plotRCS

Author by Rongrui Huo

Description

Simple drawing of restricted cubic spline (RCS) curves through ‘ggplot2’ package from a linear regression model, a logistic regression model or a Cox proportional hazards regression model.

Package NEWS

• Changes in version 0.1.5
• Support for log OR or HR, when set log = TRUE.

Installation

The stable release version can be installed directly from CRAN using:

install.packages("plotRCS")

Alternatively, the development version can be installed using the devtools R-Package:

# Install devtools (if you do not have it already)
install.packages("devtools")

devtools::install_github("kunhuo/plotRCS")

or the remotes R-Package:

install.packages("remotes")

remotes::install_github("kunhuo/plotRCS")

Bug Reports and Feature Requests

If you encounter any bugs or have any specific feature requests, please file an Issue.

Examples

RCS curves for a linear regression model

library(plotRCS)

# View data
head(cancer)

##      id age    sex  race size metastasis   status time
## 1 10274  53   Male White   27         No Censored   12
## 2 56998  32   Male Black  185         No     Dead    5
## 3 60010  69   Male White   51         No     Dead   13
## 4 24307  61   Male White   37         No Censored   50
## 5  5253  53 Female White   25         No Censored   27
## 6 39685  56   Male Other   38         No Censored   17

# RCS curves for a liear regression model
rcsplot(data = cancer,
        outcome = "size",
        exposure = "age",
        covariates = c("sex", "race", "metastasis"))

## 
## Figure: Association Between age and size Using a Restricted Cubic Spline Regression Model.
## Graphs show β for size according to age adjusted for sex, race, metastasis. Data were fitted by a linear regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of age (reference is the 5th percentile). Solid lines indicate β, and shadow shape indicate 95% CIs. CI, confidence interval.

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RCS curves for a logistic regression model

# RCS curves for a logistic regression model
rcsplot(data = cancer,
        outcome = "status",
        exposure = "age",
        covariates = c("sex", "race", "size", "metastasis"))

## 
## Figure: Association Between age and status Using a Restricted Cubic Spline Regression Model.
## Graphs show ORs for status according to age adjusted for sex, race, size, metastasis. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of age (reference is the 5th percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval.

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RCS curves for a Cox regression model

rcsplot(data = cancer,
        outcome = "status",
        time = "time",
        exposure = "age",
        covariates = c("sex", "race", "size", "metastasis"))

## 
## Figure: Association Between age and status Using a Restricted Cubic Spline Regression Model.
## Graphs show HRs for status according to age adjusted for sex, race, size, metastasis. Data were fitted by a restricted cubic spline Cox proportional hazards regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of age (reference is the 5th percentile). Solid lines indicate HRs, and shadow shape indicate 95% CIs. HR, hazard ratio; CI, confidence interval.

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