ihclust (version 0.1.0)

testchange: testchange

Description

This function identifies geographic areas with significant change over time.

Usage

testchange(data, time, perm = FALSE, nperm = 100, numclust = 4, topF = 500)

Arguments

data

a numeric matrix, each row representing a time-series and each column representing a time point

time

defines the time sequence

perm

if perm = 'TRUE', a permutation is performed

nperm

number of permuations

numclust

defines the number of clusters for the parallel processing

topF

number of top F values to be selected when perm = 'FALSE'

Value

Output if perm = 'TRUE' is a list of three items:

• perm.F - F values obtained from permutation tests

• p.values - p-values obtained from permutation tests

• p.adjusted - p-values adjusted by Benjamini-Hochberg method

Output if perm = 'False' is a list of three items:

• obs.F - conventional F-statistic values

• sig.change - areas with significant change over time pattern selected by top F-statistic values

• sel.F - top F-statistic values selected

Details

number of permutations of >=10,000 is ideal

References

1. Song, J., Carey, M., Zhu, H., Miao, H., Ram<U+00B4><U+0131>rez, J. C., & Wu, H. (2018). Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations. International Journal of Computational Biology and Drug Design, 11,135-153. doi: 10.1504/IJCBDD.2018.10011910. 2. Wu, S., & Wu, H. (2013). More powerful significant testing for time course gene expression data using functional principal component analysis approaches. BMC Bioinformatics, 14:6. 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016). Correlation-based iterative clustering methods for time course data: The identification of temporal gene response modules for influenza infection in humans. Infectious Disease Modeling, 1, 28-39.

Examples

# NOT RUN {
# This is an example not using the permutation approach

opioid_data_noNA <- opioidData[complete.cases(opioidData), ] #remove NAs

mydata <- as.matrix(opioid_data_noNA[,4:18])

testchange_results <- testchange(data=mydata,perm=FALSE,time=seq(1,15,1))
# }