kcde_prob_predict_given_lagged_lead_obs: Compute the conditional (predictive) probability that the...

In reichlab/kcde: Kernel conditional density estimation with flexible kernel specifications

Description

Compute the conditional (predictive) probability that the predictive target variables are all less than or equal to the corresponding elements of the rows of q.

Usage

kcde_prob_predict_given_lagged_lead_obs(q, n, train_lagged_obs, train_lead_obs,
  prediction_lagged_obs, kcde_fit)

Arguments

n	sample size for sample from predictive distribution used in approximating quantiles
train_lagged_obs	is a matrix (with column names) containing the lagged observation vector computed from the training data. Each row corresponds to a time point. Each column is a (lagged) variable.
train_lead_obs	is a vector with length = nrow(train_lagged_obs) with the value of the prediction target variable corresponding to each row in the train_lagged_obs matrix.
prediction_lagged_obs	is a matrix (with column names) containing the lagged observation vector computed from the prediction data. There is only one row, representing one time point. Each column is a (lagged) variable.
kcde_fit	is an object representing a fitted kcde model
p	vector of probabilities which to compute quantiles

Value

a vector of probabilities

reichlab/kcde documentation built on May 27, 2019, 4:53 a.m.