covlmc_control | R Documentation |
This function creates a list with parameters used to fine tune the coVLMC fitting algorithm.
covlmc_control(pseudo_obs = 1)
pseudo_obs |
number of fake observations of each state to add to the observed ones. |
pseudo_obs
is used to regularize the probability estimations when a
context is only observed followed by always the same state. Transition
probabilities are computed after adding pseudo_obs
pseudo observations
of each of the states (including the observed one). This corresponds to a
Bayesian posterior mean estimation with a Dirichlet prior.
a list.
dts <- rep(c(0, 1), 100)
dts_cov <- data.frame(y = rep(0, length(dts)))
default_model <- covlmc(dts, dts_cov)
contexts(default_model, type = "data.frame", model = "coef")$coef
control <- covlmc_control(pseudo_obs = 10)
model <- covlmc(dts, dts_cov, control = control)
contexts(model, type = "data.frame", model = "coef")$coef
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.