Description Usage Arguments Details Value Author(s) See Also Examples
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.
1 2 3 
object 
An R object of 
newdata 
Numeric matrix with the same number of explanatory
variables as the original 
newclin 
Numeric matrix with the same number of additional
(clinical) explanatory variables as the original 
type 
Character string, describing whether fitted values

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 insample 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, [email protected]
pelora
, also for references.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  ## 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")

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