
Post by aleixarnau on Feb 20, 2017 15:10:11 GMT
Hi,
Is it possible to get the variance explained by fixed effects?
Imagine I run a model like this: y = mu + xc(2)*bc(2) + xc(3)*bc(3) + ... + xc(t)*bc(t) + xq(1)*bq(1) + xq(2)*bq(2) + g + e where I have lots of fixed effects (categorical and continuous) as independent variables. Is there a way to know the % of variance of Y explained by each of this covariates?
Thanks.



Post by Jian Yang on Apr 24, 2017 4:09:15 GMT
variance explained is roughly var(x) * beta^2 / var(y)



Post by allielake on Nov 18, 2023 23:06:14 GMT
Hi, Is it possible to get the variance explained by fixed effects? Imagine I run a model like this: y = mu + xc(2)*bc(2) + xc(3)*bc(3) + ... + xc(t)*bc(t) + xq(1)*bq(1) + xq(2)*bq(2) + g + e where I have lots of fixed effects (categorical and continuous) as independent variables. Is there a way to know the % of variance of Y explained by each of this covariates? Thanks. Hello, I have the same question. Could you explain in more detail how to calculate the % of variance explained by a single fixed effect, when the outcome is a binary trait? For example, given the following output file, could you explain how to calculate the variance explained by the first fixed effect? V(G) 0.000313 0.000135 V(e) 0.013409 0.000170 Vp 0.013722 0.000109 V(G)/Vp 0.022774 0.009842 The estimate of variance explained on the observed scale is transformed to that on the underlying scale: (Proportion of cases in the sample = 0.014036; Userspecified disease prevalence = 0.470000) V(G)/Vp_L 0.645246 0.278845 logL 52206.615 logL0 52203.837 LRT 5.557 df 1 Pval 9.2043e03 n 31775
Fix_eff SE 0.043968 0.002359 0.000465 0.000042 0.043101 0.056215 0.081011 0.082971 0.193552 0.119570 0.020847 0.104401 0.035026 0.074360 0.049509 0.100165 0.053959 0.110962 0.053682 0.089359 0.165397 0.109448 0.148544 0.102986 0.013498 0.001334 0.013925 0.002638 Thanks very much! Allie Lake MD/PhD student Vanderbilt University

