# Summary statistics computed using AGHQ The `summary.aghq` method computes means, standard deviations, and quantiles of the transformed parameter. The associated print method prints these along with diagnostic and other information about the quadrature. ```r ## S3 method for class 'aghq' summary(object, ...) ``` ## Arguments - `object`: The return value from `aghq::aghq`. Summaries are computed for `object$transformation$fromtheta(theta)`. - `...`: not used. ## Returns A list of class `aghqsummary`, which has a print method. Elements: * mode:the mode of the log posterior * hessian:the hessian of the log posterior at the mode * covariance:the inverse of the hessian of the log posterior at the mode * cholesky:the upper Cholesky triangle of the hessian of the log posterior at the mode * quadpoints:the number of quadrature points used in each dimension * dim:the dimension of the parameter space * summarytable:a table containing the mean, median, mode, standard deviation and quantiles of each transformed parameter, computed according to the posterior normalized using AGHQ ## Examples ```r logfteta2d <- function(eta,y) { # eta is now (eta1,eta2) # y is now (y1,y2) n <- length(y) n1 <- ceiling(n/2) n2 <- floor(n/2) y1 <- y[1:n1] y2 <- y[(n1+1):(n1+n2)] eta1 <- eta[1] eta2 <- eta[2] sum(y1) * eta1 - (length(y1) + 1) * exp(eta1) - sum(lgamma(y1+1)) + eta1 + sum(y2) * eta2 - (length(y2) + 1) * exp(eta2) - sum(lgamma(y2+1)) + eta2 } set.seed(84343124) n1 <- 5 n2 <- 5 n <- n1+n2 y1 <- rpois(n1,5) y2 <- rpois(n2,5) objfunc2d <- function(x) logfteta2d(x,c(y1,y2)) funlist2d <- list( fn = objfunc2d, gr = function(x) numDeriv::grad(objfunc2d,x), he = function(x) numDeriv::hessian(objfunc2d,x) ) thequadrature <- aghq(funlist2d,3,c(0,0)) # Summarize and automatically call its print() method when called interactively: summary(thequadrature) # or, compute the summary and save for further processing: ss <- summary(thequadrature) str(ss) ``` ## See Also Other quadrature: `aghq()`, `get_hessian()`, `get_log_normconst()`, `get_mode()`, `get_nodesandweights()`, `get_numquadpoints()`, `get_opt_results()`, `get_param_dim()`, `laplace_approximation()`, `marginal_laplace_tmb()`, `marginal_laplace()`, `nested_quadrature()`, `normalize_logpost()`, `optimize_theta()`, `plot.aghq()`, `print.aghqsummary()`, `print.aghq()`, `print.laplacesummary()`, `print.laplace()`, `print.marginallaplacesummary()`, `summary.laplace()`, `summary.marginallaplace()`

summary.aghq function