step4vglm: Choose a model by AIC in a Stepwise Algorithm

Description Usage Arguments Details Value Warning See Also Examples

Description

Select a formula-based model by AIC.

Usage

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step4(object, ...)
step4vglm(object, scope, direction = c("both", "backward", "forward"),
          trace = 1, keep = NULL, steps = 1000, k = 2, ...)

Arguments

object

an object of class "vglm". This is used as the initial model in the stepwise search.

scope

See step.

direction

See step.

trace, keep

See step.

steps, k

See step.

...

any additional arguments to extractAIC.vglm, drop1.vglm and add1.vglm.

Details

This function is a direct adaptation of step for vglm-class objects. Since step is not generic, the name step4() was adopted and it is generic, as well as being S4 rather than S3. It is the intent that this function should work as similar as possible to step.

Internally, the methods function for vglm-class objects calls add1.vglm and drop1.vglm repeatedly.

Value

The results are placed in the post slot of the stepwise-selected model that is returned. There are up to two additional components. There is an "anova" component corresponding to the steps taken in the search, as well as a "keep" component if the keep= argument was supplied in the call.

Warning

In general, the same warnings in drop1.glm and drop1.vglm apply here.

This function

See Also

add1.vglm, drop1.vglm, vglm, trim.constraints, add1.glm, drop1.glm, backPain2, step, update.

Examples

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data("backPain2", package = "VGAM")
summary(backPain2)
fit1 <- vglm(pain ~ x2 + x3 + x4 + x2:x3 + x2:x4 + x3:x4,
             propodds, data = backPain2)
spom1 <- step4(fit1)
summary(spom1)
spom1@post$anova

Example output

Loading required package: stats4
Loading required package: splines
 x2     x3     x4                       pain   
 1:39   1:21   1:64   worse               : 5  
 2:62   2:52   2:37   same                :14  
        3:28          slight.improvement  :18  
                      moderate.improvement:20  
                      marked.improvement  :28  
                      complete.relief     :16  
Start:  AIC=340.44
pain ~ x2 + x3 + x4 + x2:x3 + x2:x4 + x3:x4

        Df Deviance    AIC
- x3:x4  2   312.79 336.79
- x2:x4  1   313.06 339.06
- x2:x3  2   316.00 339.99
<none>       312.44 340.44

Step:  AIC=336.79
pain ~ x2 + x3 + x4 + x2:x3 + x2:x4

        Df Deviance    AIC
- x2:x4  1   313.24 335.24
- x2:x3  2   316.28 336.28
<none>       312.79 336.79

Step:  AIC=335.24
pain ~ x2 + x3 + x4 + x2:x3

        Df Deviance    AIC
- x2:x3  2   316.40 334.40
<none>       313.24 335.24
- x4     1   320.79 340.79

Step:  AIC=334.4
pain ~ x2 + x3 + x4

       Df Deviance    AIC
<none>      316.40 334.40
- x3    2   321.53 335.53
- x4    1   322.58 338.58
- x2    1   330.48 346.48

Call:
vglm(formula = pain ~ x2 + x3 + x4, family = propodds, data = backPain2)

Coefficients: 
              Estimate Std. Error z value Pr(>|z|)    
(Intercept):1   5.4102     0.7247   7.466 8.29e-14 ***
(Intercept):2   3.8365     0.5955   6.442 1.18e-10 ***
(Intercept):3   2.8387     0.5479   5.181 2.21e-07 ***
(Intercept):4   1.8598     0.5080   3.661 0.000251 ***
(Intercept):5   0.0968     0.4758   0.203 0.838768    
x22            -1.4657     0.3968  -3.694 0.000221 ***
x32            -1.0318     0.4839  -2.132 0.032975 *  
x33            -1.1021     0.5373  -2.051 0.040227 *  
x42            -0.9241     0.3804  -2.429 0.015130 *  
---
Signif. codes:  0***0.001**0.01*0.05.’ 0.1 ‘ ’ 1

Number of linear predictors:  5 

Names of linear predictors: logitlink(P[Y>=2]), logitlink(P[Y>=3]), 
logitlink(P[Y>=4]), logitlink(P[Y>=5]), logitlink(P[Y>=6])

Residual deviance: 316.4004 on 496 degrees of freedom

Log-likelihood: -158.2002 on 496 degrees of freedom

Number of Fisher scoring iterations: 5 

No Hauck-Donner effect found in any of the estimates


Exponentiated coefficients:
      x22       x32       x33       x42 
0.2309154 0.3563712 0.3321658 0.3968965 
     Step Df  Deviance Resid. Df Resid. Dev      AIC
1         NA        NA       491   312.4383 340.4383
2 - x3:x4  2 0.3553795       493   312.7936 336.7936
3 - x2:x4  1 0.4467099       494   313.2403 335.2403
4 - x2:x3  2 3.1600873       496   316.4004 334.4004

VGAM documentation built on Jan. 16, 2021, 5:21 p.m.