Return the estimates of the conditional effects in the output of cond_effect() or cond_effect_boot().

# S3 method for cond_effect
coef(object, ...)

Arguments

object

The output of cond_effect() or cond_effect_boot().

...

Optional arguments. Ignored by the function.

Value

A numeric vector: The estimates of the conditional effects in a cond_effect-class object.

Details

It just extracts and returns the column of conditional effects in a cond_effect-class object.

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
#> 
out <- cond_effect(lm_raw, x = iv, w = mod)
out
#> The effects of iv on dv, conditional on mod:
#> 
#>   Level     mod iv Effect   S.E.      t     p Sig
#>    High 105.436   412.911 20.827 19.826 0.000 ***
#>  Medium 100.395   395.693 14.684 26.948 0.000 ***
#>     Low  95.354   378.474 19.249 19.662 0.000 ***
#> 
#> 
#> The regression model:
#> 
#> 	dv ~ iv * mod + v1 + cat1
#> 
#> Interpreting the levels of mod:
#> 
#>   Level     mod % Below From Mean (in SD)
#>    High 105.436   84.00              1.00
#>  Medium 100.395   47.40              0.00
#>     Low  95.354   17.20             -1.00
#> 
#> - % Below: The percent of cases equal to or less than a level.
#> - From Mean (in SD): Distance of a level from the mean,
#>   in standard deviation (+ve above, -ve below).
coef(out)
#>     High   Medium      Low 
#> 412.9112 395.6925 378.4739 

lm_std <- std_selected(lm_raw, to_scale = ~ iv + mod, to_center = ~ iv + mod)
out <- cond_effect(lm_std, x = iv, w = mod)
out
#> The effects of iv on dv, conditional on mod:
#> 
#>   Level    mod iv Effect   S.E.      t     p Sig
#>    High  1.000   841.990 42.468 19.826 0.000 ***
#>  Medium  0.000   806.878 29.942 26.948 0.000 ***
#>     Low -1.000   771.767 39.251 19.662 0.000 ***
#> 
#> 
#> The regression model:
#> 
#> 	dv ~ iv * mod + v1 + cat1
#> 
#> Interpreting the levels of mod:
#> 
#>   Level    mod % Below From Mean (in SD)
#>    High  1.000   84.00              1.00
#>  Medium  0.000   47.40              0.00
#>     Low -1.000   17.20             -1.00
#> 
#> - % Below: The percent of cases equal to or less than a level.
#> - From Mean (in SD): Distance of a level from the mean,
#>   in standard deviation (+ve above, -ve below).
#> 
#> Note:
#> 
#> - The variable(s) iv, mod is/are standardized.
coef(out)
#>     High   Medium      Low 
#> 841.9896 806.8781 771.7667 

# Categorical moderator
lm_cat <- lm(dv ~ iv*cat1 + v1, dat)
summary(lm_cat)
#> 
#> Call:
#> lm(formula = dv ~ iv * cat1 + v1, data = dat)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -2457.67  -506.03     3.46   437.95  2738.18 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   979.20     459.19   2.132   0.0335 *  
#> iv            391.03      29.25  13.370   <2e-16 ***
#> cat1gp2      -845.49     584.85  -1.446   0.1489    
#> cat1gp3       259.55     620.76   0.418   0.6760    
#> v1            -19.36      11.00  -1.759   0.0791 .  
#> iv:cat1gp2     43.28      38.44   1.126   0.2608    
#> iv:cat1gp3    -21.22      41.08  -0.516   0.6058    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 721.3 on 493 degrees of freedom
#> Multiple R-squared:  0.5707,	Adjusted R-squared:  0.5655 
#> F-statistic: 109.2 on 6 and 493 DF,  p-value: < 2.2e-16
#> 
out <- cond_effect(lm_cat, x = iv, w = cat1)
out
#> The effects of iv on dv, conditional on cat1:
#> 
#>  Level cat1 iv Effect   S.E.      t     p Sig
#>    gp1  gp1   391.026 29.246 13.370 0.000 ***
#>    gp2  gp2   434.302 24.937 17.416 0.000 ***
#>    gp3  gp3   369.807 28.858 12.815 0.000 ***
#> 
#> 
#> The regression model:
#> 
#> 	dv ~ iv * cat1 + v1
coef(out)
#>      gp1      gp2      gp3 
#> 391.0258 434.3016 369.8068