LDCA: Linear Discriminant Analysis based on Top Scoring Pair

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/LDCA.R

Description

Linear Discriminant Analysis based on Top Scoring Pair

Usage

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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

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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 29, 2017, 7:14 p.m.