get_individual_algebra_results

evaluates algebras for each subject in the data set. This function is useful if you have algebras with definition variables (e.g., in mnlfa).

Usage

get_individual_algebra_results(
  mxModel,
  algebra_names = NULL,
  progress_bar = TRUE
)

Arguments

mxModel

mxModel with algebras

algebra_names

optional: Only compute individual algebras for a subset of the parameters

progress_bar

should a progress bar be shown?

Value

a list of data frames. The list contains data frames for each of the algebras. The data frames contain the individual specific algebra results as well as all definition variables used to predict said algebra

Examples

library(mxsem)

set.seed(123)
dataset <- simulate_moderated_nonlinear_factor_analysis(N = 50)

model <- "
  xi  =~ x1 + x2 + x3
  eta =~ y1 + y2 + y3
  eta ~  {a := a0 + data.k*a1}*xi
  "
fit <- mxsem(model = model,
             data = dataset) |>
  mxTryHard()
#> Running untitled3 with 20 parameters
#> 
#> Beginning initial fit attempt
#> Running untitled3 with 20 parameters
#> 
#>  Lowest minimum so far:  206.07381213759
#> 
#> Solution found
#> 


#> 
#>  Solution found!  Final fit=206.07381 (started at 1259.055)  (1 attempt(s): 1 valid, 0 errors)
#>  Start values from best fit:
#> 0.860279356446424,0.93310011519778,0.818073620425412,0.917510125303794,0.0504725154057915,0.0290778597775934,0.0469073044162057,0.0419027627433044,0.0380640418077413,0.0293279504853111,0.806082523552278,0.191196377322578,0.0225271379783757,0.00927744170851602,-0.017906134668413,0.0495250696343279,-0.00882794910402525,0.0257245363438889,0.622360649597024,-0.181971897354282

algebra_results <- get_individual_algebra_results(mxModel = fit,
                                                  progress_bar = FALSE)

# the following plot will only show two data points because there is only
# two values for the definition variable k (0 or 1).

plot(x = algebra_results[["a"]]$k,
     y = algebra_results[["a"]]$algebra_result)