biprobit: Bivariate Probit model

In mets: Analysis of Multivariate Event Times

Description

Bivariate Probit model

Usage

biprobit(x, data, id, rho = ~1, num = NULL, strata = NULL,
  eqmarg = TRUE, indep = FALSE, weights = NULL, biweight,
  samecens = TRUE, randomeffect = FALSE, vcov = "robust",
  pairs.only = FALSE, allmarg = samecens & !is.null(weights),
  control = list(trace = 0), messages = 1, constrain = NULL,
  table = pairs.only, p = NULL, ...)

Arguments

x	formula (or vector)
data	data.frame
id	The name of the column in the dataset containing the cluster id-variable.
rho	Formula specifying the regression model for the dependence parameter
num	Optional name of order variable
strata	Strata
eqmarg	If TRUE same marginals are assumed (exchangeable)
indep	Independence
weights	Weights
biweight	Function defining the bivariate weight in each cluster
samecens	Same censoring
randomeffect	If TRUE a random effect model is used (otherwise correlation parameter is estimated allowing for both negative and positive dependence)
vcov	Type of standard errors to be calculated
pairs.only	Include complete pairs only?
allmarg	Should all marginal terms be included
control	Control argument parsed on to the optimization routine. Starting values may be parsed as 'start'.
messages	Control amount of messages shown
constrain	Vector of parameter constraints (NA where free). Use this to set an offset.
table	Type of estimation procedure
p	Parameter vector p in which to evaluate log-Likelihood and score function
...	Optional arguments

Examples

data(prt)
prt0 <- subset(prt,country=="Denmark")
a <- biprobit(cancer~1+zyg, ~1+zyg, data=prt0, id="id")
b <- biprobit(cancer~1+zyg, ~1+zyg, data=prt0, id="id",pairs.only=TRUE)
predict(b,newdata=Expand(prt,zyg=c("MZ")))
predict(b,newdata=Expand(prt,zyg=c("MZ","DZ")))


prtw <- ipw(Surv(time,status==0)~1,data=prt0)
b1 <- biprobit(cancer~1+zyg, ~1+zyg, data=prtw, id="id", weights="w", pairs.only=TRUE,table=FALSE)
b2 <- biprobit(cancer~1+zyg, ~1+zyg, data=prtw, id="id", weights="w", pairs.only=TRUE)




m <- lvm(c(y1,y2)~x)
covariance(m,y1~y2) <- "r"
constrain(m,r~x+a+b) <- function(x) tanh(x[2]+x[3]*x[1])
distribution(m,~x) <- uniform.lvm(a=-1,b=1)
ordinal(m) <- ~y1+y2
d <- sim(m,1000,p=c(a=0,b=-1)); d <- d[order(d$x),]
dd <- fast.reshape(d)

a <- biprobit(y~1+x,rho=~1+x,data=dd,id="id")
summary(a, mean.contrast=c(1,.5), cor.contrast=c(1,.5))
with(predict(a,data.frame(x=seq(-1,1,by=.1))), plot(p00~x,type="l"))

pp <- predict(a,data.frame(x=seq(-1,1,by=.1)),which=c(1))
plot(pp[,1]~pp$x, type="l", xlab="x", ylab="Concordance", lwd=2, xaxs="i")
confband(pp$x,pp[,2],pp[,3],polygon=TRUE,lty=0,col=Col(1))
##'
pp <- predict(a,data.frame(x=seq(-1,1,by=.1)),which=c(9)) ## rho
plot(pp[,1]~pp$x, type="l", xlab="x", ylab="Correlation", lwd=2, xaxs="i")
confband(pp$x,pp[,2],pp[,3],polygon=TRUE,lty=0,col=Col(1))
with(pp, lines(x,tanh(-x),lwd=2,lty=2))

##'
## Time
## Not run: 
    a <- biprobit.time(cancer~1, rho=~1+zyg, id="id", data=prt, eqmarg=TRUE,
                       cens.formula=Surv(time,status==0)~1,
                       breaks=seq(75,100,by=3),fix.censweights=TRUE)

    a <- biprobit.time2(cancer~1+zyg, rho=~1+zyg, id="id", data=prt0, eqmarg=TRUE,
                       cens.formula=Surv(time,status==0)~zyg,
                       breaks=100)

    a1 <- biprobit.time2(cancer~1, rho=~1, id="id", data=subset(prt0,zyg=="MZ"), eqmarg=TRUE,
                       cens.formula=Surv(time,status==0)~1,
                       breaks=100,pairs.only=TRUE)

    a2 <- biprobit.time2(cancer~1, rho=~1, id="id", data=subset(prt0,zyg=="DZ"), eqmarg=TRUE,
                        cens.formula=Surv(time,status==0)~1,
                        breaks=100,pairs.only=TRUE)


    plot(a,which=3,ylim=c(0,0.1))

## End(Not run)

mets documentation built on May 2, 2019, 4:43 p.m.