---
title: "Introduction to splineCox"
author: "Ren Teranishi"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to splineCox}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Introduction

The `splineCox` package provides functions for fitting spline-based Cox regression models.
These models allow for flexible baseline hazard shapes and efficient model selection
based on log-likelihood. The package supports predefined baseline hazard shapes as well as user-defined numeric vectors, which are normalized to have an L1 norm of 1.

# Loading the Package

```{r setup, echo = TRUE}
library(splineCox)
library(joint.Cox)  # Required for example data
```

# Example Dataset

The `dataOvarian` dataset from the `joint.Cox` package contains time-to-event data, event indicators, 
and covariates for ovarian cancer patients.

```{r example-data}
# Load the dataset
data(dataOvarian)

# Display the first few rows
head(dataOvarian)
```

# Fitting the Model with Predefined Shapes

We fit a spline-based Cox regression model using three predefined baseline hazard shapes: "constant", "increase", and "decrease".

```{r fit-predefined-model}
# Define variables
t.event <- dataOvarian$t.event
event <- dataOvarian$event
Z <- dataOvarian$CXCL12
M <- c("constant", "increase", "decrease")

# Fit the model
reg2 <- splineCox.reg2(t.event, event, Z, model = M, plot = TRUE)

# Display the results
print(reg2)
```

# Fitting the Model with Custom Numeric Vectors

The package also allows users to specify custom numeric vectors to define the baseline hazard shape. These vectors will be normalized to have an L1 norm of 1.

```{r fit-custom-model}
# Define custom numeric vectors for baseline hazard shapes
custom_models <- list(c(0.1, 0.2, 0.3, 0.2, 0.2), c(0.2, 0.3, 0.3, 0.1, 0.1))

# Fit the model
reg2_custom <- splineCox.reg2(t.event, event, Z, model = custom_models, plot = TRUE)

# Display the results
print(reg2_custom)
```

# Interpreting Results

The output of the model includes:
- The best-fitting baseline hazard shape or normalized custom vector.
- Estimates for the regression coefficients (`beta`) and the baseline hazard scale parameter (`gamma`).
- Log-likelihood for model selection.
- A plot of the estimated baseline hazard function with 95% confidence intervals (if `plot = TRUE`).

Below are the results from the predefined shapes example:

```{r display-predefined-results}
# Print a summary of the results
print(reg2)
```

And here are the results from the custom numeric vectors example:

```{r display-custom-results}
# Print a summary of the results
print(reg2_custom)
```



# Modeling Dependence with B-spline Copulas

The `splineCox` package (version 0.0.4 and later) provides the `spline.copula` function, which implements a flexible B-spline copula model based on the five-parameter M-spline basis. This allows users to model dependence structures between two variables on the unit square \([0, 1]^2\), supporting both the copula **density** and **distribution function**.

## Key Features

- Computes the copula **density** \(c(u, v)\) or the copula **distribution function** \(C(u, v)\).
- Uses a 5×5 coefficient matrix (`R`) with various built-in presets (e.g., independence, positive/negative dependence, tail dependence, etc.).
- Integrates with the `joint.Cox` package's `M.spline` and `I.spline` basis functions for computation.

## Example: Visualizing Copula Densities

Below is an example illustrating how to visualize the density of the independence copula and a positively dependent copula.

```{r copula-density-plot, message=FALSE, warning=FALSE}
library(ggplot2)

N <- 50
u <- v <- seq(from = 0, to = 1, length.out = N)
U <- rep(u, N)
V <- rep(v, each = N)

# Positive Exchangeable
c.data <- data.frame(
  U = U, V = V,
  C = spline.copula(U, V, R = "PE1", density = TRUE, mat = FALSE)
)

ggplot(aes(x=U, y=V), data=c.data) +
  geom_contour(aes(x=U,y=V,z=C,colour=after_stat(level)),
               data=c.data,bins=25)+xlab("u")+ylab("v")

# Negative Exchangeable
c.data <- data.frame(
  U = U, V = V,
  C = spline.copula(U, V, R = "NE3", density = TRUE, mat = FALSE)
)

ggplot(aes(x=U, y=V), data=c.data) +
  geom_contour(aes(x=U,y=V,z=C,colour=after_stat(level)),
               data=c.data,bins=25)+xlab("u")+ylab("v")
```

## Computing Dependence Measures

The `spline.copula()` function can also compute Kendall’s tau and Spearman’s rho analytically from the coefficient matrix \(R\).  
These are widely used rank-based measures of dependence.

```{r copula-dependence-measures}
# Compute Kendall's tau and Spearman's rho for preset R matrix "PE1"
out <- spline.copula(U, V, R = "PE1", Kendall = TRUE, Spearman = TRUE)

# Display the results
out$Kendall_tau
out$Spearman_rho
```
