Fit a cubic splines mixed model regression
with three splines parametrisation as random effect.
This function is a specific version of time_model().
Usage
egg_model(
formula,
data,
id_var,
random_complexity = "auto",
use_car1 = FALSE,
knots = c(1, 8, 12),
quiet = FALSE
)Arguments
- formula
An object of class "formula": a symbolic description of the model to be fitted with, time component as the first term in the right-hand side.
- data
A data.frame containing the variables defined in formula.
- id_var
A string indicating the name of the variable to be used as the individual identifier.
- random_complexity
A numeric (1-3) indicating the complexity of the random effect term. Default,
"auto"will try from the more complex to the less complex if no success.- use_car1
A logical indicating whether to use continuous auto-correlation, i.e., CAR(1) as correlation structure.
- knots
The knots defining the splines.
- quiet
A logical indicating whether to suppress the output.
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)
#> )
sres <- as.data.frame(summary(res)[["tTable"]])
rownames(sres) <- sub("gsp\\(.*\\)\\)", "gsp(...)", rownames(sres))
sres
#> Value Std.Error DF t-value p-value
#> (Intercept) 2.61835319 0.015188424 478 172.391363 0.000000e+00
#> gsp(...)D1(0) 0.87980825 0.048919995 478 17.984635 1.302819e-55
#> gsp(...)D2(0) -1.88100109 0.111960768 478 -16.800538 4.066470e-50
#> gsp(...)D3(0) 1.92287447 0.115451509 478 16.655256 1.893624e-49
#> gsp(...)C(1).3 -1.93037596 0.116238566 478 -16.607018 3.153465e-49
#> gsp(...)C(8).3 0.01240328 0.004270085 478 2.904692 3.846477e-03
#> gsp(...)C(12).3 -0.01521666 0.011250160 478 -1.352573 1.768316e-01