View source: R/params_mlogit.R
params_mlogit | R Documentation |
Store the parameters of a fitted multinomial logistic
regression model. The model is used to predict probabilities of K
classes, which represent the probability of transitioning to particular health
state in a discrete time state transition model. Can be used as an element of a
params_mlogit_list
to parameterize a CohortDtstmTrans
object.
params_mlogit(coefs)
coefs |
A 3D array of stacked matrices containing samples of the regression
coefficients under sampling uncertainty. May also be a
list of objects (e.g., data frames) that can be coerced into matrices with
|
Multinomial logit models are used to predict the probability of membership for subject i in each of K classes as a function of covariates:
Pr(y_i = c) = \frac{e^{β_c x_i}}{∑_{k=1}^K e^{β_k x_i}}
An object of class params_mlogit
, which is a list containing coefs
and n_samples
, where n_samples
is equal to the number of rows in each
element of coefs
. The coefs
element is always converted into
a 3D array of stacked matrices.
summary.params_mlogit()
, params_mlogit_list()
, CohortDtstmTrans
# Consider a sick-sicker model and model transitions from the sick state ## We can instantiate from a list of data frames params <- params_mlogit( coefs = list( ### Transition from sick to sicker sicker = data.frame( intercept = c(-0.33, -.2, -.15), treat = c(log(.75), log(.8), log(.9)) ), ### Transition from sick to death death = data.frame( intercept = c(-1, -1.2, -.5), treat = c(log(.6), log(.65), log(.55)) ) ) ) summary(params) params ## We can also instantiate from an array coefs_sicker <- data.frame( intercept = c(-0.33, -.2, -.15), treat = c(log(.75), log(.8), log(.9)) ) coefs_death <- data.frame( intercept = c(-1, -1.2, -.5), treat = c(log(.6), log(.65), log(.55)) ) params2 <- params_mlogit( coefs <- array( data = c(as.matrix(coefs_sicker), as.matrix(coefs_death)), dim = c(3, 2, 2), dimnames = list(NULL, c("intercept", "treat"), c("sicker", "death")) ) ) params2
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