Description Usage Arguments Details Value Examples
Linear mixed effect modeling for static functional connectivity
1 | lmmConn(dataList, op_lme)
|
dataList |
list, list of data matrices . |
op_lme |
list, options constructed by function |
lmmDyn
and lmmConn
, we need to summarize the bootstrapping by taking the mean or median of the dFC estimate at each time point.
An object of list containing all the information of the static functional connectivity linear mixed model. It stores the information of model parameters, input data, and model results.
Store the parameters for the linear mixed model
Store the information about parallel computing environment and parameters
Store the model results information for data between each pair of regions
A combined dataframe with all the model results informations
A list of matrices, contains information of confidence band estimate, see description below.
In each matrix of est_CI
:
Base line estimate, difference between condition 1 and condition 2
Dynamic functional connecvity estimates for condition 1
Dynamic functional connecvity estimates for condition 2
Lower bound of 95% confidence interval of condition-difference estimates
Upper bound of 95% confidence interval of condition-difference estimates
Lower bound of 95% confidence interval of condition-1 estimates
Upper bound of 95% confidence interval of condition-1 estimates
Lower bound of 95% confidence interval of condition-2 estimates
Upper bound of 95% confidence interval of condition-2 estimates
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Assuming user has run MLPB_boot() and has summarized the bootstrapping results
# by calculating the median or mean.
## Example with 5 subjects bootstrap-based static functional connectivity estimates data included
## Each subject's data has 731 time points in total, which includes 6 scans and 105 effective
## time point for each scan.
## 3 regions of interest (ROIs) comparision pairs are selected
data(MLPB_output_median)
subjects <- c('subject1', 'subject2', 'subject3', 'subject4', 'subject5')
# In our demo data, each subject has a scan with a total of 750 time points
time.points <- c(1:105, 126:230, 251:355,
376:480, 501:605, 626:730)
num.scan <- 6 # Each subject has 6 scans
ntps.per.scan <- 105 # Each scan has 105 time points
op <- options_lme(effective_tp = time.points,
ntps.per.scan = ntps.per.scan,
subjects = subjects,
num.scan = num.scan)
resConn <- lmmConn(MLPB_output_median, op)
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