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help > RE: ANCOVA - contrast / hypothesis testing
Dec 7, 2019 07:12 AM | Andrew Zalesky
RE: ANCOVA - contrast / hypothesis testing
Hi Athina,
1. [0 0 1] will test whether the inflammation score is positively correlated with connectivity, while controlling for the effect of diagnosis. In contrast, [0 0 -1] with test for a negative correlation. You might also want to test for an interaction between diagnosis and group, by adding a fourth column that is the multiplication of the 2nd and 3rd column. The contrast would then be [0 0 0 1] or [0 0 0 -1].
2. See above. [0 0 1] will test for a positive correlation when using t-test. [0 0 -1] will test for a negative correlation.
3. F-test is two-tailed and the only valid contrast is [0 0 1] for the F-test here. With the F-test, the subnetwork can include some connections that are +vely correlated with connectivity and some that are -vely correlated. With a t-test, the two alternative hypotheses can be tested separately.
4. I am not sure if I understand the question. I suggest testing each effect separately. Using [0 1 0] or [0 -1 0] to test the effect of diagnosis. I don't think that [0 -1 1] is sensible. To test for an interaction, see my above comment.
Andrew
Originally posted by Athina Aruldass:
1. [0 0 1] will test whether the inflammation score is positively correlated with connectivity, while controlling for the effect of diagnosis. In contrast, [0 0 -1] with test for a negative correlation. You might also want to test for an interaction between diagnosis and group, by adding a fourth column that is the multiplication of the 2nd and 3rd column. The contrast would then be [0 0 0 1] or [0 0 0 -1].
2. See above. [0 0 1] will test for a positive correlation when using t-test. [0 0 -1] will test for a negative correlation.
3. F-test is two-tailed and the only valid contrast is [0 0 1] for the F-test here. With the F-test, the subnetwork can include some connections that are +vely correlated with connectivity and some that are -vely correlated. With a t-test, the two alternative hypotheses can be tested separately.
4. I am not sure if I understand the question. I suggest testing each effect separately. Using [0 1 0] or [0 -1 0] to test the effect of diagnosis. I don't think that [0 -1 1] is sensible. To test for an interaction, see my above comment.
Andrew
Originally posted by Athina Aruldass:
Hi Andrew
I was able to perform 2-sample t-test by keeping to the manual (and snooping around the forum) with minimal issues (and heaps of fun). I think I have made some interesting finds with the functional connectivity group comparison and would now like to add covariate(s) to the GLM - I followed the ANCOVA design for this.
I would like to test the following hypothesis - inflammation ie. covariate, has a greater / stronger effect on MDD compared to HC . Partial design matrix below -
1 1 0.3
1 0 2.2
1 1 0.3
columns : 1st - intercept , 2nd - Group; MDD coded 1 (n=83) / HC coded 0 (n=46) , 3rd - inflammation index
contrast : [0 0 1] , stat test = two sample t-test
Questions -
(1) Is the above doing what I think it's doing ie. testing for the aforementioned hypothesis ? If so, contrast [0 0 -1] t-test would then be testing to see if inflammation had a weaker effect on MDD compared to HC ?
(2) When testing for a significant effect of a continuous cov. eg. my case, could one adjust the contrast or design matrix to test specifically for direction of slope ie. +ve or -ve ? Is this something one could infer from inspecting the output - if so, my apologies I have yet to turn over every rock there...
(3) Applying F-test is also appropriate here, bearing in mind that it is two-tailed ? Or is t-test opted for here because this is only testing for a single main effect ie. univariate hypothesis ?
(4) Is it then correct / ideal to test for multiple significant effects with a single contrast ? - for example, if I am also interested in testing for a significant group effect MDD < HC alongside a main inflammation effect, would contrast [0 -1 1] with F-test do the trick ? - just that, from combing through some queries on the forum, the interest mainly is on interaction effect or a single main effect i.e zero other entries in the contrast. Hence, I'm not sure if testing for multiple effects is simply ludicrous...
Apologies if my questions do not make any sense, also teeter between actual toolbox issues and stats help. I'm still trying to get my head around contrasts, stat tests and GLMs more broadly.
Please and thank you - Athina.
I was able to perform 2-sample t-test by keeping to the manual (and snooping around the forum) with minimal issues (and heaps of fun). I think I have made some interesting finds with the functional connectivity group comparison and would now like to add covariate(s) to the GLM - I followed the ANCOVA design for this.
I would like to test the following hypothesis - inflammation ie. covariate, has a greater / stronger effect on MDD compared to HC . Partial design matrix below -
1 1 0.3
1 0 2.2
1 1 0.3
columns : 1st - intercept , 2nd - Group; MDD coded 1 (n=83) / HC coded 0 (n=46) , 3rd - inflammation index
contrast : [0 0 1] , stat test = two sample t-test
Questions -
(1) Is the above doing what I think it's doing ie. testing for the aforementioned hypothesis ? If so, contrast [0 0 -1] t-test would then be testing to see if inflammation had a weaker effect on MDD compared to HC ?
(2) When testing for a significant effect of a continuous cov. eg. my case, could one adjust the contrast or design matrix to test specifically for direction of slope ie. +ve or -ve ? Is this something one could infer from inspecting the output - if so, my apologies I have yet to turn over every rock there...
(3) Applying F-test is also appropriate here, bearing in mind that it is two-tailed ? Or is t-test opted for here because this is only testing for a single main effect ie. univariate hypothesis ?
(4) Is it then correct / ideal to test for multiple significant effects with a single contrast ? - for example, if I am also interested in testing for a significant group effect MDD < HC alongside a main inflammation effect, would contrast [0 -1 1] with F-test do the trick ? - just that, from combing through some queries on the forum, the interest mainly is on interaction effect or a single main effect i.e zero other entries in the contrast. Hence, I'm not sure if testing for multiple effects is simply ludicrous...
Apologies if my questions do not make any sense, also teeter between actual toolbox issues and stats help. I'm still trying to get my head around contrasts, stat tests and GLMs more broadly.
Please and thank you - Athina.
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Title | Author | Date |
---|---|---|
Athina Aruldass | Dec 6, 2019 | |
Athina Aruldass | Dec 18, 2019 | |
Andrew Zalesky | Dec 19, 2019 | |
Athina Aruldass | Jan 6, 2020 | |
Andrew Zalesky | Jan 6, 2020 | |
Athina Aruldass | Dec 10, 2019 | |
Andrew Zalesky | Dec 13, 2019 | |
Athina Aruldass | Dec 9, 2019 | |
Andrew Zalesky | Dec 7, 2019 | |