slr {slr} | R Documentation |
Fit logistic regression model with Cauchy-Normal random probes effects: an application to aCGH data
slr(y, Z, Chrom=NULL, beta = NULL, b0 = NULL, lambda1 = 1, lambda2=1, maxiter = 15, tol = 1e-10)
y |
binary response variable |
Z |
matrix of predictor variables; rows correspond to probes and columns correspond to subjects. |
Chrom |
vector of chromsone where probes are located; only needed if probes spanned multiple chromosomes |
beta |
starting values for intercept coefficient (optional) |
b0 |
starting values for random-effect (probes) coefficients (optional) |
lambda1 |
smoothing parameter for the Cauchy part; it is recommended that users use cross-validation (cv.lambda )
to find the optimal smoothing parameters. |
lambda2 |
smoothing parameter for the Normal part; The default is lambda2=lambda1 which will give a 50-50 mixture between Normal and Cauchy distribution for the random effects. |
maxiter |
maximum iterations allowed. |
tol |
relative convergence critetion. |
A list with the following components
beta |
intercept estimate |
b |
random-effect estimates |
Huang Jian and Agus Salim
Huang, J., Lei, K., Salim, A., O'Sullivan, K., Pawitan, Y. Classification of array CGH data using smoothed logistic regression model. submitted
oscc
, phenoscc
, simacgh
, cv.lambda
# example using oral squamous-cell carcinoma data, chromosome 10 only data(oscc) data(phenoscc) Z <- oscc[oscc$Chrom==10,6:80] y <- phenoscc$p53[!is.na(phenoscc$p53)] fit <- slr(y=y,Z=Z,Chrom=oscc$Chrom[oscc$Chrom==10],lambda1=exp(3),lambda2=exp(3)) # example using simulated data from a single chromosome data(simacgh) fit2 <- slr(y=simacgh[,1], Z=t(simacgh[,-1]),lambda1=exp(10),lambda2=exp(10))