cumres.lvmfit: Cumulative residual processes for structural equation models In gof: Model-diagnostics based on cumulative residuals

Description

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

Usage

 1 2 3 4 5 ## 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

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

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 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)

gof documentation built on May 2, 2019, 5:52 p.m.