<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
<s-topic>predict.discRob</s-topic>
</s-topics>
<s-title>
Prediction Method for class <s-expression>discRob</s-expression>
</s-title>
<s-description>
Predict group membership ob observations using a
discriminant function estimated by the <s-expression>
discRob</s-expression> function.
</s-description>
<s-usage>
<s-old-style-usage>
predict.discRob(object, newdata, doubt=0, digits=.Options$digits)
</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="newdata">
a data frame containing the values at which predictions
are required. This argument can be missing, in which
case predictions are made for the training data used
to compute the discriminant function. Only those variables
used to compute the discriminant function need be
present by name in <s-expression>newdata</s-expression>.
</s-arg>
<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-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>
Predicts group membership for observations in the data
frame <s-expression>newdata</s-expression> using the
discriminant function contained in the <s-expression>
discRob</s-expression> object <s-expression>object
</s-expression>. It computes the posterior probability
of group membership for each observation, where the
prior probabilities of group membership are given by
the variable <s-expression>prior</s-expression>,
and assigns the observation to the group that has
the highest probability. This is the optimal allocation
rule, or Bayes rule. This <s-expression>predict
</s-expression> method is based on the
<s-expression>predict.discrim</s-expression>
function.
</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>
