Return the confidence intervals of estimates conditional effect in the output of cond_effect() or cond_effect_boot().

# S3 method for cond_effect
confint(object, parm, level = 0.95, type, ...)

Arguments

object

The output of cond_effect() or cond_effect_boot().

parm

Ignored by this function. The confidence intervals for all available levels will be returned.

level

The level of confidence. For the confidence intervals returned by lm(), default is .95, i.e., 95%. For the bootstrap percentile confidence intervals, default is the level used in calling cond_effect_boot().

type

The type of the confidence intervals. If est to "lm", returns the confidence interval given by the confint() method of lm(). If set to "boot", the bootstrap percentile confidence intervals are returned. Default is "boot" if bootstrap estimates are stored in object, and "lm" if bootstrap estimates are not stored.

...

Additional arguments. Ignored.

Value

A matrix of the confidence intervals.

Details

If bootstrapping is used to form the confidence interval by cond_effect_boot(), users can request the percentile confidence intervals of the bootstrap estimates. This method does not do the bootstrapping itself.

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)
print(out, t_ci = TRUE)
#> The effects of iv on dv, conditional on mod:
#> 
#>   Level     mod iv Effect   S.E.      t     p Sig CI.Lo(t) CI.Hi(t)
#>    High 105.436   412.911 20.827 19.826 0.000 ***  371.991  453.831
#>  Medium 100.395   395.693 14.684 26.948 0.000 ***  366.843  424.543
#>     Low  95.354   378.474 19.249 19.662 0.000 ***  340.654  416.293
#> 
#> [CI.Lo(t), CI.Hi(t)] shows the 95% confidence interval(s) based on t statistics.
#> 
#> 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).
confint(out)
#>           2.5 %   97.5 %
#> High   371.9915 453.8309
#> Medium 366.8425 424.5426
#> Low    340.6543 416.2935

lm_std <- std_selected(lm_raw, to_scale = ~ iv + mod, to_center = ~ iv + mod)
out <- cond_effect(lm_std, x = iv, w = mod)
print(out, t_ci = TRUE)
#> The effects of iv on dv, conditional on mod:
#> 
#>   Level    mod iv Effect   S.E.      t     p Sig CI.Lo(t) CI.Hi(t)
#>    High  1.000   841.990 42.468 19.826 0.000 ***  758.548  925.431
#>  Medium  0.000   806.878 29.942 26.948 0.000 ***  748.048  865.708
#>     Low -1.000   771.767 39.251 19.662 0.000 ***  694.647  848.887
#> 
#> [CI.Lo(t), CI.Hi(t)] shows the 95% confidence interval(s) based on t statistics.
#> They should not be used when one or more variables are standardized.
#> 
#> 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.
confint(out)
#>           2.5 %   97.5 %
#> High   758.5480 925.4311
#> Medium 748.0485 865.7078
#> Low    694.6468 848.8866

# 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)
print(out, t_ci = TRUE)
#> The effects of iv on dv, conditional on cat1:
#> 
#>  Level cat1 iv Effect   S.E.      t     p Sig CI.Lo(t) CI.Hi(t)
#>    gp1  gp1   391.026 29.246 13.370 0.000 ***  333.564  448.487
#>    gp2  gp2   434.302 24.937 17.416 0.000 ***  385.305  483.299
#>    gp3  gp3   369.807 28.858 12.815 0.000 ***  313.107  426.506
#> 
#> [CI.Lo(t), CI.Hi(t)] shows the 95% confidence interval(s) based on t statistics.
#> 
#> The regression model:
#> 
#> 	dv ~ iv * cat1 + v1
confint(out)
#>        2.5 %   97.5 %
#> gp1 333.5643 448.4873
#> gp2 385.3048 483.2985
#> gp3 313.1071 426.5064