datfbm


Data generated from a mixed Gaussian density function $f(x) = 0.65 N(2,1)
+ 0.35 N(7, 1.5^2)$, where $N(x, y)$ is the density function for a Normal
distribution with mean $x$ and variance $y$.


datasets

In a linear regression setting, priors can be assigned to the regression function using a vector space framework, and the posterior estimate of the regression function obtained. I-priors are a class of such priors based on the principle of maximum entropy. While the main interest of I-prior modelling is prediction, inference is also possible, e.g. via log-likelihood ratio tests. This package supports both formula and non-formula based input. Estimation is mainly done via an EM algorithm, but there is flexibility in using any R optimiser to maximise the log-likelihood computed by the kernel loader function.

Haziq Jamil

iprior

Linear Regression using I-Priors

datfbm function

A data frame with 53940 rows and 10 variables:
<dl> </dl>

datfbm: Simulated data to illustrate one-dimensional smoothing

Description

Usage

Arguments

Format

Examples