Cumulative residual processes for structural equation models

Calculates GoF statistics based on cumulative residual processes for structural equation models fitted with the lava package.

# S3 method for lvmfit
cumres(
  model,
  y,
  x,
  full = FALSE,
  data = model.frame(model),
  p,
  R = 1000,
  b = 0,
  plots = min(R, 50),
  ...
)

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
...	Additional arguments parsed on to lower-level functions

Value

Returns a cumres object with associated plot,print,coef methods

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).

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.

Author

Klaus K. Holst

Examples

# \donttest{
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)
#> Loading required namespace: numDeriv
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)
#> 
#>     p-value(Sup) p-value(L2)
#> eta        0.289       0.141
#> 
#> Based on 1000 realizations.
g <- cumres(e,"y1",x=x,R=1000)
plot(g)

# }