rcsplot: Plot restricted cubic splines curves

In plotRCS: Plot Restricted Cubic Splines Curves

Plot restricted cubic splines curves

Description

Drawing of restricted cubic spline (RCS) curves form a linear regression model, a logistic regression model or a Cox proportional hazards regression model.

Usage

rcsplot(
  data,
  outcome = NULL,
  time = NULL,
  exposure = NULL,
  covariates = NULL,
  positive = NULL,
  group = NULL,
  knots = c(0.05, 0.35, 0.65, 0.95),
  knots.line = FALSE,
  ref.value = "k1",
  ref.line = TRUE,
  conf.int = TRUE,
  conf.level = 0.95,
  conf.type = c("shape", "line"),
  pvalue = TRUE,
  pvalue.digits = 3,
  pvalue.position = c(0.02, 0.98),
  pvalue.label.overall = "P for overall",
  pvalue.label.nonlinear = "P for nonlinear",
  fontsize = 12,
  fontfamily = "serif",
  linesize = 0.25,
  linecolor = "#0072B5FF",
  alpha = 0.1,
  xbreaks = NULL,
  ybreaks = NULL,
  xlab = "",
  ylab = "",
  explain = TRUE,
  ...
)

Arguments

data	a data frame contain the columns of outcome, time, exposure, covariates, and group.
outcome	the name of outcome variable in the data.
time	the name of time variable in the data, for Cox regressions.
exposure	the name of exposure variable in the data.
covariates	the names of covariate variables in the data.
positive	in which positive of outcome variable to make the comparison. By default, positive is automatically defined. If outcome is a factor variable, then positive is defined as the highest level. If outcome is a numerical variable, then positive is defined as the largest value.
group	the name of group variable in the data.
knots	location of knots, detail see knot function.
knots.line	logical indicating whether or not to show the vertical lines for the knots, default FALSE.
ref.value	referrence value for the RCS curve, 'min' means using the minimum value of esposure as a reference, 'median' uses the median, 'mean' uses the mean, 'k1' uses the first knot. 'k2' uses the second knot, 'k3' uses the third 'knot', and so on. In addition, you can directly set the numerical vector as the reference value.
ref.line	logical indicating whether or not to show the referrence line, default TRUE.
conf.int	logical indicating whether or not to draw confidence interval. Defaults to TRUE.
conf.level	the confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
conf.type	confidence interval type of 'shape' (default) or 'line'.
pvalue	logical indicating whether or not to show P values, include P for overall association and P for nonlinear, default TRUE.
pvalue.digits	digits for P values, default 3.
pvalue.position	position for P value, numeric vector of length two (x-axis and y-axis).
pvalue.label.overall	label for P value of overall.
pvalue.label.nonlinear	label for P value of nonlinear.
fontsize	font size, default 12.
fontfamily	font family, default 'serif' (Times New Roman).
linesize	line size, default 0.25.
linecolor	line color, default '#0072B5FF'.
alpha	alpha for the shape of confidence interval, default 0.1.
xbreaks	breaks of x-axis.
ybreaks	breaks of y-axis.
xlab	label of x-axis.
ylab	label of y-axis.
explain	logical indicating whether or not to explain the figure, default TRUE.
...	further arguments.

Value

A ggplot2 object with class 'rcsplot' containing the attributes of 'title' and 'note'.

See Also

rcs, knot

Examples

# View data
head(cancer)

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

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

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

# Unadjusted covariates
rcsplot(data = cancer,
        outcome = "status",
        time = "time",
        exposure = "age")

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

# Set 5 knots from 'kont' function
rcsplot(data = cancer,
        outcome = "status",
        time = "time",
        exposure = "age",
        covariates = c("sex", "race", "size", "metastasis"),
        knots = knot(5))

# Set the second knot as the referrence value
rcsplot(data = cancer,
        outcome = "status",
        time = "time",
        exposure = "age",
        covariates = c("sex", "race", "size", "metastasis"),
        knots = knot(5),
        ref.value = "k2")

plotRCS documentation built on April 6, 2023, 9:23 a.m.