Shows a compact, publication-style, summary of a umx Sex Limitation model
Summarize a fitted Cholesky model returned by umxSexLim()
. Can control digits, report comparison model fits,
optionally show the Rg (genetic and environmental correlations), and show confidence intervals. The report parameter allows
drawing the tables to a web browser where they may readily be copied into non-markdown programs like Word.
umxSummarySexLim( model, digits = 2, file = getOption("umx_auto_plot"), comparison = NULL, std = TRUE, showRg = FALSE, CIs = TRUE, report = c("markdown", "html"), extended = FALSE, zero.print = ".", show = c("std", "raw"), returnStd = FALSE, ... )
model |
a |
digits |
round to how many digits (default = 2) |
file |
The name of the dot file to write: "name" = use the name of the model. Defaults to NA = do not create plot output |
comparison |
you can run mxCompare on a comparison model (NULL) |
std |
Whether to standardize the output (default = TRUE) |
showRg |
= whether to show the genetic correlations (FALSE) |
CIs |
Whether to show Confidence intervals if they exist (T) |
report |
If "html", then open an html table of the results |
extended |
how much to report (FALSE) |
zero.print |
How to show zeros (".") |
show |
Here to support being called from generic xmu_safe_run_summary. User should ignore: can be c("std", "raw") |
returnStd |
Whether to return the standardized form of the model (default = FALSE) |
... |
Other parameters to control model summary |
See documentation for summary functions for other types of umx model here: umxSummary()
.
optional mxModel()
Other Twin Modeling Functions:
power.ACE.test()
,
umxACEcov()
,
umxACEv()
,
umxACE()
,
umxCP()
,
umxDoCp()
,
umxDoC()
,
umxGxE_window()
,
umxGxEbiv()
,
umxGxE()
,
umxIP()
,
umxReduceACE()
,
umxReduceGxE()
,
umxReduce()
,
umxRotate.MxModelCP()
,
umxSexLim()
,
umxSimplex()
,
umxSummarizeTwinData()
,
umxSummaryACEv()
,
umxSummaryACE()
,
umxSummaryDoC()
,
umxSummaryGxEbiv()
,
umxSummarySimplex()
,
umxTwinMaker()
,
umx
## Not run: # ====================================================== # = Beta: Should be good to use for Boulder/March 2020 = # ====================================================== # ============================================= # = Run Qualitative Sex Differences ACE model = # ============================================= # ========================= # = Load and Process Data = # ========================= require(umx) umx_set_optimizer("SLSQP") data("us_skinfold_data") # rescale vars us_skinfold_data[, c('bic_T1', 'bic_T2')] = us_skinfold_data[, c('bic_T1', 'bic_T2')]/3.4 us_skinfold_data[, c('tri_T1', 'tri_T2')] = us_skinfold_data[, c('tri_T1', 'tri_T2')]/3 us_skinfold_data[, c('caf_T1', 'caf_T2')] = us_skinfold_data[, c('caf_T1', 'caf_T2')]/3 us_skinfold_data[, c('ssc_T1', 'ssc_T2')] = us_skinfold_data[, c('ssc_T1', 'ssc_T2')]/5 us_skinfold_data[, c('sil_T1', 'sil_T2')] = us_skinfold_data[, c('sil_T1', 'sil_T2')]/5 # Variables for Analysis selDVs = c('ssc','sil','caf','tri','bic') # Data for each of the 5 twin-type groups mzmData = subset(us_skinfold_data, zyg == 1) mzfData = subset(us_skinfold_data, zyg == 2) dzmData = subset(us_skinfold_data, zyg == 3) dzfData = subset(us_skinfold_data, zyg == 4) dzoData = subset(us_skinfold_data, zyg == 5) # ====================== # = Bivariate example = # ====================== selDVs = c('tri','bic') m1 = umxSexLim(selDVs = selDVs, sep = "_T", A_or_C = "A", tryHard = "yes", mzmData = mzmData, dzmData = dzmData, mzfData = mzfData, dzfData = dzfData, dzoData = dzoData ) umxSummary(m1, file = NA); # =============== # = Switch to C = # =============== m1 = umxSexLim(selDVs = selDVs, sep = "_T", A_or_C = "C", tryHard = "yes", mzmData = mzmData, dzmData = dzmData, mzfData = mzfData, dzfData = dzfData, dzoData = dzoData ) ## End(Not run)
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