These functions are used to apply the generic crossvalidation mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
1 2  learnNNET(data, status, params, pfun)
predictNNET(newdata, details, status, ...)

data 
The data matrix, with rows as features ("genes") and columns as the samples to be classified. 
status 
A factor, with two levels, classifying the samples. The length must
equal the number of 
params 
A list of additional parameters used by the classifier; see Details. 
pfun 
The function used to make predictions on new data, using the
crossvalidated classifier. Should always be set to

newdata 
Another data matrix, with the same number of rows as 
details 
A list of additional parameters describing details about the particular classifier; see Details. 
... 
Optional extra parameters required by the generic "predict" method. 
The input arguments to both learnNNET
and predictNNET
are dictated by the requirements of the general crossvalidation
mechanism provided by the Modelerclass
.
The NNET classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc
package. We
start by performing univariate twosample ttests to identify features
that are differentially expressed between two groups of training
samples. We then set a cutoff to select features using a bound
(alpha
) on the false discovery rate (FDR). If the number of
selected features is smaller than a prespecified goal
(minNgenes
), then we increase the FDR until we get the desired
number of features. Next, we perform PCA on the selected features
from the trqining data. we retain enough principal components (PCs)
to explain a prespecified fraction of the variance (perVar
).
We then fit a logistic regression model using these PCs to predict the
binary class of the training data. In order to use this model to make
binary predictions, you must specify a prior
probability that a
sample belongs to the first of the two groups (where the ordering is
determined by the levels of the classification factor, status
).
In order to fit the model to data, the params
argument to the
learnNNET
function should be a list containing components
named alpha
, minNgenes
, perVar
, and prior
.
It may also contain a logical value called verbose
, which
controls the amount of information that is output as the algorithm runs.
The result of fitting the model using learnNNET
is a member of
the FittedModelclass
. In additon to storing the
prediction function (pfun
) and the training data and status,
the FittedModel stores those details about the model that are required
in order to make predictions of the outcome on new data. In this
acse, the details are: the prior
probability, the set of
selected features (sel
, a logical vector), the principal
component decomposition (spca
, an object of the
SamplePCA
class), the logistic
regression model (mmod
, of class glm
), the number
of PCs used (nCompUsed
) as well as the number of components
available (nCompAvail
) and the number of genefeatures selected
(nGenesSelecets
). The details
object is appropriate for
sending as the second argument to the predictNNET
function in
order to make predictions with the model on new data. Note that the
status vector here is the one used for the training data, since
the prediction function only uses the levels of this factor to
make sure that the direction of the predicitons is interpreted
correctly.
The learnNNET
function returns an object of the
FittedModelclass
, representing a NNET classifier
that has been fitted on a training data
set.
The predictNNET
function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <krc@silicovore.com>
See Modelerclass
and Modeler
for details
about how to peform crossvalidation. See
FittedModelclass
and FittedModel
for
details about the structure of the object returned by learnNNET
.
1 2 3 4 5 6 7 8 9 10 11 12 13  # simulate some data
data < matrix(rnorm(100*20), ncol=20)
status < factor(rep(c("A", "B"), each=10))
# set up the parameter list
nnet.params < list()
# learn the model
#fm < learnNNET(data, status, nnet.params, predictNNET)
# Make predictions on some new simulated data
#newdata < matrix(rnorm(100*30), ncol=30)
#predictNNET(newdata, fm@details, status)

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