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    0.8592521   0.2700883  0.04034632
#>  2:   auc_1.5--3.5  0.85925212    1.0000000   0.7246147  0.54580347
#>  3:    auc_6.5--10  0.27008830    0.7246147   1.0000000  0.97294864
#>  4:     auc_12--17  0.04034632    0.5458035   0.9729486  1.00000000
#>  5:   slope_0--0.5 -0.25795663    0.2725898   0.8605788  0.95496221
#>  6: slope_1.5--3.5 -0.25795663    0.2725898   0.8605788  0.95496221
#>  7:  slope_6.5--10 -0.25795663    0.2725898   0.8605788  0.95496221
#>  8:   slope_12--17 -0.25795663    0.2725898   0.8605788  0.95496221
#>  9:    AP_ageyears -0.28781211    0.1467279   0.7909131  0.92641596
#> 10:    AR_ageyears  0.29075211   -0.1464326  -0.7950653 -0.93170497
#> 11:         AP_bmi  0.99381038    0.9399620   0.4353792  0.16688346
#> 12:         AR_bmi  0.92589813    0.9953601   0.6674790  0.43382020
#>     slope_0--0.5 slope_1.5--3.5 slope_6.5--10 slope_12--17 AP_ageyears
#>            <num>          <num>         <num>        <num>       <num>
#>  1:  -0.25795663    -0.25795663   -0.25795663  -0.25795663 -0.28781211
#>  2:   0.27258982     0.27258982    0.27258982   0.27258982  0.14672789
#>  3:   0.86057876     0.86057876    0.86057876   0.86057876  0.79091312
#>  4:   0.95496221     0.95496221    0.95496221   0.95496221  0.92641596
#>  5:   1.00000000     1.00000000    1.00000000   1.00000000  0.99129588
#>  6:   1.00000000     1.00000000    1.00000000   1.00000000  0.99129588
#>  7:   1.00000000     1.00000000    1.00000000   1.00000000  0.99129588
#>  8:   1.00000000     1.00000000    1.00000000   1.00000000  0.99129588
#>  9:   0.99129588     0.99129588    0.99129588   0.99129588  1.00000000
#> 10:  -0.99742818    -0.99742818   -0.99742818  -0.99742818 -0.98172755
#> 11:  -0.20272013    -0.20272013   -0.20272013  -0.20272013 -0.19277901
#> 12:   0.07743496     0.07743496    0.07743496   0.07743496  0.07898946
#>     AR_ageyears     AP_bmi      AR_bmi
#>           <num>      <num>       <num>
#>  1:   0.2907521  0.9938104  0.92589813
#>  2:  -0.1464326  0.9399620  0.99536013
#>  3:  -0.7950653  0.4353792  0.66747898
#>  4:  -0.9317050  0.1668835  0.43382020
#>  5:  -0.9974282 -0.2027201  0.07743496
#>  6:  -0.9974282 -0.2027201  0.07743496
#>  7:  -0.9974282 -0.2027201  0.07743496
#>  8:  -0.9974282 -0.2027201  0.07743496
#>  9:  -0.9817276 -0.1927790  0.07898946
#> 10:   1.0000000  0.1956851 -0.08802460
#> 11:   0.1956851  1.0000000  0.95865751
#> 12:  -0.0880246  0.9586575  1.00000000