| Title: | Classes and Methods for Training and Using Binary Prediction Models |
|---|---|
| Description: | Defines classes and methods to learn models and use them to predict binary outcomes. These are generic tools, but we also include specific examples for many common classifiers. |
| Authors: | Kevin R. Coombes |
| Maintainer: | Kevin R. Coombes <[email protected]> |
| License: | Apache License (== 2.0) |
| Version: | 3.4.5 |
| Built: | 2024-11-29 08:57:38 UTC |
| Source: | CRAN |
Functions to create functions that filter potential predictive features using statistics that do not access class labels.
filterMean(cutoff) filterMedian(cutoff) filterSD(cutoff) filterMin(cutoff) filterMax(cutoff) filterRange(cutoff) filterIQR(cutoff)filterMean(cutoff) filterMedian(cutoff) filterSD(cutoff) filterMin(cutoff) filterMax(cutoff) filterRange(cutoff) filterIQR(cutoff)
cutoff |
A real number, the level above which features with this statistic should be retained and below which should be discarded. |
Following the usual conventions introduced from the world of gene expression microarrays, a typical data matrix is constructed from columns representing samples on which we want to make predictions amd rows representing the features used to construct the predictive model. In this context, we define a filter to be a function that accepts a data matrix as its only argument and returns a logical vector, whose length equals the number of rows in the matrix, where 'TRUE' indicates features that should be retrained. Most filtering functions belong to parametrized families, with one of the most common examples being "retain all features whose mean is above some pre-specified cutoff". We implement this idea using a set of function-generating functions, whose arguments are the parameters that pick out the desired member of the family. The return value is an instantiation of a particular filtering function. The decison to define things this way is to be able to apply the methods in cross-validation (or other) loops where we want to ensure that we use the same filtering rule each time.
Each of the seven functions described here return a filter function,
f, that can be used by code that basically looks like
logicalVector <- filter(data).
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models.
set.seed(246391) data <- matrix(rnorm(1000*30), nrow=1000, ncol=30) fm <- filterMean(1) summary(fm(data)) summary(filterMedian(1)(data)) summary(filterSD(1)(data))set.seed(246391) data <- matrix(rnorm(1000*30), nrow=1000, ncol=30) fm <- filterMean(1) summary(fm(data)) summary(filterMedian(1)(data)) summary(filterSD(1)(data))
Functions to create functions that perform feature selection (or at least feature reduction) using statistics that access class labels.
keepAll(data, group) fsTtest(fdr, ming=500) fsModifiedFisher(q) fsPearson(q = NULL, rho) fsSpearman(q = NULL, rho) fsMedSplitOddsRatio(q = NULL, OR) fsChisquared(q = NULL, cutoff) fsEntropy(q = 0.9, kind=c("information.gain", "gain.ratio", "symmetric.uncertainty")) fsFisherRandomForest(q) fsTailRank(specificity=0.9, tolerance=0.5, confidence=0.5)keepAll(data, group) fsTtest(fdr, ming=500) fsModifiedFisher(q) fsPearson(q = NULL, rho) fsSpearman(q = NULL, rho) fsMedSplitOddsRatio(q = NULL, OR) fsChisquared(q = NULL, cutoff) fsEntropy(q = 0.9, kind=c("information.gain", "gain.ratio", "symmetric.uncertainty")) fsFisherRandomForest(q) fsTailRank(specificity=0.9, tolerance=0.5, confidence=0.5)
data |
A matrix containng the data; columns are samples and rows are features. |
group |
A factor with two levels defining the sample classes. |
fdr |
A real number between 0 and 1 specifying the target false discovery rate (FDR). |
ming |
An integer specifing the minimum number of features to return; overrides the FDR. |
q |
A real number between 0.5 and 1 specifiying the fraction of features to discard. |
rho |
A real number between 0 and 1 specifying the absolute value of the correlation coefficient used to filter features. |
OR |
A real number specifying the desired odds ratio for filtering features. |
cutoff |
A real number specifiyng the targeted cutoff rate when using the statistic to filter features. |
kind |
The kind of information metric to use for filtering features. |
specificity |
See |
tolerance |
See |
confidence |
See |
Following the usual conventions introduced from the world of gene expression microarrays, a typical data matrix is constructed from columns representing samples on which we want to make predictions amd rows representing the features used to construct the predictive model. In this context, we define a feature selector or pruner to be a function that accepts a data matrix and a two-level factor as its only arguments and returns a logical vector, whose length equals the number of rows in the matrix, where 'TRUE' indicates features that should be retrained. Most pruning functions belong to parametrized families. We implement this idea using a set of function-generating functions, whose arguments are the parameters that pick out the desired member of the family. The return value is an instantiation of a particular filtering function. The decison to define things this way is to be able to apply the methods in cross-validaiton (or other) loops where we want to ensure that we use the same feature selection rule each time.
We have implemented the following algorithms:
keepAll: retain all features; do nothing.
fsTtest: Keep features based on the false discovery rate
from a two-goup t-test, but always retain a specified minimum number
of genes.
fsModifiedFisher Retain the top quantile of features
for the statistic
where m is the mean and v is the variance.
fsPearson: Retain the top quantile of features based on
the absolute value of the Pearson correlation with the binary outcome.
fsSpearman: Retain the top quantile of features based on
the absolute value of the Spearman correlation with the binary outcome.
fsMedSplitOddsRatio: Retain the top quantile of
features based on the odds ratio to predict the binary outcome,
after first dichotomizing the continuous predictor using a split at
the median value.
fsChisquared: retain the top quantile of features based
on a chi-squared test comparing the binary outcome to continous
predictors discretized into ten bins.
fsEntropy: retain the top quantile of features based on
one of three information-theoretic measures of entropy.
fsFisherRandomForest: retain the top features based on
their importance in a random forest analysis, after first filtering
using the modified Fisher statistic.
fsTailRank: Retain features that are significant based
on the TailRank test, which is a measure of whether the tails of the
distributions are different.
The keepAll function is a "pruner"; it takes the data matrix and
grouping factor as arguments, and returns a logical vector indicating
which features to retain.
Each of the other nine functions described here return uses its
arguments to contruct and return a pruning function,
f, that has the same interface as keepAll.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models.
set.seed(246391) data <- matrix(rnorm(1000*36), nrow=1000, ncol=36) data[1:50, 1:18] <- data[1:50, 1:18] + 1 status <- factor(rep(c("A", "B"), each=18)) fsel <- fsPearson(q = 0.9) summary(fsel(data, status)) fsel <- fsPearson(rho=0.3) summary(fsel(data, status)) fsel <- fsEntropy(kind="gain.ratio") summary(fsel(data, status))set.seed(246391) data <- matrix(rnorm(1000*36), nrow=1000, ncol=36) data[1:50, 1:18] <- data[1:50, 1:18] + 1 status <- factor(rep(c("A", "B"), each=18)) fsel <- fsPearson(q = 0.9) summary(fsel(data, status)) fsel <- fsPearson(rho=0.3) summary(fsel(data, status)) fsel <- fsEntropy(kind="gain.ratio") summary(fsel(data, status))
Construct an object of the FittedModel-class.
FittedModel(predict, data, status, details, ...)FittedModel(predict, data, status, details, ...)
predict |
A function that applies the model to predict outcomes on new test data. |
data |
A matrix containing the training data. |
status |
A vector containing the training outcomes, which should either be a binary-valued factor or a numeric vector of contiuous outcomes. |
details |
A list of the fitted parameters for the specified model. |
... |
Any extra information that is produced while learning the model; these
wil be saved in the |
Most users will never need to use this function; instead, they will
first use an existing object of the Modeler-class,
call the learn method of that object with the training data
to obtain a FittedModel object, and then apply its
predict method to test data. Only people who want to
implement the learn-predict interface for a new classification algorithm
are likely to need to call this function directly.
Returns an object of the FittedModel-class.
Kevin R. Coombes <[email protected].
See the descriptions of the learn function and
the predict method for details on how to fit models on
training data and make predictions on new test data.
See the description of the Modeler-class for details
about the kinds of objects produced by learn.
# see the examples for learn and predict and for specific # implementations of classifiers.# see the examples for learn and predict and for specific # implementations of classifiers.
Objects of this class represent parametrized statistical
models (of the Modeler-class) after they have been fit
to a training data set. These objects can be used to
predict binary outcomes on new test data sets.
Objects can be created by calls to the constructor function,
FittedModel. In practice, however, most
FittedModel objects are created as the result of applying the
learn function to an object of the
Modeler-class.
predictFunction:Object of class "function" that
implemnts the ability to make predictions using the fitted model.
trainData:Object of class "matrix" containing
the trainng data set. Rowes are features and columns are samples.
trainStatus:Object of class "vector". Should
either be a numeric vector representing outcome or a factor with two
levels, containing the classes of the training data set.
details:Object of class "list" containing the
fitted parameters for the specific model.
extras:Object of class "list" containing any
extra information (such as diagnostics) produced a a result of
learning the model from the training data set.
fsVector:Logical vector indicating which features should be retained (TRUE) of discared (FALSE) after performing featgure selection on the training data.
signature(object = "FittedModel"): Predict the
binary outcome on a new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and learn for details on
how to fit a model to data.
showClass("FittedModel")showClass("FittedModel")
The learn function provides an abstraction that can be used to
fit a binary classification model to a training data set.
learn(model, data, status, prune=keepAll)learn(model, data, status, prune=keepAll)
model |
An object of the |
data |
A matrix containing the training data, with rows as features and columns as samples to be classified. |
status |
A factor, with two levels, containing the known classification of the training data. |
prune |
A "pruning" funciton; that is, a funciton that takes two arguments (a data matrix and a class factor) and returns a logical vector indicating which features to retain. |
Objects of the Modeler-class contain functions to learn
models from training data to make predictions on new test data. These
functions have to be prepared as pairs, since they have a shared
opinion about how to record and use specific details about the
parameters of the model. As a result, the learn function is
implemented by:
learn <- function(model, data, status) {
model@learn(data, status, model@params, model@predict)
}
An object of the FittedModel-class.
Kevin R. Coombes <[email protected]>
See predict for how to make predictions on new test data
from an object of the FittedModel-class.
# set up a generic RPART model rpart.mod <- Modeler(learnRPART, predictRPART, minsplit=2, minbucket=1) # simulate fake data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # learn the specific RPART model fm <- learn(rpart.mod, data, status) # show the predicted results from the model on the trianing data predict(fm) # set up a nearest neighbor model knn.mod <- Modeler(learnKNN, predictKNN, k=3) # fit the 3NN model on the same data fm3 <- learn(knn.mod, data, status) # show its performance predict(fm3)# set up a generic RPART model rpart.mod <- Modeler(learnRPART, predictRPART, minsplit=2, minbucket=1) # simulate fake data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # learn the specific RPART model fm <- learn(rpart.mod, data, status) # show the predicted results from the model on the trianing data predict(fm) # set up a nearest neighbor model knn.mod <- Modeler(learnKNN, predictKNN, k=3) # fit the 3NN model on the same data fm3 <- learn(knn.mod, data, status) # show its performance predict(fm3)
These functions are used to apply the generic modeling mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnCCP(data, status, params, pfun) predictCCP(newdata, details, status, ...)learnCCP(data, status, params, pfun) predictCCP(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
trained 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 learnCCP and predictCCP
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The CCP classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnCCP 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 learnCCP is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictCCP 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 learnCCP function returns an object of the
FittedModel-class, representing a CCP classifier
that has been fitted on a training data set.
The predictCCP function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnCCP.
# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list ccp.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnCCP(data, status, ccp.params, predictCCP) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictCCP(newdata, fm@details, status)# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list ccp.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnCCP(data, status, ccp.params, predictCCP) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictCCP(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a K-nearest neighbors (KNN) classifier.
learnKNN(data, status, params, pfun) predictKNN(newdata, details, status, ...)learnKNN(data, status, params, pfun) predictKNN(newdata, details, status, ...)
data |
The data matrix, with rows as features 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 trained classifier. |
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 learnKNN and predictKNN
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The implementation uses the knn method from the
class package. The params argument to
learnKNN must be alist that at least includes the component
k that specifies the number of neighbors used.
The learnKNN function returns an object of the
FittedModel-class, logically representing a KNN
classifier that has been fitted on a training data set.
The predictKNN function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnPCALR.
# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list knn.params <- list(k=5) # learn the model fm <- learnKNN(data, status, knn.params, predictKNN) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictKNN(newdata, fm@details, status)# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list knn.params <- list(k=5) # learn the model fm <- learnKNN(data, status, knn.params, predictKNN) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictKNN(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a logistic regression (LR) classifier.
learnLR(data, status, params, pfun) predictLR(newdata, details, status, type ="response", ...)learnLR(data, status, params, pfun) predictLR(newdata, details, status, type ="response", ...)
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
trained 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. |
type |
A character string indicating the type of prediciton to make. |
... |
Optional extra parameters required by the generic "predict" method. |
The input arguments to both learnLR and predictLR
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The LR classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnLR 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 learnLR is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictLR 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 learnLR function returns an object of the
FittedModel-class, representing a LR classifier
that has been fitted on a training data set.
The predictLR function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnLR.
## Not run: # simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list lr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model -- this is slow fm <- learnLR(data, status, lr.params, predictLR) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictLR(newdata, fm@details, status) ## End(Not run)## Not run: # simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list lr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model -- this is slow fm <- learnLR(data, status, lr.params, predictLR) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictLR(newdata, fm@details, status) ## End(Not run)
These functions are used to apply the generic train-and-test mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnNNET(data, status, params, pfun) predictNNET(newdata, details, status, ...)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
trained 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 train-and-test
mechanism provided by the Modeler-class.
The NNET classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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 FittedModel-class. 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 gene-features 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
FittedModel-class, 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 <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnNNET.
# 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)# 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)
These functions are used to apply the generic train-and-test mechanism to a classifier usinfg neural networks.
learnNNET2(data, status, params, pfun) predictNNET2(newdata, details, status, ...)learnNNET2(data, status, params, pfun) predictNNET2(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
trained 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 learnNNET2 and predictNNET2
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The NNET2 classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnNNET2 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 learnNNET2 is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictNNET2 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 learnNNET2 function returns an object of the
FittedModel-class, representing a NNET2 classifier
that has been fitted on a training data set.
The predictNNET2 function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnNNET2.
# 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 <- learnNNET2(data, status, nnet.params, predictNNET2) # Make predictions on some new simulated data #newdata <- matrix(rnorm(100*30), ncol=30) #predictNNET2(newdata, fm@details, status)# 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 <- learnNNET2(data, status, nnet.params, predictNNET2) # Make predictions on some new simulated data #newdata <- matrix(rnorm(100*30), ncol=30) #predictNNET2(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnPCALR(data, status, params, pfun) predictPCALR(newdata, details, status, ...)learnPCALR(data, status, params, pfun) predictPCALR(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
trained 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 learnPCALR and predictPCALR
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The PCALR classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnPCALR 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 learnPCALR is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictPCALR 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 learnPCALR function returns an object of the
FittedModel-class, representing a PCALR classifier
that has been fitted on a training data set.
The predictPCALR function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnPCALR.
# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list pcalr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnPCALR(data, status, pcalr.params, predictPCALR) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictPCALR(newdata, fm@details, status)# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list pcalr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnPCALR(data, status, pcalr.params, predictPCALR) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictPCALR(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnRF(data, status, params, pfun) predictRF(newdata, details, status, ...)learnRF(data, status, params, pfun) predictRF(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
trained 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 learnRF and predictRF
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The RF classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnRF 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 learnRF is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictRF 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 learnRF function returns an object of the
FittedModel-class, representing a RF classifier
that has been fitted on a training data set.
The predictRF function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnRF.
# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list svm.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model #fm <- learnRF(data, status, svm.params, predictRF) # Make predictions on some new simulated data #newdata <- matrix(rnorm(100*30), ncol=30) #predictRF(newdata, fm@details, status)# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list svm.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model #fm <- learnRF(data, status, svm.params, predictRF) # Make predictions on some new simulated data #newdata <- matrix(rnorm(100*30), ncol=30) #predictRF(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnRPART(data, status, params, pfun) predictRPART(newdata, details, status, ...)learnRPART(data, status, params, pfun) predictRPART(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
trained 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 learnRPART and predictRPART
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The RPART classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnRPART 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 learnRPART is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictRPART 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 learnRPART function returns an object of the
FittedModel-class, representing a RPART classifier
that has been fitted on a training data set.
The predictRPART function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnRPART.
# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list rpart.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnRPART(data, status, rpart.params, predictRPART) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictRPART(newdata, fm@details, status)# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list rpart.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnRPART(data, status, rpart.params, predictRPART) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictRPART(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnSelectedLR(data, status, params, pfun) predictSelectedLR(newdata, details, status, ...)learnSelectedLR(data, status, params, pfun) predictSelectedLR(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
trained 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 learnSelectedLR and predictSelectedLR
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The SelectedLR classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnSelectedLR 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 learnSelectedLR is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictSelectedLR 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 learnSelectedLR function returns an object of the
FittedModel-class, representing a SelectedLR classifier
that has been fitted on a training data set.
The predictSelectedLR function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to tain and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnSelectedLR.
# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list slr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnSelectedLR(data, status, slr.params, predictSelectedLR) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictSelectedLR(newdata, fm@details, status)# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list slr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnSelectedLR(data, status, slr.params, predictSelectedLR) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictSelectedLR(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnSVM(data, status, params, pfun) predictSVM(newdata, details, status, ...)learnSVM(data, status, params, pfun) predictSVM(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
trained 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 learnSVM and predictSVM
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The SVM classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnSVM 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 learnSVM is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictSVM 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 learnSVM function returns an object of the
FittedModel-class, representing a SVM classifier
that has been fitted on a training data set.
The predictSVM function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnSVM.
# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list svm.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnSVM(data, status, svm.params, predictSVM) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictSVM(newdata, fm@details, status)# simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list svm.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model fm <- learnSVM(data, status, svm.params, predictSVM) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictSVM(newdata, fm@details, status)
These functions are used to apply the generic train-and-test mechanism to a classifier that combines principal component analysis (PCA) with logistic regression (LR).
learnTailRank(data, status, params, pfun) predictTailRank(newdata, details, status, ...)learnTailRank(data, status, params, pfun) predictTailRank(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
trained 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 learnTailRank and predictTailRank
are dictated by the requirements of the general train-and-test
mechanism provided by the Modeler-class.
The TailRank classifier is similar in spirit to the "supervised principal
components" method implemented in the superpc package. We
start by performing univariate two-sample t-tests 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
learnTailRank 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 learnTailRank is a member of
the FittedModel-class. 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 gene-features selected
(nGenesSelecets). The details object is appropriate for
sending as the second argument to the predictTailRank 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 learnTailRank function returns an object of the
FittedModel-class, representing a TailRank classifier
that has been fitted on a training data set.
The predictTailRank function returns a factor containing the
predictions of the model when applied to the new data set.
Kevin R. Coombes <[email protected]>
See Modeler-class and Modeler for details
about how to train and test models. See
FittedModel-class and FittedModel for
details about the structure of the object returned by learnTailRank.
## Not run: # simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list tr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model -- this is slow fm <- learnTailRank(data, status, tr.params, predictTailRank) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictTailRank(newdata, fm@details, status) ## End(Not run)## Not run: # simulate some data data <- matrix(rnorm(100*20), ncol=20) status <- factor(rep(c("A", "B"), each=10)) # set up the parameter list tr.params <- list(minNgenes=10, alpha=0.10, perVar=0.80, prior=0.5) # learn the model -- this is slow fm <- learnTailRank(data, status, tr.params, predictTailRank) # Make predictions on some new simulated data newdata <- matrix(rnorm(100*30), ncol=30) predictTailRank(newdata, fm@details, status) ## End(Not run)
The Modeler-class represents (parametrized but not yet
fit) statistical models that can predict binary outcomes. The
Modeler function is used to construct objects of this class.
Modeler(learn, predict, ...)Modeler(learn, predict, ...)
learn |
Object of class |
predict |
Object of class |
... |
Additional parameters required for the specific kind of classificaiton model that will be constructed. See Details. |
Objects of the Modeler-class provide a general
abstraction for classification models that can be learned from one
data set and then applied to a new data set. Each type of classifier
is likely to have its own specific parameters. For instance, a
K-nearest neighbors classifier requires you to specify k. The
more complex classifier, PCA-LR has many more parameters, including
the false discovery rate (alpha) used to select features and
the percentage of variance (perVar) that should be explained by
the number of principal components created from those features. All
additional parameters should be suplied as named arguments to the
Modeler constructor; these additional parameters will be
bundled into a list and inserted into the params slot of the
resulting object of the Modeler-class.
Returns an object of the Modeler-class.
Kevin R. Coombes <[email protected]>
See the descriptions of the learn function and
the predict method for details on how to fit models on
training data and make predictions on new test data.
See the description of the FittedModel-class for details
about the kinds of objects produced by learn.
learnNNET predictNNET modelerNNET <- Modeler(learnNNET, predictNNET, size=5) modelerNNETlearnNNET predictNNET modelerNNET <- Modeler(learnNNET, predictNNET, size=5) modelerNNET
The Modeler class represents (parametrized but not
yet fit) statistical models that can predict binary outcomes.
Objects can be created by calls to the constructor fuinction, Modeler.
learnFunction:Object of class "function" that is used
to fit the model to a data set. See learn for details.
predictFunction:Object of class "function" that is
used to make predictions on new data from a fitted model. See
predict for details.
paramList:Object of class "list" that contains
parameters that are specific for one type of classifier.
No methods are defined with class "Modeler" in the signature. The only
function that can be applied to a Modeler object is
learn, which has not been made into a generic funtion.
Kevin R. Coombes <[email protected]>
See the description of the FittedModel-class for details
about the kinds of objects produced by learn.
showClass("Modeler")showClass("Modeler")