run_model: Automated Multiple Regression Modelling

In alishinski/AutoModel: Automated Hierarchical Multiple Regression with Assumptions Checking

Description

Automated Multiple Regression Modelling

Usage

run_model(outcome, block1, ..., dataset, type = "gaussian",
  assumptions.check = T, outliers.check = "significant",
  transform.outcome = F)

Arguments

outcome	The dependent variable of the hierarchical model
block1	A character vector, with names of variables. The first block of independent variables.
...	A character vector, with names of variables. Subsequent blocks of independent variables.
dataset	A data frame containing variables refered to in formulas, passed to data argument of lm
type	Family argument to pass to glm. Specify "binomial" for binary logistic regression models.
assumptions.check	Boolean, if TRUE, then assumption checks are run and output is produced. If FALSE, only model summary and coefficient tables are produced.
outliers.check	Determines how many observations to display for outliers check. Default is significant observations. "All" shows all residual and Cook's D values.
transform.outcome	A boolean. If TRUE, a variable transformation of the outcome is substituted in the final model if outcome is non-normal. NOT IMPLEMENTED YET.

Details

Calls other functions to generate model objects and test them, given specified model parameters and other options. Formatted output is produced via model_output

Examples

run_model("y", c("lag.quarterly.revenue"), c("price.index", "income.level"),
dataset=freeny)

alishinski/AutoModel documentation built on May 12, 2019, 5:37 a.m.