summary.glm_betaselect: Summary of an 'glm_betaselect'-Class Object

In betaselectr: Betas-Select in Structural Equation Models and Linear Models

Summary of an 'glm_betaselect'-Class Object

Description

The summary method for glm_betaselect-class objects.

Usage

## S3 method for class 'glm_betaselect'
summary(
  object,
  dispersion = NULL,
  correlation = FALSE,
  symbolic.cor = FALSE,
  trace = FALSE,
  test = c("LRT", "Rao"),
  se_method = c("boot", "bootstrap", "z", "glm", "default"),
  ci = TRUE,
  level = 0.95,
  boot_type = c("perc", "bc"),
  boot_pvalue_type = c("asymmetric", "norm"),
  type = c("beta", "standardized", "raw", "unstandardized"),
  print_raw = c("none", "before_ci", "after_ci"),
  transform_b = NULL,
  transform_b_name = NULL,
  ...
)

## S3 method for class 'summary.glm_betaselect'
print(
  x,
  est_digits = 3,
  symbolic.cor = x$symbolic.cor,
  signif.stars = getOption("show.signif.stars"),
  show.residuals = FALSE,
  z_digits = 3,
  pvalue_less_than = 0.001,
  ...
)

Arguments

object	The output of glm_betaselect().
dispersion	The dispersion parameter. If NULL, then it is extracted from the object. If a scalar, it will be used as the dispersion parameter. See stats::summary.glm() for details.
correlation	If TRUE, the correlation matrix of the estimates will be returned. The same argument in stats::summary.glm(). Default is FALSE.
symbolic.cor	If TRUE, correlations are printed in symbolic form as in stats::summary.glm(). Default is FALSE.
trace	Logical. Whether profiling will be traced when forming the confidence interval if se_method is "default", "z", or "glm". Ignored if ci is FALSE. See stats::confint.glm() for details.
test	The test used for se_method is "default", "z", or "glm". Ignored if ci is FALSE. See stats::confint.glm() for details.
se_method	The method used to compute the standard errors and confidence intervals (if requested). If bootstrapping was requested when calling glm_betaselect() and this argument is set to "bootstrap" or "boot", the bootstrap standard errors are returned. If bootstrapping was not requested or if this argument is set to "z", "glm", or "default", then the usual glm standard errors are returned. Default is "boot".
ci	Logical. Whether confidence intervals are computed. Default is FALSE.
level	The level of confidence, default is .95, returning the 95% confidence interval.
boot_type	The type of bootstrap confidence intervals, if requested. Currently, it supports "perc", percentile bootstrap confidence intervals, and "bc", bias-corrected bootstrap confidence interval.
boot_pvalue_type	The type of p-values if se_method is "boot" or "bootstrap". If "norm", then the z score is used to compute the p-value using a standard normal distribution. If "asymmetric", the default, then the method presented in Asparouhov and Muthén (2021) is used to compute the p-value based on the bootstrap distribution.
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".
print_raw	Control whether the estimates before selected standardization are printed when type is "beta" or "standardized". If "none", the default, then it will not be printed. If set to "before_ci" and ci is TRUE, then will be inserted to the left of the confidence intervals. If set to "after_ci"andciisTRUE⁠, then will be printed to the right of the confidence intervals. If ⁠ciisFALSE', then will be printed to the right of the standardized estimates.
transform_b	The function to be used to transform the confidence limits. For example, if set to exp, the confidence limits will be exponentiated. Users need to decide whether the transformed limits are meaningful. Default is NULL.
transform_b_name	If transform_b is a function, then this is the name of the transformed coefficients. Default is "Estimate(Transformed)"
...	Additional arguments passed to other methods.
x	The output of summary.glm_betaselect().
est_digits	The number of digits after the decimal to be displayed for the coefficient estimates, their standard errors, and confidence intervals (if present). Note that the values will be rounded to this number of digits before printing. If all digits at this position are zero for all values, the values may be displayed with fewer digits. Note that the coefficient table is printed by stats::printCoefmat(). If some numbers are vary large, the number of digits after the decimal may be smaller than est_digits due to a limit on the column width.
signif.stars	Whether "stars" (asterisks) are printed to denote the level of significance achieved for each coefficient. Default is TRUE.
show.residuals	If TRUE, a summary of the deviance residuals will be printed. Default is FALSE.
z_digits	The number of digits after the decimal to be displayed for the z or similar statistic (in the column "z value").
pvalue_less_than	If a p-value is less than this value, it will be displayed with ⁠"<(this value)".⁠ For example, if pvalue_less_than is .001, the default, p-values less than .001 will be displayed as ⁠<.001⁠. This value also determines the printout of the p-value of the F statistic. (This argument does what eps.Pvalue does in stats::printCoefmat().)

Details

By default, it returns a summary.glm_betaselect-class object for the results with selected variables standardized. By setting type to "raw" or "unstandardized", it returns the summary for the results before standardization.

The print method of summary.glm_betaselect-class objects is adapted from stdmod::print.summary.std_selected().

Value

It returns an object of class summary.glm_betaselect, which is similar to the output of stats::summary.glm(), with additional information on the standardization and bootstrapping, if requested.

The print-method of summary.glm_betaselect is called for its side effect. The object x is returned invisibly.

Author(s)

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

References

Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf

See Also

glm_betaselect()

Examples

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)
# bootstrap should be set to 2000 or 5000 in real studies
logistic_beta_x <- glm_betaselect(p ~ iv*mod + cov1 + cat1,
                                  data = data_test_mod_cat,
                                  family = binomial,
                                  to_standardize = "iv",
                                  do_boot = TRUE,
                                  bootstrap = 100,
                                  iseed = 1234)
summary(logistic_beta_x)

betaselectr documentation built on April 3, 2025, 8:51 p.m.