APIM Actor-Partner Interdependence Model Table with Multiple Moderators

Source: R/apim_table.R

[Stable]
Actor-partner interdependence model that test multiple moderators simultaneously.

APIM_table(
  data,
  predictor_a,
  predictor_p,
  outcome_a,
  outcome_p,
  mod_a = NULL,
  mod_p = NULL,
  mod_type = "mod",
  return_result = FALSE
)

Arguments

data

data frame object

predictor_a

predictor variable name for actor

predictor_p

predictor variable name for partner

outcome_a

dependent variable name for actor

outcome_p

dependent variable name for partner

mod_a

moderation variable name for actor. Support dplyr::select() syntax.

mod_p

moderation variable name for partner. Support dplyr::select() syntax.

mod_type

only 'mod' is supported for now

return_result

return lavaan::parameterestimates(). Default is FALSE

Value

data.frame of the APIM table

Examples

APIM_table(data = acitelli,
           predictor_a = 'Tension_A',
           predictor_p = 'Tension_P',
           outcome_a = 'Satisfaction_A',
           outcome_p = 'Satisfaction_P',
           mod_a = c('SelfPos_A','OtherPos_A','SimHob_A'),
           mod_p = c('SelfPos_P','OtherPos_P','SimHob_P'))
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>    Moderator  Predictor         Outcome         iAA         iPP         iAP         iPA          aa          ab          pa          pb
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>    SelfPos_A  Tension_A  Satisfaction_A   0.091       0.114 *     0.021       0.162 **   -0.368 ***   0.151 ***  -0.167 ***  -0.020    
#>   OtherPos_A  Tension_A  Satisfaction_A   0.185 ***   0.032       0.194 ***   0.133 **   -0.277 ***   0.236 ***  -0.098 ***   0.166 ***
#>     SimHob_A  Tension_A  Satisfaction_A   0.102 **   -0.032       0.122 ***  -0.029      -0.335 ***   0.144 ***  -0.162 ***   0.070 ** 
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> You can drag and resize the R console to view the entire table
#> Lab
#> iAA: Y_a ~ X_a * M_a and Y_p ~ X_p * M_p 
#> iPP: Y_a ~ X_p * M_p and Y_p ~ X_a * M_a                                               
#> iAP: Y_a ~ X_a * M_p and Y_p ~ X_p * M_a
#> iPA: Y_a ~ X_p * M_a and Y_p ~ X_a * M_p
#>  aa: Y_a ~ X_a and Y_p ~ X_p 
#>  ab: Y_a ~ M_a and Y_p ~ M_p 
#>  pa: Y_a ~ X_p and Y_p ~ X_a
#>  pb: Y_a ~ M_p and Y_p ~ M_a