minML.LA.ridgeGLM | R Documentation |
Returns the Laplace approximation (LA) of the minus log marginal likelihood of ridge penalised generalised linear models. Note: currently only implemented for linear and logistic regression.
minML.LA.ridgeGLM(loglambdas, XXblocks, Y, sigmasq = 1, Xunpen = NULL, intrcpt = TRUE, model, minlam = 0, opt.sigma = FALSE)
loglambdas |
Logarithm of the ridge penalties as returned by ecpc or squeezy; Gx1 vector. |
XXblocks |
List of sample covariance matrices X_g %*% t(X_g) for groups g = 1,...,G. |
Y |
Response data; n-dimensional vector (n: number of samples) for linear and logistic outcomes. |
sigmasq |
(linear model only) Noise level (Y~N(X*beta,sd=sqrt(sigmasq))). |
Xunpen |
Unpenalised variables; nxp_1-dimensional matrix for p_1 unpenalised variables. |
intrcpt |
Should an intercept be included? Set to TRUE by default. |
model |
Type of model for the response; linear or logistic. |
minlam |
Minimum value of lambda that is added to exp(loglambdas); set to 0 as default. |
opt.sigma |
(linear model only) TRUE/FALSE if log(sigmasq) is given as first argument of loglambdas for optimisation purposes |
Laplace approximation of the minus log marginal likelihood for the ridge penalised GLM with model parameters 'loglambdas' and 'sigmasq' (for linear regression).
#Simulate toy data n<-100 p<-300 X <- matrix(rnorm(n*p),n,p) Y <- rnorm(n) groupset <- list(1:(p/2),(p/2+1):p) sigmahat <- 2 alpha <- 0.5 tauMR <- c(0.01,0.005) XXblocks <- lapply(groupset, function(x)X[,x]%*%t(X[,x])) #compute minus log marginal likelihood minML.LA.ridgeGLM(loglambdas = log(sigmahat/tauMR), XXblocks, Y, sigmasq = sigmahat, model="linear")
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