I am posting this question here after being advised to do so on StackOverflow.

I am trying to use the rma.uni function from the metafor package to estimate the impact of fishing gears on my abundance data. Following the method published in Sciberas et al. 2018 (DOI: 10.1111/faf.12283), I think I used correctly the function, however, I am not sure how to interpret the output.

In the function, `c`

is the log response ratio and `var_c`

is the associated variance. `log2(t+1)`

represent times in days.

In my data, `gear`

is a factor with three levels: CD, QSD and KSD.

As I am not familiar with models in general and especially this type of model, I read online documentation including this :

https://faculty.nps.edu/sebuttre/home/R/contrasts.html

Thus, I understood that only two levels from my factor `gear`

need to be display in the output.

Below is the output I have when I run the rma.uni function. My questions are:

- if gearCD is considered as a ‘reference’ in the model then it would mean that the effect of gearKSD is 0.14 more positive (I don’t know how to word it) than gearCD and that on the opposite, gearQSD is 0.12 times more damaging ?
- How should I interpret the fact that the pvalues for gearKSD and gearQSD are not significant ? Does it mean that their intercept is not significantly different from the one of gearCD ? If so, is the intercept of gearCD the same thing than
`intercpt`

? - Do you know how I could obtain one intercept value for each level of my factor
`gear`

? I am aiming at distinguishing the intial impact of these three gears so it would be of interest to have one interpect per gear. - Similarly, if I had interaction terms with log2(t+1) (for example
`gearKSD:log2(t+1)`

) the interpreation would be silimar to how we interpret intercept ?

I am sorry I know these are a lot of questions..

Thank you all very much for your help !

```
rma.uni(c,var_c,mods=~gear+log2(t+1),data=data_AB,method="REML")
Mixed-Effects Model (k = 15; tau^2 estimator: REML)
tau^2 (estimated amount of residual heterogeneity): 0.0585 (SE = 0.0357)
tau (square root of estimated tau^2 value): 0.2419
I^2 (residual heterogeneity / unaccounted variability): 71.00%
H^2 (unaccounted variability / sampling variability): 3.45
R^2 (amount of heterogeneity accounted for): 30.86%
Test for Residual Heterogeneity:
QE(df = 11) = 36.6583, p-val = 0.0001
Test of Moderators (coefficients 2:4):
QM(df = 3) = 6.9723, p-val = 0.0728
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt -1.0831 0.2540 -4.2644 <.0001 -1.5810 -0.5853 ***
gearKSD 0.0912 0.2002 0.4555 0.6488 -0.3011 0.4835
gearQSD -0.0654 0.1691 -0.3867 0.6990 -0.3967 0.2660
log2(t + 1) 0.0946 0.0372 2.5449 0.0109 0.0217 0.1675 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```

Cross Validated Asked by Ena on November 21, 2021

1 Answersif gearCD is considered as a 'reference' in the model then it would mean that the effect of gearKSD is 0.14 more positive (I don't know how to word it) than gearCD and that on the opposite, gearQSD is 0.12 times more damaging ?

It's not multiplicative, so you would sau that `gearKSD`

is associated with an expected increase of 0.15 in the outcome variable, compared to `gearCD`

; and `gearQSD`

is associated with an expected decrease of 0.13 in the outcome variable, compared to `gearCD`

.

How should I interpret the fact that the pvalues for gearKSD and gearQSD are not significant ? Does it mean that their intercept is not significantly different from the one of gearCD ? If so, is the intercept of gearCD the same thing than intercpt?

You would say that, if the **true difference** associated with the outcome between `gearKSD`

and `gearCD`

was zero, then the probability of obtaining these (or more extreme) results is 0.15. if the **true difference** associated with the outcome between `gearQSD`

and `gearCD`

was zero, then the probability of obtaining these (or more extreme) results is 0.16.

Do you know how I could obtain one intercept value for each level of my factor gear ? I am aiming at distinguishing the intial impact of these three gears so it would be of interest to have one interpect per gear.

For `gearCD`

the estimated expected value of the outcome is -1.1145 because it is included in the intercept as the reference level. Then you just add the values for the other two: for `gearKSD`

it is -1.1145 + 0.1488 and for `gearQSD`

it is -1.1145 - 0.1274

Similarly, if I had interaction terms with log2(t+1) (for example gearKSD:log2(t+1)) the interpreation would be silimar to how we interpret intercept ?

The intercept is always the estimated expected value for the outcome when the other variables are at zero (or at their reference level in the case of a categorical variable/factor).

However, when a variable is involved in an interaction then the intepretation of the main effects change - the estimates for each of the main effects is conditional on the variable being zero (or at it's reference level in the case of a categorical variable/factor). The interaction term itself then estimates the diffence.

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