Hi all,
The short form of my question is: is there a way to model a bivariate model using umxACEv, where the model for one variable is ACE (or its submodels) and the model for the other variable is ADE (or its submodels)?
It seems from this thread [6] that this is theoretically possible, but I'm unsure if the umx syntax enables it.
To explain a bit more, I have tried to model a bivariate ACE model, but got a negative C variance estimate (see below). Following the recommendation here [7] I have checked what happens when fixing the relevant C component to zero (as well as rC, using umxModify), but fixing it to zero results in a significantly worse model, meaning that the C component is "significantly negative". Indeed, when estimating the univariate models for each phenotype, the model that best fits the variable with negative C is an ADE model. How can I then test a bivariate model for these two different phenotypes?
Thanks,
Noam
selDVs <- c("WM", "Mnt") model <- umxACEv(selDVs = selDVs, sep = "_", zyg = "zyg", dzData = "DZ", mzData = "MZ",nSib = 2, data = TwinData.gen.wide, addCI = TRUE, type = "FIML") umxCI(model, run = "yes") Table: Standardized parameter estimates from a 2-factor Direct variance ACE model. A: additive genetic; C: common environment; E: unique environment. | | A1|A2 | C1|C2 | E1|E2 | |:----|-----:|:----|-----:|:----|----:|:----| |WM_ | 0.78|NA | -0.36|NA | 0.58|NA | |Mnt_ | -0.20|0.38 | 0.19|0.15 | 0.09|0.47 | Table: Means (from model$top$expMean) | | WM_1| Mnt_1| WM_2| Mnt_2| |:---------|----:|-----:|----:|-----:| |intercept | 0.02| -0.02| 0.02| -0.02| lbound estimate ubound lbound Code ubound Code top.A_std[1,1] 0.275 0.778 1.181 0 0 top.A_std[2,1] -0.516 -0.201 0.105 0 0 top.A_std[1,2] -0.516 -0.201 0.105 0 0 top.A_std[2,2] -0.092 0.379 0.731 0 0 top.C_std[1,1] -0.639 -0.356 -0.046 0 0 top.C_std[2,1] -0.010 0.189 0.387 0 0 top.C_std[1,2] -0.010 0.189 0.387 0 0 top.C_std[2,2] -0.108 0.152 0.430 0 0 top.E_std[1,1] 0.418 0.578 0.808 0 0 top.E_std[2,1] -0.034 0.094 0.232 0 0 top.E_std[1,2] -0.034 0.094 0.232 0 0 top.E_std[2,2] 0.331 0.470 0.691 0 0 expMean_WM_1 -0.195 0.018 0.231 0 0 expMean_Mnt_1 -0.194 -0.021 0.152 0 0 A_r1c1 2.313 6.661 10.482 0 0 A_r2c1 -3.305 -1.264 0.665 0 0 A_r2c2 -0.424 1.756 3.445 0 0 C_r1c1 -5.644 -3.050 -0.391 0 0 C_r2c1 -0.065 1.189 2.487 0 0 C_r2c2 -0.502 0.703 2.028 0 0 E_r1c1 3.604 4.947 6.940 0 0 E_r2c1 -0.221 0.591 1.488 0 0 E_r2c2 1.559 2.178 3.186 0 0 umxSummaryACEv(model, digits = 2, comparison = NULL, std = TRUE, showRg = TRUE, CIs = TRUE, report = c("markdown"), file = getOption("umx_auto_plot"), returnStd = FALSE, extended = FALSE, zero.print = ".", show = c("std", "raw") ) Table: Standardized parameter estimates from a 2-factor Direct variance ACE model. A: additive genetic; C: common environment; E: unique environment. | | A1|A2 | C1|C2 | E1|E2 | |:----|-----:|:----|-----:|:----|----:|:----| |WM_ | 0.78|NA | -0.36|NA | 0.58|NA | |Mnt_ | -0.20|0.38 | 0.19|0.15 | 0.09|0.47 | Table: Means (from model$top$expMean) | | WM_1| Mnt_1| WM_2| Mnt_2| |:---------|----:|-----:|----:|-----:| |intercept | 0.02| -0.02| 0.02| -0.02| Table: Genetic correlations | | rA1|rA2 |rC1 |rC2 | rE1|rE2 | |:----|-----:|:---|:---|:---|----:|:---| |WM_ | 1.00| | | | 1.00| | |Mnt_ | -0.37|1 | | | 0.18|1 |