Summary of an lm_list_lmhelprs-Class Object — summary.lm_list

The summary of content of the output of many_lm().

Usage

# S3 method for class 'lm_list_lmhelprs'
summary(object, ...)

# S3 method for class 'summary_lm_list_lmhelprs'
print(x, digits = 3, ...)

Arguments

object: The output of many_lm().
...: Other arguments. For the summary method, these are arguments to be passed to the summary method for each model (usually summary.lm()). For the print method, these are arguments to be passed to stats::printCoefmat().
x: An object of class summary_lm_list_lmhelprs.
digits: The number of significant digits in printing numerical results.

Value

summary.lm_list_lmhelprs() returns a summary_lm_list_lmhelprs-class object, which is a list of the summary() outputs of the lm() outputs stored.

print.summary_lm_list_lmhelprs() returns x invisibly. Called for its side effect.

Adapted from the package manymome such that many_lm() can be used without manymome.

Functions

print(summary_lm_list_lmhelprs): Print method for output of summary for lm_list_lmhelprs.

Examples


data(data_test1)
mod <- "x3 ~ x2 + x1
        x4 ~ x3
        x5 ~ x4*x1"
out <- many_lm(mod, data_test1)
summary(out)
#> Call:
#> many_lm(models = mod, data = data_test1)
#> 
#> Model:
#> x3 ~ x2 + x1
#> <environment: 0x55b6f033e050>
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  -0.0835     0.0968   -0.86  0.39067    
#> x2           -0.0494     0.0896   -0.55  0.58289    
#> x1            0.3801     0.0955    3.98  0.00013 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.143. Adjusted R-square = 0.125. F(2, 97) = 8.079, p < .001
#> 
#> Model:
#> x4 ~ x3
#> <environment: 0x55b6f033e050>
#>             Estimate Std. Error t value Pr(>|t|)  
#> (Intercept)  -0.1144     0.0866   -1.32    0.190  
#> x3            0.2156     0.0846    2.55    0.012 *
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.062. Adjusted R-square = 0.053. F(1, 98) = 6.489, p = 0.012
#> 
#> Model:
#> x5 ~ x4 * x1
#> <environment: 0x55b6f033e050>
#>             Estimate Std. Error t value Pr(>|t|)   
#> (Intercept) -0.10644    0.10399   -1.02   0.3086   
#> x4           0.15124    0.11589    1.31   0.1950   
#> x1           0.27872    0.09885    2.82   0.0058 **
#> x4:x1        0.00945    0.12017    0.08   0.9375   
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.108. Adjusted R-square = 0.080. F(3, 96) = 3.878, p = 0.012