umx_make_TwinData: Simulate twin data with control over A, C, and E (and D)...
In umx: Structural Equation Modeling and Twin Modeling in R
umx_make_TwinData R Documentation
Simulate twin data with control over A, C, and E (and D) parameters
Description
Makes MZ and DZ twin data for standard ACE/ADE, fixed MZ/DZ correlations, thresholds,
or bivariate GxE (umxGxEbiv()). By default, variance components must sum to 1.
For univariate Purcell GxE data for umxGxE(), use umx_make_GxE_data() instead
(path form \(a + a_m M\), ground-truth attributes, optional age covariate phenotype).
Passing the old univariate style AA = c(avg = ..., min = ..., max = ...) to this
function is an error and points you to that helper.
See examples for how to use this: it is pretty flexible.
If you provide 2 varNames, they will be used for twin 1 and twin 2. If you provide one, it will be expanded to var_T1 and var_T2.
note: the function was designed around nSib = 2 and var names = var_T1. It isn't yet smart enough to do, for instance
scaling or shifting to make the min value 0 (normal for most traits we analyse) for nonstandard varNames and nSib.
Note, if you want a power calculator, see power.ACE.test() and umxPower().
Usage
You must supply nMZpairs (you can omit nDZpairs).
You can give any two of A, C, or E and the function deduces the missing parameter so A+C+E == 1.
Bivariate GxE Data (for umxGxEbiv())
To simulate data with a moderator that is not shared by both twins.
Moderated heritability is specified via the bivariate relationship (AA, CC, EE) and two moderators in each component.
AA = list(a11 = .4, a12 = .1, a22 = .15)
CC = list(c11 = .2, c12 = .1, c22 = .10)
EE = list(e11 = .4, e12 = .3, e22 = .25)
Amod = list(Beta_a1 = .025, Beta_a2 = .025)
Cmod = list(Beta_c1 = .025, Beta_c2 = .025)
Emod = list(Beta_e1 = .025, Beta_e2 = .025)
Usage
umx_make_TwinData(
nMZpairs,
nDZpairs = nMZpairs,
AA = NULL,
CC = NULL,
EE = NULL,
DD = NULL,
varNames = "var",
MZr = NULL,
DZr = MZr,
nSib = 2,
dzAr = 0.5,
scale = FALSE,
mean = 0,
sd = 1,
nThresh = NULL,
sum2one = TRUE,
bivAmod = NULL,
bivCmod = NULL,
bivEmod = NULL,
seed = NULL,
empirical = FALSE
)
Arguments
nMZpairs
Number of MZ pairs to simulate
nDZpairs
Number of DZ pairs to simulate (defaults to nMZpairs)
AA
value for A variance (scalar). For bivariate GxE, a list of Cholesky elements (see details).
CC
value for C variance.
EE
value for E variance.
DD
value for D variance (univariate ACE/ADE only).
varNames
name for variables (defaults to 'var')
MZr
If MZr and DZr are set (default = NULL), the function returns dataframes of the request n and correlation.
DZr
Set to return dataframe using MZr and Dzr (Default NULL)
nSib
Number of siblings in a family (default = 2). "3" = extra sib.
dzAr
DZ Ar (default .5)
scale
Whether to scale output to var=1 mean=0 (Default FALSE)
mean
mean for traits (default = 0) (not applied to bivariate GxE)
sd
sd of traits (default = 1) (not applied to bivariate GxE)
nThresh
If supplied, use as thresholds and return mxFactor output? (default is not to)
sum2one
Whether to enforce AA + CC + EE summing the one (default = TRUE)
bivAmod
Used for Bivariate GxE data: list(Beta_a1 = .025, Beta_a2 = .025)
bivCmod
Used for Bivariate GxE data: list(Beta_c1 = .025, Beta_c2 = .025)
bivEmod
Used for Bivariate GxE data: list(Beta_e1 = .025, Beta_e2 = .025)
seed
Allows user to set.seed() if wanting reproducible dataset
empirical
Passed to mvrnorm
Value
data.frame with twin columns and zygosity (plus moderator columns for bivariate GxE)
References
https://github.com/tbates/umx, https://tbates.github.io
See Also
-
umxACE(), umx_make_GxE_data(), umxGxE(), umxGxEbiv()
Other Twin Data functions:
umx,
umx_long2wide(),
umx_make_twin_data_nice(),
umx_residualize(),
umx_scale_wide_twin_data(),
umx_wide2longTwinData(),
umx_yj_wide_twin_data()
Other Data Functions:
noNAs(),
prolific_anonymize(),
prolific_check_ID(),
prolific_read_demog(),
prolific_scoring_stub(),
umx,
umxFactor(),
umxGSEM_sim_snps(),
umxHetCor(),
umx_as_numeric(),
umx_cont_2_quantiles(),
umx_lower2full(),
umx_make_GxE_data(),
umx_make_MR_data(),
umx_make_fake_data(),
umx_make_raw_from_cov(),
umx_merge_randomized_columns(),
umx_polychoric(),
umx_polypairwise(),
umx_polytriowise(),
umx_read_lower(),
umx_rename(),
umx_reorder(),
umx_score_scale(),
umx_select_valid(),
umx_stack(),
umx_strings2numeric()
Examples
# =====================================================================
# = Basic Example, with all elements of std univariate data specified =
# =====================================================================
tmp = umx_make_TwinData(nMZpairs = 10000, AA = .30, CC = .00, EE = .70)
# Show dataframe with 20,000 rows and 3 variables: var_T1, var_T2, and zygosity
str(tmp)
# ===============================
# = How to consume the datasets =
# ===============================
mzData = tmp[tmp$zygosity == "MZ", ]
dzData = tmp[tmp$zygosity == "DZ", ]
str(mzData); str(dzData);
cov(mzData[, c("var_T1", "var_T2")])
cov(dzData[, c("var_T1", "var_T2")])
umxAPA(mzData[, c("var_T1", "var_T2")])
# Prefer to work in path coefficient values? (little a?)
tmp = umx_make_TwinData(2000, AA = .7^2, CC = .0)
mzData = tmp[tmp$zygosity == "MZ", ]
dzData = tmp[tmp$zygosity == "DZ", ]
m1 = umxACE(selDVs="var", sep="_T", mzData= mzData, dzData= dzData)
# Examine correlations
cor(mzData[,c("var_T1","var_T2")])
cor(dzData[,c("var_T1","var_T2")])
# Example with D (left un-modeled in ACE)
tmp = umx_make_TwinData(nMZpairs = 500, AA = .4, DD = .2, CC = .2)
m1 = umxACE(selDVs="var", data = tmp, mzData= "MZ", dzData= "DZ")
# | | a1| c1| e1|
# |:---|----:|----:|----:|
# |var | 0.86| 0.24| 0.45|
m1 = umxACE(selDVs="var", data = tmp, mzData= "MZ", dzData= "DZ", dzCr=.25)
# | | a1|d1 | e1|
# |:---|---:|:--|----:|
# |var | 0.9|. | 0.44|
# =============
# = Shortcuts =
# =============
# Omit nDZpairs (equal numbers of both by default)
tmp = umx_make_TwinData(100, AA = 0.5, CC = 0.3) # omit any one of A, C, or E (sums to 1)
cov(tmp[tmp$zygosity == "DZ", c("var_T1","var_T2")])
# Not limited to unit variance
tmp = umx_make_TwinData(100, AA = 3, CC = 2, EE = 3, sum2one = FALSE)
cov(tmp[tmp$zygosity == "MZ", c("var_T1","var_T2")])
# Output can be scaled (mean=0, std=1)
tmp = umx_make_TwinData(100, AA = .7, CC = .1, scale = TRUE)
cov(tmp[tmp$zygosity == "MZ", c("var_T1","var_T2")])
# Univariate GxE for umxGxE: use umx_make_GxE_data() (not umx_make_TwinData)
# df = umx_make_GxE_data(nMZpairs = 500, nDZpairs = 500, am = 0.15)
# m1 = umxGxE(selDVs = "outcome", selDefs = "mod", sep = "_T",
# data = df, mzData = "MZ", dzData = "DZ")
## Not run:
# =====================
# = Threshold Example =
# =====================
tmp = umx_make_TwinData(100, AA = .6, CC = .2, nThresh = 3)
str(tmp)
umx_polychoric(subset(tmp, zygosity=="MZ", c("var_T1", "var_T2")))$polychorics
# Running model with 7 parameters
# var_T1 var_T2
# var_T1 1.0000000 0.7435457
# var_T2 0.7435457 1.0000000
# =================================================
# = Just use MZr and DZr (also works with nSib>2) =
# =================================================
tmp = umx_make_TwinData(100, MZr = .86, DZr = .60, nSib= 3, varNames = "IQ")
umxAPA(subset(tmp, zygosity == "MZ", paste0("IQ_T", 1:2)))
umxAPA(subset(tmp, zygosity == "DZ", paste0("IQ_T", 1:2)))
m1 = umxACE(selDVs= "IQ", data = tmp)
m1 = umxACE(selDVs= "IQ", data = tmp, nSib=3)
# TODO tmx_ examples of unmodeled D etc.
# Bivariate GxSES example (see umxGxEbiv)
AA = list(a11 = .4, a12 = .1, a22 = .15)
CC = list(c11 = .2, c12 = .1, c22 = .10)
EE = list(e11 = .4, e12 = .3, e22 = .25)
Amod = list(Beta_a1 = .025, Beta_a2 = .025)
Cmod = list(Beta_c1 = .025, Beta_c2 = .025)
Emod = list(Beta_e1 = .025, Beta_e2 = .025)
tmp = umx_make_TwinData(5000, AA =AA, CC = CC, EE = EE,
bivAmod = Amod, bivCmod =Cmod, bivEmod =Emod)
str(tmp)
# 'data.frame': 10000 obs. of 7 variables:
# $ defM_T1 : num 0.171 0.293 -0.173 0.238 -0.73 ...
# $ defM_T2 : num 0.492 -0.405 -0.696 -0.829 -0.858 ...
# $ M_T1 : num 0.171 0.293 -0.173 0.238 -0.73 ...
# $ var_T1 : num 0.011 0.1045 0.5861 0.0583 1.0225 ...
# $ M_T2 : num 0.492 -0.405 -0.696 -0.829 -0.858 ...
# $ var_T2 : num -0.502 -0.856 -0.154 0.065 -0.268 ...
# $ zygosity: Factor w/ 2 levels "MZ","DZ": 1 1 1 1 1 1 1 1 1 1 ...
## End(Not run)
umx documentation built on Oct. 11, 2026, 5:07 p.m.
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| umx_make_TwinData | R Documentation |
Simulate twin data with control over A, C, and E (and D) parameters
Description
Makes MZ and DZ twin data for standard ACE/ADE, fixed MZ/DZ correlations, thresholds,
or bivariate GxE (umxGxEbiv()). By default, variance components must sum to 1.
For univariate Purcell GxE data for umxGxE(), use umx_make_GxE_data() instead
(path form \(a + a_m M\), ground-truth attributes, optional age covariate phenotype).
Passing the old univariate style AA = c(avg = ..., min = ..., max = ...) to this
function is an error and points you to that helper.
See examples for how to use this: it is pretty flexible.
If you provide 2 varNames, they will be used for twin 1 and twin 2. If you provide one, it will be expanded to var_T1 and var_T2.
note: the function was designed around nSib = 2 and var names = var_T1. It isn't yet smart enough to do, for instance
scaling or shifting to make the min value 0 (normal for most traits we analyse) for nonstandard varNames and nSib.
Note, if you want a power calculator, see power.ACE.test() and umxPower().
Usage
You must supply nMZpairs (you can omit nDZpairs).
You can give any two of A, C, or E and the function deduces the missing parameter so A+C+E == 1.
Bivariate GxE Data (for umxGxEbiv())
To simulate data with a moderator that is not shared by both twins. Moderated heritability is specified via the bivariate relationship (AA, CC, EE) and two moderators in each component. AA = list(a11 = .4, a12 = .1, a22 = .15) CC = list(c11 = .2, c12 = .1, c22 = .10) EE = list(e11 = .4, e12 = .3, e22 = .25) Amod = list(Beta_a1 = .025, Beta_a2 = .025) Cmod = list(Beta_c1 = .025, Beta_c2 = .025) Emod = list(Beta_e1 = .025, Beta_e2 = .025)
Usage
umx_make_TwinData(
nMZpairs,
nDZpairs = nMZpairs,
AA = NULL,
CC = NULL,
EE = NULL,
DD = NULL,
varNames = "var",
MZr = NULL,
DZr = MZr,
nSib = 2,
dzAr = 0.5,
scale = FALSE,
mean = 0,
sd = 1,
nThresh = NULL,
sum2one = TRUE,
bivAmod = NULL,
bivCmod = NULL,
bivEmod = NULL,
seed = NULL,
empirical = FALSE
)
Arguments
nMZpairs |
Number of MZ pairs to simulate |
nDZpairs |
Number of DZ pairs to simulate (defaults to nMZpairs) |
AA |
value for A variance (scalar). For bivariate GxE, a list of Cholesky elements (see details). |
CC |
value for C variance. |
EE |
value for E variance. |
DD |
value for D variance (univariate ACE/ADE only). |
varNames |
name for variables (defaults to 'var') |
MZr |
If MZr and DZr are set (default = NULL), the function returns dataframes of the request n and correlation. |
DZr |
Set to return dataframe using MZr and Dzr (Default NULL) |
nSib |
Number of siblings in a family (default = 2). "3" = extra sib. |
dzAr |
DZ Ar (default .5) |
scale |
Whether to scale output to var=1 mean=0 (Default FALSE) |
mean |
mean for traits (default = 0) (not applied to bivariate GxE) |
sd |
sd of traits (default = 1) (not applied to bivariate GxE) |
nThresh |
If supplied, use as thresholds and return mxFactor output? (default is not to) |
sum2one |
Whether to enforce AA + CC + EE summing the one (default = TRUE) |
bivAmod |
Used for Bivariate GxE data: list(Beta_a1 = .025, Beta_a2 = .025) |
bivCmod |
Used for Bivariate GxE data: list(Beta_c1 = .025, Beta_c2 = .025) |
bivEmod |
Used for Bivariate GxE data: list(Beta_e1 = .025, Beta_e2 = .025) |
seed |
Allows user to set.seed() if wanting reproducible dataset |
empirical |
Passed to mvrnorm |
Value
data.frame with twin columns and zygosity (plus moderator columns for bivariate GxE)
References
https://github.com/tbates/umx, https://tbates.github.io
See Also
-
umxACE(),umx_make_GxE_data(),umxGxE(),umxGxEbiv()
Other Twin Data functions:
umx,
umx_long2wide(),
umx_make_twin_data_nice(),
umx_residualize(),
umx_scale_wide_twin_data(),
umx_wide2longTwinData(),
umx_yj_wide_twin_data()
Other Data Functions:
noNAs(),
prolific_anonymize(),
prolific_check_ID(),
prolific_read_demog(),
prolific_scoring_stub(),
umx,
umxFactor(),
umxGSEM_sim_snps(),
umxHetCor(),
umx_as_numeric(),
umx_cont_2_quantiles(),
umx_lower2full(),
umx_make_GxE_data(),
umx_make_MR_data(),
umx_make_fake_data(),
umx_make_raw_from_cov(),
umx_merge_randomized_columns(),
umx_polychoric(),
umx_polypairwise(),
umx_polytriowise(),
umx_read_lower(),
umx_rename(),
umx_reorder(),
umx_score_scale(),
umx_select_valid(),
umx_stack(),
umx_strings2numeric()
Examples
# =====================================================================
# = Basic Example, with all elements of std univariate data specified =
# =====================================================================
tmp = umx_make_TwinData(nMZpairs = 10000, AA = .30, CC = .00, EE = .70)
# Show dataframe with 20,000 rows and 3 variables: var_T1, var_T2, and zygosity
str(tmp)
# ===============================
# = How to consume the datasets =
# ===============================
mzData = tmp[tmp$zygosity == "MZ", ]
dzData = tmp[tmp$zygosity == "DZ", ]
str(mzData); str(dzData);
cov(mzData[, c("var_T1", "var_T2")])
cov(dzData[, c("var_T1", "var_T2")])
umxAPA(mzData[, c("var_T1", "var_T2")])
# Prefer to work in path coefficient values? (little a?)
tmp = umx_make_TwinData(2000, AA = .7^2, CC = .0)
mzData = tmp[tmp$zygosity == "MZ", ]
dzData = tmp[tmp$zygosity == "DZ", ]
m1 = umxACE(selDVs="var", sep="_T", mzData= mzData, dzData= dzData)
# Examine correlations
cor(mzData[,c("var_T1","var_T2")])
cor(dzData[,c("var_T1","var_T2")])
# Example with D (left un-modeled in ACE)
tmp = umx_make_TwinData(nMZpairs = 500, AA = .4, DD = .2, CC = .2)
m1 = umxACE(selDVs="var", data = tmp, mzData= "MZ", dzData= "DZ")
# | | a1| c1| e1|
# |:---|----:|----:|----:|
# |var | 0.86| 0.24| 0.45|
m1 = umxACE(selDVs="var", data = tmp, mzData= "MZ", dzData= "DZ", dzCr=.25)
# | | a1|d1 | e1|
# |:---|---:|:--|----:|
# |var | 0.9|. | 0.44|
# =============
# = Shortcuts =
# =============
# Omit nDZpairs (equal numbers of both by default)
tmp = umx_make_TwinData(100, AA = 0.5, CC = 0.3) # omit any one of A, C, or E (sums to 1)
cov(tmp[tmp$zygosity == "DZ", c("var_T1","var_T2")])
# Not limited to unit variance
tmp = umx_make_TwinData(100, AA = 3, CC = 2, EE = 3, sum2one = FALSE)
cov(tmp[tmp$zygosity == "MZ", c("var_T1","var_T2")])
# Output can be scaled (mean=0, std=1)
tmp = umx_make_TwinData(100, AA = .7, CC = .1, scale = TRUE)
cov(tmp[tmp$zygosity == "MZ", c("var_T1","var_T2")])
# Univariate GxE for umxGxE: use umx_make_GxE_data() (not umx_make_TwinData)
# df = umx_make_GxE_data(nMZpairs = 500, nDZpairs = 500, am = 0.15)
# m1 = umxGxE(selDVs = "outcome", selDefs = "mod", sep = "_T",
# data = df, mzData = "MZ", dzData = "DZ")
## Not run:
# =====================
# = Threshold Example =
# =====================
tmp = umx_make_TwinData(100, AA = .6, CC = .2, nThresh = 3)
str(tmp)
umx_polychoric(subset(tmp, zygosity=="MZ", c("var_T1", "var_T2")))$polychorics
# Running model with 7 parameters
# var_T1 var_T2
# var_T1 1.0000000 0.7435457
# var_T2 0.7435457 1.0000000
# =================================================
# = Just use MZr and DZr (also works with nSib>2) =
# =================================================
tmp = umx_make_TwinData(100, MZr = .86, DZr = .60, nSib= 3, varNames = "IQ")
umxAPA(subset(tmp, zygosity == "MZ", paste0("IQ_T", 1:2)))
umxAPA(subset(tmp, zygosity == "DZ", paste0("IQ_T", 1:2)))
m1 = umxACE(selDVs= "IQ", data = tmp)
m1 = umxACE(selDVs= "IQ", data = tmp, nSib=3)
# TODO tmx_ examples of unmodeled D etc.
# Bivariate GxSES example (see umxGxEbiv)
AA = list(a11 = .4, a12 = .1, a22 = .15)
CC = list(c11 = .2, c12 = .1, c22 = .10)
EE = list(e11 = .4, e12 = .3, e22 = .25)
Amod = list(Beta_a1 = .025, Beta_a2 = .025)
Cmod = list(Beta_c1 = .025, Beta_c2 = .025)
Emod = list(Beta_e1 = .025, Beta_e2 = .025)
tmp = umx_make_TwinData(5000, AA =AA, CC = CC, EE = EE,
bivAmod = Amod, bivCmod =Cmod, bivEmod =Emod)
str(tmp)
# 'data.frame': 10000 obs. of 7 variables:
# $ defM_T1 : num 0.171 0.293 -0.173 0.238 -0.73 ...
# $ defM_T2 : num 0.492 -0.405 -0.696 -0.829 -0.858 ...
# $ M_T1 : num 0.171 0.293 -0.173 0.238 -0.73 ...
# $ var_T1 : num 0.011 0.1045 0.5861 0.0583 1.0225 ...
# $ M_T2 : num 0.492 -0.405 -0.696 -0.829 -0.858 ...
# $ var_T2 : num -0.502 -0.856 -0.154 0.065 -0.268 ...
# $ zygosity: Factor w/ 2 levels "MZ","DZ": 1 1 1 1 1 1 1 1 1 1 ...
## End(Not run)
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