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

params_lm(coefs, sigma = 1)

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). |

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.

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, \(\epsilon \sim N(0, \hat{\sigma}^2)\).

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()`

.

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)#> parameter term mean sd 2.5% 97.5% #> 1: mean x1 0.3969149 0.3114263 0.18771386 0.6061160 #> 2: mean x2 0.5852242 0.2348049 0.42749371 0.7429547 #> 3: sd sigma 0.4493351 0.5921983 0.05152505 0.8471452params#> A "params_lm" object #> #> Summary of coefficients: #> parameter term mean sd 2.5% 97.5% #> 1: mean x1 0.3969149 0.3114263 0.1877139 0.6061160 #> 2: mean x2 0.5852242 0.2348049 0.4274937 0.7429547 #> #> Summary of sigma: #> parameter term mean sd 2.5% 97.5% #> 1: sd sigma 0.4493351 0.5921983 0.05152505 0.8471452