| params_lm {hesim} | R Documentation |
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 |
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