Summary of an 'lm_betaselect'-Class Object
Source:R/lm_betaselect_methods.R
summary.lm_betaselect.RdThe summary method
for lm_betaselect-class objects.
Usage
# S3 method for class 'lm_betaselect'
summary(
object,
correlation = FALSE,
symbolic.cor = FALSE,
se_method = c("boot", "bootstrap", "t", "lm", "ls"),
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"),
...
)
# S3 method for class 'summary.lm_betaselect'
print(
x,
est_digits = 3,
symbolic.cor = x$symbolic.cor,
signif.stars = getOption("show.signif.stars"),
tz_digits = 3,
pvalue_less_than = 0.001,
...
)Arguments
- object
The output of
lm_betaselect().- correlation
If
TRUE, the correlation matrix of the estimates will be returned. The same argument instats::summary.lm(). Default isFALSE.- symbolic.cor
If
TRUE, correlations are printed in symbolic form as instats::summary.lm(). Default isFALSE.- se_method
The method used to compute the standard errors and confidence intervals (if requested). If bootstrapping was requested when calling
lm_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"t","lm", or"ls", then the usuallmstandard errors are returned. Default is"boot".- ci
Logical. Whether confidence intervals are computed. Default is
TRUE.- 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.- ...
Additional arguments passed to other methods.
- x
The output of
summary.lm_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. This value also determines the number of digits for displayed R-squared.- signif.stars
Whether "stars" (asterisks) are printed to denote the level of significance achieved for each coefficient. Default is
TRUE.- tz_digits
The number of digits after the decimal to be displayed for the t or similar statistic (in the column
"t value"or"z value"). This value also determines the number of digits for the F statistic for the R-squared.- 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.lm_betaselect, which is
similar to the output of
stats::summary.lm(), with additional
information on the standardization
and bootstrapping, if requested.
The print-method of
summary.lm_betaselect is called
for its side effect. The object x
is returned invisibly.
Details
By default, it returns a
summary.lm_betaselect-class object
for the results with selected variables
standardized. By setting type to
"raw" or "unstandardized", it
return the summary for the results
before standardization.
The print method of
summary.lm_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(data_test_mod_cat)
# bootstrap should be set to 2000 or 5000 in real studies
lm_beta_x <- lm_betaselect(dv ~ iv*mod + cov1 + cat1,
data = data_test_mod_cat,
to_standardize = "iv",
do_boot = TRUE,
bootstrap = 100,
iseed = 1234)
summary(lm_beta_x)
#> Call to lm_betaselect():
#> betaselectr::lm_betaselect(formula = dv ~ iv * mod + cov1 + cat1,
#> data = data_test_mod_cat, to_standardize = "iv", do_boot = TRUE,
#> bootstrap = 100, iseed = 1234)
#>
#> Variable(s) standardized: iv
#>
#> Call:
#> stats::lm(formula = dv ~ iv * mod + cov1 + cat1, data = betaselectr::std_data(data = data_test_mod_cat,
#> to_standardize = "iv"))
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1987.03 -463.99 0.25 455.14 2152.48
#>
#> Coefficients:
#> Estimate CI.Lower CI.Upper Std. Error z value Pr(Boot)
#> (Intercept) 790.550 -430.112 1905.572 584.405 1.353 0.20
#> iv -94.302 -1569.880 981.851 625.286 -0.151 0.80
#> mod 57.578 45.592 70.397 6.031 9.547 <0.001 ***
#> cov1 10.024 -5.933 35.460 9.457 1.060 0.18
#> cat1gp2 -112.588 -263.297 32.218 74.809 -1.505 0.10
#> cat1gp3 -53.106 -209.176 88.391 73.524 -0.722 0.56
#> iv:mod 8.661 -1.968 22.601 6.203 1.396 0.14
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 681.1 on 493 degrees of freedom
#>
#> R-squared : 0.602
#> Adjusted R-squared : 0.597
#> ANOVA test of R-squared : F(6, 493) = 124.344, p < 0.001
#>
#> 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.
summary(lm_beta_x, ci = TRUE)
#> Call to lm_betaselect():
#> betaselectr::lm_betaselect(formula = dv ~ iv * mod + cov1 + cat1,
#> data = data_test_mod_cat, to_standardize = "iv", do_boot = TRUE,
#> bootstrap = 100, iseed = 1234)
#>
#> Variable(s) standardized: iv
#>
#> Call:
#> stats::lm(formula = dv ~ iv * mod + cov1 + cat1, data = betaselectr::std_data(data = data_test_mod_cat,
#> to_standardize = "iv"))
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1987.03 -463.99 0.25 455.14 2152.48
#>
#> Coefficients:
#> Estimate CI.Lower CI.Upper Std. Error z value Pr(Boot)
#> (Intercept) 790.550 -430.112 1905.572 584.405 1.353 0.20
#> iv -94.302 -1569.880 981.851 625.286 -0.151 0.80
#> mod 57.578 45.592 70.397 6.031 9.547 <0.001 ***
#> cov1 10.024 -5.933 35.460 9.457 1.060 0.18
#> cat1gp2 -112.588 -263.297 32.218 74.809 -1.505 0.10
#> cat1gp3 -53.106 -209.176 88.391 73.524 -0.722 0.56
#> iv:mod 8.661 -1.968 22.601 6.203 1.396 0.14
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 681.1 on 493 degrees of freedom
#>
#> R-squared : 0.602
#> Adjusted R-squared : 0.597
#> ANOVA test of R-squared : F(6, 493) = 124.344, p < 0.001
#>
#> 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.
summary(lm_beta_x, boot_pvalue_type = "norm")
#> Call to lm_betaselect():
#> betaselectr::lm_betaselect(formula = dv ~ iv * mod + cov1 + cat1,
#> data = data_test_mod_cat, to_standardize = "iv", do_boot = TRUE,
#> bootstrap = 100, iseed = 1234)
#>
#> Variable(s) standardized: iv
#>
#> Call:
#> stats::lm(formula = dv ~ iv * mod + cov1 + cat1, data = betaselectr::std_data(data = data_test_mod_cat,
#> to_standardize = "iv"))
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1987.03 -463.99 0.25 455.14 2152.48
#>
#> Coefficients:
#> Estimate CI.Lower CI.Upper Std. Error z value Pr(>|z|)
#> (Intercept) 790.550 -430.112 1905.572 584.405 1.353 0.176
#> iv -94.302 -1569.880 981.851 625.286 -0.151 0.880
#> mod 57.578 45.592 70.397 6.031 9.547 <0.001 ***
#> cov1 10.024 -5.933 35.460 9.457 1.060 0.289
#> cat1gp2 -112.588 -263.297 32.218 74.809 -1.505 0.132
#> cat1gp3 -53.106 -209.176 88.391 73.524 -0.722 0.470
#> iv:mod 8.661 -1.968 22.601 6.203 1.396 0.163
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 681.1 on 493 degrees of freedom
#>
#> R-squared : 0.602
#> Adjusted R-squared : 0.597
#> ANOVA test of R-squared : F(6, 493) = 124.344, p < 0.001
#>
#> 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 based on standard normal distribution
#> using z values.
#> - Percentile bootstrap 95.0% confidence interval reported.
summary(lm_beta_x, type = "raw")
#> Call to lm_betaselect():
#> betaselectr::lm_betaselect(formula = dv ~ iv * mod + cov1 + cat1,
#> data = data_test_mod_cat, to_standardize = "iv", do_boot = TRUE,
#> bootstrap = 100, iseed = 1234)
#>
#> Variable(s) standardized: iv
#>
#> Call:
#> stats::lm(formula = dv ~ iv * mod + cov1 + cat1, data = data_test_mod_cat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1987.03 -463.99 0.25 455.14 2152.48
#>
#> Coefficients:
#> Estimate CI.Lower CI.Upper Std. Error z value Pr(Boot)
#> (Intercept) 1488.568 -6589.967 12784.458 584.405 2.547 0.20
#> iv -46.545 -819.064 480.699 625.286 -0.074 0.80
#> mod -6.530 -117.793 72.268 6.031 -1.083 <0.001 ***
#> cov1 10.024 -5.933 35.460 9.457 1.060 0.18
#> cat1gp2 -112.588 -263.297 32.218 74.809 -1.505 0.10
#> cat1gp3 -53.106 -209.176 88.391 73.524 -0.722 0.56
#> iv:mod 4.275 -0.963 11.792 6.203 0.689 0.14
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 681.1 on 493 degrees of freedom
#>
#> R-squared : 0.602
#> Adjusted R-squared : 0.597
#> ANOVA test of R-squared : F(6, 493) = 124.344, p < 0.001
#>
#> Note:
#> - Results *before* 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.