A3 for Linear Regressions

Description

This convenience function calculates the A3 results specifically for linear regressions. It uses R's glm function and so supports logistic regressions and other link functions using the family argument. For other forms of models you may use the more general a3 function.

Usage

a3.lm(formula, data, family = gaussian, ...)

Arguments

Value

S3 A3 object; see a3.base for details

Examples

 ## Standard linear regression results:

 summary(lm(rating ~ ., attitude))

 ## A3 linear regression results:

 # In practice, p.acc should be <= 0.01 in order
 # to obtain fine grained p values.

 a3.lm(rating ~ ., attitude, p.acc = 0.1)

 # This is equivalent both to:

 a3(rating ~ ., attitude, glm, model.args = list(family = gaussian), p.acc = 0.1)

 # and also to:

 a3(rating ~ ., attitude, lm, p.acc = 0.1)
 

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(A3)
Loading required package: xtable
Loading required package: pbapply
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/A3/a3.lm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: a3.lm
> ### Title: A3 for Linear Regressions
> ### Aliases: a3.lm
> 
> ### ** Examples
> 
> ## No test: 
>  ## Standard linear regression results:
> 
>  summary(lm(rating ~ ., attitude))

Call:
lm(formula = rating ~ ., data = attitude)

Residuals:
     Min       1Q   Median       3Q      Max 
-10.9418  -4.3555   0.3158   5.5425  11.5990 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 10.78708   11.58926   0.931 0.361634    
complaints   0.61319    0.16098   3.809 0.000903 ***
privileges  -0.07305    0.13572  -0.538 0.595594    
learning     0.32033    0.16852   1.901 0.069925 .  
raises       0.08173    0.22148   0.369 0.715480    
critical     0.03838    0.14700   0.261 0.796334    
advance     -0.21706    0.17821  -1.218 0.235577    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.068 on 23 degrees of freedom
Multiple R-squared:  0.7326,	Adjusted R-squared:  0.6628 
F-statistic:  10.5 on 6 and 23 DF,  p-value: 1.24e-05

> 
>  ## A3 linear regression results:
> 
>  # In practice, p.acc should be <= 0.01 in order
>  # to obtain fine grained p values.
> 
>  a3.lm(rating ~ ., attitude, p.acc = 0.1)
             Average Slope   CV R^2 p value
-Full Model-                 57.5 %   < 0.1
(Intercept)        10.7871 -  4.5 %     0.7
complaints          0.6132 + 21.9 %   < 0.1
privileges         -0.0731 -  3.7 %     0.7
learning            0.3203 +  8.6 %     0.1
raises              0.0817 -  1.6 %     0.2
critical            0.0384 -  4.1 %     0.4
advance            -0.2171 +  2.8 %   < 0.1
> 
>  # This is equivalent both to:
> 
>  a3(rating ~ ., attitude, glm, model.args = list(family = gaussian), p.acc = 0.1)
             Average Slope   CV R^2 p value
-Full Model-                 57.4 %   < 0.1
(Intercept)        10.7871 -  3.9 %     0.8
complaints          0.6132 + 21.5 %   < 0.1
privileges         -0.0731 -  2.4 %     0.5
learning            0.3203 +  3.3 %     0.1
raises              0.0817 -  2.2 %     0.7
critical            0.0384 -  4.3 %     1.0
advance            -0.2171 -  0.6 %     0.2
> 
>  # and also to:
> 
>  a3(rating ~ ., attitude, lm, p.acc = 0.1)
             Average Slope   CV R^2 p value
-Full Model-                 50.1 %   < 0.1
(Intercept)        10.7871 -  7.3 %     1.0
complaints          0.6132 + 18.3 %   < 0.1
privileges         -0.0731 -  7.1 %     0.8
learning            0.3203 +  4.2 %     0.2
raises              0.0817 -  4.8 %     0.9
critical            0.0384 -  5.2 %     1.0
advance            -0.2171 -  1.6 %     0.4
>  
> ## End(No test)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>