Description Usage Arguments Value Author(s) References See Also Examples
Performs selection and supervised grouping of predictor variables in large (microarray gene expression) datasets, with an option for simultaneous classification. Works in a greedy forward strategy and optimizes the binomial loglikelihood, based on estimated conditional probabilities from penalized logistic regression analysis.
1 2 
x 
Numeric matrix of explanatory variables (p variables in columns, n cases in rows). For example, these can be microarray gene expression data which should be grouped. 
y 
Numeric vector of length n containing the class labels of the individuals. These labels have to be coded by 0 and 1. 
u 
Numeric matrix of additional (clinical) explanatory variables (m variables in columns, n cases in rows) that are used in the (penalized logistic regression) prediction model, but neither grouped nor averaged. For example, these can be 'traditional' clinical variables. 
noc 
Integer, the number of clusters that should be searched for on the data. 
lambda 
Real, defaults to 1/32. Rescaled penalty parameter that should be in [0,1]. 
flip 
Character string, describing a method how the 
standardize 
Logical, defaults to 
trace 
Integer >= 0; when positive, the output of the internal
loops is provided; 
pelora
returns an object of class "pelora". The functions
print
and summary
are used to obtain an overview of the
variables (genes) that have been selected and the groups that have
been formed. The function plot
yields a twodimensional
projection into the space of the first two group centroids that
pelora
found. The generic function fitted
returns
the fitted values, these are the cluster representatives. coef
returns the penalized logistic regression coefficients θ_j
for each of the predictors. Finally, predict
is used for
classifying test data with Pelora's internal penalized logistic
regression classifier on the basis of the (gene) groups that have been
found.
An object of class "pelora" is a list containing:
genes 
A list of length 
values 
A numerical matrix with dimension n \times \code{noc}, containing the fitted values, i.e. the group centroids \tilde{x}_j. 
y 
Numeric vector of length n containing the class labels of the individuals. These labels are coded by 0 and 1. 
steps 
Numerical vector of length 
lambda 
The rescaled penalty parameter. 
noc 
The number of clusters that has been searched for on the data. 
px 
The number of columns (genes) in the 
flip 
The method that has been chosen for signflipping the

var.type 
A factor with 
crit 
A list of length 
signs 
Numerical vector of length p, saying whether the ith variable (gene) should be signflipped (1) or not (+1). 
samp.names 
The names of the samples (rows) in the

gene.names 
The names of the variables (columns) in the

call 
The function call. 
Marcel Dettling, [email protected]
Marcel Dettling (2003) Finding Predictive Gene Groups from Microarray Data, see http://stat.ethz.ch/~dettling/supervised.html
Marcel Dettling and Peter B<c3><bc>hlmann (2002). Supervised Clustering of Genes. Genome Biology, 3(12): research0069.10069.15.
Marcel Dettling and Peter B<c3><bc>hlmann (2004). Finding Predictive Gene Groups from Microarray Data. Journal of Multivariate Analysis 90, 106–131.
wilma
for another supervised clustering technique.
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44  ## 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)
## Working with the output
fit
summary(fit)
plot(fit)
fitted(fit)
coef(fit)
## 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])
## Working with the output
fit
summary(fit)
plot(fit)
fitted(fit)
coef(fit)
## 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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