Print a 'cond_effect' Class Object — print.cond

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

Usage

# 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.
#> - One or more variables are scaled by SD or standardized. OLS standard
#>   errors and confidence intervals may be biased for their coefficients.
#>   Please use `cond_effect_boot()`.