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<table width="100%" summary="page for all.class"><tr><td>all.class</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Different generic functions for class PK.</h2>

<h3>Description</h3>

<p>Generic functions for summarizing an object of class PK</p>


<h3>Usage</h3>

<pre>
## S3 method for class 'PK'
print(x, digits=max(3, getOption("digits") - 4), ...)

## S3 method for class 'PK'
summary(object, ...)

## S3 method for class 'PK'
plot(x, bygroup=FALSE, col=NULL, pch=NULL, main=NULL, xlab="Time", 
         ylab="Concentration", ylim=NULL, xlim=NULL, add=FALSE, ...)

</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
<p> An output object of class PK.</p>
</td></tr>
<tr valign="top"><td><code>digits</code></td>
<td>
<p>Number of significant digits to be printed.</p>
</td></tr>
<tr valign="top"><td><code>object</code></td>
<td>
<p> An output object of class PK.</p>
</td></tr>
<tr valign="top"><td><code>bygroup</code></td>
<td>
<p> A logical value indicating whether the plot should highlight the groups.</p>
</td></tr>
<tr valign="top"><td><code>col</code></td>
<td>
<p>A specification for the default plotting color (default=<code>NULL</code>). See <code>par</code> for more details.</p>
</td></tr>
<tr valign="top"><td><code>pch</code></td>
<td>
<p>Either an integer specifying a symbol or a single character
to be used as the default in plotting points (default=<code>NULL</code>). See <code>par</code> for more details.</p>
</td></tr>
<tr valign="top"><td><code>main</code></td>
<td>
<p>An overall title for the plot (default=<code>NULL</code>). The default setting produces <code>"Concentration versus time plot (Design)"</code>.</p>
</td></tr>
<tr valign="top"><td><code>xlab</code></td>
<td>
<p>A title for the x axis (default=<code>"time"</code>).</p>
</td></tr>
<tr valign="top"><td><code>ylab</code></td>
<td>
<p>A title for the y axis (default=<code>"concentration"</code>).</p>
</td></tr>
<tr valign="top"><td><code>xlim</code></td>
<td>
<p>Numeric vector of length 2, giving the x coordinates range. (default=<code>"NULL"</code>).</p>
</td></tr>
<tr valign="top"><td><code>ylim</code></td>
<td>
<p>Numeric vector of length 2, giving the y coordinates range. (default=<code>"NULL"</code>).</p>
</td></tr>
<tr valign="top"><td><code>add</code></td>
<td>
<p> A logical value indicating whether to add plot to current plot (default=<code>FALSE</code>). </p>
</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
<p>Further (graphical) arguments to be passed to methods.</p>
</td></tr>
</table>


<h3>Details</h3>

<p><code>print.PK</code> produces a minimal summary of an estimation object from class PK including point estimate, standard error and confidence interval. The confidence interval is the first of <code>"boott"</code>, <code>"fieller"</code>, <code>"t"</code> or <code>"z"</code> that was originally requested. <br /><br />
</p>
<p><code>summary.PK</code> prints a more detailed summary of an estimation object from class PK. Most notably all confidence intervals originally requested are printed.<br /><br />
</p>
<p><code>plot.PK</code> produces as concentration versus time plot of the data used of an estimation object from class PK.
</p>


<h3>Value</h3>

<p>Screen or graphics output.</p>


<h3>Author(s)</h3>

<p>Thomas Jaki and Martin J. Wolfsegger</p>


<h3>References</h3>

<p>Hand, D. and Crowder, M. (1996), <em>Practical Longitudinal Data Analysis</em>, Chapman and Hall, London.<br /><br />
</p>
<p>Holder D. J., Hsuan F., Dixit R. and Soper K. (1999). A method for estimating and testing area under the curve in serial sacrifice, batch, and complete data designs. <em>Journal of Biopharmaceutical Statistics</em>, 9(3):451-464.<br /><br />
</p>
<p>Jaki T. and Wolfsegger M. J. (2009). A theoretical framework for estimation of AUCs in complete and incomplete sampling designs. <em>Statistics in Biopharmaceutical Research</em>, 1(2):176-184. <br /><br />
</p>
<p>Nedelman J. R., Gibiansky E. and Lau D. T. W. (1995). Applying Bailer's method for AUC confidence intervals to sparse sampling. <em>Pharmaceutical Research</em>, 12(1):124-128. <br /><br />
</p>


<h3>See Also</h3>

<p><code>estimator</code>, <code>ci</code> and <code>test</code></p>


<h3>Examples</h3>

<pre>
## serial sampling desing: example from Nedelman et al. (1995)
conc &lt;- c(2790, 3280, 4980, 7550, 5500, 6650, 2250, 3220, 213, 636)
time &lt;- c(1, 1, 2, 2, 4, 4, 8, 8, 24, 24)

obj &lt;- auc(conc=conc, time=time, method=c("z", "t"), design="ssd")

print(obj)

summary(obj)

## serial sampling design: example from Nedelman et al. (1995)
conc.m &lt;- c(391, 396, 649, 1990, 3290, 3820, 844, 1650, 75.7, 288)
conc.f &lt;- c(353, 384, 625, 1410, 1020, 1500, 933, 1030, 0, 80.5)
time &lt;- c(1, 1, 2, 2, 4, 4, 8, 8, 24, 24)

res1 &lt;- auc(conc=conc.m, time=time, method=c('t','z'), design='ssd')
res2 &lt;- auc(conc=conc.f, time=time, method=c('t','z'), design='ssd')

plot(res1, pch=19, ylim=c(0,5000), xlim=c(0,25))
plot(res2, pch=21, col='red', add=TRUE)
legend(x=25, y=5000, xjust=1, pch=c(19,21), col=c('black','red'), 
       legend=c('Male', 'Female'))


## batch design: example from Jaki and Wolfsegger (2009),
## originally in Holder et al. (1999) using data for call
data(Rats)

data1 &lt;- subset(Rats,Rats$dose==100)
data2 &lt;- subset(Rats,Rats$dose==300)
res1 &lt;- auc(data=data1,method='t', design='batch')
res2 &lt;- auc(data=data2,method='t', design='batch')

plot(res1, col='black', ylim=c(0,8), xlim=c(0,25))
plot(res2, col='red', add=TRUE)
legend(x=0, y=8, xjust=0, lty=1, col=c('black','red'), 
       legend=c('Dose of 100', 'Dose of 300'))

data3 &lt;- subset(Rats,Rats$dose==100 | Rats$dose==300)
data3$group &lt;- data3$dose
res3 &lt;- auc(data=data3,method='t', design='batch')
plot(res3,bygroup=TRUE)

## complete data design example
## originally in Hand and Crowler (1996)
data(Glucose)
data1 &lt;- subset(Glucose, date==1)
data2 &lt;- subset(Glucose, date==2)
res1 &lt;- auc(conc=data1$conc, time=data1$time, design='complete', method='t')
res2 &lt;- auc(conc=data2$conc, time=data2$time, design='complete', method='t')
plot(res1, pch=19, col='black', ylim=c(0,5))
plot(res2, pch=21, col='red', add=TRUE)

## more informative plot 
plot(x=c(0, 30), y=c(0, 5), type='n', main='Complete Data Design', xlab='Time', 
      ylab='Concentration')
for(i in unique(Glucose$id)){
   for(j in unique(Glucose$date)){
        temp &lt;- subset(Glucose, id==i &amp; date==j)
        col &lt;- ifelse(j==1, 'black', 'red')
        lty &lt;- ifelse(j==1, 1, 2)
        pch &lt;- ifelse(j==1, 19, 21)
	points(x=temp$time, y=temp$conc, col=col, lty=lty, pch=pch, type='b')
   }
}
legend(x=30, y=5, xjust=1, pch=c(19,21), col=c('black','red'), lty=c(1,2), 
       legend=c('Date 1', 'Date 2'))


</pre>


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