Print the output of cond_effect() or cond_effect_boot().

# S3 method for cond_effect
print(
x,
nd = 3,
nd_stat = 3,
nd_p = 3,
title = TRUE,
model = TRUE,
level_info = TRUE,
standardized = TRUE,
boot_info = TRUE,
table_only = FALSE,
t_ci = FALSE,
t_ci_level = 0.95,
...
)

## Arguments

x

The output of cond_effect() or cond_effect_boot().

nd

The number of digits for the variables.

nd_stat

The number of digits for test statistics (e.g., t).

nd_p

The number of digits for p-values.

title

If TRUE, print a title. Default is TRUE.

model

If TRUE, print the regression model. Default is TRUE.

level_info

If TRUE, print information for interpreting the levels of the moderator, such as the values of the levels and distance from the mean. Default is TRUE.

standardized

If TRUE and one or more variables are standardized, report it. Default is TRUE.

boot_info

If TRUE and bootstrap estimates are in x, print information about the bootstrapping, such as the number of bootstrap samples. Default is TRUE.

table_only

If TRUE, will suppress of other elements except for the table of conditional effects. Override arguments such as title, model, and level_info.

t_ci

If TRUE, will print the confidence intervals based on t statistics. These confidence intervals should not be used if some variables are standardized.

t_ci_level

The level of confidence of the confidence intervals based on t statistics. Default is .95.

...

Additional arguments. Ignored by this function.

## Value

x is returned invisibility.

## Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

## 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)

cond_effect(lm_raw, x = iv, w = mod)
#> 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).

lm_std <- std_selected(lm_raw, to_scale = ~ iv + mod,
to_center = ~ iv + mod)

cond_effect(lm_std, x = iv, w = mod)
#> 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.

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