Creates an outcome with Multinomial distribution with the chosen parameters.
Usage
Multinom(p, data, offset = as.matrix(data)^0, base.class = NULL)Arguments
- p
character: a vector with the name of the linear predictor associated with the probability of each category (except the base one, which is assumed to be the last).
- data
vector: Values of the observed data.
- offset
vector: The offset at each observation. Must have the same shape as data.
- base.class
character or integer: The name or index of the base class. Default is to use the last column of data.
Details
For evaluating the posterior parameters, we use the method proposed in ArtigokParametrico;textualkDGLM.
For the details about the implementation see ArtigoPacote;textualkDGLM.
See also
Other auxiliary functions for a creating outcomes:
Gamma(),
Normal(),
Poisson(),
summary.dlm_distr()
Examples
structure <- (
polynomial_block(p = 1, order = 2, D = 0.95) +
harmonic_block(p = 1, period = 12, D = 0.975) +
noise_block(p = 1, R1 = 0.1) +
regression_block(p = chickenPox$date >= as.Date("2013-09-01"))
# Vaccine was introduced in September of 2013
) * 4
outcome <- Multinom(p = structure$pred.names, data = chickenPox[, c(2, 3, 4, 6, 5)])
fitted.data <- fit_model(structure, chickenPox = outcome)
summary(fitted.data)
#> Fitted DGLM with 1 outcomes.
#>
#> distributions:
#> chickenPox: Multinomial
#>
#> Static coeficients (smoothed):
#> Estimate Std. Error t value Pr(>|t|)
#> Var.Reg.1 0.39743 0.25059 1.58601 0.113
#> Var.Reg.2 0.47441 0.26448 1.79376 0.073
#> Var.Reg.3 0.48811 0.28497 1.71284 0.087
#> Var.Reg.4 -0.26900 0.23557 -1.14192 0.253
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> ---
#> See the coef.fitted_dlm for the coeficients with temporal dynamic.
#>
#> One-step-ahead prediction
#> Log-likelihood : -1952.613
#> Interval Score : 165.55741
#> Mean Abs. Scaled Error: 0.77058
#> ---
plot(fitted.data, plot.pkg = "base")
