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<table width="100%" summary="page for summary.rlm {MASS}"><tr><td>summary.rlm {MASS}</td><td align="right">R Documentation</td></tr></table>
<h2>Summary Method for Robust Linear Models</h2>
<h3>Description</h3>
<p>
<code>summary</code> method for objects of class <code>"rlm"</code>
</p>
<h3>Usage</h3>
<pre>
## S3 method for class 'rlm':
summary(object, method = c("XtX", "XtWX"), correlation = FALSE, ...)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>object</code></td>
<td>
the fitted model.
This is assumed to be the result of some fit that produces
an object inheriting from the class <code>rlm</code>, in the sense that
the components returned by the <code>rlm</code> function will be available.
</td></tr>
<tr valign="top"><td><code>method</code></td>
<td>
Should the weighted (by the IWLS weights) or unweighted cross-products
matrix be used?
</td></tr>
<tr valign="top"><td><code>correlation</code></td>
<td>
logical. Should correlations be computed (and printed)?
</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
arguments passed to or from other methods.
</td></tr>
</table>
<h3>Details</h3>
<p>
This function is a method for the generic function
<code>summary()</code> for class <code>"rlm"</code>.
It can be invoked by calling <code>summary(x)</code> for an
object <code>x</code> of the appropriate class, or directly by
calling <code>summary.rlm(x)</code> regardless of the
class of the object.
</p>
<h3>Value</h3>
<p>
If printing takes place, only a null value is returned.
Otherwise, a list is returned with the following components.
Printing always takes place if this function is invoked automatically
as a method for the <code>summary</code> function.
</p>
<table summary="R argblock">
<tr valign="top"><td><code>correlation</code></td>
<td>
The computed correlation coefficient matrix for the coefficients in the model.
</td></tr>
<tr valign="top"><td><code>cov.unscaled</code></td>
<td>
The unscaled covariance matrix; i.e, a matrix such that multiplying it by
an estimate of the error variance produces an estimated covariance matrix
for the coefficients.
</td></tr>
<tr valign="top"><td><code>sigma</code></td>
<td>
The scale estimate.
</td></tr>
<tr valign="top"><td><code>stddev</code></td>
<td>
A scale estimate used for the standard errors.
</td></tr>
<tr valign="top"><td><code>df</code></td>
<td>
The number of degrees of freedom for the model and for residuals.
</td></tr>
<tr valign="top"><td><code>coefficients</code></td>
<td>
A matrix with three columns, containing the coefficients, their standard errors
and the corresponding t statistic.
</td></tr>
<tr valign="top"><td><code>terms</code></td>
<td>
The terms object used in fitting this model.
</td></tr>
</table>
<h3>References</h3>
<p>
Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM> Fourth edition. Springer.
</p>
<h3>See Also</h3>
<p>
<code><a href="../../Zelig/html/summary.zelig.html">summary</a></code>
</p>
<h3>Examples</h3>
<pre>
summary(rlm(calls ~ year, data = phones, maxit = 50))
## Not run:
Call:
rlm(formula = calls ~ year, data = phones, maxit = 50)
Residuals:
Min 1Q Median 3Q Max
-18.31 -5.95 -1.68 26.46 173.77
Coefficients:
Value Std. Error t value
(Intercept) -102.622 26.553 -3.86
year 2.041 0.429 4.76
Residual standard error: 9.03 on 22 degrees of freedom
Correlation of Coefficients:
[1] -0.994
## End(Not run)</pre>
<hr><div align="center">[Package <em>MASS</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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