Predict average slopes for several intervals using a model fitted by time_model().

Comoute average slopes for "clubic slope", "linear splines" and "cubic splines" fitted using time_model().

Usage

compute_slopes(
  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 slopes are to be computed.
knots: The knots as defined fit and according to method.

Value

A data.frame with slopes 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_slopes(
  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 pred_period_0 pred_period_0.5 pred_period_1.5 pred_period_3.5
#> 1 082      2.723878        2.924006        3.052294        3.127556
#> 2 083      2.589648        2.776216        2.890202        2.945406
#> 3 080      2.664667        2.768827        2.846761        2.915703
#> 4 031      2.563305        2.687913        2.759908        2.785750
#> 5 007      2.482096        2.801064        3.013571        3.154967
#> 6 033      2.714582        2.790200        2.830887        2.838559
#>   pred_period_6.5 pred_period_10 pred_period_12 pred_period_17 slope_0--0.5
#> 1        3.250941       3.419375       3.535776       3.877164    0.4002572
#> 2        3.069558       3.310879       3.424940       3.650504    0.3731377
#> 3        3.070117       3.369271       3.510101       3.786891    0.2083212
#> 4        2.902621       3.221219       3.342805       3.495594    0.2492165
#> 5        3.322661       3.414705       3.516817       3.895885    0.6379362
#> 6        2.944679       3.289247       3.420563       3.584907    0.1512362
#>   slope_1.5--3.5 slope_6.5--10 slope_12--17
#> 1    0.037630884    0.04812388   0.06827756
#> 2    0.027601764    0.06894873   0.04511272
#> 3    0.034471055    0.08547234   0.05535803
#> 4    0.012921103    0.09102806   0.03055786
#> 5    0.070697987    0.02629820   0.07581357
#> 6    0.003836079    0.09844796   0.03286874