trendyspliner: Trendyspliner

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

Trendyspliner

Description

Test for a significant non-zero trend in a response over time.

Usage

Permutation to test whether there is a non-zero trend among a set
of individuals/samples over a continuous variable -- such as time. So, there
does not need to be two groups in this test. The x variable datapoints are
permuated within each case/individual, thus maintaining the distribution in
the y component but shuffling the hypothesized trend.

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.
group	If more than one group in the data, the group to compare.
mean_center	Before processing, mean center data by individual/case (default FALSE)
perms	The number of permutations to generate
set_spar	Set the spar parameter for splines
cut_low	Remove data with fewer than __ points
ints	Number of x intervals over which to measure area
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', group = '1')
result$pval

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