qgraph (version 1.3.5)

qgraph.semModel: qgraph: SEM model pathdiagram

Description

This is a watered down version of qgraph.sem that only plots a diagram. It is called if a "mod" object is supplied to qgraph

Usage

qgraph.semModel(mod, manifest = NULL, layout = "spring", vsize.man = 3,  vsize.lat = 6, residuals = TRUE, latres = TRUE, curve = 0.2, residSize = 0.2, ...)

Arguments

mod

A "mod" object (model of the sem package (Fox; 2010) or a "sem" object

manifest

Vector containing the names of the manifest variables

layout

The layout used for the path diagram. Can be "tree", "spring", "circle" and "springtree". Defaults to "spring"

vsize.man

Size of the manifest variables in the path diagram

vsize.lat

Size of the latent variables in the path diagram

residuals

Logical indicating if the residuals should be included in the path diagram. If this is FALSE then residual variances will be shown as colors on the nodes. Default is TRUE

latres

This is currently not supported, leave to TRUE

curve

Numerical value indicating the curve of edges that are on the same level in the "tree" layout, See details. This represent an offset from the middle of the straight edge through where the curved edge must be drawn. 0 indicates no curve, and any other value indicates a curve of that strength. Defaults to 0.2

residSize

Size of the residual arrows

...

Arguments passed to qgraph

References

Sacha Epskamp, Angelique O. J. Cramer, Lourens J. Waldorp, Verena D. Schmittmann, Denny Borsboom (2012). qgraph: Network Visualizations of Relationships in Psychometric Data. Journal of Statistical Software, 48(4), 1-18. URL http://www.jstatsoft.org/v48/i04/.

John Fox with contributions from Adam Kramer and Michael Friendly (2010). sem: Structural Equation Models. R package version 0.9-21. http://CRAN.R-project.org/package=sem

See Also

qgraph

Examples

## Not run: 
# 
# require('sem')
# 
# # This example is taken from the examples of the sem function. 
# # Only names were changed to better suit the path diagram.
# 
# # ----------------------- Thurstone data ---------------------------------------
# #  Second-order confirmatory factor analysis, from the SAS manual for PROC CALIS
# 
# R.thur <- readMoments(diag=FALSE, names=c('Sen','Voc',
#         'SC','FL','4LW','Suf',
#         'LS','Ped', 'LG'))
#     .828                                              
#     .776   .779                                        
#     .439   .493    .46                                 
#     .432   .464    .425   .674                           
#     .447   .489    .443   .59    .541                    
#     .447   .432    .401   .381    .402   .288              
#     .541   .537    .534   .35    .367   .32   .555        
#     .38   .358    .359   .424    .446   .325   .598   .452  
#             
# model.thur <- specifyModel()
#     F1 -> Sen,               *l11, NA
#     F1 -> Voc,               *l21, NA
#     F1 -> SC,                *l31, NA
#     F2 -> FL,                *l41, NA
#     F2 -> 4LW,               *l52, NA
#     F2 -> Suf,               *l62, NA
#     F3 -> LS,                *l73, NA
#     F3 -> Ped,               *l83, NA
#     F3 -> LG,                *l93, NA
#     F4 -> F1,                *g1,  NA
#     F4 -> F2,                *g2,  NA
#     F4 -> F3,                *g3,  NA 
#     Sen <-> Sen,             q*1,   NA
#     Voc<-> Voc,              q*2,   NA
#     SC <-> SC,               q*3,   NA
#     FL <-> FL,               q*4,   NA
#     4LW <-> 4LW,             q*5,   NA
#     Suf<-> Suf,              q*6,   NA
#     LS <-> LS,               q*7,   NA
#     Ped<-> Ped,              q*8,   NA
#     LG <-> LG,               q*9,   NA
#     F1 <-> F1,               NA,     1
#     F2 <-> F2,               NA,     1
#     F3 <-> F3,               NA,     1
#     F4 <-> F4,               NA,     1
# 
# 
# 
# # Run qgraph:
# qgraph(model.thur)
# 
# # Tree layout:
# qgraph(model.thur,layout="tree",manifest=c('Sen','Voc','SC','FL','4LW','Suf','LS','Ped', 'LG'))
# 
# ## End(Not run)