Type: Package
Title: Visualization of Spline Effects in GAM and GLM Models
Version: 0.1.1
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>
Description: Creates 'ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models) fitted with 'mgcv' and spline effects from GLM (Generalized Linear Models). Supports interaction terms and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models.
License: Apache License 2.0
Encoding: UTF-8
Imports: ggplot2 (≥ 3.0.0), stats
Suggests: mgcv, survival, splines, testthat (≥ 3.0.0), knitr, rmarkdown, covr, pkgdown
RoxygenNote: 7.3.2
VignetteBuilder: knitr
URL: https://github.com/jinseob2kim/splineplot, https://jinseob2kim.github.io/splineplot/
BugReports: https://github.com/jinseob2kim/splineplot/issues
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-09-23 15:09:22 UTC; js
Author: Jinseob Kim ORCID iD [aut, cre], Zarathu [cph, fnd]
Repository: CRAN
Date/Publication: 2025-09-30 08:30:14 UTC

Extract Spline Data

Description

Extract predictions and confidence intervals from fitted models

Usage

extract_spline_data(
  fit,
  data,
  xvar,
  refx,
  model_info,
  term_index = 1,
  log_scale = FALSE,
  ci_level = 0.95
)

Arguments

fit

Fitted model object

data

Data frame

xvar

Variable name

refx

Reference value

model_info

Model information list

term_index

Which smooth term to use (for multiple s() terms)

log_scale

Whether to use log scale

ci_level

Confidence level

Value

Data frame with predictions

Examples

# Create sample data
set.seed(123)
n <- 100
x <- rnorm(n, mean = 50, sd = 10)
y <- rbinom(n, 1, plogis(-0.05*(x - 50)))
dat <- data.frame(x = x, y = y)

# Fit GLM with splines
library(splines)
fit <- glm(y ~ ns(x, df = 4), family = binomial(), data = dat)

# Extract spline data
model_info <- list(type = "glm", family = "binomial", ylabel = "Odds Ratio")
df <- extract_spline_data(fit, dat, "x", refx = 50, model_info,
                          log_scale = FALSE, ci_level = 0.95)
head(df)

Spline Plot for GAM and GLM Models

Description

Create ggplot2 visualizations of smooth or spline effects from GAM and GLM models. Supports Linear, Logistic, Poisson, and Cox models with interaction terms. Handles GAM smooth terms (s(), te(), ti()), GLM splines (ns(), bs()), and Cox pspline().

Usage

splineplot(
  fit,
  data,
  xvar = NULL,
  by_var = NULL,
  refx = NULL,
  term_index = 1,
  bins = 12,
  xlim = NULL,
  ylim = NULL,
  show_hist = NULL,
  log_scale = FALSE,
  ci_level = 0.95,
  show_ref_point = TRUE,
  colors = NULL,
  ribbon_ci = FALSE,
  xlab = NULL,
  ylab = NULL,
  ylab_right = "Percent of Population"
)

Arguments

fit

A fitted model object (gam, glm, lm, coxph)

data

The data frame used to fit the model

xvar

Character string specifying the variable name for x-axis (default: first spline term)

by_var

Character string specifying the interaction variable (default: auto-detect from model)

refx

Reference value for the x variable (default: median)

term_index

For GAM with multiple smooth terms, which term to plot (default: 1)

bins

Number of bins for histogram (default: 12)

xlim

X-axis limits (default: range of x variable)

ylim

Y-axis limits (default: auto-determined, e.g., c(0.25, 2.0) for HR/OR/RR)

show_hist

Logical, whether to show histogram (default: TRUE)

log_scale

Logical, whether to use log scale for OR/RR/HR (default: FALSE)

ci_level

Confidence interval level (default: 0.95)

show_ref_point

Logical, whether to show reference point marker (default: TRUE)

colors

Named vector of colors for by_var levels

ribbon_ci

Logical, whether to use ribbon style for CI (default: FALSE, uses dotted lines)

xlab

Custom x-axis label (default: xvar name)

ylab

Custom y-axis label (default: auto-determined based on model type)

ylab_right

Custom right y-axis label for histogram (default: "Percent of Population")

Value

A ggplot2 object

Examples

# Create sample data
set.seed(123)
n <- 200
x <- rnorm(n, mean = 50, sd = 10)
lp <- -0.05*(x - 50) + 0.001*(x - 50)^2
y <- rbinom(n, 1, plogis(lp))
dat <- data.frame(x = x, y = y)

# GLM with natural splines
library(splines)
fit_glm <- glm(y ~ ns(x, df = 4), family = binomial(), data = dat)
p <- splineplot(fit_glm, dat)


# GAM example (requires mgcv)
if (requireNamespace("mgcv", quietly = TRUE)) {
  fit_gam <- mgcv::gam(y ~ s(x), family = binomial(), data = dat)
  p2 <- splineplot(fit_gam, dat)
}

# Cox model example (requires survival)
if (requireNamespace("survival", quietly = TRUE)) {
  time <- rexp(n, rate = exp(lp/2))
  status <- rbinom(n, 1, 0.8)
  dat$time <- time
  dat$status <- status
  fit_cox <- survival::coxph(survival::Surv(time, status) ~ ns(x, df = 4),
                              data = dat)
  p3 <- splineplot(fit_cox, dat)
}