FCSlib (version 1.3.0)

pcf: Pair Correlation Function

Description

Calculates the correlation between the pixel i and pixel i + dr.

Usage

pcf(img, nPoints = 1000, one.col = FALSE, dr = 1)

Arguments

img

The image to analyze

nPoints

The size of the sub-vectors in which the input vectors will be divided. This number must be less than N/2.

one.col

By default FALSE. If TRUE the correlation will be performed in the fixed colum mode, else the distance mode.

dr

Distance between pixel at which the correlation is calculated. For a value of delta_r = 3, the columns are correlated as follows: (1,4), (2,5), ..., (n-3, n), with n being the last column.

Value

An image depicting the correlation between the pixel i and pixel i + dr.

Details

The pair correlation function (pCF) analyzes data of a periodically scanned line, measuring the time it takes a particle to go from one pixel to another, i.e. calculates the spatial cross-correlation function between pixels. G(tau,deltar) = (<F(t,0) F(t + tau, deltar)>/<F(t,0)> <F(t,deltar)>)-1

See Also

fcs, pcomb

Examples

# NOT RUN {
### Load the FCSlib package

library(FCSlib)

### As an example, we will use a data set that corresponds to a population of Venus dimers
# diffusing in HEK-293 cells. Use the readFileTiff() function to extract the information
# from the '.tiff' files.

dmv2 <- data.matrix(V2)
pB <- pcf(dmv2, nPoints = 2500, dr = 10)

### Plot the result
library(fields)
di <- dim(pB)
tau <- (1:(di[2]))
image.plot( x = 1:di[1], y = log10(tau), z = pB, main = "Column Distance 10",
xlab = "Pixel", ylab = "Logarithmic tau",
cex.lab = 1.2, cex.main = 1.2, cex.axis = 1)
# }