multipleRegion_plot.RdThis function can draw connection line(s) among multiple brain regions, based on the result from funciton permkCCA_multipleRegion(...).
multipleRegion_plot(input, significance = 0.05, regionCodeProvided = FALSE,
view = "coronal", color = "blue", screenShot = "None")| input | it can accept two types of argument: (1) output generated by "permkCCA_multipleRegion(...)" (with p value). (2) output generated by summary_kcca() or meanConnection() (without p value). |
|---|---|
| significance | significance level of hypothesis testing. If there is no p value in your data (i.e. only region name and region index, then set significance as NA) |
| regionCodeProvided | use TRUE if region code was provided in nii2RData() funciton and FALSE if not. |
| view | view of brain to see. One can choose coronal, axial, SL, or SR. |
| color | color of dots and lines. User can specify multiple colors in this argument and the function will use these colors periodically. |
| screenShot | whether to save figure as png. If it is "None", then the figure will not be saved; If it is a name, for example, "plot.png", then it will be saved as "plot.png". |
1-4 view(s) (depends on the number of views user would like to see) of brain graphic network. All significant regions are connected by colored lines.
If you provide an input with multiple patients, the function will ask you to specify which patients data you would like to use.
Xubo Yue, Chia-Wei Hsu (tester), Jian Kang (maintainer)
# \donttest{
#It will take more than 3 min to run
#write data into temp file
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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multipleRegion_plot(result1, view="axial")
#> p value of kccc between PreCG.L and ORBsup.L is: 1
#> p value of kccc between PreCG.L and ORBmid.R is: 0.33
#> p value of kccc between ORBsup.L and ORBmid.R is: 0
# }