sits_glm: Train SITS classifiction models with Generalised Linear...

In luizassis/sits: Satellite Image Time Series Analysis

Description

Use generalized liner model (glm) via penalized maximim likelihood to classify data. This function is a front-end to the "cv.glmnet" method in the "glmnet" package. Please refer to the documentation in that package for more details.

Usage

sits_glm(distances.tb = NULL, family = "multinomial", alpha = 1,
  lambda_kfolds = 10, ...)

Arguments

distances.tb	a time series with a set of distance measures for each training sample
family	Response type. Can be either "gaussian", "binomial", "poisson", "multinomial", "cox", or "mgaussian". (default: "multinomial")
alpha	the elasticnet mixing parameter, with 0<=alpha<=1. Set alpha = 0.0 to obtain ridge model, and alpha = 1.0 to obtain lasso model). (refer to 'glmnet::cv.glmnet' function for more details)
lambda_kfolds	number of folds to find best lambda parameter (default: 10)
...	other parameters to be passed to 'glmnet::cv.glmnet' function

Value

result either an model function to be passed in sits_predict or an function prepared that can be called further to compute multinom training model

Author(s)

Alexandre Xavier Ywata de Carvalho, [e-mail]

Rolf Simoes, [e-mail]

luizassis/sits documentation built on May 30, 2019, 7:15 p.m.