3dMEMA error

AFNI Message Board
1

Hello,

I’m trying to run a between-group analysis with two groups and two continuous covariates. I’d like to use the same center for the covariates but different slopes, but when I specify this (-covariates_model center=same slope=different) I get this error:

Error in lop$covData * lop$xMat[, 2, drop = F] : non-conformable arrays
Calls: process.MEMA.opts → cbind

The program runs fine when I leave out the -covariates_model option (which I assume defaults to same center and same slope?). So it doesn’t seem to be a problem with any other aspect of my code. Am I leaving something out, e.g. some further specification because there are two covariates?

This is what my covariates file looks like:

Subj Age meanFD
RED_CAT_107 12.64 0.14256525
RED_CAT_108 11.48 0.2176973

…etc (including subjects from both groups)

2

Could you paste the whole script here if you don’t mind?

3

3dMEMA
-prefix ASDvTD_${N}_Age+meanFD_P-C_G
-cio -max_zeros 2 -missing_data 0
-model_outliers -jobs $job -verb 1
-groups ASD TD
-set ASD
RED_CAT_201 RED_CAT_201_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_201_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_204 RED_CAT_204_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_204_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_205 RED_CAT_205_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_205_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_206 RED_CAT_206_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_206_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_207 RED_CAT_207_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_207_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_212 RED_CAT_212_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_212_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_216 RED_CAT_216_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_216_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_217 RED_CAT_217_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_217_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_218 RED_CAT_218_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_218_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_224 RED_CAT_224_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_224_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
-set TD
RED_CMNT_130 RED_CMNT_130_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CMNT_130_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CMNT_137 RED_CMNT_137_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CMNT_137_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CMNT_166 RED_CMNT_166_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CMNT_166_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CMNT_170 RED_CMNT_170_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CMNT_170_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_107 RED_CAT_107_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_107_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_108 RED_CAT_108_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_108_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_109 RED_CAT_109_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_109_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_110 RED_CAT_110_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_110_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_111 RED_CAT_111_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_111_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
RED_CAT_117 RED_CAT_117_stats_dm_REML+tlrc’[P-C_G#0_Coef]’ RED_CAT_117_stats_dm_REML+tlrc’[P-C_G#0_Tstat]’
-mask Haskins_mask+tlrc
-covariates Covariates.txt
-covariates_model center=same slope=different

This is what the Covariates file looks like:

Subj Age meanFD
RED_CAT_107 12.64 0.14256525
RED_CAT_108 11.48 0.2176973
etc…