[R-meta] Wald_test error
Cátia Ferreira De Oliveira
cm|o500 @end|ng |rom york@@c@uk
Sun Aug 15 15:10:10 CEST 2021
Dear James,
Apologies for not adding the mailing list to the email.
I am planning many contrasts, they could be accomplished by subgrouping the
data but not sure if that's a good approach. What I am aiming to accomplish
is to determine whether there are
a) any differences between the TD group and DD/DLD groups for grammar,
vocabulary and phonology;
b) comparing whether the effect for grammar, phonology and vocabulary == 0;
c) pairwise comparisons of the effect for grammar, phonology and vocabulary
to see if they are different;
d) check whether the correlation is different from 0 for the reference
level (TD) for each component - grammar, vocabulary, phonology (not sure if
I would need to run a different model for this one).
The reference level is the TD group, there are three groups (TD, DD and
DLD) and three components (grammar, vocabulary and phonology).
*component <- robu(formula = yi ~ 0 + Component + Group:Component, data =
df,*
* studynum = Study, var.eff.size = vi,*
* rho = .8, small = TRUE)*
*print(component)*
*RVE: Correlated Effects Model with Small-Sample Corrections*
*Model: yi ~ 0 + Component + Group:Component*
*Number of studies = 34 *
*Number of outcomes = 305 (min = 1 , mean = 8.97 , median = 4 , max = 48 )*
*Rho = 0.8 *
*I.sq = 47.09732 **Tau.sq = 0.02458387 *
* Estimate StdErr t-value dfs P(|t|>) 95%
CI.L 95% CI.U Sig*
*1 ComponentGrammar 0.05897 0.0456 1.293 11.45 0.2217
-0.0410 0.1589 *
*2 ComponentPhonology -0.00383 0.0356 -0.108 8.05 0.9169
-0.0857 0.0781 *
*3 ComponentVocabulary 0.09662 0.0592 1.631 5.35 0.1600
-0.0527 0.2460 *
*4 ComponentGrammar.GroupDD 0.03277 0.0590 0.555 1.05 0.6740
-0.6412 0.7068 *
*5 ComponentPhonology.GroupDD 0.01557 0.0929 0.168 5.60 0.8728
-0.2159 0.2470 *
*6 ComponentVocabulary.GroupDD -0.16516 0.0592 -2.788 5.35 0.0358
-0.3145 -0.0158 ***
*7 ComponentGrammar.GroupDLD -0.08097 0.0819 -0.989 14.99 0.3386
-0.2556 0.0936 *
*8 ComponentPhonology.GroupDLD 0.35854 0.1760 2.037 5.77 0.0897
-0.0763 0.7934 **
*9 ComponentVocabulary.GroupDLD -0.10642 0.0692 -1.537 9.94 0.1555
-0.2608 0.0480 *
*---*
*Signif. codes: < .01 *** < .05 ** < .10 **
*---*
*Note: If df < 4, do not trust the results*
Thank you! I am a bit new to using the constraints.
Best wishes,
Catia
On Sun, 15 Aug 2021 at 03:38, James Pustejovsky <jepusto using gmail.com> wrote:
> Please keep the list cc'd. Responses below.
>
> James
>
> On Fri, Aug 13, 2021 at 9:56 AM Cátia Ferreira De Oliveira <
> cmfo500 using york.ac.uk> wrote:
>
>> Dear James,
>>
>> Thank you for your reply! I wasn't able to find any examples similar to
>> mine, so could you give me an idea of how one would go about doing the
>> constraints when you want to test for an overall group effect and there's
>> an interaction term?
>>
>> group <- robu(formula = yi ~ 0 + Group + Component:Group, data = Data,
>> studynum = Study, var.eff.size = vi,
>> rho = .8, small = TRUE)
>> print(group)
>>
>
> To answer this question, we need to know what the null hypothesis of
> interest is. In the model as you've specified it, the definition of the
> group effects depends on how you specify the contrasts for the Component
> term. As a result, it's not clear what the main effects of the Group term
> mean. Could state in words what hypothesis you're trying to test?
>
>
>>
>> Also, just to confirm, if there is only one predictor with three levels
>> (yi ~ 0 + Variable), would the constraints be the following:
>>
>> Wald_test(model, constraints = matrix(c(1,0,0,0,1,0,0,0,1),3,3), vcov =
>> "CR2")
>>
>> Again, we need to know what the null hypothesis of interest is. Using a
> diagonal constraint matrix (as you've specified) will test the null that
> the average effect size is equal to zero for each of three levels of
> Variable. That might be of interest, or perhaps you instead want to test
> whether the average effect sizes are *identical* across the three levels of
> Variable (but not necessarily all zero). For the latter null, you would
> instead use
>
> constraints = constrain_equal(1:3)
>
> or
>
> constraints = constrain_equal("Variable", reg_ex = TRUE)
>
>
--
Cátia Margarida Ferreira de Oliveira
Psychology PhD Student
Department of Psychology, Room B214
University of York, YO10 5DD
pronouns: she, her
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