3dMVM error

Laura · January 30, 2019, 5:09pm

Hi all,

I’m new to 3dMVM (used to run 3dANOVAX, but now my models are a bit too complex, and I have unequal sample sizes), and I’m having some trouble getting the program to run. First of all, I want to explore whether there is a significant group (3 groups: TD, RD, EL) by condition (8 conditions: SPEECH, NOISE, PSW, FF, OPplus, OPminus, SEM, UNREL) interaction. I’m worried that I may find no evidence of differences between groups on any of the conditions (possibly due to a power issue (whether there’s a way to check that is a different questions!). If I do find evidence of a significant groupxcondition interaction, I’d like to explore further with GLTs (e.g., -gltLabel 1 SPEECH_TDvRD -gltCode 1 'group : 1TD -1RD condition : 1SPEECH \ -gltLabel 2 SPEECHvNOISE_TDvRD -gltCode 2 'group : 1TD -1RD condition : 1SPEECH -1*NOISE).

The issue right now, however, is that I can’t even get the program to run even without glts. When I run the code below, the program kills itself a minute or so after it begins reading the input files (see output below the data table file contents). Can you help me identify the source of the problem?

Thank you,
Laura

Data table file:
Subj group condition inputFile
C03 RD SPEECH stats.C03.read+tlrc[SPEECH#0_Coef]
C03 RD NOISE stats.C03.read+tlrc[NOISE#0_Coef]
C03 RD UNREL stats.C03.read+tlrc[UNREL#0_Coef]
C03 RD PSW stats.C03.read+tlrc[PSW#0_Coef]
C03 RD FF stats.C03.read+tlrc[FF#0_Coef]
C03 RD OPplus stats.C03.read+tlrc[OPplus#0_Coef]
C03 RD OPminus stats.C03.read+tlrc[OPminus#0_Coef]
C03 RD SEM stats.C03.read+tlrc[SEM#0_Coef]
C05 RD SPEECH stats.C05.read+tlrc[SPEECH#0_Coef]
C05 RD NOISE stats.C05.read+tlrc[NOISE#0_Coef]
C05 RD UNREL stats.C05.read+tlrc[UNREL#0_Coef]
C05 RD PSW stats.C05.read+tlrc[PSW#0_Coef]
C05 RD FF stats.C05.read+tlrc[FF#0_Coef]
C05 RD OPplus stats.C05.read+tlrc[OPplus#0_Coef]
C05 RD OPminus stats.C05.read+tlrc[OPminus#0_Coef]
C05 RD SEM stats.C05.read+tlrc[SEM#0_Coef]
C07 RD SPEECH stats.C07.read+tlrc[SPEECH#0_Coef]
C07 RD NOISE stats.C07.read+tlrc[NOISE#0_Coef]
C07 RD UNREL stats.C07.read+tlrc[UNREL#0_Coef]
C07 RD PSW stats.C07.read+tlrc[PSW#0_Coef]
C07 RD FF stats.C07.read+tlrc[FF#0_Coef]
C07 RD OPplus stats.C07.read+tlrc[OPplus#0_Coef]
C07 RD OPminus stats.C07.read+tlrc[OPminus#0_Coef]
C07 RD SEM stats.C07.read+tlrc[SEM#0_Coef]
C08 RD SPEECH stats.C08.read+tlrc[SPEECH#0_Coef]
C08 RD NOISE stats.C08.read+tlrc[NOISE#0_Coef]
C08 RD UNREL stats.C08.read+tlrc[UNREL#0_Coef]
C08 RD PSW stats.C08.read+tlrc[PSW#0_Coef]
C08 RD FF stats.C08.read+tlrc[FF#0_Coef]
C08 RD OPplus stats.C08.read+tlrc[OPplus#0_Coef]
C08 RD OPminus stats.C08.read+tlrc[OPminus#0_Coef]
C08 RD SEM stats.C08.read+tlrc[SEM#0_Coef]
C10 RD SPEECH stats.C10.read+tlrc[SPEECH#0_Coef]
C10 RD NOISE stats.C10.read+tlrc[NOISE#0_Coef]
C10 RD UNREL stats.C10.read+tlrc[UNREL#0_Coef]
C10 RD PSW stats.C10.read+tlrc[PSW#0_Coef]
C10 RD FF stats.C10.read+tlrc[FF#0_Coef]
C10 RD OPplus stats.C10.read+tlrc[OPplus#0_Coef]
C10 RD OPminus stats.C10.read+tlrc[OPminus#0_Coef]
C10 RD SEM stats.C10.read+tlrc[SEM#0_Coef]
C12 RD SPEECH stats.C12.read+tlrc[SPEECH#0_Coef]
C12 RD NOISE stats.C12.read+tlrc[NOISE#0_Coef]
C12 RD UNREL stats.C12.read+tlrc[UNREL#0_Coef]
C12 RD PSW stats.C12.read+tlrc[PSW#0_Coef]
C12 RD FF stats.C12.read+tlrc[FF#0_Coef]
C12 RD OPplus stats.C12.read+tlrc[OPplus#0_Coef]
C12 RD OPminus stats.C12.read+tlrc[OPminus#0_Coef]
C12 RD SEM stats.C12.read+tlrc[SEM#0_Coef]
C13 RD SPEECH stats.C13.read+tlrc[SPEECH#0_Coef]
C13 RD NOISE stats.C13.read+tlrc[NOISE#0_Coef]
C13 RD UNREL stats.C13.read+tlrc[UNREL#0_Coef]
C13 RD PSW stats.C13.read+tlrc[PSW#0_Coef]
C13 RD FF stats.C13.read+tlrc[FF#0_Coef]
C13 RD OPplus stats.C13.read+tlrc[OPplus#0_Coef]
C13 RD OPminus stats.C13.read+tlrc[OPminus#0_Coef]
C13 RD SEM stats.C13.read+tlrc[SEM#0_Coef]
C34 RD SPEECH stats.C34.read+tlrc[SPEECH#0_Coef]
C34 RD NOISE stats.C34.read+tlrc[NOISE#0_Coef]
C34 RD UNREL stats.C34.read+tlrc[UNREL#0_Coef]
C34 RD PSW stats.C34.read+tlrc[PSW#0_Coef]
C34 RD FF stats.C34.read+tlrc[FF#0_Coef]
C34 RD OPplus stats.C34.read+tlrc[OPplus#0_Coef]
C34 RD OPminus stats.C34.read+tlrc[OPminus#0_Coef]
C34 RD SEM stats.C34.read+tlrc[SEM#0_Coef]
C36 RD SPEECH stats.C36.read+tlrc[SPEECH#0_Coef]
C36 RD NOISE stats.C36.read+tlrc[NOISE#0_Coef]
C36 RD UNREL stats.C36.read+tlrc[UNREL#0_Coef]
C36 RD PSW stats.C36.read+tlrc[PSW#0_Coef]
C36 RD FF stats.C36.read+tlrc[FF#0_Coef]
C36 RD OPplus stats.C36.read+tlrc[OPplus#0_Coef]
C36 RD OPminus stats.C36.read+tlrc[OPminus#0_Coef]
C36 RD SEM stats.C36.read+tlrc[SEM#0_Coef]
C43 RD SPEECH stats.C43.read+tlrc[SPEECH#0_Coef]
C43 RD NOISE stats.C43.read+tlrc[NOISE#0_Coef]
C43 RD UNREL stats.C43.read+tlrc[UNREL#0_Coef]
C43 RD PSW stats.C43.read+tlrc[PSW#0_Coef]
C43 RD FF stats.C43.read+tlrc[FF#0_Coef]
C43 RD OPplus stats.C43.read+tlrc[OPplus#0_Coef]
C43 RD OPminus stats.C43.read+tlrc[OPminus#0_Coef]
C43 RD SEM stats.C43.read+tlrc[SEM#0_Coef]
C81 RD SPEECH stats.C81.read+tlrc[SPEECH#0_Coef]
C81 RD NOISE stats.C81.read+tlrc[NOISE#0_Coef]
C81 RD UNREL stats.C81.read+tlrc[UNREL#0_Coef]
C81 RD PSW stats.C81.read+tlrc[PSW#0_Coef]
C81 RD FF stats.C81.read+tlrc[FF#0_Coef]
C81 RD OPplus stats.C81.read+tlrc[OPplus#0_Coef]
C81 RD OPminus stats.C81.read+tlrc[OPminus#0_Coef]
C81 RD SEM stats.C81.read+tlrc[SEM#0_Coef]
C95 RD SPEECH stats.C95.read+tlrc[SPEECH#0_Coef]
C95 RD NOISE stats.C95.read+tlrc[NOISE#0_Coef]
C95 RD UNREL stats.C95.read+tlrc[UNREL#0_Coef]
C95 RD PSW stats.C95.read+tlrc[PSW#0_Coef]
C95 RD FF stats.C95.read+tlrc[FF#0_Coef]
C95 RD OPplus stats.C95.read+tlrc[OPplus#0_Coef]
C95 RD OPminus stats.C95.read+tlrc[OPminus#0_Coef]
C95 RD SEM stats.C95.read+tlrc[SEM#0_Coef]
C97 RD SPEECH stats.C97.read+tlrc[SPEECH#0_Coef]
C97 RD NOISE stats.C97.read+tlrc[NOISE#0_Coef]
C97 RD UNREL stats.C97.read+tlrc[UNREL#0_Coef]
C97 RD PSW stats.C97.read+tlrc[PSW#0_Coef]
C97 RD FF stats.C97.read+tlrc[FF#0_Coef]
C97 RD OPplus stats.C97.read+tlrc[OPplus#0_Coef]
C97 RD OPminus stats.C97.read+tlrc[OPminus#0_Coef]
C97 RD SEM stats.C97.read+tlrc[SEM#0_Coef]
C19 EL SPEECH stats.C19.read+tlrc[SPEECH#0_Coef]
C19 EL NOISE stats.C19.read+tlrc[NOISE#0_Coef]
C19 EL UNREL stats.C19.read+tlrc[UNREL#0_Coef]
C19 EL PSW stats.C19.read+tlrc[PSW#0_Coef]
C19 EL FF stats.C19.read+tlrc[FF#0_Coef]
C19 EL OPplus stats.C19.read+tlrc[OPplus#0_Coef]
C19 EL OPminus stats.C19.read+tlrc[OPminus#0_Coef]
C19 EL SEM stats.C19.read+tlrc[SEM#0_Coef]
C20 EL SPEECH stats.C20.read+tlrc[SPEECH#0_Coef]
C20 EL NOISE stats.C20.read+tlrc[NOISE#0_Coef]
C20 EL UNREL stats.C20.read+tlrc[UNREL#0_Coef]
C20 EL PSW stats.C20.read+tlrc[PSW#0_Coef]
C20 EL FF stats.C20.read+tlrc[FF#0_Coef]
C20 EL OPplus stats.C20.read+tlrc[OPplus#0_Coef]
C20 EL OPminus stats.C20.read+tlrc[OPminus#0_Coef]
C20 EL SEM stats.C20.read+tlrc[SEM#0_Coef]
C21 EL SPEECH stats.C21.read+tlrc[SPEECH#0_Coef]
C21 EL NOISE stats.C21.read+tlrc[NOISE#0_Coef]
C21 EL UNREL stats.C21.read+tlrc[UNREL#0_Coef]
C21 EL PSW stats.C21.read+tlrc[PSW#0_Coef]
C21 EL FF stats.C21.read+tlrc[FF#0_Coef]
C21 EL OPplus stats.C21.read+tlrc[OPplus#0_Coef]
C21 EL OPminus stats.C21.read+tlrc[OPminus#0_Coef]
C21 EL SEM stats.C21.read+tlrc[SEM#0_Coef]
C29 EL SPEECH stats.C29.read+tlrc[SPEECH#0_Coef]
C29 EL NOISE stats.C29.read+tlrc[NOISE#0_Coef]
C29 EL UNREL stats.C29.read+tlrc[UNREL#0_Coef]
C29 EL PSW stats.C29.read+tlrc[PSW#0_Coef]
C29 EL FF stats.C29.read+tlrc[FF#0_Coef]
C29 EL OPplus stats.C29.read+tlrc[OPplus#0_Coef]
C29 EL OPminus stats.C29.read+tlrc[OPminus#0_Coef]
C29 EL SEM stats.C29.read+tlrc[SEM#0_Coef]
C30 EL SPEECH stats.C30.read+tlrc[SPEECH#0_Coef]
C30 EL NOISE stats.C30.read+tlrc[NOISE#0_Coef]
C30 EL UNREL stats.C30.read+tlrc[UNREL#0_Coef]
C30 EL PSW stats.C30.read+tlrc[PSW#0_Coef]
C30 EL FF stats.C30.read+tlrc[FF#0_Coef]
C30 EL OPplus stats.C30.read+tlrc[OPplus#0_Coef]
C30 EL OPminus stats.C30.read+tlrc[OPminus#0_Coef]
C30 EL SEM stats.C30.read+tlrc[SEM#0_Coef]
C38 EL SPEECH stats.C38.read+tlrc[SPEECH#0_Coef]
C38 EL NOISE stats.C38.read+tlrc[NOISE#0_Coef]
C38 EL UNREL stats.C38.read+tlrc[UNREL#0_Coef]
C38 EL PSW stats.C38.read+tlrc[PSW#0_Coef]
C38 EL FF stats.C38.read+tlrc[FF#0_Coef]
C38 EL OPplus stats.C38.read+tlrc[OPplus#0_Coef]
C38 EL OPminus stats.C38.read+tlrc[OPminus#0_Coef]
C38 EL SEM stats.C38.read+tlrc[SEM#0_Coef]
C41 EL SPEECH stats.C41.read+tlrc[SPEECH#0_Coef]
C41 EL NOISE stats.C41.read+tlrc[NOISE#0_Coef]
C41 EL UNREL stats.C41.read+tlrc[UNREL#0_Coef]
C41 EL PSW stats.C41.read+tlrc[PSW#0_Coef]
C41 EL FF stats.C41.read+tlrc[FF#0_Coef]
C41 EL OPplus stats.C41.read+tlrc[OPplus#0_Coef]
C41 EL OPminus stats.C41.read+tlrc[OPminus#0_Coef]
C41 EL SEM stats.C41.read+tlrc[SEM#0_Coef]
C59 EL SPEECH stats.C59.read+tlrc[SPEECH#0_Coef]
C59 EL NOISE stats.C59.read+tlrc[NOISE#0_Coef]
C59 EL UNREL stats.C59.read+tlrc[UNREL#0_Coef]
C59 EL PSW stats.C59.read+tlrc[PSW#0_Coef]
C59 EL FF stats.C59.read+tlrc[FF#0_Coef]
C59 EL OPplus stats.C59.read+tlrc[OPplus#0_Coef]
C59 EL OPminus stats.C59.read+tlrc[OPminus#0_Coef]
C59 EL SEM stats.C59.read+tlrc[SEM#0_Coef]
C66 EL SPEECH stats.C66.read+tlrc[SPEECH#0_Coef]
C66 EL NOISE stats.C66.read+tlrc[NOISE#0_Coef]
C66 EL UNREL stats.C66.read+tlrc[UNREL#0_Coef]
C66 EL PSW stats.C66.read+tlrc[PSW#0_Coef]
C66 EL FF stats.C66.read+tlrc[FF#0_Coef]
C66 EL OPplus stats.C66.read+tlrc[OPplus#0_Coef]
C66 EL OPminus stats.C66.read+tlrc[OPminus#0_Coef]
C66 EL SEM stats.C66.read+tlrc[SEM#0_Coef]
C67 EL SPEECH stats.C67.read+tlrc[SPEECH#0_Coef]
C67 EL NOISE stats.C67.read+tlrc[NOISE#0_Coef]
C67 EL UNREL stats.C67.read+tlrc[UNREL#0_Coef]
C67 EL PSW stats.C67.read+tlrc[PSW#0_Coef]
C67 EL FF stats.C67.read+tlrc[FF#0_Coef]
C67 EL OPplus stats.C67.read+tlrc[OPplus#0_Coef]
C67 EL OPminus stats.C67.read+tlrc[OPminus#0_Coef]
C67 EL SEM stats.C67.read+tlrc[SEM#0_Coef]
C70 EL SPEECH stats.C70.read+tlrc[SPEECH#0_Coef]
C70 EL NOISE stats.C70.read+tlrc[NOISE#0_Coef]
C70 EL UNREL stats.C70.read+tlrc[UNREL#0_Coef]
C70 EL PSW stats.C70.read+tlrc[PSW#0_Coef]
C70 EL FF stats.C70.read+tlrc[FF#0_Coef]
C70 EL OPplus stats.C70.read+tlrc[OPplus#0_Coef]
C70 EL OPminus stats.C70.read+tlrc[OPminus#0_Coef]
C70 EL SEM stats.C70.read+tlrc[SEM#0_Coef]
C73 EL SPEECH stats.C73.read+tlrc[SPEECH#0_Coef]
C73 EL NOISE stats.C73.read+tlrc[NOISE#0_Coef]
C73 EL UNREL stats.C73.read+tlrc[UNREL#0_Coef]
C73 EL PSW stats.C73.read+tlrc[PSW#0_Coef]
C73 EL FF stats.C73.read+tlrc[FF#0_Coef]
C73 EL OPplus stats.C73.read+tlrc[OPplus#0_Coef]
C73 EL OPminus stats.C73.read+tlrc[OPminus#0_Coef]
C73 EL SEM stats.C73.read+tlrc[SEM#0_Coef]
C76 EL SPEECH stats.C76.read+tlrc[SPEECH#0_Coef]
C76 EL NOISE stats.C76.read+tlrc[NOISE#0_Coef]
C76 EL UNREL stats.C76.read+tlrc[UNREL#0_Coef]
C76 EL PSW stats.C76.read+tlrc[PSW#0_Coef]
C76 EL FF stats.C76.read+tlrc[FF#0_Coef]
C76 EL OPplus stats.C76.read+tlrc[OPplus#0_Coef]
C76 EL OPminus stats.C76.read+tlrc[OPminus#0_Coef]
C76 EL SEM stats.C76.read+tlrc[SEM#0_Coef]
C90 EL SPEECH stats.C90.read+tlrc[SPEECH#0_Coef]
C90 EL NOISE stats.C90.read+tlrc[NOISE#0_Coef]
C90 EL UNREL stats.C90.read+tlrc[UNREL#0_Coef]
C90 EL PSW stats.C90.read+tlrc[PSW#0_Coef]
C90 EL FF stats.C90.read+tlrc[FF#0_Coef]
C90 EL OPplus stats.C90.read+tlrc[OPplus#0_Coef]
C90 EL OPminus stats.C90.read+tlrc[OPminus#0_Coef]
C90 EL SEM stats.C90.read+tlrc[SEM#0_Coef]
C33 TD SPEECH stats.C33.read+tlrc[SPEECH#0_Coef]
C33 TD NOISE stats.C33.read+tlrc[NOISE#0_Coef]
C33 TD UNREL stats.C33.read+tlrc[UNREL#0_Coef]
C33 TD PSW stats.C33.read+tlrc[PSW#0_Coef]
C33 TD FF stats.C33.read+tlrc[FF#0_Coef]
C33 TD OPplus stats.C33.read+tlrc[OPplus#0_Coef]
C33 TD OPminus stats.C33.read+tlrc[OPminus#0_Coef]
C33 TD SEM stats.C33.read+tlrc[SEM#0_Coef]
C35 TD SPEECH stats.C35.read+tlrc[SPEECH#0_Coef]
C35 TD NOISE stats.C35.read+tlrc[NOISE#0_Coef]
C35 TD UNREL stats.C35.read+tlrc[UNREL#0_Coef]
C35 TD PSW stats.C35.read+tlrc[PSW#0_Coef]
C35 TD FF stats.C35.read+tlrc[FF#0_Coef]
C35 TD OPplus stats.C35.read+tlrc[OPplus#0_Coef]
C35 TD OPminus stats.C35.read+tlrc[OPminus#0_Coef]
C35 TD SEM stats.C35.read+tlrc[SEM#0_Coef]
C37 TD SPEECH stats.C37.read+tlrc[SPEECH#0_Coef]
C37 TD NOISE stats.C37.read+tlrc[NOISE#0_Coef]
C37 TD UNREL stats.C37.read+tlrc[UNREL#0_Coef]
C37 TD PSW stats.C37.read+tlrc[PSW#0_Coef]
C37 TD FF stats.C37.read+tlrc[FF#0_Coef]
C37 TD OPplus stats.C37.read+tlrc[OPplus#0_Coef]
C37 TD OPminus stats.C37.read+tlrc[OPminus#0_Coef]
C37 TD SEM stats.C37.read+tlrc[SEM#0_Coef]
C39 TD SPEECH stats.C39.read+tlrc[SPEECH#0_Coef]
C39 TD NOISE stats.C39.read+tlrc[NOISE#0_Coef]
C39 TD UNREL stats.C39.read+tlrc[UNREL#0_Coef]
C39 TD PSW stats.C39.read+tlrc[PSW#0_Coef]
C39 TD FF stats.C39.read+tlrc[FF#0_Coef]
C39 TD OPplus stats.C39.read+tlrc[OPplus#0_Coef]
C39 TD OPminus stats.C39.read+tlrc[OPminus#0_Coef]
C39 TD SEM stats.C39.read+tlrc[SEM#0_Coef]
C45 TD SPEECH stats.C45.read+tlrc[SPEECH#0_Coef]
C45 TD NOISE stats.C45.read+tlrc[NOISE#0_Coef]
C45 TD UNREL stats.C45.read+tlrc[UNREL#0_Coef]
C45 TD PSW stats.C45.read+tlrc[PSW#0_Coef]
C45 TD FF stats.C45.read+tlrc[FF#0_Coef]
C45 TD OPplus stats.C45.read+tlrc[OPplus#0_Coef]
C45 TD OPminus stats.C45.read+tlrc[OPminus#0_Coef]
C45 TD SEM stats.C45.read+tlrc[SEM#0_Coef]
C46 TD SPEECH stats.C46.read+tlrc[SPEECH#0_Coef]
C46 TD NOISE stats.C46.read+tlrc[NOISE#0_Coef]
C46 TD UNREL stats.C46.read+tlrc[UNREL#0_Coef]
C46 TD PSW stats.C46.read+tlrc[PSW#0_Coef]
C46 TD FF stats.C46.read+tlrc[FF#0_Coef]
C46 TD OPplus stats.C46.read+tlrc[OPplus#0_Coef]
C46 TD OPminus stats.C46.read+tlrc[OPminus#0_Coef]
C46 TD SEM stats.C46.read+tlrc[SEM#0_Coef]
C54 TD SPEECH stats.C54.read+tlrc[SPEECH#0_Coef]
C54 TD NOISE stats.C54.read+tlrc[NOISE#0_Coef]
C54 TD UNREL stats.C54.read+tlrc[UNREL#0_Coef]
C54 TD PSW stats.C54.read+tlrc[PSW#0_Coef]
C54 TD FF stats.C54.read+tlrc[FF#0_Coef]
C54 TD OPplus stats.C54.read+tlrc[OPplus#0_Coef]
C54 TD OPminus stats.C54.read+tlrc[OPminus#0_Coef]
C54 TD SEM stats.C54.read+tlrc[SEM#0_Coef]
C55 TD SPEECH stats.C55.read+tlrc[SPEECH#0_Coef]
C55 TD NOISE stats.C55.read+tlrc[NOISE#0_Coef]
C55 TD UNREL stats.C55.read+tlrc[UNREL#0_Coef]
C55 TD PSW stats.C55.read+tlrc[PSW#0_Coef]
C55 TD FF stats.C55.read+tlrc[FF#0_Coef]
C55 TD OPplus stats.C55.read+tlrc[OPplus#0_Coef]
C55 TD OPminus stats.C55.read+tlrc[OPminus#0_Coef]
C55 TD SEM stats.C55.read+tlrc[SEM#0_Coef]
C57 TD SPEECH stats.C57.read+tlrc[SPEECH#0_Coef]
C57 TD NOISE stats.C57.read+tlrc[NOISE#0_Coef]
C57 TD UNREL stats.C57.read+tlrc[UNREL#0_Coef]
C57 TD PSW stats.C57.read+tlrc[PSW#0_Coef]
C57 TD FF stats.C57.read+tlrc[FF#0_Coef]
C57 TD OPplus stats.C57.read+tlrc[OPplus#0_Coef]
C57 TD OPminus stats.C57.read+tlrc[OPminus#0_Coef]
C57 TD SEM stats.C57.read+tlrc[SEM#0_Coef]
C64 TD SPEECH stats.C64.read+tlrc[SPEECH#0_Coef]
C64 TD NOISE stats.C64.read+tlrc[NOISE#0_Coef]
C64 TD UNREL stats.C64.read+tlrc[UNREL#0_Coef]
C64 TD PSW stats.C64.read+tlrc[PSW#0_Coef]
C64 TD FF stats.C64.read+tlrc[FF#0_Coef]
C64 TD OPplus stats.C64.read+tlrc[OPplus#0_Coef]
C64 TD OPminus stats.C64.read+tlrc[OPminus#0_Coef]
C64 TD SEM stats.C64.read+tlrc[SEM#0_Coef]
C68 TD SPEECH stats.C68.read+tlrc[SPEECH#0_Coef]
C68 TD NOISE stats.C68.read+tlrc[NOISE#0_Coef]
C68 TD UNREL stats.C68.read+tlrc[UNREL#0_Coef]
C68 TD PSW stats.C68.read+tlrc[PSW#0_Coef]
C68 TD FF stats.C68.read+tlrc[FF#0_Coef]
C68 TD OPplus stats.C68.read+tlrc[OPplus#0_Coef]
C68 TD OPminus stats.C68.read+tlrc[OPminus#0_Coef]
C68 TD SEM stats.C68.read+tlrc[SEM#0_Coef]
C74 TD SPEECH stats.C74.read+tlrc[SPEECH#0_Coef]
C74 TD NOISE stats.C74.read+tlrc[NOISE#0_Coef]
C74 TD UNREL stats.C74.read+tlrc[UNREL#0_Coef]
C74 TD PSW stats.C74.read+tlrc[PSW#0_Coef]
C74 TD FF stats.C74.read+tlrc[FF#0_Coef]
C74 TD OPplus stats.C74.read+tlrc[OPplus#0_Coef]
C74 TD OPminus stats.C74.read+tlrc[OPminus#0_Coef]
C74 TD SEM stats.C74.read+tlrc[SEM#0_Coef]
C75 TD SPEECH stats.C75.read+tlrc[SPEECH#0_Coef]
C75 TD NOISE stats.C75.read+tlrc[NOISE#0_Coef]
C75 TD UNREL stats.C75.read+tlrc[UNREL#0_Coef]
C75 TD PSW stats.C75.read+tlrc[PSW#0_Coef]
C75 TD FF stats.C75.read+tlrc[FF#0_Coef]
C75 TD OPplus stats.C75.read+tlrc[OPplus#0_Coef]
C75 TD OPminus stats.C75.read+tlrc[OPminus#0_Coef]
C75 TD SEM stats.C75.read+tlrc[SEM#0_Coef]
C77 TD SPEECH stats.C77.read+tlrc[SPEECH#0_Coef]
C77 TD NOISE stats.C77.read+tlrc[NOISE#0_Coef]
C77 TD UNREL stats.C77.read+tlrc[UNREL#0_Coef]
C77 TD PSW stats.C77.read+tlrc[PSW#0_Coef]
C77 TD FF stats.C77.read+tlrc[FF#0_Coef]
C77 TD OPplus stats.C77.read+tlrc[OPplus#0_Coef]
C77 TD OPminus stats.C77.read+tlrc[OPminus#0_Coef]
C77 TD SEM stats.C77.read+tlrc[SEM#0_Coef]
C86 TD SPEECH stats.C86.read+tlrc[SPEECH#0_Coef]
C86 TD NOISE stats.C86.read+tlrc[NOISE#0_Coef]
C86 TD UNREL stats.C86.read+tlrc[UNREL#0_Coef]
C86 TD PSW stats.C86.read+tlrc[PSW#0_Coef]
C86 TD FF stats.C86.read+tlrc[FF#0_Coef]
C86 TD OPplus stats.C86.read+tlrc[OPplus#0_Coef]
C86 TD OPminus stats.C86.read+tlrc[OPminus#0_Coef]
C86 TD SEM stats.C86.read+tlrc[SEM#0_Coef]
C89 TD SPEECH stats.C89.read+tlrc[SPEECH#0_Coef]
C89 TD NOISE stats.C89.read+tlrc[NOISE#0_Coef]
C89 TD UNREL stats.C89.read+tlrc[UNREL#0_Coef]
C89 TD PSW stats.C89.read+tlrc[PSW#0_Coef]
C89 TD FF stats.C89.read+tlrc[FF#0_Coef]
C89 TD OPplus stats.C89.read+tlrc[OPplus#0_Coef]
C89 TD OPminus stats.C89.read+tlrc[OPminus#0_Coef]
C89 TD SEM stats.C89.read+tlrc[SEM#0_Coef]

3dMVM -prefix AllSubjects -jobs 8 -bsVars group -wsVars condition -SS_type 2 -mask group_mask.nii -num_glt 0 -dataTable @table.txt
Read 1724 items
Loading required package: lme4
Loading required package: Matrix

Welcome to afex. For support visit: http://afex.singmann.science/

Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
Methods for calculating p-values with mixed(): ‘KR’, ‘S’, ‘LRT’, and ‘PB’
‘afex_aov’ and ‘mixed’ objects can be passed to emmeans() for follow-up tests
NEWS: library(‘emmeans’) now needs to be called explicitly!
Get and set global package options with: afex_options()
Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
For example analyses see: browseVignettes(“afex”)

Attaching package: ‘afex’

The following object is masked from ‘package:lme4’:

lmer

Loading required package: car
Loading required package: carData

++++++++++++++++++++++++++++++++++++++++++++++++++++
***** Summary information of data structure *****
43 subjects : C03 C05 C07 C08 C10 C12 C13 C19 C20 C21 C29 C30 C33 C34 C35 C36 C37 C38 C39 C41 C43 C45 C46 C54 C55 C57 C59 C64 C66 C67 C68 C70 C73 C74 C75 C76 C77 C81 C86 C89 C90 C95 C97
344 response values
3 levels for factor group : EL RD TD
8 levels for factor condition : FF NOISE OPminus OPplus PSW SEM SPEECH UNREL
0 post hoc tests

Contingency tables of subject distributions among the categorical variables:

 condition

group FF NOISE OPminus OPplus PSW SEM SPEECH UNREL
EL 14 14 14 14 14 14 14 14
RD 13 13 13 13 13 13 13 13
TD 16 16 16 16 16 16 16 16

Tabulation of subjects against each of the categorical variables:

lop$nSubj vs group:
     
      EL RD TD
  C03  0  8  0
  C05  0  8  0
  C07  0  8  0
  C08  0  8  0
  C10  0  8  0
  C12  0  8  0
  C13  0  8  0
  C19  8  0  0
  C20  8  0  0
  C21  8  0  0
  C29  8  0  0
  C30  8  0  0
  C33  0  0  8
  C34  0  8  0
  C35  0  0  8
  C36  0  8  0
  C37  0  0  8
  C38  8  0  0
  C39  0  0  8
  C41  8  0  0
  C43  0  8  0
  C45  0  0  8
  C46  0  0  8
  C54  0  0  8
  C55  0  0  8
  C57  0  0  8
  C59  8  0  0
  C64  0  0  8
  C66  8  0  0
  C67  8  0  0
  C68  0  0  8
  C70  8  0  0
  C73  8  0  0
  C74  0  0  8
  C75  0  0  8
  C76  8  0  0
  C77  0  0  8
  C81  0  8  0
  C86  0  0  8
  C89  0  0  8
  C90  8  0  0
  C95  0  8  0
  C97  0  8  0

lop$nSubj vs condition:

  FF NOISE OPminus OPplus PSW SEM SPEECH UNREL

C03 1 1 1 1 1 1 1 1
C05 1 1 1 1 1 1 1 1
C07 1 1 1 1 1 1 1 1
C08 1 1 1 1 1 1 1 1
C10 1 1 1 1 1 1 1 1
C12 1 1 1 1 1 1 1 1
C13 1 1 1 1 1 1 1 1
C19 1 1 1 1 1 1 1 1
C20 1 1 1 1 1 1 1 1
C21 1 1 1 1 1 1 1 1
C29 1 1 1 1 1 1 1 1
C30 1 1 1 1 1 1 1 1
C33 1 1 1 1 1 1 1 1
C34 1 1 1 1 1 1 1 1
C35 1 1 1 1 1 1 1 1
C36 1 1 1 1 1 1 1 1
C37 1 1 1 1 1 1 1 1
C38 1 1 1 1 1 1 1 1
C39 1 1 1 1 1 1 1 1
C41 1 1 1 1 1 1 1 1
C43 1 1 1 1 1 1 1 1
C45 1 1 1 1 1 1 1 1
C46 1 1 1 1 1 1 1 1
C54 1 1 1 1 1 1 1 1
C55 1 1 1 1 1 1 1 1
C57 1 1 1 1 1 1 1 1
C59 1 1 1 1 1 1 1 1
C64 1 1 1 1 1 1 1 1
C66 1 1 1 1 1 1 1 1
C67 1 1 1 1 1 1 1 1
C68 1 1 1 1 1 1 1 1
C70 1 1 1 1 1 1 1 1
C73 1 1 1 1 1 1 1 1
C74 1 1 1 1 1 1 1 1
C75 1 1 1 1 1 1 1 1
C76 1 1 1 1 1 1 1 1
C77 1 1 1 1 1 1 1 1
C81 1 1 1 1 1 1 1 1
C86 1 1 1 1 1 1 1 1
C89 1 1 1 1 1 1 1 1
C90 1 1 1 1 1 1 1 1
C95 1 1 1 1 1 1 1 1
C97 1 1 1 1 1 1 1 1

***** End of data structure information *****
++++++++++++++++++++++++++++++++++++++++++++++++++++

Reading input files now…

Killed

Gang · January 31, 2019, 5:34pm

Laura,

I’m puzzled by the error message too. Were you running the analysis on a remote machine? Do you still get the same error?

Laura · January 31, 2019, 6:09pm

Hi Gang,

Thanks for your quick response! I’m running the analysis on our computer cluster at Harvard. All of the necessary software has to be loaded through a modules system every time we log on (included in my .bashrc-see below). When I run the afni_system_check.py -check_all, the necessary R packages seem to be recognized. Am I missing something? Should I try on my local machine to see if I can get it to run there?

Thanks,
Laura

relevant .bashrc code:
module load afni/18.3.05-fasrc01
module load gcc/7.1.0-fasrc01
module load R/3.5.1-fasrc02
export R_LIBS_USER=~/apps/R_3.5.1-fasrc02:$R_LIBS_USER

output of afni_system_check.py
-------------------------------- general ---------------------------------
architecture: 64bit ELF
system: Linux
release: 3.10.0-693.21.1.el7.x86_64
version: #1 SMP Wed Mar 7 19:03:37 UTC 2018
distribution: CentOS Linux 7.4.1708 Core
number of CPUs: 40
apparent login shell: bash
shell RC file: .bashrc (exists)

--------------------- AFNI and related program tests ---------------------
which afni : /n/helmod/apps/centos7/Core/afni/18.3.05-fasrc01/afni
afni version : Precompiled binary linux_openmp_64: Nov 19 2018
: AFNI_18.3.05
AFNI_version.txt : AFNI_18.3.05, linux_openmp_64, Nov 19 2018
which python : /bin/python
python version : 2.7.5
which R : /n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/R_core/3.5.1-fasrc02/bin/R
R version : R version 3.5.1 (2018-07-02) – “Feather Spray”
which tcsh : /bin/tcsh

instances of various programs found in PATH:
afni : 1 (/n/sw/helmod/apps/centos7/Core/afni/18.3.05-fasrc01/afni)
R : 2
/n/sw/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/R_core/3.5.1-fasrc02/bin/R
/n/sw/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/R_core/3.5.1-fasrc02/lib64/R/bin/R
python : 1 (/usr/bin/python2.7)
python2 : 1 (/usr/bin/python2.7)
python3 : 0

testing ability to start various programs…
afni : success
suma : success
3dSkullStrip : success
uber_subject.py : success
3dAllineate : success
3dRSFC : success
SurfMesh : success
3dClustSim : success

checking for R packages…
rPkgsInstall -pkgs ALL -check : success

checking for $HOME files…
.afnirc : missing
.sumarc : missing
.afni/help/all_progs.COMP : missing

------------------------------ python libs -------------------------------
** python module not found: PyQt4
– PyQt4 is no longer needed for an AFNI bootcamp

-------------------------------- env vars --------------------------------
PATH = /n/helmod/apps/centos7/Core/fsl/6.0.0-fasrc01/bin:/n/helmod/apps/centos7/Core/OpenBLAS/0.2.20-fasrc03/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/libxml2/2.7.8-fasrc03/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/gsl/2.4-fasrc01/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/netcdf/4.5.0-fasrc02/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/hdf5/1.10.1-fasrc02/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/R_core/3.5.1-fasrc02/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/R_core/3.5.1-fasrc02/lib64/R/bin:/n/helmod/apps/centos7/Core/libtiff/4.0.9-fasrc01/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/pcre/8.41-fasrc01/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/xz/5.2.2-fasrc02/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/bzip2/1.0.6-fasrc02/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/curl/7.45.0-fasrc02/bin:/n/helmod/apps/centos7/Core/jdk/1.8.0_45-fasrc01/jre/bin:/n/helmod/apps/centos7/Core/jdk/1.8.0_45-fasrc01/db/bin:/n/helmod/apps/centos7/Core/jdk/1.8.0_45-fasrc01/bin:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/readline/6.3-fasrc03/bin:/n/helmod/apps/centos7/Core/gcc/7.1.0-fasrc01/bin:/n/helmod/apps/centos7/Core/afni/18.3.05-fasrc01:/n/helmod/apps/centos7/Core/freesurfer/6.0.0-fasrc01/mni/bin:/n/helmod/apps/centos7/Core/freesurfer/6.0.0-fasrc01/fsfast/bin:/n/helmod/apps/centos7/Core/freesurfer/6.0.0-fasrc01/tktools:/n/helmod/apps/centos7/Core/freesurfer/6.0.0-fasrc01/bin:/ncf/nrg/sw/apps/mricrogl/2017_07_14:/usr/local/bin:/opt/TurboVNC/bin:/usr/lib64/qt-3.3/bin:/bin:/usr/bin:/opt/dell/srvadmin/bin

PYTHONPATH = /n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/libxml2/2.7.8-fasrc03/lib64/python2.7/site-packages:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/nlopt/2.4.2-fasrc03/lib/python2.7/site-packages:/opt/websockify/lib/python2.7/site-packages::

R_LIBS =
LD_LIBRARY_PATH = /n/helmod/apps/centos7/Core/OpenBLAS/0.2.20-fasrc03/lib:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/libxml2/2.7.8-fasrc03/lib64:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/nlopt/2.4.2-fasrc03/lib:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/gsl/2.4-fasrc01/lib64:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/netcdf/4.5.0-fasrc02/lib64:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/hdf5/1.10.1-fasrc02/lib:/n/helmod/apps/centos7/Core/szip/2.1-fasrc02/lib64:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/R_core/3.5.1-fasrc02/lib64:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/R_core/3.5.1-fasrc02/lib64/R/lib:/n/helmod/apps/centos7/Core/libtiff/4.0.9-fasrc01/lib64:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/pcre/8.41-fasrc01/lib:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/xz/5.2.2-fasrc02/lib64:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/bzip2/1.0.6-fasrc02/lib:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/zlib/1.2.8-fasrc08/lib:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/curl/7.45.0-fasrc02/lib:/n/helmod/apps/centos7/Core/jdk/1.8.0_45-fasrc01/jre/lib:/n/helmod/apps/centos7/Core/jdk/1.8.0_45-fasrc01/db/lib:/n/helmod/apps/centos7/Core/jdk/1.8.0_45-fasrc01/lib:/n/helmod/apps/centos7/Comp/gcc/7.1.0-fasrc01/readline/6.3-fasrc03/lib64:/n/helmod/apps/centos7/Core/gcc/7.1.0-fasrc01/lib64:/n/helmod/apps/centos7/Core/gcc/7.1.0-fasrc01/lib:/n/helmod/apps/centos7/Core/mpc/1.0.3-fasrc06/lib64:/n/helmod/apps/centos7/Core/mpfr/3.1.5-fasrc01/lib64:/n/helmod/apps/centos7/Core/gmp/6.1.2-fasrc01/lib64

DYLD_LIBRARY_PATH =
DYLD_FALLBACK_LIBRARY_PATH =

------------------------------ data checks -------------------------------
data dir : missing AFNI_data6
data dir : missing AFNI_demos
data dir : missing suma_demo
data dir : missing afni_handouts
atlas : found TT_N27+tlrc under /n/helmod/apps/centos7/Core/afni/18.3.05-fasrc01

------------------------------ OS specific -------------------------------
which yum : /bin/yum
yum version : There was a problem importing one of the Python modules

========================= summary, please fix: =========================

login shell ‘bash’, trusting user to translate code examples from ‘tcsh’
‘afni’ executable is owned by root
consider running: cp /n/helmod/apps/centos7/Core/afni/18.3.05-fasrc01/AFNI.afnirc ~/.afnirc
consider running “suma -update_env” for .sumarc
consider running: apsearch -update_all_afni_help
insufficient data for AFNI bootcamp
consider running: yum install PyQt4

ptaylor · January 31, 2019, 6:49pm

I wonder if the “killed” message means that you are running out of resources, such as memory or CPUs-- I don’t know if you have an interface to check the amount of memory for jobs running on the cluster, and seeing it reach an allocated ceiling? If using multiple CPUs, you might need more. My first guess would be that you are running out of memory, so might want to flag/allocate more?

–pt

Laura · January 31, 2019, 11:09pm

Thanks, Paul! That was extremely helpful. I just needed to increase my allocated memory and then it ran! I did find a significant group by condition interaction when FDR corrected, so that makes me feel better, but now I have to learn to tackle the GLT coding and do some post-hoc digging. I’ll be back if I can’t figure it out!

Thanks,
Laura

Laura · February 4, 2019, 12:36pm

Hi all,

Unfortunately my worst nightmare was realized, and even though it looks like the group x condition interaction was significant (see post above), the post hoc GLTs are all non-significant (at FDR-corrected 0.05). How is that possible? Am I coding the GLTs incorrectly, missing post-hoc tests, reading the interaction incorrectly (I’m setting the interaction sub-brick for both the overlay and threshold), thresholding incorrectly, etc.? I’ve included the code below (see previous posts in this thread for the data table), and would be willing to send the MVM output file if necessary.

Thanks,
Laura

3dMVM -prefix WithGLTs
-jobs 8
-bsVars group
-wsVars condition
-SS_type 2
-mask group_mask.nii
-num_glt 24
-gltLabel 1 SPEECH_TDvRD -gltCode 1 ‘group : 1TD -1RD condition : 1SPEECH’
-gltLabel 2 NOISE_TDvRD -gltCode 2 'group : 1TD -1RD condition : 1NOISE’
-gltLabel 3 UNREL_TDvRD -gltCode 3 ‘group : 1TD -1RD condition : 1UNREL’
-gltLabel 4 PSW_TDvRD -gltCode 4 'group : 1TD -1RD condition : 1PSW’
-gltLabel 5 FF_TDvRD -gltCode 5 ‘group : 1TD -1RD condition : 1FF’
-gltLabel 6 OPplus_TDvRD -gltCode 6 'group : 1TD -1RD condition : 1OPplus’
-gltLabel 7 OPminus_TDvRD -gltCode 7 ‘group : 1TD -1RD condition : 1OPminus’
-gltLabel 8 SEM_TDvRD -gltCode 8 'group : 1TD -1RD condition : 1SEM’
-gltLabel 9 SPEECH_TDvEL -gltCode 9 ‘group : 1TD -1EL condition : 1SPEECH’
-gltLabel 10 NOISE_TDvEL -gltCode 10 'group : 1TD -1EL condition : 1NOISE’
-gltLabel 11 UNREL_TDvEL -gltCode 11 ‘group : 1TD -1EL condition : 1UNREL’
-gltLabel 12 PSW_TDvEL -gltCode 12 'group : 1TD -1EL condition : 1PSW’
-gltLabel 13 FF_TDvEL -gltCode 13 ‘group : 1TD -1EL condition : 1FF’
-gltLabel 14 OPplus_TDvEL -gltCode 14 'group : 1TD -1EL condition : 1OPplus’
-gltLabel 15 OPminus_TDvEL -gltCode 15 ‘group : 1TD -1EL condition : 1OPminus’
-gltLabel 16 SEM_TDvEL -gltCode 16 'group : 1TD -1EL condition : 1SEM’
-gltLabel 17 SPEECH_RDvEL -gltCode 17 ‘group : 1RD -1EL condition : 1SPEECH’
-gltLabel 18 NOISE_RDvEL -gltCode 18 'group : 1RD -1EL condition : 1NOISE’
-gltLabel 19 UNREL_RDvEL -gltCode 19 ‘group : 1RD -1EL condition : 1UNREL’
-gltLabel 20 PSW_RDvEL -gltCode 20 'group : 1RD -1EL condition : 1PSW’
-gltLabel 21 FF_RDvEL -gltCode 21 ‘group : 1RD -1EL condition : 1FF’
-gltLabel 22 OPplus_RDvEL -gltCode 22 'group : 1RD -1EL condition : 1OPplus’
-gltLabel 23 OPminus_RDvEL -gltCode 23 ‘group : 1RD -1EL condition : 1OPminus’
-gltLabel 24 SEM_RDvEL -gltCode 24 'group : 1RD -1EL condition : 1SEM’
-dataTable @table.txt

Gang · February 4, 2019, 10:00pm

Laura,

A couple of comments:

You have 3 groups and 8 conditions. The F-stat for the interaction between the two factors shows the statistical evidence about the differences of differences, i.e., whether there are any differences among the 3 groups in terms of the differences among the 8 conditions. For example, use a simple case with both factors (A and B) having only 2 levels as an example. The F-stat tests the following

(A1B1 - A1B2) - (A2B1 - A2B2)

or

(A1B1 - A2B1) - (A1B2 - A2B2)

However, none of those post hoc tests you specified in your 3dMVM script are directly teasing apart the interaction effect even though they are related and helpful. For example, this following is one such test you may consider that would be part of the interaction effect:

-gltLabel ? … -gltCode ? ‘group : 1TD -1RD condition : 1SPEECH -1NOISE’ \

FDR correction could be too stringent. I would simply choose a voxel-wise p-value of 0.05 or even 0.1 to simply examine/visualize the results and see you have some meaningful data. Consider cluster-based FWE correction later on (e.g., 3dClustSim) if you want to get a stamp for publication.