LDCA {BigTSP}  R Documentation 
Linear Discriminant Analysis based on Top Scoring Pair
LDCA(X,y,nlambda=100,lambda=NULL,threshold=1e07)
X 
input matrix, of dimension nobs x nvars; each row is an observation vector. 
y 
response variable. 
nlambda 
The number of 
lambda 
user specified lambda sequence 
threshold 
Convergence threshold for coordinate descent. A parameter from "glmnet" package. Defaults value is 
An object with S3 class "LDCA","glmnet"
call 
the call that produced this object 
a0 
Intercept sequence of length 
beta 
For 
lambda 
The actual sequence of 
dev.ratio 
The fraction of (null) deviance explained (for 
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

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). 
Xiaolin Yang, Han Liu
Geman, D., dAvignon, C.: Classifying gene expression profiles from pairwise mRNA comparisons. Statistical Applications in Genetics and Molecular Biology, 3(1):19 (2007)
summary.LDCA
,print.LDCA
,predict.LDCA
,plot.LDCA
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