<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
<s-topic>crossvalidate.discRob</s-topic>
</s-topics>

<s-title>
Crossvalidation Method for class <s-expression>discRob</s-expression>
</s-title>

<s-description>
Compute leave-one-out crossvalidation of a
<s-expression>discRob</s-expression> object.
</s-description>

<s-usage>
<s-old-style-usage>
crossvalidate.discRob(object, doubt=0, digits=.Options$digits, 
                      trace=T, subset)
</s-old-style-usage>
</s-usage>

<s-args-required>
<s-arg name="object">
the <s-expression>discRob</s-expression> object constructed by the
<s-expression>discRob</s-expression> function.
</s-arg>
</s-args-required>

<s-args-optional>
<s-arg name="doubt">
a numerical variable, 0 <= <s-expression>doubt</s-expression>
< <s-expression>(g-1) / g</s-expression>, where
<s-expression>g</s-expression> is the number of groups,
defining the minimum difference the maximum posterior
probability of group membership must exceed all others
before assigning the observation to that group. If there
does not exist a posterior probability of a group
membership for an observation that exceeds
<s-expression>doubt + 1/g</s-expression>, the observation
is assigned to an "unknown" group.
</s-arg>
<s-arg name="digits">
the number of significant digits to retain in the posterior
probability. Rounding the posterior probabilities is odne
mostly for printing purposes.
</s-arg>
<s-arg name="trace">
a logical flag for printing trace information. A classification
table is printed for every 10 iterations.
</s-arg>
<s-arg name="subset">
an integer or logical vector specifying which observations
to crossvalidate.
</s-arg>
</s-args-optional>
<s-value>
an n x (g+1) data frame, where n is the number of observations and g is the
number of groups, containing a <s-expression>group</s-expression> factor
column followed by the posterior probability of group membership for each
observation in the <s-expression>newdata</s-expression> data frame or the
training data.
</s-value>
<s-details>
Performs crossvalidation of the discriminant function contained in
<s-expression>object</s-expression>. This function may take a considerable
amount of time to compute the leave-one-out error rate.
</s-details>
<s-keywords>
<s-keyword>librobust</s-keyword>
<s-keyword>robust</s-keyword>
<s-keyword>multivariate</s-keyword>
</s-keywords>
<s-docclass>
function
</s-docclass>
</s-function-doc>
