splineplot

The splineplot package provides a unified interface for visualizing spline effects from GAM (Generalized Additive Models) and GLM (Generalized Linear Models) in R. It creates publication-ready plots with confidence intervals, supporting various model types including Linear, Logistic, Poisson, and Cox proportional hazards models.

Key Features

📊 Unified visualization for spline effects across different model types
🎨 Publication-ready plots with customizable aesthetics
📈 Automatic detection of spline terms and interaction variables
🔄 Support for multiple spline types: GAM smooths (s(), te(), ti()), GLM splines (ns(), bs()), and Cox pspline()
📉 Flexible confidence intervals: dotted lines or ribbon style
📊 Built-in histograms showing data distribution
🎯 Reference point marking with automatic SE = 0 at reference value
🔀 Interaction term support with by-variable visualization

Installation

You can install the released version of splineplot from CRAN:

install.packages("splineplot")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("jinseob2kim/splineplot")

Basic Usage

library(splineplot)
library(mgcv)
library(survival)
library(splines)
library(ggplot2)

# Generate sample data
set.seed(123)
n <- 500
x <- rnorm(n, mean = 35, sd = 8)
lp <- -0.06*(x - 35) + 0.0009*(x - 35)^3/(8^2)
time <- rexp(n, rate = exp(lp))
status <- rbinom(n, 1, 0.8)
binary_y <- rbinom(n, 1, plogis(lp))

dat <- data.frame(x, time, status, binary_y)

GAM with Cox family

# Fit GAM Cox model
fit_gam_cox <- gam(time ~ s(x),
                   family = cox.ph(), weights = status, data = dat)

# Create spline plot
splineplot(fit_gam_cox, dat,
          ylim = c(0.2, 2.0),
          xlab = "Age (years)",
          ylab = "Hazard Ratio")
#> Using 'x' as x variable
#> Using refx = 35.17 (median of x)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_rect()`).
#> Warning: Removed 39 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 56 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 49 rows containing missing values or values outside the scale range
#> (`geom_line()`).

Logistic regression with natural splines

# Fit logistic model with natural splines
fit_glm <- glm(binary_y ~ ns(x, df = 4),
               family = binomial(), data = dat)

# Create spline plot
splineplot(fit_glm, dat,
          ylim = c(0.2, 2.0),
          ylab = "Odds Ratio")
#> Using 'x' as x variable
#> Using refx = 35.17 (median of x)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_rect()`).
#> Warning: Removed 22 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 85 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 59 rows containing missing values or values outside the scale range
#> (`geom_line()`).

GAM with interaction terms

# Add a grouping variable
dat$group <- factor(sample(c("A", "B"), n, replace = TRUE))

# Fit model with interaction
fit_interaction <- gam(time ~ s(x, by = group),
                      family = cox.ph(),
                      weights = status,
                      data = dat)

# Plot with interaction
splineplot(fit_interaction, dat,
          ylim = c(0.2, 2.0))
#> Using 'x' as x variable
#> Detected interaction with 'group'
#> Using refx = 35.17 (median of x)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_rect()`).
#> Warning: Removed 136 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 116 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 126 rows containing missing values or values outside the scale range
#> (`geom_line()`).

Advanced Features

Customizing confidence intervals

# Default: dotted lines
splineplot(fit_gam_cox, dat, ribbon_ci = FALSE)

# Alternative: ribbon/shaded area
splineplot(fit_gam_cox, dat, ribbon_ci = TRUE)

Log scale for odds/rate/hazard ratios

# Use log scale for y-axis
splineplot(fit_glm, dat, log_scale = TRUE)
#> Using 'x' as x variable
#> Using refx = 35.17 (median of x)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_rect()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).

Custom reference values

# Set custom reference point (default is median)
splineplot(fit_gam_cox, dat,
          refx = 40,  # Reference at x = 40
          show_ref_point = TRUE)  # Show diamond marker
#> Using 'x' as x variable
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_rect()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).

Supported Model Types

Model Type	Model Function	Spline Types	Outcome
GAM	`mgcv::gam()`	`s()`, `te()`, `ti()`	HR, OR, RR, Effect
GLM	`stats::glm()`	`ns()`, `bs()`	OR, RR, Effect
Linear	`stats::lm()`	`ns()`, `bs()`	Effect
Cox	`survival::coxph()`	`ns()`, `bs()`, `pspline()`*	HR

*Note: pspline() has limited support due to its internal structure. We recommend using ns() or bs() with Cox models for optimal results.

Function Parameters

Parameter	Description	Default
`fit`	Fitted model object	Required
`data`	Data frame used for fitting	Required
`xvar`	Variable name for x-axis	Auto-detected
`by_var`	Interaction variable	Auto-detected
`refx`	Reference x value	Median of x
`xlim`	X-axis limits	Data range
`ylim`	Y-axis limits	Auto
`show_hist`	Show histogram	TRUE
`ribbon_ci`	Use ribbon CI style	FALSE
`log_scale`	Use log scale for y-axis	FALSE
`show_ref_point`	Show reference point marker	TRUE
`xlab`	X-axis label	Variable name
`ylab`	Y-axis label	Auto by model
`ylab_right`	Right y-axis label	“Percent of Population”

Citation

If you use splineplot in your research, please cite:

citation("splineplot")

Getting Help

Documentation: Visit our pkgdown site
Bug reports: GitHub Issues

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

Acknowledgments

Special thanks to the developers of mgcv, survival, and ggplot2 packages.