Multiple Regression Output

Description

Multiple Regression Output

Usage

model_output(models, data, checkList = NULL, formulas, outliers)

Arguments

Details

Creates plots and text output to summarize models and check assumptions via objects created by assumptions_check. Uses full model with all predictors.

Examples

freeny_model_formulas <- create_formula_objects("y",
c("lag.quarterly.revenue"), c("price.index"))
freeny_models <- create_model_objects(freeny_model_formulas,
dataset = freeny)
freeny_model <- freeny_models[[length(freeny_models)]]
checks <- assumptions_check(freeny_model)
model_output(freeny_models, freeny, checks, freeny_model_formulas,
outliers = "significant")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AutoModel)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AutoModel/model_output.Rd_%03d_medium.png", width=480, height=480)
> ### Name: model_output
> ### Title: Multiple Regression Output
> ### Aliases: model_output
> 
> ### ** Examples
> 
> freeny_model_formulas <- create_formula_objects("y",
+ c("lag.quarterly.revenue"), c("price.index"))
> freeny_models <- create_model_objects(freeny_model_formulas,
+ dataset = freeny)
> freeny_model <- freeny_models[[length(freeny_models)]]
> checks <- assumptions_check(freeny_model)
> model_output(freeny_models, freeny, checks, freeny_model_formulas,
+ outliers = "significant")


REGRESSION OUTPUT

Durbin-Watson =  2.357 p value =  0.797 

Partial Regression plots (all relationships should be linear):

Plot of studentized residuals: uniform distibution across predicted values requiredCorrelation Matrix for model (correlation >.70 indicates severe multicollinearity)

                            y lag.quarterly.revenue price.index
y                      1.0000                0.9978     -0.9895
lag.quarterly.revenue  0.9978                1.0000     -0.9894
price.index           -0.9895               -0.9894      1.0000

Variance inflation factor (<10 desired):

lag.quarterly.revenue           price.index 
                 47.5                  47.5 

Standardized Residuals (observations > 3.00 problematic):

1963.25 
  3.073 

Cook's distance (values >.2 problematic):

1963.25 
  1.169 

Normality of standardized model residuals:  Shapiro-Wilk (p-value):  0.1296 

Model change statistics

             R    R^2 Adj R^2 SE Est. Delta R^2  F Change df1 df2  p Fch Sig
Model 1 0.9978 0.9956  0.9955  0.0212    0.9956 8360.3793   1  37      0 ***
Model 2 0.9979 0.9958  0.9956  0.0209    0.0002    2.1304   1  36 0.1531    
Model 1 : y ~ lag.quarterly.revenue 
Model 2 : y ~ lag.quarterly.revenue + price.index 

Model Coefficients

   Model                  term estimate std.error statistic p.value sig
 Model 1           (Intercept)  0.04169   0.10138    0.4112  0.6833    
 Model 1 lag.quarterly.revenue  0.99827   0.01092   91.4351  0.0000 ***
 Model 2           (Intercept)  2.18577   1.47236    1.4845  0.1464    
 Model 2 lag.quarterly.revenue  0.89122   0.07412   12.0240  0.0000 ***
 Model 2           price.index -0.25592   0.17534   -1.4596  0.1531    
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>