By means of the utility methods pls model descrpitions inheriting from
class plsm
can easily be altered or investigated.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41  plsmEdit(model, ...)
## S3 method for class 'plsm'
plsmEdit(model, data, ...)
addLV(model, ...)
## S3 method for class 'plsm'
addLV(model, data, LV, mode, MVs, pred, succ, ...)
invertLVs(model, ...)
## S3 method for class 'plsm'
invertLVs(model, LVs, ...)
removeLVs(model, ...)
## S3 method for class 'plsm'
removeLVs(model, which, ...)
addMVs(model, ...)
## S3 method for class 'plsm'
addMVs(model, data, LV, MVs, ...)
removeMVs(model, ...)
## S3 method for class 'plsm'
removeMVs(model, MVs, ...)
addPath(model, ...)
## S3 method for class 'plsm'
addPath(model, from, to, ...)
removePath(model, ...)
## S3 method for class 'plsm'
removePath(model, from, to, ...)
exogenous(model)
endogenous(model)
reflective(model)
formative(model)
indicators(model, LV)
predecessors(model)
successors(model)
connected(model)
acyclic(model)

model 
An object inheriting from class 
data 
A 
LV 
A 
mode 
A

MVs 
A 
pred 
A 
succ 
A 
LVs 
A 
which 
A 
from 
A 
to 
A 
... 
Currently unused. 
exogenous
returns the exogenous latent variables contained in a path
model.
endogenous
returns the endogenous latent variables contained in a path
model.
reflective
returns the reflectively measured latent variables
contained in a path model.
formative
returns the formatively measured latent variables
contained in a path model.
indicators
returns the index or observed variables related to
the given latent variable.
predecessors
returns a list with the names of predecessors for
each latent variable.
successors
returns a list with the names of successors for
each latent variable.
connected
returns TRUE
if the inner model is a connected
graph.
acyclic
returns TRUE
if the inner model is recursive,
thus represented by an acyclic graph.
All utility methods return an object of class plsm
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41  data(mobi)
data(ECSIsm)
data(ECSImm)
ECSI < plsm(data=mobi, strucmod=ECSIsm, measuremod=ECSImm)
### Some sense free examples
# Print the block of MVs associated with "Expectation".
ECSI[["blocks"]]["Expectation"]
# Change measurement model for "Expectation" from reflective to
# formative and print its block MVs
invertLVs(model=ECSI, LVs=c("Expectation"))[["blocks"]]["Expectation"]
# Print the adjacency matrix for the inner model.
ECSI[["D"]]
# Add an additional path from "Quality" to "Loyalty" and
# print the resulting adjacency matrix.
addPath(model=ECSI, from="Quality", to="Loyalty")[["D"]]
# Remove the path previously added.
removePath(model=ECSI, from="Image", to=c("Satisfaction", "Loyalty"))[["D"]]
# Print all MVs used in the model
ECSI$manifest
# Remove some MVs and print the MVs used in the model
removeMVs(model=ECSI, MVs=c("IMAG3", "CUEX1", "PERQ7"))$manifest
### some handy functions
exogenous(ECSI)
endogenous(ECSI)
reflective(ECSI)
formative(ECSI)
indicators(ECSI, "Image")
predecessors(ECSI)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.