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

`as.matrix()`

. Each matrix must have the same number of columns and the number of matrices must be equal to \(K-1\).

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.

Multinomial logit models are used to predict the probability of membership for subject \(i\) in each of \(K\) classes as a function of covariates: $$Pr(y_i = c) = \frac{e^{\beta_c x_i}}{\sum_{k=1}^K e^{\beta_k x_i}}$$

```
# 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)
#> to term mean sd 2.5% 97.5%
#> <char> <char> <num> <num> <num> <num>
#> 1: sicker intercept -0.2266667 0.09291573 -0.3235000 -0.1525000
#> 2: sicker treat -0.2053954 0.09244748 -0.2844551 -0.1112497
#> 3: death intercept -0.9000000 0.36055513 -1.1900000 -0.5250000
#> 4: death treat -0.5131485 0.08355126 -0.5934864 -0.4347851
params
#> A "params_mlogit" object
#>
#> Summary of coefficients:
#> to term mean sd 2.5% 97.5%
#> <char> <char> <num> <num> <num> <num>
#> 1: sicker intercept -0.2266667 0.09291573 -0.3235000 -0.1525000
#> 2: sicker treat -0.2053954 0.09244748 -0.2844551 -0.1112497
#> 3: death intercept -0.9000000 0.36055513 -1.1900000 -0.5250000
#> 4: death treat -0.5131485 0.08355126 -0.5934864 -0.4347851
#>
#> Number of parameter samples: 3
#> Number of transitions: 2
## 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
#> A "params_mlogit" object
#>
#> Summary of coefficients:
#> to term mean sd 2.5% 97.5%
#> <char> <char> <num> <num> <num> <num>
#> 1: sicker intercept -0.2266667 0.09291573 -0.3235000 -0.1525000
#> 2: sicker treat -0.2053954 0.09244748 -0.2844551 -0.1112497
#> 3: death intercept -0.9000000 0.36055513 -1.1900000 -0.5250000
#> 4: death treat -0.5131485 0.08355126 -0.5934864 -0.4347851
#>
#> Number of parameter samples: 3
#> Number of transitions: 2
```