model_output: Multiple Regression Output

Description Usage Arguments Details Examples

Description

Multiple Regression Output

Usage

1
model_output(models, data, checkList = NULL, formulas, outliers)

Arguments

models

A list of lm model objects. A set of model objects created by create_model_object.

data

The dataframe from which the model was built.

checkList

a list object created by assumptions_check used to create output.

formulas

Formula list produced by create_formula_objects, used for summary table.

outliers

Outlier option, select the number of observations to examine for outliers.

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

1
2
3
4
5
6
7
8
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")

Example output

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    

AutoModel documentation built on May 1, 2019, 9:14 p.m.