params_lm | R Documentation |
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). |
Fitted linear models are used to predict values, y, as a function of covariates, x,
y = x^Tβ + ε.
Predicted means are given by x^T\hat{β} where \hat{β} is the vector of estimated regression coefficients. Random samples are obtained by sampling the error term from a normal distribution, ε ~ N(0, \hat{σ}^2).
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.
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) params
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