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<table width="100%" summary="page for TGROC {unknown}"><tr><td>TGROC {unknown}</td><td align="right">R Documentation</td></tr></table>
<h2>TG-ROC - Two Graphic Receiver Operating Characteristic</h2>
<h3>Description</h3>
<p>
TGROC draws a graphic of sensitivity and specificity with the variations of a diagnostic test scale. Also, it demonstrates which cut-offs (or decision thresholds) may trichotomize the test results into a range where the test is good to identify those with the target condition, a inconclusive range and a range where the test is good to identify those without the target disease according with the researcher tolerance. Also, it estimates and graphically demonstrates good cut-offs by different methods.
</p>
<h3>Usage</h3>
<pre>
TGROC(gold, test, Cost = 1, CL = 0.95, Inconclusive = 0.95, Prevalence = 0, Plot = TRUE, Plot.inc.range = TRUE, Plot.Cl = FALSE, Plot.cutoff = "None", cex.sub = 0.85, Print = TRUE)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>gold</code></td>
<td>
The reference standard. A column in a data frame or a vector indicating the classification by the reference test. The reference standard must have two levels: must be coded either as 0 - without target disease - or 1 - with the target disease; or could be coded <a href="../../base/html/as.factor.html">as.factor</a> with the words "negative" - without target disease - and "positive" - with the target disease.</td></tr>
<tr valign="top"><td><code>test</code></td>
<td>
The index test or test under evaluation. A column in a dataframe or vector indicating the test results in a continuous scale. May also work with discrete ordinal scale.</td></tr>
<tr valign="top"><td><code>Cost</code></td>
<td>
Cost = cost(FN)/cost(FP). MCT (misclassification cost term) will be used to estimate a good cut-off. It is a value in a range from 0 to infinite. Could be financial cost or a health outcome with the perception that FN are more undesirable than FP (or the other way around). This item will run into MCT - (1-prevalence)*(1-Sp)+Cost*prevalence(1-Se). Cost=1 means FN and FP have even cost. Cost = 0.9 means FP are 10 percent more costly. Cost = 0.769 means that FP are 30 percent more costly. Cost = 0.555 means that FP are 80 percent more costly. Cost = 0.3 means that FP are 3 times more costly. Cost = 0.2 means that FP are 5 times more costly. Also, it can be inserted as any ratio such as 1/2.5 or 1/4.</td></tr>
<tr valign="top"><td><code>CL</code></td>
<td>
Confidence limit. The limits of the confidence interval. Must be coded as number in range from 0 to 1. Default value is 0.95</td></tr>
<tr valign="top"><td><code>Inconclusive</code></td>
<td>
Inconclusive is a value that ranges from 0 to 1 that identify the test range where the performance of the test is not acceptable and thus considered inconclusive. It represents the researcher tolerance of how good the test should be. If it is set to 0.95 (which is the default value), test results that have less than 0.95 sensitivity and specificity will be in the inconclusive range. </td></tr>
<tr valign="top"><td><code>Prevalence</code></td>
<td>
Prevalence of the disease in the population who the test will be performed. It must be a value from 0 to 1. If left 0 (the default value), this will be replaced by the disease prevalence in the sample. This values will be used in the MCT and Efficiency formulas to estimate good cut-offs.</td></tr>
<tr valign="top"><td><code>Plot</code></td>
<td>
If FALSE, the TG-ROC curve plot will not be displayed. Default is TRUE.</td></tr>
<tr valign="top"><td><code>Plot.inc.range</code></td>
<td>
Plot inconclusive range. If FALSE, the lines representing the limits of the inconclusive range will not be displayed. Default is TRUE. If Plot is FALSE than Plot.inc.range is not considered.</td></tr>
<tr valign="top"><td><code>Plot.Cl</code></td>
<td>
Plot confidence limits. If TRUE, confidence bands for sensitivity and specificity curves will be displayed. Default is FLASE. If Plot is FALSE than Plot.Cl is not considered.</td></tr>
<tr valign="top"><td><code>Plot.cutoff</code></td>
<td>
Draws a line representing the estimated best cut-off (threshold) will be displayed. If Plot is FALSE than Plot.cutoff is not considered. Default is "None". Possible values are:
"Se=Sp" - the cut-off which Sensitivity is equal to Specificity;
<br>
"Max.Efficiency" - the cut-off which maximize the efficiency;
<br>
"Min.MCT" - the cut-off which minimize the misclassification cost term. </td></tr>
<tr valign="top"><td><code>cex.sub</code></td>
<td>
See <a href="../../base/html/par.html">par</a>. Controls the font size in the subtitle. If Plot is FALSE than cex.sub is not considered.</td></tr>
<tr valign="top"><td><code>Print</code></td>
<td>
If FALSE, statistics estimated by TG-ROC will not be displayed in the output window. Default is TRUE.</td></tr>
</table>
<h3>Details</h3>
<p>
There are two main advantages of TG-ROC over ROC analysis: (1) for the uninitiated is much easier to understand how sensitivity and specificity changes with different cut-offs; (2) and because of the graphical display is much easier to understand and estimate reasonable inconclusive test ranges. Occasionally the MCT or Efficiency cut-offs may be set outside the inconclusive range. This may happens with extreme values of Cost and population prevalence. If this is the case, perhaps the inconclusive range may not be of interest or not applicable. Tests results matching the cut-off values will be considered a positive test. TG-ROC assumes that subjects with higher values of the test are with the target condition and those with lower values are without the target condition. Tests that behave like glucose (middle values are supposed to be normal and extreme values are supposed to be abnormal) and immunefluorescence (lower values - higher dilutions - are suppose to be abnormal) will not be correctly analyzed. In the latter, multiplying the test results by -1 or other transformation before analysis could make it work. The validity measures such as Sensitivity, Specificity and Likelihood ratios and its confidence limits are estimated as in <code><a href="../../../doc/html/search/SearchObject.html?diagnosis">diagnosis</a></code> function. MCT and Efficiency are estimated as in <code><a href="../../../doc/html/search/SearchObject.html?ROC">ROC</a></code> function. So far, TG-ROC estimates only non-parametric statistics.
</p>
<h3>Value</h3>
<table summary="R argblock">
<tr valign="top"><td><code>Sample size</code></td>
<td>
Amount of subjects analyzed.</td></tr>
<tr valign="top"><td><code>Sample prevalence</code></td>
<td>
Prevalence of target condition in the sample.</td></tr>
<tr valign="top"><td><code>Population prevalence.</code></td>
<td>
Informed prevalence in the population.</td></tr>
<tr valign="top"><td><code>Test summary</code></td>
<td>
A summary of central and dispersion tendencies of test results.</td></tr>
<tr valign="top"><td><code>Non-parametric inconclusive limits.</code></td>
<td>
Estimate of the inconclusive limits of the tests and its corresponding validity measures.</td></tr>
<tr valign="top"><td><code>Best cut-off</code></td>
<td>
The cut-offs estimated by different methods and its corresponding validity measures.</td></tr>
</table>
<h3>Note</h3>
<p>
Bug reports, malfunctioning, or suggestions for further improvements or contributions can be sent, preferentially, through the DiagnosisMed email list, or R-Forge website <a href="https://r-forge.r-project.org/projects/diagnosismed/">https://r-forge.r-project.org/projects/diagnosismed/</a>.
</p>
<h3>Author(s)</h3>
<p>
Pedro Brasil; - <a href="mailto:diagnosismed-list@lists.r-forge.r-project.org">diagnosismed-list@lists.r-forge.r-project.org</a>
</p>
<h3>References</h3>
<p>
Greiner, M. (1996) Two-graph receiver operating characteristic (TG-ROC): update version supports optimisation of cut-off values that minimize overall misclassification costs. J.Immunol.Methods 191:93-94.
</p>
<p>
M. Greiner (1995) Two-graph receiver operating characteristic (TG-ROC): a Microsoft-EXCEL template for the selection of cut-off values in diagnostic tests. Journal of Immunological Methods. 185(1):145-146.
</p>
<p>
M. Greiner, D. Sohr, P. Göbel (1995) A modified ROC analysis for the selection of cut-off values and the definition of intermediate results of serodiagnostic tests. Journal of immunological methods. 185(1):123-132.
</p>
<h3>See Also</h3>
<p>
<code><a href="../../../doc/html/search/SearchObject.html?interact.ROC">interact.ROC</a></code>,<code><a href="../../../doc/html/search/SearchObject.html?ROC">ROC</a></code>,<code><a href="../../../doc/html/search/SearchObject.html?diagnosis">diagnosis</a></code>,<a href="../../ROCR/html/performance.html">performance</a>,<a href="../../epitools/html/binom.conf.int.html">binom.conf.int</a>,
</p>
<h3>Examples</h3>
<pre>
# Loading a dataset.
data(tutorial)
# Attaching dataset
attach(tutorial)
# Running the analysis
TGROC(gold=Gold,test=Test_B)
</pre>
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