Summarize the results of std_selected() or std_selected_boot().

# S3 method for std_selected
summary(object, ...)

Arguments

object

The output of std_selected() or std_selected_boot().

...

Additional arguments. Ignored by this function.

Value

An object of class summary.std_selected, with bootstrap confidence intervals added if present in the object. The object is a list. Its main element coefficients is similar to the coefficient table in the summary() printout of lm(). This object is for printing summary information of the results from std_selected() or std_selected_boot().

Examples


# Load a sample data set

dat <- test_x_1_w_1_v_1_cat1_n_500

# Do a moderated regression by lm
lm_raw <- lm(dv ~ iv*mod + v1 + cat1, dat)
summary(lm_raw)
#> 
#> Call:
#> lm(formula = dv ~ iv * mod + v1 + cat1, data = dat)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2146.0  -431.9   -25.0   411.2  2309.3 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)  
#> (Intercept)  308.767   4075.066   0.076   0.9396  
#> iv            52.760    271.242   0.195   0.8459  
#> mod            5.127     40.772   0.126   0.9000  
#> v1           -12.760     10.174  -1.254   0.2104  
#> cat1gp2     -158.673     71.834  -2.209   0.0276 *
#> cat1gp3      -43.166     75.283  -0.573   0.5666  
#> iv:mod         3.416      2.709   1.261   0.2080  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 665 on 493 degrees of freedom
#> Multiple R-squared:  0.6352,	Adjusted R-squared:  0.6307 
#> F-statistic:   143 on 6 and 493 DF,  p-value: < 2.2e-16
#> 

# Standardize all variables except for categorical variables.
# Interaction terms are formed after standardization.
lm_std <- std_selected(lm_raw, to_scale = ~ .,
                               to_center = ~ .)
summary(lm_std)
#> 
#> Selected variable(s) are centered by mean and/or scaled by SD
#> - Variable(s) centered: dv iv mod v1 cat1
#> - Variable(s) scaled: dv iv mod v1 cat1
#> 
#>      centered_by   scaled_by                            Note
#> dv    6565.02965 1094.244465 Standardized (mean = 0, SD = 1)
#> iv      15.01576    2.039154 Standardized (mean = 0, SD = 1)
#> mod    100.39502    5.040823 Standardized (mean = 0, SD = 1)
#> v1      10.13884    2.938932 Standardized (mean = 0, SD = 1)
#> cat1          NA          NA Nonnumeric                     
#> 
#> Note:
#> - Categorical variables will not be centered or scaled even if requested.
#> 
#> Call:
#> lm(formula = dv ~ iv * mod + v1 + cat1, data = dat_mod)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -1.96117 -0.39474 -0.02285  0.37579  2.11040 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  0.06458    0.04825   1.338   0.1814    
#> iv           0.73738    0.02736  26.948   <2e-16 ***
#> mod          0.25990    0.02737   9.496   <2e-16 ***
#> v1          -0.03427    0.02733  -1.254   0.2104    
#> cat1gp2     -0.14501    0.06565  -2.209   0.0276 *  
#> cat1gp3     -0.03945    0.06880  -0.573   0.5666    
#> iv:mod       0.03209    0.02545   1.261   0.2080    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.6077 on 493 degrees of freedom
#> Multiple R-squared:  0.6352,	Adjusted R-squared:  0.6307 
#> F-statistic:   143 on 6 and 493 DF,  p-value: < 2.2e-16
#> 
#> Note:
#> - Estimates and their statistics are based on the data after mean-centering, scaling, or standardization.

# With bootstrapping
# nboot = 100 just for illustration. nboot >= 2000 should be used in read
# research.
lm_std_boot <- std_selected_boot(lm_raw, to_scale = ~ .,
                                         to_center = ~ .,
                                         nboot = 100)
summary(lm_std_boot)
#> 
#> Selected variable(s) are centered by mean and/or scaled by SD
#> - Variable(s) centered: dv iv mod v1 cat1
#> - Variable(s) scaled: dv iv mod v1 cat1
#> 
#>      centered_by   scaled_by                            Note
#> dv    6565.02965 1094.244465 Standardized (mean = 0, SD = 1)
#> iv      15.01576    2.039154 Standardized (mean = 0, SD = 1)
#> mod    100.39502    5.040823 Standardized (mean = 0, SD = 1)
#> v1      10.13884    2.938932 Standardized (mean = 0, SD = 1)
#> cat1          NA          NA Nonnumeric                     
#> 
#> Note:
#> - Categorical variables will not be centered or scaled even if requested.
#> - Nonparametric bootstrapping 95% confidence intervals computed.
#> - The number of bootstrap samples is 100.
#> 
#> Call:
#> lm(formula = dv ~ iv * mod + v1 + cat1, data = dat_mod)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -1.96117 -0.39474 -0.02285  0.37579  2.11040 
#> 
#> Coefficients:
#>               Estimate   CI Lower   CI Upper Std. Error t value Pr(>|t|)    
#> (Intercept)  0.0645819 -0.0005936  0.1491718  0.0482503   1.338   0.1814    
#> iv           0.7373838  0.6887268  0.7763855  0.0273632  26.948   <2e-16 ***
#> mod          0.2598987  0.2008832  0.3074666  0.0273687   9.496   <2e-16 ***
#> v1          -0.0342709 -0.0902115  0.0272759  0.0273250  -1.254   0.2104    
#> cat1gp2     -0.1450065 -0.2768718 -0.0106607  0.0656469  -2.209   0.0276 *  
#> cat1gp3     -0.0394483 -0.1964224  0.0802801  0.0687988  -0.573   0.5666    
#> iv:mod       0.0320874 -0.0048116  0.0747295  0.0254507   1.261   0.2080    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.6077 on 493 degrees of freedom
#> Multiple R-squared:  0.6352,	Adjusted R-squared:  0.6307 
#> F-statistic:   143 on 6 and 493 DF,  p-value: < 2.2e-16
#> 
#> Note:
#> - Estimates and their statistics are based on the data after mean-centering, scaling, or standardization.
#> - [CI Lower, CI Upper] are bootstrap percentile confidence intervals.
#> - Std. Error are not bootstrap SEs.