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# nipype.algorithms.icc module

## ICC

Link to code

> Bases: BaseInterface

> 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.

> mask : a pathlike object or string representing an existing file subjects_sessions : a list of items which are a list of items which are a pathlike object or string representing an existing file

> > N subjects m sessions 3D stat files.

> icc_map : a pathlike object or string representing an existing file session_var_map : a pathlike object or string representing an existing file

> > Variance between sessions.

> subject_var_mapa pathlike object or string representing an existing file

>   Variance between subjects.

nipype.algorithms.icc.ICC_rep_anova(Y)

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

  One Sample Repeated measure ANOVA

  Y = XB + E with X = [FaTor / Subjects]