Skip to contents

ANOVA Tables For 'lm_betaselect' and 'glm_betaselect' Objects

Source: R/lm_betaselect_methods.R
anova.lm_betaselect.Rd

Return the analysis of variance tables for the outputs of lm_betaselect() and glm_betaselect().

Usage

# S3 method for class 'lm_betaselect'
anova(object, ..., type = c("beta", "standardized", "raw", "unstandardized"))

# S3 method for class 'glm_betaselect'
anova(
  object,
  ...,
  type = c("beta", "standardized", "raw", "unstandardized"),
  dispersion = NULL,
  test = NULL
)

Arguments

object

The output of lm_betaselect() or glm_betaselect().

...

Additional outputs of lm_betaselect() or glm_betaselect().

type

String. If "unstandardized" or "raw", the output before standardization are used If "beta" or "standardized", then the output after selected variables standardized are returned. Default is "beta".

dispersion

To be passed to stats::anova.glm(). The dispersion parameter. Default ia NULL and it is extracted from the model.

test

String. The test to be conducted. Please refer to stats::anova.glm() for details.

Value

It returns an object of class anova, which is identical to the output of stats::anova() in structure.

Details

By default, it calls stats::anova() on the results with selected variables standardized. By setting type to "raw" or "unstandardized", it calls stats::anova() on the results before standardization.

See also

lm_betaselect()

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples


data(data_test_mod_cat)

lm_beta_x <- lm_betaselect(dv ~ iv*mod + cov1 + cat1,
                           data = data_test_mod_cat,
                           to_standardize = "iv",
                           do_boot = FALSE)
anova(lm_beta_x)
#> Analysis of Variance Table
#> 
#> Response: dv
#>            Df    Sum Sq   Mean Sq  F value Pr(>F)    
#> iv          1 302739738 302739738 652.6310 <2e-16 ***
#> mod         1  40936174  40936174  88.2481 <2e-16 ***
#> cov1        1    341505    341505   0.7362 0.3913    
#> cat1        2   1118110    559055   1.2052 0.3005    
#> iv:mod      1    946107    946107   2.0396 0.1539    
#> Residuals 493 228690783    463876                    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(lm_beta_x, type = "raw")
#> Analysis of Variance Table
#> 
#> Response: dv
#>            Df    Sum Sq   Mean Sq  F value Pr(>F)    
#> iv          1 302739738 302739738 652.6310 <2e-16 ***
#> mod         1  40936174  40936174  88.2481 <2e-16 ***
#> cov1        1    341505    341505   0.7362 0.3913    
#> cat1        2   1118110    559055   1.2052 0.3005    
#> iv:mod      1    946107    946107   2.0396 0.1539    
#> Residuals 493 228690783    463876                    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


data_test_mod_cat$p <- scale(data_test_mod_cat$dv)[, 1]
data_test_mod_cat$p <- ifelse(data_test_mod_cat$p > 0,
                              yes = 1,
                              no = 0)
logistic_beta_x <- glm_betaselect(p ~ iv*mod + cov1 + cat1,
                                  data = data_test_mod_cat,
                                  family = binomial,
                                  to_standardize = "iv")
anova(logistic_beta_x)
#> Analysis of Deviance Table
#> 
#> Model: binomial, link: logit
#> 
#> Response: p
#> 
#> Terms added sequentially (first to last)
#> 
#> 
#>        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
#> NULL                     499     692.86              
#> iv      1  206.034       498     486.83 < 2.2e-16 ***
#> mod     1   45.127       497     441.70 1.847e-11 ***
#> cov1    1    0.034       496     441.66    0.8531    
#> cat1    2    0.675       494     440.99    0.7136    
#> iv:mod  1    0.886       493     440.10    0.3465    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(logistic_beta_x, type = "raw")
#> Analysis of Deviance Table
#> 
#> Model: binomial, link: logit
#> 
#> Response: p
#> 
#> Terms added sequentially (first to last)
#> 
#> 
#>        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
#> NULL                     499     692.86              
#> iv      1  206.034       498     486.83 < 2.2e-16 ***
#> mod     1   45.127       497     441.70 1.847e-11 ***
#> cov1    1    0.034       496     441.66    0.8531    
#> cat1    2    0.675       494     440.99    0.7136    
#> iv:mod  1    0.886       493     440.10    0.3465    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1