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# algorithms.icc

## ICC

Link to code

Calculates Interclass Correlation Coefficient (3,1) as defined in P. E. Shrout & Joseph L. Fleiss (1979). “Intraclass Correlations: Uses in Assessing Rater Reliability”. Psychological Bulletin 86 (2): 420-428. This particular implementation is aimed at relaibility (test-retest) studies.

Inputs:

    [Mandatory]
    mask: (an existing file name)
    subjects_sessions: (a list of items which are a list of items which
             are an existing file name)
            n subjects m sessions 3D stat files

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mask: (an existing file name)
    subjects_sessions: (a list of items which are a list of items which
             are an existing file name)
            n subjects m sessions 3D stat files

Outputs:

    icc_map: (an existing file name)
    session_var_map: (an existing file name)
            variance between sessions
    subject_var_map: (an existing file name)
            variance between subjects

## `ICC_rep_anova()`

Link to code

the data Y are entered as a ‘table’ ie subjects are in rows and repeated measures in columns