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.

Mandatory Inputs

  • 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.

Outputs

  • 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_map (a 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]