umx (version 4.0.0)

umxSummarySexLim: Shows a compact, publication-style, summary of a umx Sex Limitation model

Description

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.

Usage

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,
  ...
)

Arguments

model

a umxSexLim() model to summarize

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

Value

• optional mxModel()

Details

See documentation for summary functions for other types of umx model here: umxSummary().

References

• https://tbates.github.io, https://github.com/tbates/umx

See Also

• umxSexLim(), umxPlotSexLim()

Other Twin Reporting Functions: umxPlotCP(), umxPlotDoC(), umxReduceACE(), umxReduceGxE(), umxReduce(), umxSummarizeTwinData(), umxSummaryACEcov(), umxSummaryACEv(), umxSummaryACE(), umxSummaryCP(), umxSummaryDoC(), umxSummaryGxEbiv(), umxSummaryGxE(), umxSummaryIP(), umxSummarySimplex(), umx

Examples

# 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
)
# }