params_lm: Parameters of a linear model

In InnovationValueInitiative/hesim: Health Economic Simulation Modeling and Decision Analysis

Parameters of a linear model

Description

Create a list containing the parameters of a fitted linear regression model.

Usage

params_lm(coefs, sigma = 1)

Arguments

coefs	Samples of the coefficients under sampling uncertainty. Must be a matrix or any object coercible to a matrix such as data.frame or data.table.
sigma	A vector of samples of the standard error of the regression model. Default value is 1 for all samples. Only used if the model is used to randomly simulate values (rather than to predict means).

Details

Fitted linear models are used to predict values, y, as a function of covariates, x,

y = x^T\beta + \epsilon.

Predicted means are given by x^T\hat{\beta} where \hat{\beta} is the vector of estimated regression coefficients. Random samples are obtained by sampling the error term from a normal distribution, \epsilon \sim N(0, \hat{\sigma}^2).

Value

An object of class params_lm, which is a list containing coefs, sigma, and n_samples. n_samples is equal to the number of rows in coefs. The coefs element is always converted into a matrix.

See Also

This parameter object is useful for modeling health state values when values can vary across patients and/or health states as a function of covariates. In many cases it will, however, be simpler, and more flexible to use a stateval_tbl. For an example use case see the documentation for create_StateVals.lm().

Examples

library("MASS")
n <- 2
params <- params_lm(
  coefs = mvrnorm(n, mu = c(.5,.6),
                  Sigma = matrix(c(.05, .01, .01, .05), nrow = 2)),
  sigma <- rgamma(n, shape = .5, rate = 4)
)
summary(params)
params

InnovationValueInitiative/hesim documentation built on Feb. 12, 2024, 10:39 p.m.