# biprobit: Bivariate Probit model In mets: Analysis of Multivariate Event Times

## Description

Bivariate Probit model

## Usage

 1 2 3 4 5 6 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

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 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 31, 2017, 1:52 a.m.