Compute area under the curves for several intervals using a model fitted by time_model().

Compute area under the curves for "clubic slope", "linear splines" and "cubic splines" fitted using time_model().

Usage

compute_aucs(
  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 data.frame with AUC for each individuals/samples.

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)
#> )
head(compute_aucs(
  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]]
))
#>      ID auc_0--0.5 auc_1.5--3.5 auc_6.5--10 auc_12--17
#> 001 001   1.411971     6.179849    11.67305   18.53235
#> 004 004   1.341466     5.835608    11.16576   17.68861
#> 005 005   1.358373     5.762464    11.26893   18.24248
#> 006 006   1.312805     5.545659    10.71672   17.09600
#> 007 007   1.320790     6.168538    11.79039   18.53175
#> 009 009   1.376196     5.669445    10.90937   17.51368