## Introduction

This vignette provides a detailed guide to specific functions within
the `BGmisc`

package that aid in the identification and
fitting of variance component models common in behavior genetics. We
will explore key functions such as `identifyComponentModel`

,
providing practical examples and theoretical background. Identification
ensures a unique set of parameters that define the model-implied
covariance matrix, preventing free parameters from trading off one
another.

### Loading Required Libraries

Ensure that the `BGmisc`

package is installed and
loaded.

Ensure that the following dependencies are installed before proceeding as they provide us with behavior genetic data and models:

`EasyMx`

`OpenMx`

Note: If any of the libraries are not installed, you can install them
using install.packages(“`package_name`

”).

## Working with Variance Component Models

In this section, we will demonstrate core functions related to the identification and fitting of variance component models.

### Using `comp2vech`

Function

The `comp2vech`

function is used to vectorize a components
model. The function is often used in conjunction with the identification
process. In this example, we apply it to a list of matrices:

The result showcases how the matrices have been transformed, reflecting their role in subsequent variance component analysis.

### Using `identifyComponentModel`

Function

The `identifyComponentModel`

function helps determine if a
variance components model is identified. It accepts relatedness
component matrices and returns information about identified and
non-identified parameters.

Here’s an example using the classical twin model *with only MZ
twins*:

```
identifyComponentModel(
A = list(matrix(1, 2, 2)),
C = list(matrix(1, 2, 2)),
E = diag(1, 2)
)
#> Component model is not identified.
#> Non-identified parameters are A, C
#> $identified
#> [1] FALSE
#>
#> $nidp
#> [1] "A" "C"
```

As you can see, the model is not identified. We need to add an additional group so that we have sufficient information. Let us add the rest of the classical twin model, in this case DZ twins.

```
identifyComponentModel(
A = list(matrix(c(1, .5, .5, 1), 2, 2), matrix(1, 2, 2)),
C = list(matrix(1, 2, 2), matrix(1, 2, 2)),
E = diag(1, 4)
)
#> Component model is identified.
#> $identified
#> [1] TRUE
#>
#> $nidp
#> character(0)
```

As you can see the model is identified, now that we’ve added another group. Let us confirm by fitting a model. First we prepare the data.

```
require(dplyr)
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
```

```
# require(purrr)
data(twinData, package = "OpenMx")
selVars <- c("ht1", "ht2")
mzdzData <- subset(
twinData, zyg %in% c(1, 3),
c(selVars, "zyg")
)
mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg]
mzData <- mzdzData %>% filter(zyg == 1)
```

Let us fit the data with MZ twins by themselves.

```
run1 <- emxTwinModel(
model = "Cholesky",
relatedness = "RCoef",
data = mzData,
use = selVars,
run = TRUE, name = "TwCh"
)
#> Running TwCh with 4 parameters
#> Warning: In model 'TwCh' Optimizer returned a non-zero status code 5. The
#> Hessian at the solution does not appear to be convex. See
#> ?mxCheckIdentification for possible diagnosis (Mx status RED).
```

```
summary(run1)
#> Summary of TwCh
#>
#> The Hessian at the solution does not appear to be convex. See ?mxCheckIdentification for possible diagnosis (Mx status RED).
#>
#> free parameters:
#> name matrix row col Estimate Std.Error A lbound ubound
#> 1 sqrtA11 sqrtA 1 1 0.05090090 NA 1e-06
#> 2 sqrtC11 sqrtC 1 1 0.03565565 NA ! 0!
#> 3 sqrtE11 sqrtE 1 1 0.02325722 0.0007017955 ! 0!
#> 4 Mht1 Means ht1 1 1.62974907 0.0027023908
#>
#> Model Statistics:
#> | Parameters | Degrees of Freedom | Fit (-2lnL units)
#> Model: 4 1112 -3693.148
#> Saturated: 5 1111 NA
#> Independence: 4 1112 NA
#> Number of observations/statistics: 569/1116
#>
#>
#> ** Information matrix is not positive definite (not at a candidate optimum).
#> Be suspicious of these results. At minimum, do not trust the standard errors.
#>
#> Information Criteria:
#> | df Penalty | Parameters Penalty | Sample-Size Adjusted
#> AIC: -5917.148 -3685.148 -3685.078
#> BIC: -10747.543 -3667.773 -3680.471
#> To get additional fit indices, see help(mxRefModels)
#> timestamp: 2024-06-13 18:03:16
#> Wall clock time: 0.2029054 secs
#> optimizer: SLSQP
#> OpenMx version number: 2.21.11
#> Need help? See help(mxSummary)
```

As you can see the model was unsuccessful because it was not identified. But when we add another group, so that the model is identified, the model now fits.

```
run2 <- emxTwinModel(
model = "Cholesky",
relatedness = "RCoef",
data = mzdzData,
use = selVars,
run = TRUE, name = "TwCh"
)
#> Running TwCh with 4 parameters
```

```
summary(run2)
#> Summary of TwCh
#>
#> free parameters:
#> name matrix row col Estimate Std.Error A lbound ubound
#> 1 sqrtA11 sqrtA 1 1 0.06339271 0.0014377690 1e-06
#> 2 sqrtC11 sqrtC 1 1 0.00000100 0.0250258713 ! 0!
#> 3 sqrtE11 sqrtE 1 1 0.02330040 0.0007015267 0!
#> 4 Mht1 Means ht1 1 1.63295540 0.0020511844
#>
#> Model Statistics:
#> | Parameters | Degrees of Freedom | Fit (-2lnL units)
#> Model: 4 1803 -5507.092
#> Saturated: 5 1802 NA
#> Independence: 4 1803 NA
#> Number of observations/statistics: 920/1807
#>
#> Information Criteria:
#> | df Penalty | Parameters Penalty | Sample-Size Adjusted
#> AIC: -9113.092 -5499.092 -5499.048
#> BIC: -17811.437 -5479.794 -5492.498
#> To get additional fit indices, see help(mxRefModels)
#> timestamp: 2024-06-13 18:03:16
#> Wall clock time: 0.05672002 secs
#> optimizer: SLSQP
#> OpenMx version number: 2.21.11
#> Need help? See help(mxSummary)
```