LDCA: Linear Discriminant Analysis based on Top Scoring Pair

Description

Linear Discriminant Analysis based on Top Scoring Pair

Usage

LDCA(X,y,nlambda=100,lambda=NULL,threshold=1e-07)

Arguments

input matrix, of dimension nobs x nvars; each row is an observation vector.

response variable.

nlambda

The number of lambda values - default is 100.

lambda

user specified lambda sequence

threshold

Convergence threshold for coordinate descent. A parameter from "glmnet" package. Defaults value is 1E-7.

Value

An object with S3 class "LDCA","glmnet"
callthe call that produced this object
a0Intercept sequence of length length(lambda)
betaFor "elnet" and "lognet" models, a nvars x length(lambda) matrix of coefficients, stored in sparse column format ("CsparseMatrix"). For "multnet", a list of nc such matrices, one for each class.
lambdaThe actual sequence of lambda values used
dev.ratioThe fraction of (null) deviance explained (for "elnet", this is the R-square). The deviance calculations incorporate weights if present in the model. The deviance is defined to be 2*(loglike_sat - loglike), where loglike_sat is the log-likelihood for the saturated model (a model with a free parameter per observation). Hence dev.ratio=1-dev/nulldev.
nulldevNull deviance (per observation). This is defined to be 2*(loglike_sat -loglike(Null)); The NULL model refers to the intercept model, except for the Cox, where it is the 0 model.
dfThe number of nonzero coefficients for each value of lambda. For "multnet", this is the number of variables with a nonzero coefficient for any class.
dimdimension of coefficient matrix (ices)
nobsnumber of observations
npassestotal passes over the data summed over all lambda values
offseta logical variable indicating whether an offset was included in the model
jerrerror flag, for warnings and errors (largely for internal debugging).

References

Geman, D., dAvignon, C.: Classifying gene expression profiles from pairwise mRNA comparisons. Statistical Applications in Genetics and Molecular Biology, 3(1):19 (2007)

Examples

Run this code

library(glmnet)
x=matrix(rnorm(100*20),100,20)
y=rbinom(100,1,0.5)
fit=LDCA(x,y)
print(fit)
predict(fit,newx=x[1:10,]) # make predictions