spline.plot: Plot Spline

In drsmooth: Dose-Response Modeling with Smoothing Splines

Description

This function generates a spline model with the input dose and target response columns, and plots the spline-estimated dose-response function with its upper and lower 95 percent confidence bounds in green and red respectively along with the actual data. Note that the confidence bounds depicted on the plot are for the dose-response function itself, and not for the raw data.

Usage

spline.plot(dosecolumn = "", targetcolumn = "", k = 4, data_type = "", data = NA)

Arguments

dosecolumn	Name of dose column.
targetcolumn	Name of response column.
k	Dimension of the basis used to represent the smooth term.
data_type	Allowed values "continuous" or "dichotomous".
data	Input dataframe.

Value

A plot of the spline-estimated dose-response function along with the actual data.

Examples

# Produces and plots the spline model with confidence bounds.
# For the same plot with key metrics, see drsmooth().
# For continuous outcomes:
data(DRdata)
spline.plot("dose", "MF_Log",  k = 4, data_type = "continuous", data=DRdata)

# For dichotomous outcomes:
data(DIdata)
# If necessary, convert summarized dataframe into 1 row per case dataframe (see drsmooth::expand)
DIdata_expanded <- expand(dosecolumn = "Dose", targetcolumn = "Tumor", ncolumn = "n", data = DIdata)
spline.plot("Dose", "Tumor", k = 4, data_type = "dichotomous", data=DIdata_expanded)

drsmooth documentation built on May 1, 2019, 10:28 p.m.