Compute the derived parameters correlations from a cubic splines mixed-effects model by egg_model().

Based on computed area under the curves (i.e., egg_aucs()) and slopes (i.e., egg_slopes()) for several intervals using a model fitted by egg_model(), compute the correlations between each intervals derived parameters.

Usage

egg_correlations(
  fit,
  period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17),
  knots = c(1, 8, 12),
  start = 0.25,
  end = 10,
  step = 0.01,
  filter = NULL
)

Arguments

fit: A model object from a statistical model such as from a call to egg_model().
period: The intervals knots on which slopes are to be computed.
knots: The knots as defined fit and according to method.
start: The start of the time window to compute AP and AR.
end: The end of the time window to compute AP and AR.
step: The step to increment the sequence.
filter: A string following data.table syntax for filtering on "i" (i.e., row elements), e.g., filter = "source == 'A'". Argument pass through compute_apar() (see predict_bmi()). Default is NULL.

Value

A data.table object with correlations between each intervals derived parameters.

Examples

data("bmigrowth")
res <- egg_model(
  formula = log(bmi) ~ age,
  data = bmigrowth[bmigrowth[["sex"]] == 0, ],
  id_var = "ID",
  random_complexity = 1
)
#> Fitting model:
#>   nlme::lme(
#>     fixed = log(bmi) ~ gsp(age, knots = c(1, 8, 12), degree = rep(3, 4), smooth = rep(2, 3)),
#>     data = data,
#>     random = ~ gsp(age, knots = c(1, 8, 12), degree = rep(1, 4), smooth = rep(2, 3)) | ID,
#>     na.action = stats::na.omit,
#>     method = "ML",
#>     control = nlme::lmeControl(opt = "optim", niterEM = 25, maxIter = 500, msMaxIter = 500)
#>   )
egg_correlations(
  fit = res,
  period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17),
  knots = c(1, 8, 12)
)
#>               term  auc_0--0.5 auc_1.5--3.5 auc_6.5--10  auc_12--17
#>             <char>       <num>        <num>       <num>       <num>
#>  1:     auc_0--0.5  1.00000000  0.859252125   0.2700883  0.04034632
#>  2:   auc_1.5--3.5  0.85925212  1.000000000   0.7246147  0.54580347
#>  3:    auc_6.5--10  0.27008830  0.724614715   1.0000000  0.97294864
#>  4:     auc_12--17  0.04034632  0.545803466   0.9729486  1.00000000
#>  5:   slope_0--0.5 -0.25795663  0.272589824   0.8605788  0.95496221
#>  6: slope_1.5--3.5 -0.25795663  0.272589824   0.8605788  0.95496221
#>  7:  slope_6.5--10 -0.25795663  0.272589824   0.8605788  0.95496221
#>  8:   slope_12--17 -0.25795663  0.272589824   0.8605788  0.95496221
#>  9:    AP_ageyears -0.13447702 -0.027491854   0.1436239  0.18508442
#> 10:    AR_ageyears  0.11381106  0.008036503  -0.1520585 -0.18841085
#> 11:         AP_bmi  0.04998238  0.203225927   0.3332056  0.33380622
#> 12:         AR_bmi  0.02613407  0.211353846   0.3847213  0.39375324
#>     slope_0--0.5 slope_1.5--3.5 slope_6.5--10 slope_12--17 AP_ageyears
#>            <num>          <num>         <num>        <num>       <num>
#>  1:   -0.2579566     -0.2579566    -0.2579566   -0.2579566 -0.13447702
#>  2:    0.2725898      0.2725898     0.2725898    0.2725898 -0.02749185
#>  3:    0.8605788      0.8605788     0.8605788    0.8605788  0.14362395
#>  4:    0.9549622      0.9549622     0.9549622    0.9549622  0.18508442
#>  5:    1.0000000      1.0000000     1.0000000    1.0000000  0.22028245
#>  6:    1.0000000      1.0000000     1.0000000    1.0000000  0.22028245
#>  7:    1.0000000      1.0000000     1.0000000    1.0000000  0.22028245
#>  8:    1.0000000      1.0000000     1.0000000    1.0000000  0.22028245
#>  9:    0.2202824      0.2202824     0.2202824    0.2202824  1.00000000
#> 10:   -0.2167862     -0.2167862    -0.2167862   -0.2167862 -0.98172755
#> 11:    0.3028107      0.3028107     0.3028107    0.3028107 -0.19277901
#> 12:    0.3677929      0.3677929     0.3677929    0.3677929  0.07898946
#>      AR_ageyears      AP_bmi      AR_bmi
#>            <num>       <num>       <num>
#>  1:  0.113811056  0.04998238  0.02613407
#>  2:  0.008036503  0.20322593  0.21135385
#>  3: -0.152058462  0.33320556  0.38472126
#>  4: -0.188410854  0.33380622  0.39375324
#>  5: -0.216786218  0.30281065  0.36779288
#>  6: -0.216786218  0.30281065  0.36779288
#>  7: -0.216786218  0.30281065  0.36779288
#>  8: -0.216786218  0.30281065  0.36779288
#>  9: -0.981727551 -0.19277901  0.07898946
#> 10:  1.000000000  0.19568510 -0.08802460
#> 11:  0.195685103  1.00000000  0.95865751
#> 12: -0.088024597  0.95865751  1.00000000