FIAR (version 0.6)

hrfConvolve: Convolution function

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

Value

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

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.

References

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

Examples

# NOT RUN {
# 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]))
# }