| umxSummarySexLim {umx} | R Documentation |
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 |
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 |
Details
See documentation for summary functions for other types of umx model here: umxSummary().
Value
optional
mxModel()
References
See Also
Other Twin Modeling Functions:
power.ACE.test(),
umxACEcov(),
umxACEv(),
umxACE(),
umxCP(),
umxDiffMZ(),
umxDiscTwin(),
umxDoCp(),
umxDoC(),
umxGxE_window(),
umxGxEbiv(),
umxGxE(),
umxIP(),
umxMRDoC(),
umxReduceACE(),
umxReduceGxE(),
umxReduce(),
umxRotate.MxModelCP(),
umxSexLim(),
umxSimplex(),
umxSummarizeTwinData(),
umxSummaryACEv(),
umxSummaryACE(),
umxSummaryDoC(),
umxSummaryGxEbiv(),
umxSummarySimplex(),
umxTwinMaker(),
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
)
## End(Not run)