mc_dist: Distance Models Structure

In mcglm: Multivariate Covariance Generalized Linear Models

Distance Models Structure

Description

The function mc_dist helps to build the components of the matrix linear predictor using matrices based on distances. This function is generaly used for the analysis of longitudinal and spatial data. The idea is to use the inverse of some measure of distance as for example the Euclidean distance to model the covariance structure within response variables. The model can also use the inverse of distance squared or high order power.

Usage

mc_dist(id, time, data, method = "euclidean")

Arguments

id	name of the column (string) containing the subject index. For spatial data use the same id for all observations (one unit sample).
time	name of the column (string) containing the index indicating the time. For spatial data use the same index for all observations.
data	data set.
method	distance measure to be used.

Details

The distance measure must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". This function is a customize call of the dist function.

Value

A matrix of dgCMatrix class.

Author(s)

Wagner Hugo Bonat, [e-mail]

Source

Bonat, W. H. (2018). Multiple Response Variables Regression Models in R: The mcglm Package. Journal of Statistical Software, 84(4):1–30.

See Also

dist, mc_id, mc_conditional_test, mc_car, mc_ma, mc_rw and mc_mixed.

Examples

id <- rep(1:2, each = 4)
time <- rep(1:4, 2)
data <- data.frame("id" = id, "time" = time)
mc_dist(id = "id", time = "time", data = data)
mc_dist(id = "id", time = "time", data = data, method = "canberra")

mcglm documentation built on Sept. 16, 2022, 1:06 a.m.