Shows a compact, publication-style, summary of a umx Direction of Causation model
Summarize a fitted model returned by umxDoC(). 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.
umxSummaryDoC(
model,
digits = 2,
comparison = NULL,
std = TRUE,
showRg = FALSE,
CIs = TRUE,
report = c("markdown", "html"),
file = getOption("umx_auto_plot"),
returnStd = FALSE,
zero.print = ".",
...
)model |
a fitted |
digits |
round to how many digits (default = 2). |
comparison |
Run mxCompare on a comparison model (default 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 (TRUE). |
report |
Print tables to the console (as 'markdown'), or open in browser ('html') |
file |
The name of the dot file to write: "name" = use the name of the model. Defaults to NA = do not create plot output. |
returnStd |
Whether to return the standardized form of the model (default = FALSE). |
zero.print |
How to show zeros (".") |
... |
Other parameters to control model summary. |
See documentation for other umx models here: umxSummary().
optional mxModel()
umxDoC(), plot.MxModelDoC(), umxModify(), umxCP(), plot(), umxSummary() work for IP, CP, GxE, SAT, and ACE models.
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(),
umxSummaryGxEbiv(),
umxSummarySexLim(),
umxSummarySimplex(),
umxTwinMaker(),
umx
## Not run:
# ================
# = 1. Load Data =
# ================
umx_set_auto_plot(FALSE) # turn off autoplotting for CRAN
data(docData)
mzData = subset(docData, zygosity %in% c("MZFF", "MZMM"))
dzData = subset(docData, zygosity %in% c("DZFF", "DZMM"))
# =======================================
# = 2. Define manifests for var 1 and 2 =
# =======================================
var1 = paste0("varA", 1:3)
var2 = paste0("varB", 1:3)
# =======================================================
# = 2. Make the non-causal (Cholesky) and causal models =
# =======================================================
Chol= umxDoC(var1= var1, var2= var2, mzData= mzData, dzData= dzData, causal= FALSE)
DoC = umxDoC(var1= var1, var2= var2, mzData= mzData, dzData= dzData, causal= TRUE)
# ================================================
# = Make the directional models by modifying DoC =
# ================================================
A2B = umxModify(DoC, "a2b", free = TRUE, name = "A2B")
A2B = umxModify(DoC, "a2b", free = TRUE, name = "A2B", comp=TRUE)
B2A = umxModify(DoC, "b2a", free = TRUE, name = "B2A", comp=TRUE)
umxCompare(B2A, A2B)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.