntaCg4MiRI2Hfxte3QQjpdDouHr3g5Ru5p3oaGxsNBgPfAQx8j57gEmlEDHoUrP09cw2TyoAKaLmKBPU/YAgccjhy5Ej79u15xavkQCQK/blz5wh2keb+AG61WqOjqfdx4zvnhWM7QI/H43A4BBw/R6HntSy4l6ysrMrKSpY38M22+9KhQweO8xh+/fVXASUevkfPsaLn0vTx4MGD+JGboJBN0AsT+urqar6rCzAERuk///zzqVOnCtiUtESc0OP35/OD+9dVnAVj+X6vODYSENx9sHv37kePHg37Nr4heoauXbseP36c5Q044zGdO3fmaNP/8ssvQ4YM4bt9fI8+7DqC3Hd0+PBhUjNL/Dh+/DjBMXwBDZoQxoxfvyi91Wrdv38/kRn1IhNxQr9582b2Bfb4wt2j59WMXjBc+lb6wtH0FPxoz3GkQcDSfQihLl26nDhxguUNOMOAnTp14hi82bNnT9++ffluX6/XC1uF3AvHHj6tW7cOK46XLl3iO3k4FH6SSnAkFgkVesHGkV+UfsWKFXfddZewTUlLJAo933QEO9zrMgEVvYCVFkpLS8O2M/OF47Pw4cOHBT/ahzVYEEKVlZUCpjVxqegFqwzHir66ujohIYHvzFsicIxX5ufnnz59muUNZEsQv4ElnI8gEAHWTX19fXx8vLDd+bWk/+qrryZNmiRsU9IScUJ/9uxZXjoYFu6Dsbwa3TAEnX7CzoEDB3iV3hyj9DjTJgsKCg4fPsz+nj179gjoGh027Y7jG7Rr146LR79169bBgwcL2wUmV65c4SL0HTt2LC0tZXkDkQS9F7/qhO8jJjuxsbF8/S6cyE2bNm2WLl3KTGc5d+5cXFwc8RXGxSGyhJ6sXcig1Wo5Ln8hIP4hIErPd1oTx3WmhDUSYLjmmmvCjsfu2rVLgPsRdoBEmCPEwHHm7S+//EL2GZE7DoeDZelqL2GFnm9xwI6fFrOvry0AvlE0nOTonXfeOXHixGHDhn3//fcLFy586KGHhG1HciJL6Ddv3ixg0CwsHG1oAd0B+bakt9lser2eVwMpLhU933ZdfoQN3rjdbovFIiwewzJ/2Gq1YvZsSEpKqqurY38PWZWkQYcOHc6cOcPyBrIjsb69qclO6WJITk7mvhAxEtrRzMuYMWM2bNiwcePGpUuXjhgxQvB2pCXihJ5G8cVxurwA64ZvS3oBTjoXoccUgvbt27N7IMIS+gx+47EvvPDC3LlzmX+fOnUK0zQIOz+WGVrgOwNAZMI+95C10X17U5MdiWXgOx6LP1sqKSlp/vz5R44ckfkHzUIECb3b7TaZTDR6zoUdEmQQMOTFt6IXMDmbizuE2ddQo9GwT27auXPnDTfcIGzjfn/8rVu3/vjjj8zKWcePH8fspRU2eLNu3bpRo0YJ3j5O+0leY/ssnQM8Ho+A1ZdY8O2CQMMs5TseS2parAiTYOgRQUJ/4MCBXr160dgyR6EXNhjLS+iF5fzCgj9tkr003rFjh+Bssm9FX1JS0qVLl7feeuvvf/87IqEynTp1Ys+rrF27duTIkYK3j7OcE6/1Edu1axeqL9DZs2fx29D74ls64N9rA+HViQ+R7n+gUCJI6Cn5Noiz0J8/f57vhPWWLVuaTCbu7+eyVGwgiYmJ7AYR977HoWAP3nBvzhOI7x//hx9+uOOOOwYMGGAwGLZu3Yov9OymnNPpvHz5cps2bQRvP7BhAHd4ta5kSVgeOnSI7FQpX6Gn0bdSgHVDsHWlQokgod+yZQulGFx6enpYOa6vr1+7du3o0aN5bXngwIFbt27l+Gam+6CAItH7LHzx4sXAupuZ1IPpTrIEb5iMoODatmXLlt52PRs3bmSGy95+++3Zs2eXl5djOnVZWVlBF8Zj2LVrF9/WxH4kJCTwGlf0hdc4Z8eOHUMJ/b59+8g+6Xo9+gsXLtTU1MTFxRHcOOJp3TQ2NjY2NgrO0auGSBF6u93e1NQkOGkXlrCrHb3//vtPP/003wXVunTpwr0r+v79+4W1yc7Ly5s+ffqgQYOeffbZv/zlL34Dd0SWdWYJ3uDLJdN9rLy8vFWrVow5lpGRMW7cOJxtemFpLbd27dpbb70VZ+O9evXat2+fsN/dtWsX90o8Pz8/VMLyt99+w/z7+xEfH3/hwoVZs2ZNnTr1ww8/JLhlhuzsbGYMhgtLliy57777iB+D4ogUod+9ezfZq9kP9qiyyWTasmXLvffeK2DL3FfAENwm+7HHHlu8ePH27duXLFkyZcqUxYsX+/50/vz599xzj4DN+sKyKhvOSCwD88dnfBvvi9OmTfPGb3DIyckJ1SLit99+w2xX269fv927dwv73TVr1nC/zYRKWFZUVKSmppIdZkxJSfniiy8GDx78888/9+vXj+CWGXQ6Hffp4kVFRRMnTiR+DIojUoT+22+/pTqrhd2mnzNnzvPPPy/MnRg2bNjPP//M5Z3FxcUCJpcihJKTk/Py8ph/M0Lv/SKVl5efOHFi+PDhAjbrR6hMuuDD9tK1a9cTJ06sXr16zJgx3hf1ej2RW3uo4A0jkZhTga6//vri4mIBv7h3796CggLuAh0qYcnrbsGRO++88+jRo4WFhWQ364tGo+Gi9Xv37s3PzxfcvlRNqF/oGxsbJ02aFBMTc8stt9DbS9euXY8dOxb0RxcuXDh27Jjgr9Mtt9yyadMmLu/kviY4C7GxsUOHDl21ahXz3zlz5vzjH//A3CZDQUFBoKi5XC6r1YppoXbt2vW3337TaDQ0rLnOnTsHvYWvX78eJ1jJwAyDCwhZCmi6ErRN6dq1a2+77Ta+e5ccLt2TEEKffvrpY489JsLxyB+VC/3Ro0dHjhw5bty49957j+pkh27duoWq6F9++eVXXnlF8JYzMzPNZnPYufg4c/39mD59+rx58xBC58+fP3fuHKm5xI8++uiLL7544MAB3xePHDmC32WlS5cuCxYs8C3nCTJy5MiioqJA3wnfoGfgNQzD4HA4iouL+fpdgQty2e32qqoqUk0rxYTLeGx9ff3p06cVt+YfJVQu9B9++GFRURHVp0iG1q1bX7x4MfD1lStXajQazAbW11133d69e9nfs2/fvj59+uDsxUtmZmZeXl5xcfEbb7wxe/ZsIttECLVr1+77779/6qmntmzZ4n0R36BHCOXl5bndbl+DniApKSmvvfbaE0884fui0+msqKjAf35CCPXr12/Xrl28fmX9+vUjRozg6wQGBm+2bt1KcKk1MeEi9F9//fX9998vzvHIH34hEMXx+eefi7MjjUbD+Ia+na8vX7781ltvbdiwAXPjjE3P7jjjO92+zJw58/HHH4+Pjye7NmZWVtbKlSvHjRu3d+9eu91+9uzZHTt2rF27FnOzGo1myZIlrVu3JnKQgQwfPnz16tVFRUVe4cBPCnnp16/fxx9/PHnyZO6/UlRU9Prrr/PdUWDwZs2aNePHj+e7HTmQl5cXtoP0kiVL8C8t1aDyil5M2rRpc+HCBe9/3W73ww8//J///Ac/wzt48OBff/2V/T179+4lKPTdunXLzMx88cUXSW3QS3Jy8k8//ZSenn7dddc999xzBw8exJlw5IWSb+Pl7bffnj9/fllZWU1NzQcffDBr1ixSEtmtW7dQoztBqaurM5lMAuaXBQbDKM2jFoGwc6Z+/vnn3r17Q3zei8orejFhvrHe1e7ffffdoUOHErEI4+Pj3W53U1NTqLkndXV1VVVVgtsIB2Xp0qUEt+ZLixYtHnjgAUobp0RMTMy8efNGjBiRnZ394IMPbtmyhdSqkDqdLiYmhuXD9WP58uXC0q5+CctTp0517NiR1JrdIsNu3bhcrtdee+27774T85BkjiI/Znnim7DcsWPH9u3bZ86cSWrjN910E0tR/9FHH0G6gDY9e/YsLi7evHnzgw8+SHbtXy5jMAwej2fx4sXCHib8mmmvXr1aiXkbhsA1u3354osvbr/9doWuEEIJEHpieIW+oqJi1qxZX375JcFyadiwYaFClhaLZeXKlcJmYwG8wGnKzwL3aVMrVqy44YYbuLe48YOZQsz8e8OGDTjt2CQnVCa1vr5+0aJFTz31lMjHI3NA6InBdF232+0TJ0786KOPyBopffv23b59e9AqZuHChQ8++CDf5gqAfOAo9C6X6/3332cacwqDSVjW19dPmTKloKAgOTlZ8KYkJ9QUsDlz5jzzzDOKbilMAxB6Yuh0OpfL9dRTT02ePFlYzxkW9Hr9K6+88swzz/i97nA4ioqKeGU2ALmRkZFhNBrDvm3RokV33HEHzmyJ/Pz8xYsXjxw58p577nn33XcFb0cO5ObmBgaaz507d+DAgbFjx0pySHIGhJ4k2dnZMTExlNaJHzVqlNPp9GuH8M0334wdOxZzwTxAcsL2WLdarQsWLHjyySdx9tKrV6/Tp0//9NNPfLuoyhC/8djKyso5c+bcd99977zzjoRHJVvgeZ8k7733Ho0VrLzMnTt3zJgxN9xwA5PQcLvdn3322bp16+jtERCH/v37b9++fcKECaHe8NFHH02ZMgVzEHj48OFE2hbJgY4dO86ePfurr76Kjo42mUxWq3Xq1Knbt28nuFSWmgChJwmRqZIstGzZcsaMGbNnz54+ffqyZcvWrFlz//33UxohBMTk7rvvfvjhh7ds2fLCCy+0bduWedFms505c+bUqVOnT59esWJF2LkUEcV99903YMAAZrGE2NhYWEOKHVGE3nZxW9HnRet2HyszN9g0MclZ+b2HjH1o6h3dk4QvpBap3HvvvZs2bXrppZfGjRs3Y8YMMG3UQevWrdetW7dt27Zp06bFxsY2NDRYrdbo6OgOHTrk5+d36dJlxYoVMN7ui0ajITLVLkKgf+m4Tn9y59B3bKOnjn/wtrbpCQZ3c21F6b4Nc25dtGXh1n8PSwGt58unn34q9SEAVLjxxhtvvPFG3xVUAIAI1IXeeXjxp7XT1mx7oavvrsY+MHXo9L6frKsfNiEpxC+uWrXKbwUMhNChQ4e8U08BQJXQNgCBCIS60GsMUXq7xeL025XHYbUhvZ4l9HPLLbcErh+/cuVKCscIAACgZqgLva7b5Jn5N43uX3Lv2MEFbdMSDB5rXUXp/o3L1tRPWPYByzBiixYt2rdv7/diVlZWVVUV1QMGAABQGfQ9em3uhG/291hV9O36PeuLzY0ObUxKdn7PSQt3jRvYGmavAQAAUEeUcXxtSkHhEwXUF/8AAAAAggAzYwEAAFQOCD0AAIDKAaEHAABQOSD0AAAAKgeEHgAAQOWA0AMAAKgcEHoAAACVo7B+eElJSXPmzFmxYgXLe8rLyy9fvqyOttQOh0Oj0aijbaHdbtdqtao5F51Op9PppD4QAthsNr1er5pz6d69e2JiotQHIoTy8nJ6new0odbYVS6LFi1CCKljdb358+enpqaOHz9e6gMhwNy5czt27FhYqIaJc2+++Wbfvn3VsYjHiy++OHLkyEGDBkl9IAR45plnJkyY0KdPH6kPRHaAdQMAAKByQOgBAABUDgg9AACAygGhBwAAUDkg9AAAACpHDVk3P9QRFGNQTYYPwbnIFa1Wq9WqpOBT07mQRYXxSrvdjhCKioqS+kAIYLVadTqdOuYENDc3GwwGdeToLRZLdHS0OrS+qampRYsW6tDHxsbGuLg4jUYj9YHIDhUKPQAAAOCLGm7jAAAAAAsg9AAAACoHhB4AAEDlgNADAACoHBB6AAAAlQNCDwAAoHJA6AEAAFSO7pVXXpH6GHjTbL7cYEho4Z2tYru4Y/nXyzYeqIzOy8+Ku3rvaj6/9duvvl1ffM6a2qF96tXZU07zoTVLl204UpvQpn1ajBymVXA6l6s4GswNnriYqzOOlHouzRf3rPn2uw2HG1vmK/1zCfqi7M7FXn25GiXEeSfdOSv3/rhk6epdZfq8zjnxV8/QU3fi52X/W73XGN26Q+bvpxL0RUnhdC72ij3ff71k9Y6TdQntOqRf/Qjkdy6iosATdp7879ixH592Xf2v48R/x/Sb8vXpxvqD/73ruvu/ueRGCNkPvjO830NfltQ3nf3uyQH9Z26u8yDkrvxuSt8x7+yquLjxn7cMmrW5XvqpYlzO5Sqeus2z+nZ/aqMNIaTcc2nY+fLQG6cvO1NXsXbW4BFvHbQhxZ5L0Bfldy6eiqLJI9/Y47j6X3vJvDH9JnxyuLb+8Px7+t//zUU3QshTv3nmgCHPr7to3PNeYf8pyys9oV6UFi7n4j77xd3Xjf1g95Xmio2zh/ae/F2FW5bnIjYeBeGu3bPw+QcHt2lh6PX6USfzmmPX37u0m76p2ePxeGqW3N1y4PtnXB7bz4/ndn9ut9Xj8Xicp9+7Iblw8RW3s+SN6zLvW1bt9nhcFz8d2WrEp5fcCjiXq283r3q4a3yL5Ek/MSelzHNxnn5/UOsJy664PR6P8/T/nv/Ht2edSj2X4Ccoq3OxlSx98dFRnRL0eU9usTMvNa2cnHXN7P12j8fjsWyb2bXniwccHvelT0e2HDa/3OXxuGu+vz/7ujdKnMFflP+5OA6+dG3eQ6vrPR6Px21cdHvqwPfPuGR2LlKgqIpeo09p3/f2aVOHtPI5bL1e73a63Aghj9vlRnq9DiFnbNe7nri7ZzRCCOlaZWVorM0OT13xrtO9RwxJ1iCkzbpleKdDO/bbJToRxONcEELIXbF8xqv1k5+4/uojq0LPxVO3ffPRPqP6VG7+7uv/rW8Y/Oqb49rpFHouIU5QVueiTcjtPeKhx0e38RpQbmNpqa1Hn24GhBBq0bt/9/O79lR57Ad3HsofPjRLi5Am+eYRvU/v3FMb/EX5nwvSth321MQb4hFCSJOYmRlra7bK7VykQFkdpuI6Dr6royN16ws/eV/S95r2+sjbnxrz8Oie1t9WGx/7ZGKeFmluePLDGxBCCDkvr33pX3sGPPFhmueKqSo6LT1egxBC2lbprZpN5kYPipbKROV6Lgi5y75+4m3dCyvHX7r/X5cQQgi5lXku7tOmKtfBOfc82mtQ37gjLz3z/pNr1s7orsxzQZpgJ1gqq3PR595wZ66r5Pi7/zVdfUXbKi9Xt+jAccedPQ2o+dCeo9aqa6+4mkxmS8ub05g7W1xaemyVMfiLbtRSqjZuHM/FrS+Y8q8ChBBC7uod77y5uvXd6ztpm/bI6lykQFEVfTA8V0p2HLVnd2mXm9uhU2rtnt2nG6/eqz21BxY+Pui6v564q+jLh/K0yONBvl3tPMjjdgffplQEPRdn6WeP/6fVa+/fmeHzWSn0XBDyOKJu+2DDkg/nLlj/wyM1b731U71SzyX4i3I/l4Qxzz+fumDsyIdn/u3+kU+stBg0yIMQ8iDfe5HH43GHelFGhDgXhBBqOrns78N73bO+z/ylz/TQI/mfC3WUVdEH4jqxYHZRhzdPfHxHAkKeCa1v7f7qt1NWT82wn1j4QOErl2+bs+7QX65J1iKEtGmZrazbqiweFK1B7ipzdUxaeoIMMhF/EPRcvs9446XjGRP3zHttT/2e85ZL/5vzoWHqE/0UeS6r7mjVMqagb+9YhBAydO7V3bPhghndoshz+WnAwiAnOFbu54KirpmiucnWAAAF30lEQVS5rvimdZuONKZNefrWV3p/lZ6mi0tPj71ivuJGKTqELFXm5pYDWwV9UWZlYZBz0SLkKvt++u1P7enzz6UHH+mfrkcIIQWcC22Uf7oe5Ha5mHiEx+VyeRDSINfJ/zz8uv35XzZ/MJFReYSQJvH6AZ0ObN7WiBDymH/95VTPQX3k1rI+yLnoO939z+lDcxLi4+Njo7UafUx8bLROoeeiSbm+f/vjew40I4SQ4+SBEl2Hjhk6ZZ5L8BOU/bm4L/zw+pydqaMnTfnLyLzDG4o73TyolSaq18Cepb/8avYghBq2bz6QP6BfSvAXpT78PxH0XDwVXz3xt5P3r9m+8PGrKo8Qkv+5UEfpFb2u84PPDB8yY/TUQ3fk2w4sLap6aNHdae5zi1ccbJF5fMFLLzDv0nca+/yU67tMeW7owJnjnzs1uHHl56ZHF92XJa/bXNBz0Sff/vQshBBC7vLoNZ/uufOpR4ZEI5StxHPR6Fo9/Ow1A+4fbZ06tMWer75r+fzKUfFIp8zPJTHoCabJ/Fy0mW10GwrHnTk0rv3FnxYfvfeLD7vqkCbz3r9P/HDy+KfMY1psWVB827/e7hbiRVkR7Fw81Ru++0Uf03bJ6y8sQQghpGs98ulpN8v+XKijwIVH3BfWzdsQN+HhG9Ou3pQ9jac3/7DhoEmX2++2woF5Mchdtv6//7er9g8bTt+xcMb9vWIRchgPrF29rczQZVjh8C5J0t/Uw5/LH3jqi7/67FL/GXd2Yi5ShZ6Lo2L38uVbLxk63Hh7Yd/sqzEiZZ5LiBOU17l4zNsWLGka8cSoNr/fcppOb/x29VFHbt8Rowa0jbt6gO7qYxtXbT7pyBtcOPraNB3LixIS/lw8VdsXfLLpssv7K7rsW/768KBWGtmdi8goUOgBAAAAPsjrwRIAAAAgDgg9AACAygGhBwAAUDkg9AAAACoHhB4AAEDlgNADAACoHBB6AAAAlQNCDwAAoHJA6AEAAFQOCD0AAIDKAaEHAABQOSD0AAAAKgeEHgAAQOWA0AMAAKgcEHoAAACVA0IPAACgckDoAQAAVA4IPQAAgMoBoQciG9eplf/58aQr/BsBQLmA0AORjbNkyevfHHFKfRgAQBMQeiCCcZV++ddXN9dteWXM7I2NlcufvuPlzU0IIYQ8phUzCmdvqEeuil/nzfjL7aNuu2vyC4sP1nkQQq5TXzzy7PJ9K1+b/Oinx+FRAFACIPRABKNrd987swYl3jBr8fOD49M6tfz/duwvpKkoDuD4udu6U5nZ1h+S/oxRTG0lbS2fGiFS25p/itgioR5Mqpcegh4iGkQvFlgE0UMv/SMIo4cSySJsiEnoYIipD0OQzIfRH5CZY3Rztwezx1hPN4/fz9OBezj8Lhy+XO6n+12DeSGE/u31wyeac5ct8/hM7F4+eP7K5XbPRLy1I/lTCP371FBX/OKz0qa2kNNs9CsARbAYPQBgIHNphU1VrOUOmypETWPEfuxl8sfBffN93Ul/7O5GxVJ7+sGjQHBnhaK7Gqs73s4siDqzEIWc59StC0ftitHzA0Xhix74zVLbFFb7eke1uUR30h+NbFAUx3bX3ItLrYcCPs/+q8Pa0k7zpip3OZXHskHogSUWb3NIe9ObSvQM74lF1isi23MuHB/3tHc+HRhNdTZY/+xUrSqdx/JB6IFCobC4WOVvCc4+j98e9EbDaxVR+JKe1AMn2xp2VJbNJ99PaH8/BvhfEXqsbCbHOtvA9RM33uWEEEKtaz7wOZHyRkN2RQiTM3Lc8+ps/ZFYS/3hO18rbf03r/VndaNHBv6VouvcW6xo+czYyEfVvdftMAkhtMyHoYzdv3tzyeLThez0WHp29bZq1xptemQ8t8VXVTKTSptqfFvLDB0bKB6hBwDJ8esGACRH6AFAcoQeACRH6AFAcoQeACRH6AFAcoQeACRH6AFAcoQeACRH6AFAcoQeACRH6AFAcoQeACRH6AFAcoQeACRH6AFAcoQeACT3C0qI/qj5EcitAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-31" /></p>
<p>Suppose we want to estimate the cycle lengths. One way of doing this
is as follows:</p>
<pre><code class="language-r">yyy &lt;- lynx &gt; mean(lynx)
head(yyy)
#&gt; [1] FALSE FALSE FALSE FALSE FALSE  TRUE
sss &lt;- subSeq(yyy, TRUE)
sss
#&gt;    first last slength midpoint value
#&gt; 1      6   10       5        8  TRUE
#&gt; 2     16   19       4       18  TRUE
#&gt; 3     27   28       2       28  TRUE
#&gt; 4     35   38       4       37  TRUE
#&gt; 5     44   47       4       46  TRUE
#&gt; 6     53   55       3       54  TRUE
#&gt; 7     63   66       4       65  TRUE
#&gt; 8     75   76       2       76  TRUE
#&gt; 9     83   87       5       85  TRUE
#&gt; 10    92   96       5       94  TRUE
#&gt; 11   104  106       3      105  TRUE
#&gt; 12   112  114       3      113  TRUE
</code></pre>
<pre><code class="language-r">plot(tvar, lynx, type=&quot;l&quot;)
rug(tvar[sss$midpoint], col=&quot;blue&quot;, lwd=4)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-33" /></p>
<p>Create the “event vector”</p>
<pre><code class="language-r">yvar &lt;- rep(0, length(lynx))
yvar[sss$midpoint] &lt;- 1
str(yvar)
#&gt;  num [1:114] 0 0 0 0 0 0 0 1 0 0 ...
</code></pre>
<pre><code class="language-r">tse &lt;- timeSinceEvent(yvar,tvar)
#&gt; Warning: 'timeSinceEvent' is deprecated.
#&gt; Use 'time_since_event' instead.
#&gt; See help(&quot;Deprecated&quot;)
head(tse, 20)
#&gt;    yvar tvar abs.tse sign.tse ewin run tae tbe
#&gt; 1     0 1821       7       -7    1  NA  NA  -7
#&gt; 2     0 1822       6       -6    1  NA  NA  -6
#&gt; 3     0 1823       5       -5    1  NA  NA  -5
#&gt; 4     0 1824       4       -4    1  NA  NA  -4
#&gt; 5     0 1825       3       -3    1  NA  NA  -3
#&gt; 6     0 1826       2       -2    1  NA  NA  -2
#&gt; 7     0 1827       1       -1    1  NA  NA  -1
#&gt; 8     1 1828       0        0    1   1   0   0
#&gt; 9     0 1829       1        1    1   1   1  -9
#&gt; 10    0 1830       2        2    1   1   2  -8
#&gt; 11    0 1831       3        3    1   1   3  -7
#&gt; 12    0 1832       4        4    1   1   4  -6
#&gt; 13    0 1833       5        5    1   1   5  -5
#&gt; 14    0 1834       4       -4    2   1   6  -4
#&gt; 15    0 1835       3       -3    2   1   7  -3
#&gt; 16    0 1836       2       -2    2   1   8  -2
#&gt; 17    0 1837       1       -1    2   1   9  -1
#&gt; 18    1 1838       0        0    2   2   0   0
#&gt; 19    0 1839       1        1    2   2   1  -9
#&gt; 20    0 1840       2        2    2   2   2  -8
</code></pre>
<p>We get two different (not that different) estimates of period
lengths:</p>
<pre><code class="language-r">len1 &lt;- tapply(tse$ewin, tse$ewin, length)
len2 &lt;- tapply(tse$run, tse$run, length)
c(median(len1), median(len2), mean(len1), mean(len2))
#&gt; [1] 9.50 9.00 9.50 8.92
</code></pre>
<p>We can overlay the cycles as:</p>
<pre><code class="language-r">tse$lynx &lt;- lynx
tse2 &lt;- na.omit(tse)
plot(lynx ~ tae, data=tse2)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-37" /></p>
<pre><code class="language-r">plot(tvar, lynx, type=&quot;l&quot;, lty=2)
mm &lt;- lm(lynx ~ tae + I(tae^2) + I(tae^3), data=tse2)
lines(fitted(mm) ~ tvar, data=tse2, col=&quot;red&quot;)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-38" /></p>
<p>\section{Acknowledgements}
\label{discussion}</p>
<p>Credit is due to
Dennis Chabot, Gabor Grothendieck, Paul Murrell and Jim Robison-Cox for
reporting various bugs and making various suggestions to the
functionality in the \doby{} package.</p>
<!-- ```  -->
<!-- \end{document} -->
<!-- % \appendix -->
<!-- % \section{The data} -->
<!-- % \label{sec:appdata} -->
<!-- % The reduced \code{C02} are: -->
<!-- % ```{r} -->
<!-- % CO2 -->
<!-- % ```  -->
<!-- % The reduced \code{airquality} data are: -->
<!-- % ```{r} -->
<!-- % head(airquality, n=20) -->
<!-- % ```  -->
<!-- % On the to-do-list is to allow to write as follows (but this feature is not -->
<!-- % implemented yet): -->
<!-- % <<eval=F>>=  -->
<!-- % summaryBy(list(c("conc", "uptake", lu="log(uptake)"), "Plant"),  -->
<!-- %           data=CO2, FUN=c(mean, sd)) -->
<!-- % ``` -->
<!-- % Inspired by the \pkg{dplyr} package, there is a \verb|summary_by| function which -->
<!-- % does the samme as \summaryby{} but with the data argument being the first so -->
<!-- % that one may write -->
<!-- % <<results=hide>>=  -->
<!-- % CO2 |> summary_by(cbind(conc, uptake, lu=log(uptake)) ~ Plant,  -->
<!-- %                    FUN=myfun1) -->
<!-- % ``` -->
<!-- % which is the same as writing: -->
<!-- % <<results=hide>>=  -->
<!-- % summary_by(CO2, cbind(conc, uptake, lu=log(uptake)) ~ Plant,  -->
<!-- %            FUN=myfun1) -->
<!-- % ``` -->
<!-- %% Same as -->
<!-- %% <<results=hide>>=  -->
<!-- %% aggregate(cbind(conc, uptake, log(uptake)) ~ Plant, data=CO2, FUN=myfun1) -->
<!-- %% aggregate(conc ~ Plant, data=CO2, FUN=mean) -->
<!-- %% ``` -->
<!-- %%  -->
<!-- %%<< >>=  -->
<!-- %%library(magrittr) -->
<!-- %%CO2 |> summary_by(cbind(conc, uptake) ~ Plant, FUN=myfun1) -->
<!-- %%``` -->
<!-- %% -->
<!-- %% -->
<!-- %%<< >>=  -->
<!-- %%summaryBy2( list(c("conc","uptake"), "Plant"), data=CO2, FUN=myfun1) -->
<!-- %%``` -->
<!-- %% -->
<!-- %% -->
<!-- %%Above \code{myfun1()} is a function that returns a vector of named -->
<!-- %%values. Note that the values returned by the function has been named as -->
<!-- %%\code{m} and \code{v}. An alternative specification is: -->
<!-- %% -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy( list(c("conc","uptake"), "Plant"), data=CO2, FUN=myfun1) -->
<!-- %%```  -->
<!-- %% -->
<!-- %%If the result of the function(s) are not named, then the names in the -->
<!-- %%output data in general become less intuitive: -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%myfun2 <- function(x){c(mean(x), var(x))} -->
<!-- %%summaryBy( conc + uptake ~ Plant, data=CO2, FUN=myfun2) -->
<!-- %%```  -->
<!-- %% -->
<!-- %% -->
<!-- %%Another usage is to specify a list of functions each of which returns -->
<!-- %%a single value: -->
<!-- %% -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryByOLD( conc + uptake ~ Plant, data=CO2, FUN=list( mean, var ) ) -->
<!-- %%```  -->
<!-- %% -->
<!-- %%Notice that if we specify a list of functions of which some returns a -->
<!-- %%vector with more than one element, then the proper names are not -->
<!-- %%retrieved: -->
<!-- %%%%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(uptake~Plant, data=CO2, FUN=list( mean, var, myfun1 )) -->
<!-- %%```  -->
<!-- %% -->
<!-- %%One can ``hard code'' the function names into the output as -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryByOLD(uptake~Plant, data=CO2, FUN=list( mean, var, myfun1 ), -->
<!-- %%          fun.names=c("mean","var","mm","vv")) -->
<!-- %%```  -->
<!-- %% -->
<!-- % \subsubsection{Statistics on functions of data} -->
<!-- % \label{sec:xxx} -->
<!-- % We may want to calculate the mean and variance for the logarithm of -->
<!-- % \code{uptake}, for \code{uptake}+\code{conc} (not likely to be a -->
<!-- % useful statistic) as well as for \code{uptake} and -->
<!-- % \code{conc}. This can be achieved as: -->
<!-- % ``` -->
<!-- % ```{r} -->
<!-- % summaryByOLD(log(uptake) + I(conc+uptake) + conc+uptake ~ Plant, data=CO2, -->
<!-- %           FUN=myfun1) -->
<!-- % ```  -->
<!-- %% -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(cbind(lu=log(uptake), conc + uptake, conc, uptake) ~ Plant, data=CO2, -->
<!-- %%          FUN=myfun1) -->
<!-- %%```  -->
<!-- %% -->
<!-- %% -->
<!-- %% -->
<!-- %%The names of the variables become involved with this. The user may -->
<!-- %%control the names of the variables directly: -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(log(uptake) + I(conc+uptake) + conc + uptake ~ Plant, data=CO2, -->
<!-- %%          FUN=myfun1, var.names=c("log.upt", "conc+upt", "conc", "upt")) -->
<!-- %%```  -->
<!-- %% -->
<!-- %%If one does not want output variables to contain parentheses then -->
<!-- %%setting \code{p2d=TRUE} causes the parentheses to be replaced by dots -->
<!-- %%(``.''). -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(log(uptake)+I(conc+uptake)~Plant, data=CO2, p2d=TRUE, -->
<!-- %%FUN=myfun1) -->
<!-- %%```  -->
<!-- %% -->
<!-- %% -->
<!-- %% -->
<!-- %% -->
<!-- %% -->
<!-- %%\subsubsection{Copying variables out with the \code{id} argument} -->
<!-- %%\label{sec:xxx} -->
<!-- %% -->
<!-- %%To get the value of the \code{Type} and \code{Treat} in the first row of the -->
<!-- %%groups (defined by the values of \code{Plant}) copied to the output -->
<!-- %%dataframe we use the \code{id} argument in one of the following forms: -->
<!-- %% -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(conc+uptake~Plant, data=CO2, FUN=myfun1, id=~Type+Treat) -->
<!-- %%summaryBy(conc+uptake~Plant, data=CO2, FUN=myfun1, id=c("Type","Treat")) -->
<!-- %%```  -->
<!-- %% -->
<!-- %%\subsubsection{Using '.' on the left hand side of a formula} -->
<!-- %%\label{sec:xxx} -->
<!-- %% -->
<!-- %%It is possible  to use the dot (".") on the left hand side of -->
<!-- %%the formula. The dot means "all numerical variables which do not -->
<!-- %%appear elsewhere" (i.e.\ on the right hand side of the formula and in -->
<!-- %%the \code{id} statement): -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(log(uptake)+I(conc+uptake)+. ~Plant, data=CO2, FUN=myfun1) -->
<!-- %%```  -->
<!-- %% -->
<!-- %% -->
<!-- %%\subsubsection{Using '.' on the right hand side of a formula} -->
<!-- %%\label{sec:xxx} -->
<!-- %% -->
<!-- %%The dot (".") can also be used on the right hand side of the formula -->
<!-- %%where it refers to "all non--numerical variables which are not -->
<!-- %%specified elsewhere": -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(log(uptake) ~Plant+., data=CO2, FUN=myfun1) -->
<!-- %%```  -->
<!-- %% -->
<!-- %%\subsubsection{Using '1' on the right hand side of the formula} -->
<!-- %%\label{sec:xxx} -->
<!-- %% -->
<!-- %%Using 1 on the -->
<!-- %%  right hand side means no grouping: -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(log(uptake) ~ 1, data=CO2, FUN=myfun1) -->
<!-- %%```  -->
<!-- %% -->
<!-- %% -->
<!-- %% -->
<!-- %%\subsubsection{Preserving names of variables using \code{keep.names}} -->
<!-- %%\label{sec:xxx} -->
<!-- %%If the function applied to data only returns one value, it is possible -->
<!-- %%to force that the summary variables retain the original names by -->
<!-- %%setting \code{keep.names=TRUE}. A -->
<!-- %%typical use of this could be -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%summaryBy(conc+uptake+log(uptake)~Plant, -->
<!-- %%data=CO2, FUN=mean, id=~Type+Treat, keep.names=TRUE) -->
<!-- %%```  -->
<!-- %% -->
<!-- %%\subsection{The \code{splitBy} function} -->
<!-- %%\label{splitBy} -->
<!-- %% -->
<!-- %%Suppose we want to split the \code{airquality} data into a list of dataframes, e.g.\ one -->
<!-- %%dataframe for each month. This can be achieved by: -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%x<-splitBy(~Month, data=airquality) -->
<!-- %%x -->
<!-- %%```  -->
<!-- %%Hence for month 5, the relevant entry-name in the list is '5' and this -->
<!-- %%part of data  can -->
<!-- %%be extracted as -->
<!-- %%``` -->
<!-- %%%<<eval=F, results=hide>>= -->
<!-- %%%x[['5']] -->
<!-- %%%```  -->
<!-- %% -->
<!-- %%Information about the grouping is stored as a dataframe -->
<!-- %%in an attribute called \code{groupid} and can be retrieved with: -->
<!-- %%```{r} -->
<!-- %%attr(x,"groupid") -->
<!-- %%```  -->
<!-- %% -->
<!-- %% \subsection{The \code{sampleBy} function} -->
<!-- %% \label{sampleBy} -->
<!-- %%  -->
<!-- %% Suppose we want a random sample of 50 \% of the observations from a -->
<!-- %% dataframe. This can be achieved with: -->
<!-- %% ``` -->
<!-- %% <<results=hide>>= -->
<!-- %% sampleBy(~1, frac=0.5, data=airquality) -->
<!-- %% ```  -->
<!-- %%  -->
<!-- %% Suppose instead that we want a  systematic sample of  every fifth -->
<!-- %% observation within each month. This is achieved with: -->
<!-- %% ``` -->
<!-- %% <<results=hide>>= -->
<!-- %% sampleBy(~Month, frac=0.2, data=airquality,systematic=T) -->
<!-- %% ```  -->
<!-- %%  -->
<!-- %%  -->
<!-- %% Inspired by the \pkg{dplyr} package, there is an \verb|order_by| function -->
<!-- %% << >>=  -->
<!-- %% x5 <- airquality |> order_by(c("Temp", "Month")) -->
<!-- %% x6 <- airquality |> order_by(c("-Temp", "Month")) -->
<!-- %% ``` -->
<!-- %% which is the same as -->
<!-- %% << >>=  -->
<!-- %% x5 <- order_by(airquality, c("Temp", "Month")) -->
<!-- %% x6 <- order_by(airquality, c("-Temp", "Month")) -->
<!-- %% ``` -->
<!-- %% <<echo=F, results=hide>>=  -->
<!-- %% c(all.equal(x1, x3), all.equal(x1, x5),  -->
<!-- %%   all.equal(x2, x4), all.equal(x2, x6)) -->
<!-- %% ``` -->
<!-- %% Suppose we want to calculate the weekwise feed efficiency of the pigs -->
<!-- %% in the \code{dietox} data, i.e. weight gain divided by feed intake. -->
<!-- %% ``` -->
<!-- %% ```{r} -->
<!-- %% data(dietox) -->
<!-- %% dietox <- orderBy(~Pig+Time, data=dietox) -->
<!-- %% FEfun  <- function(d){c(NA, diff(d$Weight)/diff(d$Feed))} -->
<!-- %% v      <- lapplyBy(~Pig, data=dietox, FEfun) -->
<!-- %% dietox$FE <- unlist(v) -->
<!-- %% ```  -->
<!-- %%  -->
<!-- %% Technically, the above is the same as -->
<!-- %% ``` -->
<!-- %% ```{r} -->
<!-- %% dietox <- orderBy(~Pig+Time, data=dietox) -->
<!-- %% wdata  <- splitBy(~Pig, data=dietox) -->
<!-- %% v      <- lapply(wdata, FEfun) -->
<!-- %% dietox$FE <- unlist(v) -->
<!-- %% ```  -->
<!-- %%  -->
<!-- %% -->
<!-- %% \subsection{The \code{scaleBy} function} -->
<!-- %%  -->
<!-- %% Standardize the \code{iris} data within each value of \code{"Species"}: -->
<!-- %% ``` -->
<!-- %% ```{r} -->
<!-- %% x <- scaleBy(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ~ Species,      -->
<!-- %%              data=iris) -->
<!-- %% lapply(x, head, 4) -->
<!-- %% head(iris) -->
<!-- %% ``` def -->
<!-- %%  -->
<!-- %% Alternative forms: -->
<!-- %% << >>=  -->
<!-- %% x <- scaleBy( list(c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), -->
<!-- %%                  "Species"),     data=iris) -->
<!-- %% x <- scaleBy(. ~ Species, data=iris) -->
<!-- %% x <- scaleBy(list(".", "Species"),  data=iris) -->
<!-- %% x <- iris  >% scale_by(. ~ Species) -->
<!-- %% ``` -->
<!-- %%  -->
<!-- %%\section{Create By--functions on the fly} -->
<!-- %%\label{sec:create-functions-fly} -->
<!-- %% -->
<!-- %%Create a function for creating groupwise t-tests -->
<!-- %% -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%mydata <- data.frame(y=rnorm(32), x=rnorm(32), -->
<!-- %%g1=factor(rep(c(1,2),each=16)), g2=factor(rep(c(1,2), each=8)), -->
<!-- %%g3=factor(rep(c(1,2),each=4))) -->
<!-- %%head(mydata) -->
<!-- %%```  -->
<!-- %% -->
<!-- %% -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%## Based on the formula interface to t.test -->
<!-- %%t.testBy1 <- function(formula, group, data, ...){ -->
<!-- %%  formulaFunBy(formula, group, data, FUN=t.test, class="t.testBy1", ...) -->
<!-- %%} -->
<!-- %%## Based on the default interface to t.test -->
<!-- %%t.testBy2 <- function(formula, group, data, ...){ -->
<!-- %%  xyFunBy(formula, group, data, FUN=t.test, class="t.testBy1", ...) -->
<!-- %%} -->
<!-- %%```  -->
<!-- %% -->
<!-- %%Notice: The optional \code{class} argument will facilitate that you -->
<!-- %%create your own print / summary methods etc. -->
<!-- %% -->
<!-- %%``` -->
<!-- %%```{r} -->
<!-- %%t.testBy1(y~g1, ~g2, data=mydata) -->
<!-- %%t.testBy2(y~x,  ~g2, data=mydata) -->
<!-- %%```  -->
<!-- %% -->
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