R: Cumulative residual processes for structural equation models
cumres.lvmfit
R Documentation
Cumulative residual processes for structural equation models
Description
Calculates GoF statistics based on cumulative residual
processes for structural equation models fitted with the
lava package.
Usage
## S3 method for class 'lvmfit'
cumres(model, y, x, full = FALSE,
data = model.frame(model), p, R = 1000, b = 0,
plots = min(R, 50), seed = round(runif(1, 1, 1e+09)),
...)
Arguments
model
lvm object
y
A formula specifying the association to be
checked. Alternatively the outcome specified as a
function or a string with the name of the outcome in the
model.
x
Predictor. A function, vector or character
full
If FALSE the prediction, Pr, of the variable
that are ordered after is only calculated based on the
conditional distribution given covariates. If TRUE the
conditional expectation is based on the largest set of
covariates and endogenous variables such that the
residual and Pr are uncorrelated.
data
data.frame (default is the model.frame of the
model)
p
Optional parameter vector
R
Number of processes to simulate
b
Moving average parameter
plots
Number of processes to save for use with the
plot method
seed
Random seed
...
Additional arguments parsed on to
lower-level functions
Details
With y and x given as functions the user
can decide which variables to use in the prediction of
the outcome and predictor (use the predict method
as below).
Value
Returns a cumres object with associated
plot,print,confint methods
Author(s)
Klaus K. Holst
References
B.N. Sanchez and E. A. Houseman and L. M. Ryan (2009)
Residual-Based Diagnostics for Structural Equation
Models. Biometrics Volume 65 (1), pp 104-115.
Examples
library(lava)
m <- lvm(list(c(y1,y2,y3)~eta,eta~x)); latent(m) <- ~eta
## simulate some data with non-linear covariate effect
functional(m,eta~x) <- function(x) 0.3*x^2
d <- sim(m,100)
e <- estimate(m,d)
## Checking the functional form of eta on x
g <- cumres(e,eta~x,R=1000)
plot(g)
x <- function(p) predict(e,x=~y2+y3,p=p)[,"eta"]
## Checking the functional form of y1 on eta
cumres(e,y1~eta,R=1000)
g <- cumres(e,"y1",x=x,R=1000)
plot(g)