Summary of an lm_list-Class Object — summary.lm

The summary of content of the output of lm2list().

Usage

# S3 method for class 'lm_list'
summary(object, betaselect = FALSE, ci = FALSE, level = 0.95, ...)

# S3 method for class 'summary_lm_list'
print(x, digits = 3, digits_decimal = NULL, ...)

Arguments

object: The output of lm2list().
betaselect: If TRUE, standardized coefficients are computed and included in the printout. Only numeric variables will be computed, and any derived terms, such as product terms, will be formed after standardization. Default is FALSE.
ci: If TRUE, confidence interval based on t statistic and standard error will be computed and added to the output. Default is FALSE.
level: The level of confidence of the confidence interval. Ignored if ci is not TRUE.
...: Other arguments. Not used.
x: An object of class summary_lm_list.
digits: The number of significant digits in printing numerical results.
digits_decimal: The number of digits after the decimal in printing numerical results. Default is NULL. If set to an integer, numerical results in the coefficient table will be printed according this setting, and digits will be ignored.

Value

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

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

Functions

print(summary_lm_list): Print method for output of summary for lm_list.

Examples


data(data_serial_parallel)
lm_m11 <- lm(m11 ~ x + c1 + c2, data_serial_parallel)
lm_m12 <- lm(m12 ~ m11 + x + c1 + c2, data_serial_parallel)
lm_m2 <- lm(m2 ~ x + c1 + c2, data_serial_parallel)
lm_y <- lm(y ~ m11 + m12 + m2 + x + c1 + c2, data_serial_parallel)
# Join them to form a lm_list-class object
lm_serial_parallel <- lm2list(lm_m11, lm_m12, lm_m2, lm_y)
lm_serial_parallel
#> 
#> The model(s):
#> m11 ~ x + c1 + c2
#> m12 ~ m11 + x + c1 + c2
#> m2 ~ x + c1 + c2
#> y ~ m11 + m12 + m2 + x + c1 + c2
#> 
summary(lm_serial_parallel)
#> 
#> 
#> Model:
#> m11 ~ x + c1 + c2
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  11.4546     1.1258   10.17  < 2e-16 ***
#> x             0.8001     0.0953    8.39  4.2e-13 ***
#> c1            0.0855     0.1020    0.84    0.404    
#> c2           -0.2444     0.1002   -2.44    0.017 *  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.459. Adjusted R-square = 0.442. F(3, 96) = 27.148, p < .001
#> 
#> Model:
#> m12 ~ m11 + x + c1 + c2
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   9.8742     1.5048    6.56  2.8e-09 ***
#> m11           0.4652     0.0946    4.92  3.7e-06 ***
#> x             0.1146     0.1164    0.98   0.3274    
#> c1            0.1934     0.0949    2.04   0.0444 *  
#> c2           -0.2848     0.0957   -2.97   0.0037 ** 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.469. Adjusted R-square = 0.446. F(4, 95) = 20.963, p < .001
#> 
#> Model:
#> m2 ~ x + c1 + c2
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)    2.354      1.236    1.91     0.06 .  
#> x              0.435      0.105    4.15  7.1e-05 ***
#> c1             0.178      0.112    1.59     0.12    
#> c2            -0.167      0.110   -1.52     0.13    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.196. Adjusted R-square = 0.171. F(3, 96) = 7.812, p < .001
#> 
#> Model:
#> y ~ m11 + m12 + m2 + x + c1 + c2
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -1.791908   4.613263   -0.39  0.69859    
#> m11          0.203249   0.266930    0.76  0.44832    
#> m12          0.519112   0.255389    2.03  0.04494 *  
#> m2           0.838632   0.217639    3.85  0.00021 ***
#> x            0.071421   0.317264    0.23  0.82238    
#> c1          -0.000114   0.244934    0.00  0.99963    
#> c2          -0.069787   0.253231   -0.28  0.78348    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.315. Adjusted R-square = 0.271. F(6, 93) = 7.133, p < .001