In jtimonen/lgpr: Longitudinal Gaussian Process Regression

knitr::opts_chunk$set(echo = TRUE)

Model

print(fit@model)

Fit

print(fit)
plot(fit)

Plots

# Compute and plot predictions at data points (reduce = NULL)
alpha <- 0.3
p1 <- pred(fit, reduce = NULL, draws = DRAW_INDS, verbose = verbose)
plot_components(fit, p1,  t_name = "age", group_by = "id", alpha = alpha,
                color_by = c(NA, NA, "group", "sex", "group", NA))
plot_pred(fit, p1, t_name = "age", group_by = "id", alpha = alpha)

# Compute and plot out-of-sample predictions (reduce = mean)
x_pred <- new_x(fit, x_values = seq(0, 200, by = 2), group_by = "id",
                x = "age", x_ns = "diseaseAge")
p2 <- pred(fit, x_pred, reduce = mean, verbose = verbose)
plot_components(fit, p2, t_name = "age", group_by = "id",
                color_by = c(NA, NA, "group", "sex", "group", NA))
plot_pred(fit, p2, t_name = "age", group_by = "id")

Relevances and selection

r <- relevances(fit)
s <- select(fit)
print(r)
print(s)

sessionInfo()

sessionInfo()

jtimonen/lgpr documentation built on Oct. 12, 2023, 11:13 p.m.