meanConnection.RdThis function can create a list of significant (threshold is defined by user) region pairs.
meanConnection(path = getwd(), threshold = 0.2)| path | the path where csv files located |
|---|---|
| threshold | the threshold for significance of percentage of connection (if percentage exceeds threhold, then the region pair is significant). Typically, it can be 15-30%. |
the object containing significant regions.
you need to specify the path where csv files (containing KCCA information)locoated. This function will read all csv files listed in the path.
Xubo Yue, Chia-Wei Hsu (tester), Jian Kang (maintainer)
# \donttest{
#It will take more than 3 min to run
filePath <- tempdir()
#the nii.gz fMRI imaging file is created (toy example)
oro.nifti::writeNIfTI(brainKCCA::input_img, paste(filePath, "/", "temp", sep=""))
#read fMRI data
testcase1 <- nii2RData(niiFile1 = "temp", resolution = "3mm", imgPath = filePath)
#> Reading nii files...this progress may take a long time...
#>
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#> reading data 1
#>
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#> Completed...
#>
result1<-permkCCA_multipleRegion(imageDat = testcase1, region = c(1,5,10))
#>
#> Checking data format...
#>
#> processed image data detected, do not need to read nii data again...
#> Calculating KCC...
#>
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#>
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#>
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summary_result1 <- summary_kcca(kcca_object=result1, saveFormat = "excel")
#>
#> summary of kcca object 1 generated by 'permkCCA_multipleRegion' function:
write.csv(summary_result1, paste(filePath, "/", "temp.csv", sep=""))
summary_data <- meanConnection(path = filePath, threshold=0.25)
multipleRegion_plot(summary_data, significance=NA)
#> Warning: no DISPLAY variable so Tk is not available
#> NULL
# }