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Coefficients of Beta-Select in Linear Models

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

Return the estimates of coefficients in an lm_betaselect-class or glm_betaselect-class object.

Usage

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

# S3 method for class 'glm_betaselect'
coef(
  object,
  complete = FALSE,
  type = c("beta", "standardized", "raw", "unstandardized"),
  ...
)

Arguments

object

The output of lm_betaselect() or glm_betaselect(), or an lm_betaselect-class or glm_betaselect-class object.

complete

If TRUE, it returns the full vector of coefficients, including those of terms dropped in an over-determined system. See stats::coef() for further information. Default is FALSE.

type

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

...

Other arguments. Ignored.

Value

A numeric vector: The estimate of regression coefficients.

Details

By default, it extracts the regression coefficients after the selected variables have been standardized. If requested, it can also return the regression coefficients before standardization.

See also

lm_betaselect() and glm_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")
coef(lm_beta_x)
#> (Intercept)          iv         mod        cov1     cat1gp2     cat1gp3 
#>  790.549933  -94.301982   57.578053   10.024281 -112.587966  -53.106405 
#>      iv:mod 
#>    8.661027 
coef(lm_beta_x, type = "raw")
#> (Intercept)          iv         mod        cov1     cat1gp2     cat1gp3 
#> 1488.568116  -46.545177   -6.530403   10.024281 -112.587966  -53.106405 
#>      iv:mod 
#>    4.274873 


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")
coef(logistic_beta_x)
#>   (Intercept)            iv           mod          cov1       cat1gp2 
#> -16.205361985  -1.148482578   0.165201690  -0.004369295  -0.210517691 
#>       cat1gp3        iv:mod 
#>  -0.189052500   0.030878129 
coef(logistic_beta_x, type = "raw")
#>  (Intercept)           iv          mod         cov1      cat1gp2      cat1gp3 
#> -7.704355870 -0.566863225 -0.063356556 -0.004369295 -0.210517691 -0.189052500 
#>       iv:mod 
#>  0.015240698