calibrate_model: Build a Linear Regression for Statistical Downscaling

In leerichardson/sdsmR: Implementation of Statistical Downscaling

Description

This function is meant to replicate the calibrate model section of the SDSM tool. It will date in a dataframe, the response variable, the dates column, as well as other specific modeling oprions. As an output, calibrate_model with generate a list with appropriate number of linear models. The reason it outputs a list of models, rather than just one, is for consistency when the "monthly" or "seasonal" option is chosen, which will fit 4 or 12 seperate models to the dataframe.

Usage

calibrate_model(dataframe, y, model_type = "annual",
  autoregression = "false", process = "unconditional", date_column = NULL)

Arguments

dataframe	a dataframe object which contains the data you are fitting the model with
y	the repsonse variable. Input must be a character vector
model_type	"annual", "monthly", or "seasonal". The default used is "annual"
autoregression	Whether or not to include an autoregressive term in the model
process	Either conditional or unconditional
date_column	Column which contains the dates. Should be of the class Date in R to work, input must be a character!

Value

A list of either one, four, or twelve linear models, with the length of the list determined by the model_type parameter.

leerichardson/sdsmR documentation built on May 21, 2019, 1:39 a.m.