LDCA: Linear Discriminant Analysis based on Top Scoring Pair

In BigTSP: Top Scoring Pair based methods for classification

Description

Linear Discriminant Analysis based on Top Scoring Pair

Usage

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

Arguments

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

call	the call that produced this object
a0	Intercept sequence of length length(lambda)
beta	For "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.
lambda	The actual sequence of lambda values used
dev.ratio	The 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.
nulldev	Null 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.
df	The number of nonzero coefficients for each value of lambda. For "multnet", this is the number of variables with a nonzero coefficient for any class.
dim	dimension of coefficient matrix (ices)
nobs	number of observations
npasses	total passes over the data summed over all lambda values
offset	a logical variable indicating whether an offset was included in the model
jerr	error flag, for warnings and errors (largely for internal debugging).

Author(s)

Xiaolin Yang, Han Liu

References

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

See Also

summary.LDCA,print.LDCA,predict.LDCA,plot.LDCA

Examples

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

BigTSP documentation built on May 2, 2019, 6:09 a.m.