Skip to contents

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

Source: R/compute_correlations.R
compute_correlations.Rd

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

Usage

compute_correlations(
  fit,
  method,
  period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17),
  knots = list(cubic_slope = NULL, linear_splines = c(0.75, 5.5, 11), cubic_splines =
    c(1, 8, 12))[[method]]
)

Arguments

fit

A model object from a statistical model such as from a call to time_model().

method

The type of model provided in fit, i.e., one of "cubic_slope", "linear_splines" or "cubic_splines".

period

The intervals knots on which AUCs are to be computed.

knots

The knots as defined fit and according to method.

Value

A list object with correlations between each intervals derived parameters.

Examples

data("bmigrowth")
ls_mod <- time_model(
  x = "age",
  y = "log(bmi)",
  cov = NULL,
  data = bmigrowth[bmigrowth[["sex"]] == 0, ],
  method = "linear_splines"
)
#> nlme::lme(
#>   fixed = log(bmi) ~ gsp(age, knots = c(0.75, 5.5, 11), degree = rep(1, 4), smooth = rep(0, 3)),
#>   data = data,
#>   random = ~ gsp(age, knots = c(0.75, 5.5, 11), degree = rep(1, 4), smooth = rep(0, 3)) | ID,
#>   na.action = stats::na.omit,
#>   method = "ML",
#>   control = nlme::lmeControl(opt = "optim", maxIter = 500, msMaxIter = 500)
#> )
compute_correlations(
  fit = ls_mod,
  method = "linear_splines",
  period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17)#,
  # knots = list(
  #   "cubic_slope" = NULL,
  #   "linear_splines" = c(0.75, 5.5, 11),
  #   "cubic_splines" = c(1, 8, 12)
  # )[[method]]
)
#> $AUC
#>            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    0.5282555   0.1096091 0.03004233
#> 2: auc_1.5--3.5 0.52825550    1.0000000   0.7244223 0.51476239
#> 3:  auc_6.5--10 0.10960912    0.7244223   1.0000000 0.95171508
#> 4:   auc_12--17 0.03004233    0.5147624   0.9517151 1.00000000
#> 
#> $SLOPE
#>              term slope_0--0.5 slope_1.5--3.5 slope_6.5--10 slope_12--17
#>            <char>        <num>          <num>         <num>        <num>
#> 1:   slope_0--0.5    1.0000000      0.2967416    -0.8479564    0.2391561
#> 2: slope_1.5--3.5    0.2967416      1.0000000    -0.3552805    0.8843912
#> 3:  slope_6.5--10   -0.8479564     -0.3552805     1.0000000   -0.5354321
#> 4:   slope_12--17    0.2391561      0.8843912    -0.5354321    1.0000000
#>