Summary of an 'glm_betaselect'-Class Object
Source:R/lm_betaselect_methods.R
summary.glm_betaselect.RdThe 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. Seestats::summary.glm()for details.- correlation
If
TRUE, the correlation matrix of the estimates will be returned. The same argument instats::summary.glm(). Default isFALSE.- symbolic.cor
If
TRUE, correlations are printed in symbolic form as instats::summary.glm(). Default isFALSE.- trace
Logical. Whether profiling will be traced when forming the confidence interval if
se_methodis"default","z", or"glm". Ignored ifciisFALSE. Seestats::confint.glm()for details.- test
The test used for
se_methodis"default","z", or"glm". Ignored ifciisFALSE. Seestats::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 usualglmstandard 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_methodis"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
typeis"beta"or"standardized". If"none", the default, then it will not be printed. If set to"before_ci"andciisTRUE, 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. IfciisFALSE`, 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 isNULL.- transform_b_name
If
transform_bis 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 thanest_digitsdue 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 isFALSE.- 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, ifpvalue_less_thanis .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 whateps.Pvaluedoes instats::printCoefmat().)
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.
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().
References
Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
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)
#> Call to glm_betaselect():
#> betaselectr::lm_betaselect(formula = p ~ iv * mod + cov1 + cat1,
#> family = binomial, data = data_test_mod_cat, to_standardize = "iv",
#> do_boot = TRUE, bootstrap = 100, iseed = 1234, model_call = "glm")
#>
#> Variable(s) standardized: iv
#>
#> Call:
#> stats::glm(formula = p ~ iv * mod + cov1 + cat1, family = binomial,
#> data = betaselectr::std_data(data = data_test_mod_cat, to_standardize = "iv"))
#>
#> Coefficients:
#> Estimate CI.Lower CI.Upper Std. Error z value Pr(Boot)
#> (Intercept) -16.205 -23.001 -10.040 2.966 -5.464 <0.001 ***
#> iv -1.148 -10.721 4.958 4.066 -0.282 0.74
#> mod 0.165 0.105 0.229 0.030 5.557 <0.001 ***
#> cov1 -0.004 -0.077 0.098 0.042 -0.103 0.78
#> cat1gp2 -0.211 -0.706 0.294 0.290 -0.725 0.50
#> cat1gp3 -0.189 -0.772 0.297 0.271 -0.697 0.54
#> iv:mod 0.031 -0.028 0.129 0.041 0.755 0.38
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 692.86 on 499 degrees of freedom
#> Residual deviance: 440.10 on 493 degrees of freedom
#> AIC: 454.1
#>
#> Number of Fisher Scoring iterations: 5
#>
#> Transformed Parameter Estimates:
#> Exp(B) CI.Lower CI.Upper
#> (Intercept) 0.000 0.000 0.000
#> iv 0.317 0.000 143.965
#> mod 1.180 1.110 1.257
#> cov1 0.996 0.926 1.103
#> cat1gp2 0.810 0.494 1.341
#> cat1gp3 0.828 0.463 1.346
#> iv:mod 1.031 0.973 1.137
#>
#> Note:
#> - Results *after* standardization are reported.
#> - Nonparametric bootstrapping conducted.
#> - The number of bootstrap samples is 100.
#> - Standard errors are bootstrap standard errors.
#> - Z values are computed by 'Estimate / Std. Error'.
#> - The bootstrap p-values are asymmetric p-values by Asparouhov and
#> Muthén (2021).
#> - Percentile bootstrap 95.0% confidence interval reported.