# NOT RUN {
require(umx)
# ============================
# = How heritable is height? =
# ============================
# 1. Height in meters has a tiny variance, and this makes optimising hard.
# We'll scale it by 10x to make the Optimizer's task easier.
data(twinData) # ?twinData from Australian twins.
twinData[, c("ht1", "ht2")] = twinData[, c("ht1", "ht2")] * 10
# 2. Make mz & dz data.frames (no need to select variables: umx will do this)
mzData = twinData[twinData$zygosity %in% "MZFF", ]
dzData = twinData[twinData$zygosity %in% "DZFF", ]
# 3. Built & run the model, controlling for age in the means model
m1 = umxACE(selDVs = "ht", selCovs = "age", sep = "", dzData = dzData, mzData = mzData)
# sidebar: umxACE figures out variable names using sep:
# e.g. selVars = "wt" + sep= "_T" -> "wt_T1" "wt_T2"
umxSummary(m1, std = FALSE) # un-standardized
# tip 1: set report = "html" and umxSummary prints the table to your browser!
# tip 2: plot works for umx: Get a figure of the model and parameters
# plot(m1) # Also, look at the options for ?plot.MxModel.
# }
# NOT RUN {
# ============================
# = Model, with 2 covariates =
# ============================
# Create another covariate: cohort
twinData$cohort1 = twinData$cohort2 =twinData$part
mzData = twinData[twinData$zygosity %in% "MZFF", ]
dzData = twinData[twinData$zygosity %in% "DZFF", ]
# 1. def var approach
m2 = umxACE(selDVs = "ht", selCovs = c("age", "cohort"), sep = "", dzData = dzData, mzData = mzData)
# 2. Residualized approach: remove height variance accounted-for by age.
FFdata = twinData[twinData$zygosity %in% c("MZFF", "DZFF"), ]
FFdata = umx_residualize("ht", "age", suffixes = 1:2, data = FFdata)
mzData = FFdata[FFdata$zygosity %in% "MZFF", ]
dzData = FFdata[FFdata$zygosity %in% "DZFF", ]
m3 = umxACE(selDVs = "ht", sep = "", dzData = dzData, mzData = mzData)
# =============================================================
# = ADE: Evidence for dominance ? (DZ correlation set to .25) =
# =============================================================
m2 = umxACE(selDVs = "ht", sep = "", dzData = dzData, mzData = mzData, dzCr = .25)
umxCompare(m2, m1) # ADE is better
umxSummary(m2, comparison = m1)
# nb: Although summary is smart enough to print d, the underlying
# matrices are still called a, c & e.
# tip: try umxReduce(m1) to automatically build and compare ACE, ADE, AE, CE
# including conditional probabilities!
# ===================================================
# = WLS example using diagonal weight least squares =
# ===================================================
m3 = umxACE(selDVs = "ht", sep = "", dzData = dzData, mzData = mzData,
type = "DWLS", allContinuousMethod='marginals'
)
# ==============================
# = Univariate model of weight =
# ==============================
# Things to note:
# 1. Weight has a large variance, and this makes solution finding very hard.
# Here, we residualize the data for age, which also scales weight and height.
data(twinData)
tmp = umx_residualize(c("wt", "ht"), cov = "age", suffixes= c(1, 2), data = twinData)
mzData = tmp[tmp$zygosity %in% "MZFF", ]
dzData = tmp[tmp$zygosity %in% "DZFF", ]
# tip: You might also want transform variables
# tmp = twinData$wt1[!is.na(twinData$wt1)]
# car::powerTransform(tmp, family="bcPower"); hist(tmp^-0.6848438)
# twinData$wt1 = twinData$wt1^-0.6848438
# twinData$wt2 = twinData$wt2^-0.6848438
# 4. note: the default boundDiag = 0 lower-bounds a, c, and e at 0.
# Prevents mirror-solutions. If not desired: set boundDiag = NULL.
m2 = umxACE(selDVs = "wt", dzData = dzData, mzData = mzData, sep = "", boundDiag = NULL)
# A short cut (which is even shorter for "_T" twin data with "MZ"/"DZ" data in zygosity column is:
m1 = umxACE(selDVs = "wt", sep = "", data = twinData,
dzData = c("DZMM", "DZFF", "DZOS"), mzData = c("MZMM", "MZFF"))
# | | a1|c1 | e1|
# |:--|----:|:--|----:|
# |wt | 0.93|. | 0.38|
# tip: umx_make_twin_data_nice() will make data into this nice format for you!
# ======================
# = MODEL MODIFICATION =
# ======================
# We can modify this model, e.g. test shared environment.
# Set comparison to modify, and show effect in one step.
m2 = umxModify(m1, update = "c_r1c1", name = "no_C", comparison = TRUE)
#*tip* call umxModify(m1) with no parameters, and it will print the labels available to fix!
# nb: You can see parameters of any model with parameters(m1)
# =========================================================
# = Well done! Now you can make modify twin models in umx =
# =========================================================
# =====================================
# = Bivariate height and weight model =
# =====================================
data(twinData)
# We'll scale height (ht1 and ht2) and weight
twinData = umx_scale_wide_twin_data(data = twinData, varsToScale = c("ht", "wt"), sep = "")
mzData = twinData[twinData$zygosity %in% c("MZFF", "MZMM"),]
dzData = twinData[twinData$zygosity %in% c("DZFF", "DZMM", "DZOS"), ]
m1 = umxACE(selDVs = c("ht", "wt"), sep = '', dzData = dzData, mzData = mzData)
umxSummary(m1)
# ===================
# = Ordinal example =
# ===================
# Prep data
require(umx)
data(twinData)
# Cut BMI column to form ordinal obesity variables
obLevels = c('normal', 'overweight', 'obese')
cuts = quantile(twinData[, "bmi1"], probs = c(.5, .2), na.rm = TRUE)
twinData$obese1=cut(twinData$bmi1, breaks=c(-Inf,cuts,Inf), labels=obLevels)
twinData$obese2=cut(twinData$bmi2, breaks=c(-Inf,cuts,Inf), labels=obLevels)
# Make the ordinal variables into umxFactors
ordDVs = c("obese1", "obese2")
twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
mzData = twinData[twinData$zygosity %in% "MZFF", ]
dzData = twinData[twinData$zygosity %in% "DZFF", ]
# Model and summary!
m1 = umxACE(selDVs = "obese", dzData = dzData, mzData = mzData, sep = '')
# And controlling age (otherwise manifests appearance as latent C)
m1 = umxACE(selDVs = "obese", selCov= "age", dzData = dzData, mzData = mzData, sep = '')
# umxSummary(m1)
# ============================================
# = Bivariate continuous and ordinal example =
# ============================================
data(twinData)
twinData= umx_scale_wide_twin_data(data=twinData,varsToScale="wt",sep= "")
# Cut BMI column to form ordinal obesity variables
obLevels = c('normal', 'overweight', 'obese')
cuts = quantile(twinData[, "bmi1"], probs = c(.5, .2), na.rm = TRUE)
twinData$obese1=cut(twinData$bmi1,breaks=c(-Inf,cuts,Inf),labels=obLevels)
twinData$obese2=cut(twinData$bmi2,breaks=c(-Inf,cuts,Inf),labels=obLevels)
# Make the ordinal variables into mxFactors
ordDVs = c("obese1", "obese2")
twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
mzData = twinData[twinData$zygosity %in% "MZFF",]
dzData = twinData[twinData$zygosity %in% "DZFF",]
mzData = mzData[1:80,] # just top 80 so example runs in a couple of secs
dzData = dzData[1:80,]
m1 = umxACE(selDVs= c("wt","obese"), dzData= dzData, mzData= mzData, sep='')
# And controlling age
m1 = umxACE(selDVs = c("wt","obese"), selCov= "age", dzData = dzData, mzData = mzData, sep = '')
# =======================================
# = Mixed continuous and binary example =
# =======================================
require(umx)
data(twinData)
twinData= umx_scale_wide_twin_data(data= twinData,varsToScale= "wt", sep="")
# Cut to form category of 20% obese subjects
# and make into mxFactors (ensure ordered is TRUE, and require levels)
obLevels = c('normal', 'obese')
cuts = quantile(twinData[, "bmi1"], probs = .2, na.rm = TRUE)
twinData$obese1= cut(twinData$bmi1, breaks=c(-Inf,cuts,Inf), labels=obLevels)
twinData$obese2= cut(twinData$bmi2, breaks=c(-Inf,cuts,Inf), labels=obLevels)
ordDVs = c("obese1", "obese2")
twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
selDVs = c("wt", "obese")
mzData = twinData[twinData$zygosity %in% "MZFF",]
dzData = twinData[twinData$zygosity %in% "DZFF",]
m1 = umxACE(selDVs = selDVs, dzData = dzData, mzData = mzData, sep = '')
umxSummary(m1)
# ===================================
# Example with covariance data only =
# ===================================
require(umx)
data(twinData)
twinData= umx_scale_wide_twin_data(data=twinData, varsToScale= "wt", sep="")
selDVs = c("wt1", "wt2")
mz = cov(twinData[twinData$zygosity %in% "MZFF", selDVs], use = "complete")
dz = cov(twinData[twinData$zygosity %in% "DZFF", selDVs], use = "complete")
m1 = umxACE(selDVs=selDVs, dzData=dz, mzData=mz, numObsDZ=569, numObsMZ=351)
umxSummary(m1)
plot(m1)
# }
# NOT RUN {
# }
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