sim {lava} | R Documentation

## Simulate model

### Description

Simulate data from a general SEM model including non-linear effects and general link and distribution of variables.

### Usage

    ## S3 method for class 'lvm'
    sim(x, n = NULL, p = NULL, normal = FALSE, cond = FALSE,
    sigma = 1, rho = 0.5, X = NULL, unlink=FALSE, latent=TRUE,
    use.labels = TRUE, seed=NULL, ...)

### Arguments

`x` | Model object
`...` | Additional arguments to be passed to the low level functions
`n` | Number of simulated values/individuals
`p` | Parameter value (optional)
`normal` | Logical indicating whether to simulate data from a multivariate normal distribution conditional on exogenous variables hence ignoring functional/distribution definition
`cond` | for internal use
`sigma` | Default residual variance (1)
`rho` | Default covariance parameter (0.5)
`X` | Optional matrix of covariates
`unlink` | Return Inverse link transformed data
`latent` | Include latent variables (default TRUE)
`use.labels` | convert categorical variables to factors before applying transformation
`seed` | Random seed

### Author(s)

Klaus K. Holst

### Examples

    ##################################################
    ## Logistic regression
    ##################################################
    m <- lvm(y~x+z)
    regression(m) <- x~z
    distribution(m,~y+z) <- binomial.lvm("logit")
    d <- sim(m,1e3)
    head(d)
    e <- estimate(m,d,estimator="glm")
    e
    ## Simulate a few observation from estimated model
    sim(e,n=5)
    ##################################################
    ## Poisson
    ##################################################
    distribution(m,~y) <- poisson.lvm()
    d <- sim(m,1e4,p=c(y=-1,"y~x"=2,z=1))
    head(d)
    estimate(m,d,estimator="glm")
    mean(d$z); lava:::expit(1)
    summary(lm(y~x,sim(lvm(y[1:2]~4*x),1e3)))
    ##################################################
    ### Gamma distribution
    ##################################################
    m <- lvm(y~x)
    distribution(m,~y+x) <- list(Gamma.lvm(shape=2),binomial.lvm())
    intercept(m,~y) <- 0.5
    d <- sim(m,1e4)
    summary(g <- glm(y~x,family=Gamma(),data=d))
    ## Not run: MASS::gamma.shape(g)
    args(lava::Gamma.lvm)
    distribution(m,~y) <- Gamma.lvm(shape=2,log=TRUE)
    sim(m,10,p=c(y=0.5))[,"y"]
    ##################################################
    ### Beta
    ##################################################
    m <- lvm()
    distribution(m,~y) <- beta.lvm(alpha=2,beta=1)
    var(sim(m,100,"y,y"=2))
    distribution(m,~y) <- beta.lvm(alpha=2,beta=1,scale=FALSE)
    var(sim(m,100))
    ##################################################
    ### Transform
    ##################################################
    m <- lvm()
    transform(m,xz~x+z) <- function(x) x[1]*(x[2]>0)
    regression(m) <- y~x+z+xz
    d <- sim(m,1e3)
    summary(lm(y~x+z + x*I(z>0),d))
    ##################################################
    ### Non-random variables
    ##################################################
    m <- lvm()
    distribution(m,~x+z+v+w) <- list(sequence.lvm(0,5),## Seq. 0 to 5 by 1/n
                                   ones.lvm(),       ## Vector of ones
                                   ones.lvm(0.5),    ##  0.8n 0, 0.2n 1
                                   ones.lvm(interval=list(c(0.3,0.5),c(0.8,1))))
    sim(m,10)
    ##################################################
    ### Cox model
    ### piecewise constant hazard
    ################################################
    m <- lvm(t~x)
    rates <- c(1,0.5); cuts <- c(0,5)
    ## Constant rate: 1 in [0,5), 0.5 in [5,Inf)
    distribution(m,~t) <- coxExponential.lvm(rate=rates,timecut=cuts)
    ## Not run:
        d <- sim(m,2e4,p=c("t~x"=0.1)); d$status <- TRUE
        plot(timereg::aalen(survival::Surv(t,status)~x,data=d,
                            resample.iid=0,robust=0),spec=1)
        L <- approxfun(c(cuts,max(d$t)),f=1,
                       cumsum(c(0,rates*diff(c(cuts,max(d$t))))),
                       method="linear")
        curve(L,0,100,add=TRUE,col="blue")

    ## End(Not run)
    ##################################################
    ### Cox model
    ### piecewise constant hazard, gamma frailty
    ##################################################
    m <- lvm(y~x+z)
    rates <- c(0.3,0.5); cuts <- c(0,5)
    distribution(m,~y+z) <- list(coxExponential.lvm(rate=rates,timecut=cuts),
                                 loggamma.lvm(rate=1,shape=1))
    ## Not run:
        d <- sim(m,2e4,p=c("y~x"=0,"y~z"=0)); d$status <- TRUE
        plot(timereg::aalen(survival::Surv(y,status)~x,data=d,
                            resample.iid=0,robust=0),spec=1)
        L <- approxfun(c(cuts,max(d$y)),f=1,
                       cumsum(c(0,rates*diff(c(cuts,max(d$y))))),
                       method="linear")
        curve(L,0,100,add=TRUE,col="blue")

    ## End(Not run)
    ## Equivalent via transform (here with Aalens additive hazard model)
    m <- lvm(y~x)
    distribution(m,~y) <- aalenExponential.lvm(rate=rates,timecut=cuts)
    distribution(m,~z) <- Gamma.lvm(rate=1,shape=1)
    transform(m,t~y+z) <- prod
    sim(m,10)
    ## Shared frailty
    m <- lvm(c(t1,t2)~x+z)
    rates <- c(1,0.5); cuts <- c(0,5)
    distribution(m,~y) <- aalenExponential.lvm(rate=rates,timecut=cuts)
    distribution(m,~z) <- loggamma.lvm(rate=1,shape=1)
    ## Not run:
    mets::fast.reshape(sim(m,100),varying="t")

    ## End(Not run)
    ##################################################
    ### General multivariate distributions
    ##################################################
    ## Not run:
    m <- lvm()
    distribution(m,~y1+y2,oratio=4) <- VGAM::rbiplackcop
    ksmooth2(sim(m,1e4),rgl=FALSE,theta=-20,phi=25)
    m <- lvm()
    distribution(m,~z1+z2,"or1") <- VGAM::rbiplackcop
    distribution(m,~y1+y2,"or2") <- VGAM::rbiplackcop
    sim(m,10,p=c(or1=0.1,or2=4))

    ## End(Not run)
    m <- lvm()
    distribution(m,~y1+y2+y3,TRUE) <- function(n,...) rmvn(n,sigma=diag(3)+1)
    var(sim(m,100))
    ## Syntax also useful for univariate generators, e.g.
    m <- lvm(y~x+z)
    distribution(m,~y,TRUE) <- function(n) rnorm(n,mean=1000)
    sim(m,5)
    distribution(m,~y,"m1",0) <- rnorm
    sim(m,5)
    sim(m,5,p=c(m1=100))
    ##################################################
    ### Regression design in other parameters
    ##################################################
    ## Variance heterogeneity
    m <- lvm(y~x)
    distribution(m,~y) <- function(n,mean,x) rnorm(n,mean,exp(x)^.5)
    if (interactive()) plot(y~x,sim(m,1e3))
    ## Alternaively, calculate the standard error directly
    addvar(m) <- ~sd ## If 'sd' should be part of the resulting data.frame
    constrain(m,sd~x) <- function(x) exp(x)^.5
    distribution(m,~y) <- function(n,mean,sd) rnorm(n,mean,sd)
    if (interactive()) plot(y~x,sim(m,1e3))
    ## Regression on variance parameter
    m <- lvm()
    regression(m) <- y~x
    regression(m) <- v~x
    ##distribution(m,~v) <- 0 # No stochastic term
    ## Alternative:
    ## regression(m) <- v[NA:0]~x
    distribution(m,~y) <- function(n,mean,v) rnorm(n,mean,exp(v)^.5)
    if (interactive()) plot(y~x,sim(m,1e3))
    ## Regression on shape parameter in Weibull model
    m <- lvm()
    regression(m) <- y ~ z+v
    regression(m) <- s ~ exp(0.6*x-0.5*z)
    distribution(m,~x+z) <- binomial.lvm()
    distribution(m,~cens) <- coxWeibull.lvm(scale=1)
    distribution(m,~y) <- coxWeibull.lvm(scale=0.1,shape=~s)
    eventTime(m) <- time ~ min(y=1,cens=0)
    if (interactive()) {
        d <- sim(m,1e3)
        require(survival)
        (cc <- coxph(Surv(time,status)~v+strata(x,z),data=d))
        plot(survfit(cc) ,col=1:4,mark.time=FALSE)
    }
    ##################################################
    ### Categorical predictor
    ##################################################
    m <- lvm()
    ## categorical(m,K=3) <- "v"
    categorical(m,labels=c("A","B","C")) <- "v"
    regression(m,additive=FALSE) <- y~v
    ## Not run:
    plot(y~v,sim(m,1000,p=c("y~v:2"=3)))

    ## End(Not run)
    m <- lvm()
    categorical(m,labels=c("A","B","C"),p=c(0.5,0.3)) <- "v"
    regression(m,additive=FALSE,beta=c(0,2,-1)) <- y~v
    ## equivalent to:
    ## regression(m,y~v,additive=FALSE) <- c(0,2,-1)
    regression(m,additive=FALSE,beta=c(0,4,-1)) <- z~v
    table(sim(m,1e4)$v)
    glm(y~v, data=sim(m,1e4))
    glm(y~v, data=sim(m,1e4,p=c("y~v:1"=3)))

    transform(m,v2~v) <- function(x) x=='A'
    sim(m,10)

    ##################################################
    ### Pre-calculate object
    ##################################################
    m <- lvm(y~x)
    m2 <- sim(m,'y~x'=2)
    sim(m,10,'y~x'=2)
    sim(m2,10) ## Faster

[Package lava version 1.5.1 Index]