sliding_spliner: Sliding_spliner

In RRShieldsCutler/splinectomeR: Test for significance in longitudinal data with one or two populations

Sliding_spliner

Description

Test for a significant difference in two groups at imputed intervals

Usage

Test usage

Arguments

data	A dataframe object containing your data.
category	The column name of the category to be tested, if present.
cases	The column name defining the individual cases, e.g. patients.
groups	If more than two groups, the two groups to compare as character vector.
set_spar	Set the spar parameter for splines
cut_low	Remove low prevalence with fewer than __ data points (default 4)
test_density	Minimum density of cases in each group to report p-value (default 3)
ints	Number of x intervals over which to measure significance
quiet	Silence all text outputs
x	The independent variable; is continuous, e.g. time.
y	The dependent variable; is continuous, e.g. temperature.

Details

The data object needs to be organized with each observation as a row, and have a column that identifies the case, patient, animal, etc, and columns with the continuous x and y variables (row with x = NA will be removed). If there are multiple groups in the data, you can filter to the single group of interest with the category and group arguments. Otherwise it assumes the entire dataset is the single population.

Examples

result <- trendyspliner(data = ChickWeight, xvar = 'Time',
             yvar = 'weight', category = 'Diet',
             cases = 'Chick', groups = c(1,2))
result$pval

RRShieldsCutler/splinectomeR documentation built on April 24, 2022, 2:20 a.m.