predict.pelora {supclust} | R Documentation |
Yields fitted values, predicted class labels and
conditional probability estimates for training and test data, which
are based on the gene groups pelora
found, and on its internal
penalized logistic regression classifier.
predict.pelora(object, newdata = NULL, newclin = NULL, type = c("fitted", "probs", "class"), noc = object$noc, ...)
object |
An R object of class "pelora" ,
typically the result of pelora() . |
newdata |
Numeric matrix with the same number of explanatory
variables as the original x -matrix (p variables in
columns, r cases in rows). For example, these can be
additional microarray gene expression data which should be
predicted. |
newclin |
Numeric matrix with the same number of additional
(clinical) explanatory variables as the original u -matrix
(m variables in columns, r cases in rows) that are used
in the (penalized logistic regression) prediction model, but neither
grouped nor averaged. Only needs to be given, if the model fit
included an u -matrix. For example, these can be 'traditional'
clinical variables. |
type |
Character string, describing whether fitted values
"fitted" , estimated conditional probabilites "probs"
or class labels "class" should be returned. |
noc |
Integer, saying with how many clusters the fitted values, probability estimates or class labels should be determined. Also numeric vectors are allowed as an argument. The output is then a numeric matrix with fitted values, probability estimates or class labels for a multiple number of clusters. |
... |
Further arguments passed to and from methods. |
If newdata = NULL
, then the in-sample fitted values,
probability estimates and class label predictions are returned.
Depending on whether noc
is a single number or a numeric
vector. In the first case, a numeric vector of length r is
returned, which contains fitted values for noc
clusters, or
probability estimates/class label predictions with noc
clusters.
In the latter case, a numeric matrix with length(noc)
columns,
each containing fitted values for noc
clusters, or
probability estimates/class label predictions with noc
clusters, is returned.
Marcel Dettling, dettling@stat.math.ethz.ch
Marcel Dettling (2003) Finding Groups of Predictive Genes from Microarray Data, see http://stat.ethz.ch/~dettling/supervised.html
Marcel Dettling and Peter Bühlmann (2004). Finding Predictive Gene Groups from Microarray Data. To appear in the Journal of Multivariate Analysis.
## Working with a "real" microarray dataset data(leukemia, package="supclust") ## Generating random test data: 3 observations and 250 variables (genes) set.seed(724) xN <- matrix(rnorm(750), nrow = 3, ncol = 250) ## Fitting Pelora fit <- pelora(leukemia.x, leukemia.y, noc = 3) ## Fitted values and class probabilities for the training data predict(fit, type = "cla") predict(fit, type = "prob") ## Predicting fitted values and class labels for the random test data predict(fit, newdata = xN) predict(fit, newdata = xN, type = "cla", noc = c(1,2,3)) predict(fit, newdata = xN, type = "pro", noc = c(1,3)) ## Fitting Pelora such that the first 70 variables (genes) are not grouped fit <- pelora(leukemia.x[, -(1:70)], leukemia.y, leukemia.x[,1:70]) ## Fitted values and class probabilities for the training data predict(fit, type = "cla") predict(fit, type = "prob") ## Predicting fitted values and class labels for the random test data predict(fit, newdata = xN[, -(1:70)], newclin = xN[, 1:70]) predict(fit, newdata = xN[, -(1:70)], newclin = xN[, 1:70], "cla", noc = 1:10) predict(fit, newdata = xN[, -(1:70)], newclin = xN[, 1:70], type = "pro")