hrfConvolve: Convolution function

In FIAR: Functional Integration Analysis in R

Description

Convolute a timeseries with any double gamma function.

Usage

hrfConvolve(x = NULL, scans = NA, onsets = c(), durations = c(), 
             rt = NA, SNR = 0, mean = FALSE, a1 = 6, a2 = 12, b1 = 0.9, 
             b2 = 0.9, cc = 0.35)

Arguments

x	Single timeseries (default = NULL)
scans	number of scans
onsets	onsets of experimental condition
durations	duration of experimental condition
rt	repetition time
SNR	signal to noise ratio of data
mean	logical if mean is TRUE the timeseries is centered around 0.
a1	parameter of the double gamma function
a2	parameter of the double gamma function
b1	parameter of the double gamma function
b2	parameter of the double gamma function
cc	parameter of the double gamma function

Details

The function is an extension of the fmri.stimulus function in the ‘fmri’ pacakge (see ref.). If x = NULL, the to be convolved stimulusfunction can be specified with the parameters 'scans', 'onsets', 'durations', 'rt', and 'SNR'. If x is entered, the timeseries x is convolved and the other parameters need not be specified. The default convolution function is the canonical HRF, but can be altered by changing the parameters of the double gamma function.

Value

returns convolved timeseries. The timeseries is convolved with a mixture of 2 gamma functions (default = canonical HRF).

Author(s)

Bjorn Roelstraete

References

Polzehl, J. and Tabelow, K. (2007) _fmri: A Package for Analyzing fmri Data_, R News, 7:13-17 .

Examples

# Specify a stimulusfunction without noise and convolve with canonical HRF

hrfConvolve(scans = 240, onsets = c(0,60,120,180), durations = c(30), 
             rt = 3, SNR = 0)

# Convolve a (part of a) timeseries with a canonical HRF.

hrfConvolve(x=grangerdata[1:100,1])
plot(hrfConvolve(grangerdata[1:100,1]))

# Compare the convolved timeseries with the raw
par(mfrow=c(2,1))
plot(x=semdata[1:100,1])
plot(hrfConvolve(x=semdata[1:100,1]))

FIAR documentation built on June 5, 2018, 5:03 p.m.