sim: Simulate model

Description Usage Arguments Author(s) Examples

Description

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

Usage

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## 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 fixed values of variables (manipulation)

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

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##################################################
## 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
                               Binary.lvm(),       ## Vector of ones
                               Binary.lvm(0.5),    ##  0.5n 0, 0.5n 1
                               Binary.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,...) rmvn0(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

lava documentation built on Sept. 5, 2021, 5:43 p.m.