umx (version 4.0.0)

umxIP: umxIP: Build and run an Independent pathway twin model

Description

Make a 2-group Independent Pathway twin model (Common-factor independent-pathway multivariate model). The following figure shows the IP model diagrammatically:

As can be seen, each phenotype also by default has A, C, and E influences specific to that phenotype.

Features of the model include the ability to include add more one set of independent pathways, different numbers of pathways for a, c, and e, as well the ability to use ordinal data, and different fit functions, e.g. WLS.

note: The function umx_set_mvn_optimization_options() allows users to see and set mvnRelEps and mvnMaxPointsA mvnRelEps defaults to .005. For ordinal models, you might find that '0.01' works better.

Usage

umxIP(
  name = "IP",
  selDVs,
  dzData,
  mzData,
  sep = NULL,
  nFac = c(a = 1, c = 1, e = 1),
  data = NULL,
  zyg = "zygosity",
  type = c("Auto", "FIML", "cov", "cor", "WLS", "DWLS", "ULS"),
  allContinuousMethod = c("cumulants", "marginals"),
  dzAr = 0.5,
  dzCr = 1,
  correlatedA = FALSE,
  numObsDZ = NULL,
  numObsMZ = NULL,
  autoRun = getOption("umx_auto_run"),
  tryHard = c("no", "yes", "ordinal", "search"),
  optimizer = NULL,
  equateMeans = TRUE,
  weightVar = NULL,
  addStd = TRUE,
  addCI = TRUE,
  freeLowerA = FALSE,
  freeLowerC = FALSE,
  freeLowerE = FALSE
)

Arguments

name

The name of the model (defaults to "IP").

selDVs

The base names of the variables to model. note: Omit suffixes - just "dep" not c("dep_T1", "dep_T2")

dzData

The DZ dataframe.

mzData

The MZ dataframe.

sep

The suffix for twin 1 and twin 2. e.g. selDVs= "dep", sep= "_T" -> c("dep_T1", "dep_T2")

nFac

How many common factors for a, c, and e. If one number is given, applies to all three.

data

If provided, dzData and mzData are treated as levels of zyg to select() MZ and DZ data sets (default = NULL)

zyg

If data provided, this column is used to select rows by zygosity (Default = "zygosity")

type

Analysis method one of c("Auto", "FIML", "cov", "cor", "WLS", "DWLS", "ULS")

allContinuousMethod

"cumulants" or "marginals". Used in all-continuous WLS data to determine if a means model needed.

dzAr

The DZ genetic correlation (defaults to .5, vary to examine assortative mating).

dzCr

The DZ "C" correlation (defaults to 1: set to .25 to make an ADE model).

correlatedA

Whether factors are allowed to correlate (not implemented yet: FALSE).

numObsDZ

= For cov data, the number of DZ pairs.

numObsMZ

= For cov data, the number of MZ pairs.

autoRun

Whether to run and return the model (default), or just to create and return without running.

tryHard

Whether to tryHard (default 'no' uses normal mxRun). options: "mxTryHard", "mxTryHardOrdinal", or "mxTryHardWideSearch"

optimizer

optionally set the optimizer (default NULL does nothing).

equateMeans

Whether to equate the means across twins (defaults to TRUE).

weightVar

If a weighting variable is provided, a vector objective will be used to weight the data. (default = NULL).

addStd

Whether to add algebras for a standardized model (defaults to TRUE).

addCI

Whether to add CIs (defaults to TRUE).

freeLowerA

ignore: Whether to leave the lower triangle of A free (default = FALSE).

freeLowerC

ignore: Whether to leave the lower triangle of C free (default = FALSE).

freeLowerE

ignore: Whether to leave the lower triangle of E free (default = FALSE).

Value

• mxModel()

Details

Like the umxACE() model, the CP model decomposes phenotypic variance into Additive genetic, unique environmental (E) and, optionally, either common or shared-environment (C) or non-additive genetic effects (D).

Unlike the Cholesky, these factors do not act directly on the phenotype. Instead latent A, C, and E influences impact on one or more latent common factors which, in turn, account for variance in the phenotypes (see Figure).

Data Input Currently, umxIP accepts only raw data. This may change in future versions. You can choose other fit functions, e.g. WLS.

Ordinal Data

In an important capability, the model transparently handles ordinal (binary or multi-level ordered factor data) inputs, and can handle mixtures of continuous, binary, and ordinal data in any combination.

Additional features

umxIP supports varying the DZ genetic association (defaulting to .5) to allow exploring assortative mating effects, as well as varying the DZ “C” factor from 1 (the default for modeling family-level effects shared 100% by twins in a pair), to .25 to model dominance effects.

Matrices and Labels in IP model

A good way to see which matrices are used in umxIP is to run an example model and plot it.

All the shared matrices are in the model "top".

Matrices as, cs, and es contain the path loadings specific to each variable on their diagonals.

To see the 'as' values, you can simply execute:

m1$top#as$values

m1$top#as$labels

m1$top#as$free

Labels relevant to modifying the specific loadings take the form "as_r1c1", "as_r2c2" etc.

The independent-pathway loadings on the manifests are in matrices a_ip, c_ip, e_ip.

Less commonly-modified matrices are the mean matrix expMean. This has 1 row, and the columns are laid out for each variable for twin 1, followed by each variable for twin 2.

So, in a model where the means for twin 1 and twin 2 had been equated (set = to T1), you could make them independent again with this line:

m1$top$expMean$labels[1,4:6] = c("expMean_r1c4", "expMean_r1c5", "expMean_r1c6")

References

• https://www.github.com/tbates/umx

See Also

• plot(), umxSummary() work for IP, CP, GxE, SAT, and ACE models.

Other Twin Modeling Functions: plot.MxModelTwinMaker(), power.ACE.test(), umxACEcov(), umxACEv(), umxACE(), umxCP(), umxDoCp(), umxDoC(), umxGxE_window(), umxGxEbiv(), umxGxE(), umxRotate.MxModelCP(), umxSexLim(), umxSimplex(), umx

Examples

# NOT RUN {
require(umx)
data(GFF)
mzData <- subset(GFF, zyg_2grp == "MZ")
dzData <- subset(GFF, zyg_2grp == "DZ")
selDVs = c("gff","fc","qol","hap","sat","AD") # These will be expanded into "gff_T1" "gff_T2" etc.
m1 =    umxIP(selDVs = selDVs, sep = "_T", dzData = dzData, mzData = mzData)

# WLS example: Use "marginals" method to enable all continuous data with missingness.
m3 = umxIP(selDVs = selDVs, sep = "_T", dzData = dzData, mzData = mzData, 
	type = "DWLS", allContinuousMethod='marginals')
# omit missing to enable default WLS method to work on all continuous data
dzD = na.omit(dzData[, tvars(selDVs, "_T")])
mzD = na.omit(dzData[, tvars(selDVs, "_T")])
m4 = umxIP(selDVs = selDVs, sep = "_T", dzData = dzD, mzData = mzD, type = "DWLS")

# ====================================================================
# = Try with a non-default number of a, c, and e independent factors =
# ====================================================================
nFac = c(a = 2, c = 1, e = 1)
m2 = umxIP(selDVs = selDVs, sep = "_T", dzData = dzData, mzData = mzData, nFac = nFac, 
	tryHard = "yes")
umxCompare(m1, m2)
# }