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| @@ -1,166 +1,166 @@ | ||
| # | ||
| # event : A Library of Special Functions for Event Histories | ||
| # Copyright (C) 1998, 1999, 2000, 2001 J.K. Lindsey | ||
| # | ||
| # This program is free software; you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public License as published by | ||
| # the Free Software Foundation; either version 2 of the License, or | ||
| # (at your option) any later version. | ||
| # | ||
| # This program is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public License for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public License | ||
| # along with this program; if not, write to the Free Software | ||
| # Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. | ||
| # | ||
| # SYNOPSIS | ||
| # | ||
| # hboxcox(y,m,s,f) | ||
| # hburr(y,m,s,f) | ||
| # hcauchy(y,m,s) | ||
| # hexp(y,m) | ||
| # hgextval(y,s,m,f) | ||
| # hgamma(y,s,m) | ||
| # hggamma(y,s,m,f) | ||
| # hhjorth(y,m,s,f) | ||
| # hinvgauss(y,m,s) | ||
| # hlaplace(y,m,s) | ||
| # hlnorm(y,m,s) | ||
| # hlogis(y,m,s) | ||
| # hglogis(y,m,s,f) | ||
| # hnorm(y,m,s) | ||
| # hpareto(y,m,s) | ||
| # hstudent(y,m,s,f) | ||
| # hweibull(y,s,m) | ||
| # hgweibull(y,s,m,f) | ||
| # hskewlaplace(y,s,m,f) | ||
| # | ||
| # DESCRIPTION | ||
| # | ||
| # Functions for various log hazards or intensities | ||
|
|
||
| # for log distributions, subtract log(y) from the intensity function | ||
|
|
||
| ### Box-Cox intensity | ||
| ### | ||
| # f=1 gives truncated normal | ||
| hboxcox <- function(y,m,s,f) { | ||
| y1 <- y^f/f | ||
| -(y1-m)^2/s/2+(f-1)*log(y)-log(2*pi*s)/2-log(1-pnorm(y1,m,sqrt(s))+(f<0)*(1-pnorm(0,m,sqrt(s))))} | ||
|
|
||
| ### Burr intensity | ||
| ### | ||
| hburr <- function(y,m,s,f) { | ||
| y1 <- y/m | ||
| y2 <- y1^s | ||
| log(f*s/m)+(s-1)*log(y1)-log(1+y2)} | ||
|
|
||
| ### Cauchy intensity | ||
| ### | ||
| hcauchy <- function(y,m,s) log(dcauchy(y,m,s))-log(1-pcauchy(y,m,s)) | ||
|
|
||
| ### exponential intensity | ||
| ### | ||
| hexp <- function(y,rate) | ||
| if(length(rate)==1)rep(log(rate),length(y)) else log(rate) | ||
|
|
||
| ### generalized extreme value intensity | ||
| ### | ||
| # f=1 gives truncated extreme value | ||
| hgextval <- function(y,s,m,f) { | ||
| y1 <- y^f/f | ||
| ey <-exp(y1) | ||
| log(s)+s*(y1-log(m))-(ey/m)^s+(f-1)*log(y)-log(1-pweibull(ey,s,m)-(f<0)*exp(-m^-s))} | ||
|
|
||
| ### gamma intensity | ||
| ### | ||
| hgamma <- function(y,shape,rate=1,scale=1/rate) | ||
| dgamma(y,shape,scale=scale,log=TRUE)- | ||
| pgamma(y,shape,scale=scale,lower=FALSE,log=TRUE) | ||
|
|
||
| ### generalized gamma intensity | ||
| ### | ||
| hggamma <- function(y,s,m,f) { | ||
| t <- m/s | ||
| u <- t^f | ||
| y1 <- y^f | ||
| v <- s*f | ||
| -v*log(t)-y1/u+log(f)+(v-1)*log(y)-lgamma(s)- | ||
| log(1-pgamma(y1,s,scale=u))} | ||
|
|
||
| ### Hjorth intensity | ||
| ### | ||
| hhjorth <- function(y,m,s,f) log(y/m^2+f/(1+s*y)) | ||
|
|
||
| ### inverse Gaussian intensity | ||
| ### | ||
| hinvgauss <- function(y,m,s) { | ||
| t <- y/m | ||
| v <- sqrt(y*s) | ||
| -((t-1)^2/(y*s)+log(2*s*pi*y^3))/2-log(1-pnorm((t-1)/v) | ||
| -exp(2/(m*s))*pnorm(-(t+1)/v))} | ||
| ### Laplace intensity | ||
| ### | ||
| hlaplace <- function(y,m=0,s=1){ | ||
| plp <- function(u){ | ||
| t <- exp(-abs(u))/2 | ||
| ifelse(u<0,t,1-t)} | ||
| -abs(y-m)/s-log(2*s)-log(1-plp((y-m)/s))} | ||
|
|
||
| ### log normal intensity | ||
| ### | ||
| hlnorm <- function(y,m,s) dlnorm(y,m,s,TRUE)-plnorm(y,m,s,FALSE,TRUE) | ||
|
|
||
| ### logistic intensity | ||
| ### | ||
| hlogis <- function(y,m,s) dlogis(y,m,s,TRUE)-plogis(y,m,s,FALSE,TRUE) | ||
|
|
||
| ### generalized logistic intensity | ||
| ### | ||
| # f=1 gives hlogis | ||
| hglogis <- function(y,m,s,f) { | ||
| y1 <- (y-m)/s | ||
| ey <- exp(-y1) | ||
| -log(s/f)-y1-(f+1)*log(1+ey)-log(1-(1+ey)^-f)} | ||
|
|
||
| ### normal intensity | ||
| ### | ||
| hnorm <- function(y,m,s) dnorm(y,m,s,TRUE)-pnorm(y,m,s,FALSE,TRUE) | ||
|
|
||
| ### Pareto intensity | ||
| ### | ||
| hpareto <- function(y,m,s) (s+1)/(m*s+y) | ||
|
|
||
| ### Student t intensity | ||
| ### | ||
| hstudent <- function(y,m,s,f){ | ||
| pst <- function(u,f){ | ||
| t <- 0.5*pbeta(f/(f+u^2),f/2,0.5) | ||
| ifelse(u<0,t,1-t)} | ||
| t <- (f+1)/2 | ||
| u <- (y-m)/s | ||
| lgamma(t)-lgamma(f/2)-log(f)/2-(t)*log(1+u^2/f) | ||
| -log(pi)/2-log(1-pst(u,f))} | ||
| ### Weibull intensity | ||
| ### | ||
| hweibull <- function(y,s,m) log(s)+(s-1)*log(y)-s*log(m) | ||
|
|
||
| ### generalized Weibull intensity | ||
| ### | ||
| # Mudholkar, Srivastava, & Freimer (1995) Technometrics 37: 436-445 | ||
| hgweibull <- function(y,s,m,f) { | ||
| y1 <- y/m | ||
| y2 <- y1^s | ||
| y3 <- exp(-y2) | ||
| log(s*f/m)+(s-1)*log(y1)+(f-1)*log(1-y3)-y2-log(1-(1-y3)^f)} | ||
|
|
||
| ### skew Laplace intensity | ||
| ### | ||
| hskewlaplace <- function(y,m=0,s=1,f=1){ | ||
| plp <- function(u) | ||
| ifelse(u>0,1-exp(-f*abs(u))/(1+f^2),f^2*exp(-abs(u)/f)/(1+f^2)) | ||
| log(f)+ifelse(y>m,-f*(y-m),(y-m)/f)/s-log((1+f^2)*s)- | ||
| log(1-plp((y-m)/s))} | ||
| # | ||
| # event : A Library of Special Functions for Event Histories | ||
| # Copyright (C) 1998, 1999, 2000, 2001 J.K. Lindsey | ||
| # | ||
| # This program is free software; you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public License as published by | ||
| # the Free Software Foundation; either version 2 of the License, or | ||
| # (at your option) any later version. | ||
| # | ||
| # This program is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public License for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public License | ||
| # along with this program; if not, write to the Free Software | ||
| # Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. | ||
| # | ||
| # SYNOPSIS | ||
| # | ||
| # hboxcox(y,m,s,f) | ||
| # hburr(y,m,s,f) | ||
| # hcauchy(y,m,s) | ||
| # hexp(y,m) | ||
| # hgextval(y,s,m,f) | ||
| # hgamma(y,s,m) | ||
| # hggamma(y,s,m,f) | ||
| # hhjorth(y,m,s,f) | ||
| # hinvgauss(y,m,s) | ||
| # hlaplace(y,m,s) | ||
| # hlnorm(y,m,s) | ||
| # hlogis(y,m,s) | ||
| # hglogis(y,m,s,f) | ||
| # hnorm(y,m,s) | ||
| # hpareto(y,m,s) | ||
| # hstudent(y,m,s,f) | ||
| # hweibull(y,s,m) | ||
| # hgweibull(y,s,m,f) | ||
| # hskewlaplace(y,s,m,f) | ||
| # | ||
| # DESCRIPTION | ||
| # | ||
| # Functions for various log hazards or intensities | ||
|
|
||
| # for log distributions, subtract log(y) from the intensity function | ||
|
|
||
| ### Box-Cox intensity | ||
| ### | ||
| # f=1 gives truncated normal | ||
| hboxcox <- function(y,m,s,f) { | ||
| y1 <- y^f/f | ||
| -(y1-m)^2/s/2+(f-1)*log(y)-log(2*pi*s)/2-log(1-pnorm(y1,m,sqrt(s))+(f<0)*(1-pnorm(0,m,sqrt(s))))} | ||
|
|
||
| ### Burr intensity | ||
| ### | ||
| hburr <- function(y,m,s,f) { | ||
| y1 <- y/m | ||
| y2 <- y1^s | ||
| log(f*s/m)+(s-1)*log(y1)-log(1+y2)} | ||
|
|
||
| ### Cauchy intensity | ||
| ### | ||
| hcauchy <- function(y,m,s) log(dcauchy(y,m,s))-log(1-pcauchy(y,m,s)) | ||
|
|
||
| ### exponential intensity | ||
| ### | ||
| hexp <- function(y,rate) | ||
| if(length(rate)==1)rep(log(rate),length(y)) else log(rate) | ||
|
|
||
| ### generalized extreme value intensity | ||
| ### | ||
| # f=1 gives truncated extreme value | ||
| hgextval <- function(y,s,m,f) { | ||
| y1 <- y^f/f | ||
| ey <-exp(y1) | ||
| log(s)+s*(y1-log(m))-(ey/m)^s+(f-1)*log(y)-log(1-pweibull(ey,s,m)-(f<0)*exp(-m^-s))} | ||
|
|
||
| ### gamma intensity | ||
| ### | ||
| hgamma <- function(y,shape,rate=1,scale=1/rate) | ||
| dgamma(y,shape,scale=scale,log=TRUE)- | ||
| pgamma(y,shape,scale=scale,lower.tail=FALSE,log.p=TRUE) | ||
|
|
||
| ### generalized gamma intensity | ||
| ### | ||
| hggamma <- function(y,s,m,f) { | ||
| t <- m/s | ||
| u <- t^f | ||
| y1 <- y^f | ||
| v <- s*f | ||
| -v*log(t)-y1/u+log(f)+(v-1)*log(y)-lgamma(s)- | ||
| log(1-pgamma(y1,s,scale=u))} | ||
|
|
||
| ### Hjorth intensity | ||
| ### | ||
| hhjorth <- function(y,m,s,f) log(y/m^2+f/(1+s*y)) | ||
|
|
||
| ### inverse Gaussian intensity | ||
| ### | ||
| hinvgauss <- function(y,m,s) { | ||
| t <- y/m | ||
| v <- sqrt(y*s) | ||
| -((t-1)^2/(y*s)+log(2*s*pi*y^3))/2-log(1-pnorm((t-1)/v) | ||
| -exp(2/(m*s))*pnorm(-(t+1)/v))} | ||
| ### Laplace intensity | ||
| ### | ||
| hlaplace <- function(y,m=0,s=1){ | ||
| plp <- function(u){ | ||
| t <- exp(-abs(u))/2 | ||
| ifelse(u<0,t,1-t)} | ||
| -abs(y-m)/s-log(2*s)-log(1-plp((y-m)/s))} | ||
|
|
||
| ### log normal intensity | ||
| ### | ||
| hlnorm <- function(y,m,s) dlnorm(y,m,s,TRUE)-plnorm(y,m,s,FALSE,TRUE) | ||
|
|
||
| ### logistic intensity | ||
| ### | ||
| hlogis <- function(y,m,s) dlogis(y,m,s,TRUE)-plogis(y,m,s,FALSE,TRUE) | ||
|
|
||
| ### generalized logistic intensity | ||
| ### | ||
| # f=1 gives hlogis | ||
| hglogis <- function(y,m,s,f) { | ||
| y1 <- (y-m)/s | ||
| ey <- exp(-y1) | ||
| -log(s/f)-y1-(f+1)*log(1+ey)-log(1-(1+ey)^-f)} | ||
|
|
||
| ### normal intensity | ||
| ### | ||
| hnorm <- function(y,m,s) dnorm(y,m,s,TRUE)-pnorm(y,m,s,FALSE,TRUE) | ||
|
|
||
| ### Pareto intensity | ||
| ### | ||
| hpareto <- function(y,m,s) (s+1)/(m*s+y) | ||
|
|
||
| ### Student t intensity | ||
| ### | ||
| hstudent <- function(y,m,s,f){ | ||
| pst <- function(u,f){ | ||
| t <- 0.5*pbeta(f/(f+u^2),f/2,0.5) | ||
| ifelse(u<0,t,1-t)} | ||
| t <- (f+1)/2 | ||
| u <- (y-m)/s | ||
| lgamma(t)-lgamma(f/2)-log(f)/2-(t)*log(1+u^2/f) | ||
| -log(pi)/2-log(1-pst(u,f))} | ||
| ### Weibull intensity | ||
| ### | ||
| hweibull <- function(y,s,m) log(s)+(s-1)*log(y)-s*log(m) | ||
|
|
||
| ### generalized Weibull intensity | ||
| ### | ||
| # Mudholkar, Srivastava, & Freimer (1995) Technometrics 37: 436-445 | ||
| hgweibull <- function(y,s,m,f) { | ||
| y1 <- y/m | ||
| y2 <- y1^s | ||
| y3 <- exp(-y2) | ||
| log(s*f/m)+(s-1)*log(y1)+(f-1)*log(1-y3)-y2-log(1-(1-y3)^f)} | ||
|
|
||
| ### skew Laplace intensity | ||
| ### | ||
| hskewlaplace <- function(y,m=0,s=1,f=1){ | ||
| plp <- function(u) | ||
| ifelse(u>0,1-exp(-f*abs(u))/(1+f^2),f^2*exp(-abs(u)/f)/(1+f^2)) | ||
| log(f)+ifelse(y>m,-f*(y-m),(y-m)/f)/s-log((1+f^2)*s)- | ||
| log(1-plp((y-m)/s))} |
| @@ -1,180 +1,183 @@ | ||
| # | ||
| # event : A Library of Special Functions for Event Histories | ||
| # Copyright (C) 1998, 1999, 2000, 2001 J.K. Lindsey | ||
| # | ||
| # This program is free software; you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public Licence as published by | ||
| # the Free Software Foundation; either version 2 of the Licence, or | ||
| # (at your option) any later version. | ||
| # | ||
| # This program is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public Licence for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public Licence | ||
| # along with this program; if not, write to the Free Software | ||
| # Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. | ||
| # | ||
| # SYNOPSIS | ||
| # | ||
| # pbirth(frequencies, p, intensity="negative binomial", | ||
| # type="spectral decomposition", print.level=0, ndigit=10, | ||
| # gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, | ||
| # typsize=abs(p), stepmax=10*sqrt(p%*%p)) | ||
| # | ||
| # DESCRIPTION | ||
| # | ||
| # Function to fit overdispersed count data as a birth process | ||
|
|
||
| pbirth <- function(frequencies, p, intensity="negative binomial", | ||
| type="spectral decomposition", print.level=0, ndigit=10, | ||
| gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, | ||
| typsize=abs(p), stepmax=10*sqrt(p%*%p)){ | ||
| call <- sys.call() | ||
| # | ||
| # find distribution and method | ||
| # | ||
| intensity <- match.arg(intensity,c("binomial","binomial exponential", | ||
| "binomial logistic","binomial total","Poisson","Poisson exponential", | ||
| "negative binomial","gen negative binomial")) | ||
| type <- match.arg(type,c("spectral decomposition","series approximation")) | ||
| # | ||
| # set up frequencies | ||
| # | ||
| if(is.vector(frequencies))frequencies <- matrix(frequencies,nrow=1) | ||
| n <- dim(frequencies)[2]-1 | ||
| nr <- dim(frequencies)[1] | ||
| np <- length(p) | ||
| n1 <- NULL | ||
| for(i in 1:nr)n1 <- c(n1,length(frequencies[i,!is.na(frequencies[i,])])-1) | ||
| # | ||
| # create appropriate intensity function | ||
| # | ||
| lambda <- switch(intensity, | ||
| "binomial"=function(p,nn,n) (n-nn)*exp(p[1]), | ||
| "binomial exponential"=function(p,nn,n) (n-nn)*exp(p[1]+p[2]*nn), | ||
| "binomial logistic"=function(p,nn,n) | ||
| (n-nn)*exp(p[1])/(1+exp(p[2]+p[3]*nn)), | ||
| "binomial total"=function(p,nn,n) | ||
| (n-nn)*exp(p[1]+p[4]*n)/(1+exp(p[2]+p[3]*nn)), | ||
| "Poisson"=function(p,nn,n) rep(exp(p[1]),length(nn)), | ||
| "Poisson exponential"=function(p,nn,n) exp(p[1]+p[2]*nn), | ||
| "negative binomial"=function(p,nn,n) exp(p[1])*(exp(p[2])+nn), | ||
| "gen negative binomial"= | ||
| function(p,nn,n) exp(p[1])*(exp(p[2])+nn)^(1-exp(p[3]))) | ||
| # | ||
| # create probability functions | ||
| # | ||
| x <- matrix(0,n+1,n+1) | ||
| i1 <- matrix(1:(n+1),n+1,2) | ||
| i2 <- cbind(1:n,2:(n+1)) | ||
| prob <- function(p,n) { | ||
| x[i1[1:(n+1),]] <- -lambda(p,0:n,n) | ||
| x[i2[1:n,,drop=FALSE]] <- lambda(p,0:(n-1),n) | ||
| pr <- mexp(x[1:(n+1),1:(n+1)],type=type)[1,]} | ||
| # z <- eigen(x[1:(n+1),1:(n+1)],sym=FALSE) | ||
| # pr <- (z$vectors%*%diag(exp(z$values))%*%solve(z$vectors))[1,]} | ||
| prob1 <- function(p){ | ||
| pr <- NULL | ||
| for(i in 1:nr)pr <- c(pr,prob(p,n1[i]))} | ||
| # | ||
| # call nlm to optimize | ||
| # | ||
| freq <- as.vector(t(frequencies)) | ||
| freq <- freq[!is.na(freq)] | ||
| like <- function(p) -sum(freq*log(prob1(p)),na.rm=TRUE) | ||
| z0 <- nlm(like,p=p, hessian=TRUE, print.level=print.level, | ||
| typsize=typsize, ndigit=ndigit, gradtol=gradtol, stepmax=stepmax, | ||
| steptol=steptol, iterlim=iterlim, fscale=fscale) | ||
| # | ||
| # calculate se's | ||
| # | ||
| if(np==1)cov <- 1/z0$hessian | ||
| else { | ||
| a <- if(any(is.na(z0$hessian))||any(abs(z0$hessian)==Inf))0 | ||
| else qr(z0$hessian)$rank | ||
| if(a==np)cov <- solve(z0$hessian) | ||
| else cov <- matrix(NA,ncol=np,nrow=np)} | ||
| se <- sqrt(diag(cov)) | ||
| nn <- sum(frequencies) | ||
| # | ||
| # back transform coefficients | ||
| # | ||
| p <- z0$estimate | ||
| pr <- prob(p,n) | ||
| pt <- vector(mode="double",np) | ||
| pr1 <- exp(-exp(p[1])) | ||
| if(intensity=="binomial")pt <- 1-pr1 | ||
| else if(intensity=="Poisson")pt <- sum(pr*0:n) | ||
| else if(intensity=="negative binomial"||intensity=="gen negative binomial"){ | ||
| pr2 <- exp(p[2]) | ||
| pt[1] <- pr2/pr1-pr2 | ||
| pt[2] <- pr2 | ||
| if(intensity=="gen negative binomial")pt[3] <- 1-exp(p[3])} | ||
| else pt <- exp(-exp(p)) | ||
| # | ||
| # create values from intensity function | ||
| # | ||
| an <- switch(intensity, | ||
| "binomial"=rep(exp(p[1]),n+1), | ||
| "binomial exponential"=exp(p[1]+p[2]*0:n), | ||
| "binomial logistic"=exp(p[1])/(1+exp(p[2]+p[3]*0:n)), | ||
| "binomial total"=exp(p[1]+p[4]*n)/(1+exp(p[2]+p[3]*0:n)), | ||
| "Poisson"=rep(exp(p[1]),n+1), | ||
| "Poisson exponential"=exp(p[1]+p[2]*0:n), | ||
| "negative binomial"=exp(p[1])*(exp(p[2])+0:n), | ||
| "gen negative binomial"= | ||
| exp(p[1])*(exp(p[2])+0:n)^(1-exp(p[3]))) | ||
| fitted.values <- nn*pr | ||
| residuals <- (frequencies-fitted.values)/sqrt(fitted.values) | ||
| z1 <- list( | ||
| call=call, | ||
| intensity=intensity, | ||
| lambda=lambda, | ||
| an=an, | ||
| frequencies=frequencies, | ||
| maxlike=z0$minimum, | ||
| aic=z0$minimum+np, | ||
| fitted.values=fitted.values, | ||
| prob=pr, | ||
| residuals=residuals, | ||
| initial.values=p, | ||
| coefficients=p, | ||
| pt=pt, | ||
| se=se, | ||
| cov=cov, | ||
| corr=cov/(se%o%se), | ||
| gradient=z0$gradient, | ||
| iterations=z0$iterations, | ||
| error=z0$error, | ||
| code=z0$code) | ||
| class(z1) <- "pbirth" | ||
| return(z1)} | ||
|
|
||
| ### standard method | ||
| ### | ||
|
|
||
| deviance.pbirth <- function(z) 2*z$maxlike | ||
|
|
||
| ### print method | ||
| ### | ||
| print.pbirth <- function(z){ | ||
| np <- length(z$coefficients) | ||
| cat("\nCall:",deparse(z$call),sep="\n") | ||
| cat("\n") | ||
| t <- deparse(z$lambda) | ||
| cat(z$intensity,"intensity function:",t[2:length(t)],sep="\n") | ||
| cat("-Log likelihood ",z$maxlike,"\n") | ||
| cat("AIC ",z$aic,"\n") | ||
| cat("Iterations ",z$iterations,"\n\n") | ||
| cat("Coefficients:\n") | ||
| coef.table <- cbind(z$coefficients,z$se,z$pt) | ||
| dn <- paste("p",1:np,sep="") | ||
| dimnames(coef.table) <- list(dn,c("estimate","se","parameter")) | ||
| print.default(coef.table,digits=4,print.gap=2) | ||
| if(np>1){ | ||
| cat("\nCorrelations:\n") | ||
| dimnames(z$corr) <- list(seq(1,np),seq(1,np)) | ||
| print.default(z$corr,digits=4)} | ||
| invisible(z)} | ||
| # | ||
| # event : A Library of Special Functions for Event Histories | ||
| # Copyright (C) 1998, 1999, 2000, 2001 J.K. Lindsey | ||
| # | ||
| # This program is free software; you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public Licence as published by | ||
| # the Free Software Foundation; either version 2 of the Licence, or | ||
| # (at your option) any later version. | ||
| # | ||
| # This program is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public Licence for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public Licence | ||
| # along with this program; if not, write to the Free Software | ||
| # Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. | ||
| # | ||
| # SYNOPSIS | ||
| # | ||
| # pbirth(frequencies, p, intensity="negative binomial", | ||
| # type="spectral decomposition", print.level=0, ndigit=10, | ||
| # gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, | ||
| # typsize=abs(p), stepmax=10*sqrt(p%*%p)) | ||
| # | ||
| # DESCRIPTION | ||
| # | ||
| # Function to fit overdispersed count data as a birth process | ||
|
|
||
| pbirth <- function(frequencies, p, intensity="negative binomial", | ||
| type="spectral decomposition", print.level=0, ndigit=10, | ||
| gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, | ||
| typsize=abs(p), stepmax=10*sqrt(p%*%p)){ | ||
| call <- sys.call() | ||
| # | ||
| # find distribution and method | ||
| # | ||
| intensity <- match.arg(intensity,c("binomial","binomial exponential", | ||
| "binomial logistic","binomial total","Poisson","Poisson exponential", | ||
| "negative binomial","gen negative binomial")) | ||
| type <- match.arg(type,c("spectral decomposition","series approximation")) | ||
| # | ||
| # set up frequencies | ||
| # | ||
| if(is.vector(frequencies))frequencies <- matrix(frequencies,nrow=1) | ||
| n <- dim(frequencies)[2]-1 | ||
| nr <- dim(frequencies)[1] | ||
| np <- length(p) | ||
| n1 <- NULL | ||
| for(i in 1:nr)n1 <- c(n1,length(frequencies[i,!is.na(frequencies[i,])])-1) | ||
| # | ||
| # create appropriate intensity function | ||
| # | ||
| lambda <- switch(intensity, | ||
| "binomial"=function(p,nn,n) (n-nn)*exp(p[1]), | ||
| "binomial exponential"=function(p,nn,n) (n-nn)*exp(p[1]+p[2]*nn), | ||
| "binomial logistic"=function(p,nn,n) | ||
| (n-nn)*exp(p[1])/(1+exp(p[2]+p[3]*nn)), | ||
| "binomial total"=function(p,nn,n) | ||
| (n-nn)*exp(p[1]+p[4]*n)/(1+exp(p[2]+p[3]*nn)), | ||
| "Poisson"=function(p,nn,n) rep(exp(p[1]),length(nn)), | ||
| "Poisson exponential"=function(p,nn,n) exp(p[1]+p[2]*nn), | ||
| "negative binomial"=function(p,nn,n) exp(p[1])*(exp(p[2])+nn), | ||
| "gen negative binomial"= | ||
| function(p,nn,n) exp(p[1])*(exp(p[2])+nn)^(1-exp(p[3]))) | ||
| # | ||
| # create probability functions | ||
| # | ||
| x <- matrix(0,n+1,n+1) | ||
| i1 <- matrix(1:(n+1),n+1,2) | ||
| i2 <- cbind(1:n,2:(n+1)) | ||
| prob <- function(p,n) { | ||
| x[i1[1:(n+1),]] <- -lambda(p,0:n,n) | ||
| x[i2[1:n,,drop=FALSE]] <- lambda(p,0:(n-1),n) | ||
| pr <- mexp(x[1:(n+1),1:(n+1)],type=type)[1,] | ||
| pr} | ||
| # z <- eigen(x[1:(n+1),1:(n+1)],sym=FALSE) | ||
| # pr <- (z$vectors%*%diag(exp(z$values))%*%solve(z$vectors))[1,]} | ||
| prob1 <- function(p){ | ||
| pr <- NULL | ||
| for(i in 1:nr)pr <- c(pr,prob(p,n1[i])) | ||
| pr} | ||
| # | ||
| # call nlm to optimize | ||
| # | ||
| freq <- as.vector(t(frequencies)) | ||
| freq <- freq[!is.na(freq)] | ||
| like <- function(p) -sum(freq*log(prob1(p)),na.rm=TRUE) | ||
| z0 <- nlm(like,p=p, hessian=TRUE, print.level=print.level, | ||
| typsize=typsize, ndigit=ndigit, gradtol=gradtol, stepmax=stepmax, | ||
| steptol=steptol, iterlim=iterlim, fscale=fscale) | ||
| # | ||
| # calculate se's | ||
| # | ||
| if(np==1)cov <- 1/z0$hessian | ||
| else { | ||
| a <- if(any(is.na(z0$hessian))||any(abs(z0$hessian)==Inf))0 | ||
| else qr(z0$hessian)$rank | ||
| if(a==np)cov <- solve(z0$hessian) | ||
| else cov <- matrix(NA,ncol=np,nrow=np)} | ||
| se <- sqrt(diag(cov)) | ||
| nn <- sum(frequencies) | ||
| # | ||
| # back transform coefficients | ||
| # | ||
| p <- z0$estimate | ||
| pr <- prob(p,n) | ||
| pt <- vector(mode="double",np) | ||
| pr1 <- exp(-exp(p[1])) | ||
| if(intensity=="binomial")pt <- 1-pr1 | ||
| else if(intensity=="Poisson")pt <- sum(pr*0:n) | ||
| else if(intensity=="negative binomial"||intensity=="gen negative binomial"){ | ||
| pr2 <- exp(p[2]) | ||
| pt[1] <- pr2/pr1-pr2 | ||
| pt[2] <- pr2 | ||
| if(intensity=="gen negative binomial")pt[3] <- 1-exp(p[3])} | ||
| else pt <- exp(-exp(p)) | ||
| # | ||
| # create values from intensity function | ||
| # | ||
| an <- switch(intensity, | ||
| "binomial"=rep(exp(p[1]),n+1), | ||
| "binomial exponential"=exp(p[1]+p[2]*0:n), | ||
| "binomial logistic"=exp(p[1])/(1+exp(p[2]+p[3]*0:n)), | ||
| "binomial total"=exp(p[1]+p[4]*n)/(1+exp(p[2]+p[3]*0:n)), | ||
| "Poisson"=rep(exp(p[1]),n+1), | ||
| "Poisson exponential"=exp(p[1]+p[2]*0:n), | ||
| "negative binomial"=exp(p[1])*(exp(p[2])+0:n), | ||
| "gen negative binomial"= | ||
| exp(p[1])*(exp(p[2])+0:n)^(1-exp(p[3]))) | ||
| fitted.values <- nn*pr | ||
| residuals <- (frequencies-fitted.values)/sqrt(fitted.values) | ||
| z1 <- list( | ||
| call=call, | ||
| intensity=intensity, | ||
| lambda=lambda, | ||
| an=an, | ||
| frequencies=frequencies, | ||
| maxlike=z0$minimum, | ||
| aic=z0$minimum+np, | ||
| fitted.values=fitted.values, | ||
| prob=pr, | ||
| residuals=residuals, | ||
| initial.values=p, | ||
| coefficients=p, | ||
| pt=pt, | ||
| se=se, | ||
| cov=cov, | ||
| corr=cov/(se%o%se), | ||
| gradient=z0$gradient, | ||
| iterations=z0$iterations, | ||
| error=z0$error, | ||
| code=z0$code) | ||
| class(z1) <- "pbirth" | ||
| return(z1)} | ||
|
|
||
| ### standard method | ||
| ### | ||
|
|
||
| deviance.pbirth <- function(object, ...) 2*object$maxlike | ||
|
|
||
| ### print method | ||
| ### | ||
| print.pbirth <- function(x, ...){ | ||
| z <- x ## legacy / S3methods consistency | ||
| np <- length(z$coefficients) | ||
| cat("\nCall:",deparse(z$call),sep="\n") | ||
| cat("\n") | ||
| t <- deparse(z$lambda) | ||
| cat(z$intensity,"intensity function:",t[2:length(t)],sep="\n") | ||
| cat("-Log likelihood ",z$maxlike,"\n") | ||
| cat("AIC ",z$aic,"\n") | ||
| cat("Iterations ",z$iterations,"\n\n") | ||
| cat("Coefficients:\n") | ||
| coef.table <- cbind(z$coefficients,z$se,z$pt) | ||
| dn <- paste("p",1:np,sep="") | ||
| dimnames(coef.table) <- list(dn,c("estimate","se","parameter")) | ||
| print.default(coef.table,digits=4,print.gap=2) | ||
| if(np>1){ | ||
| cat("\nCorrelations:\n") | ||
| dimnames(z$corr) <- list(seq(1,np),seq(1,np)) | ||
| print.default(z$corr,digits=4)} | ||
| invisible(z)} |
| @@ -0,0 +1,10 @@ | ||
|
|
||
| [](https://travis-ci.org/swihart/event) [](https://cran.r-project.org/package=event)  | ||
|
|
||
| <!-- README.md is generated from README.Rmd. Please edit README.Rmd (this file)--> | ||
| `event` R package | ||
| ================= | ||
|
|
||
| This package is intended to be the developmental version to the CRAN version of [Jim Lindsey's event](http://www.commanster.eu/rcode.html). The .zip files listed on his homepage have been listed as version 1.0 since 2005. For the subsequent maintenance on this github and CRAN, we will start at 1.1.0. | ||
|
|
||
| To compare this version with the static v1.0 files on [Jim Lindsey's Homepage](http://www.commanster.eu/rcode.html), it may be useful to use [the compare page for this repo's two branches](https://github.com/swihart/event/compare/jim-lindsey-homepage-version-1.0...master?diff=split&name=master). |
| @@ -0,0 +1,59 @@ | ||
| # event R package | ||
| Bruce Swihart | ||
| November 2016 | ||
|
|
||
| ## Resubmission 1 | ||
| I removed survkit.r/.Rd/.f. Turns out survkit is available as of 2013 in a nice update | ||
| from the original authors of survkit.f. Added this info to NEWS. | ||
|
|
||
| ## Test environments | ||
| * local OS X install: R version 3.3.2 (2016-10-31) | ||
| * ubuntu 12.04 (on travis-ci): R version 3.3.1 (2016-06-21) | ||
| * win-builder: R Under development (unstable) (2016-11-29 r71708) | ||
|
|
||
| ## R CMD check results | ||
| There were no ERRORs or WARNINGs. | ||
|
|
||
| There was 1 NOTE: | ||
|
|
||
| * 1st submission (Package was archived on CRAN, see Miscellaneous) | ||
|
|
||
| ## Downstream dependencies | ||
| There are currently no downstream dependencies for this package. | ||
|
|
||
| ## Miscellaneous | ||
| As per the CRAN Repository Policy Version Revision 3577, | ||
|
|
||
| * Explain any change in the maintainer’s email address and if possible send confirmation from the previous address (by a separate email to CRAN@R-project.org) or explain why it is not possible. | ||
|
|
||
| This Package was archived on CRAN. I am resurrecting it. | ||
| I have Jim Lindsey's permission to be maintainer of his packages on CRAN. Currently he has them in .zip files on his homepage: http://www.commanster.eu/rcode.html. He does not have access to the original email address he used when this package was on CRAN, and thus cannot send a separate confirmation email. However, you can contact him at James Lindsey <jlindsey@gen.unimaas.nl>. | ||
|
|
||
| He sent this confirmation message on 2016-11-24: | ||
|
|
||
| ---------- Forwarded message ---------- | ||
| From: James Lindsey <jlindsey@gen.unimaas.nl> | ||
| Date: Thu, Nov 24, 2016 at 9:26 AM | ||
| Subject: transferring maintainer role to Bruce Swihart for 'rmutil', 'repeated', 'gnlm', 'growth', 'event', 'stable' | ||
| To: CRAN@r-project.org, ligges@statistik.tu-dortmund.de, Kurt.Hornik@wu.ac.at | ||
| Cc: bruce.swihart@gmail.com | ||
|
|
||
|
|
||
| Dear CRAN, | ||
|
|
||
| Bruce Swihart is taking on the role of maintainer for my R-packages which have been available on my homepage, http://www.commanster.eu/rcode.html. Some of these R-packages were on CRAN but have since been archived. | ||
| I know it is CRAN repository policy to email confirmation from the previous maintainer's email address (jlindsey@luc.ac.be), but alas I can't due to LUC changing its name. | ||
|
|
||
| Please accept this email as confirmation of the maintainer role changing for R packages 'rmutil', 'repeated', 'gnlm', 'growth', 'event', 'stable'. | ||
|
|
||
| Regards, | ||
| Jim Lindsey | ||
|
|
||
|
|
||
|
|
||
|
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||
|
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||
|
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||
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|
|
| @@ -1,43 +1,50 @@ | ||
| \name{autointensity} | ||
| \title{Plot Autointensity Function of a Point Process} | ||
| \usage{ | ||
| autointensity(times, window=NULL, maxlag=total/10, | ||
| ylab="Conditional probability", xlab="Lag", | ||
| main="Autointensity function", xlim=c(0,max(times)), | ||
| ylim=c(0,if(plotse)max(se1)else max(z$density)), | ||
| lty=1, plot=TRUE, plotse=TRUE, add=FALSE, ...) | ||
| } | ||
| \description{ | ||
| \code{autointensity} plots the autointensity function of a point | ||
| process, including a solid horizontal line indicating the constant | ||
| intensity of a Poisson process. | ||
| } | ||
| \arguments{ | ||
| \item{times}{Vector of times between events.} | ||
| \item{window}{Width of grouping interval.} | ||
| \item{maxlag}{Maximum lag to be calculated, by default the maximum | ||
| interevent time.} | ||
| \item{plot}{If FALSE, values are returned but the function is not | ||
| plotted.} | ||
| \item{plotse}{If TRUE, plots pointwise two-standard error bands around | ||
| the curve.} | ||
| \item{add}{If TRUE, add curve to an existing plot.} | ||
| \item{others}{Plotting control options.} | ||
| } | ||
| \value{ | ||
| A list containing the coordinates of the plotted function and the | ||
| standard error bands. | ||
| } | ||
| \references{ | ||
| Guttorp, P. (1995) Stochastic Modeling of Scientific Data. Chapman & | ||
| Hall, pp. 229, 238-240. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{cprocess}}. | ||
| } | ||
| \examples{ | ||
| times <- rgamma(100,2,scale=4) | ||
| autointensity(times, window=3) | ||
| } | ||
| \keyword{hplot} | ||
| \name{autointensity} | ||
| \title{Plot Autointensity Function of a Point Process} | ||
| \usage{ | ||
| autointensity(times, window=NULL, maxlag=max(times), | ||
| ylab="Conditional probability", xlab="Lag", | ||
| main="Autointensity function", xlim=c(0,max(times)), | ||
| ylim=c(0,if(plotse)max(se1)else max(z$density)), | ||
| lty=1, plot=TRUE, plotse=TRUE, add=FALSE, ...) | ||
| } | ||
| \alias{autointensity} | ||
| \description{ | ||
| \code{autointensity} plots the autointensity function of a point | ||
| process, including a solid horizontal line indicating the constant | ||
| intensity of a Poisson process. | ||
| } | ||
| \arguments{ | ||
| \item{times}{Vector of times between events.} | ||
| \item{window}{Width of grouping interval.} | ||
| \item{maxlag}{Maximum lag to be calculated, by default the maximum | ||
| interevent time.} | ||
| \item{plot}{If FALSE, values are returned but the function is not | ||
| plotted.} | ||
| \item{plotse}{If TRUE, plots pointwise two-standard error bands around | ||
| the curve.} | ||
| \item{add}{If TRUE, add curve to an existing plot.} | ||
| \item{ylab}{Plotting control options.} | ||
| \item{xlab}{Plotting control options.} | ||
| \item{main}{Plotting control options.} | ||
| \item{xlim}{Plotting control options.} | ||
| \item{ylim}{Plotting control options.} | ||
| \item{lty}{Plotting control options.} | ||
| \item{...}{Plotting control options.} | ||
| } | ||
| \value{ | ||
| A list containing the coordinates of the plotted function and the | ||
| standard error bands. | ||
| } | ||
| \references{ | ||
| Guttorp, P. (1995) Stochastic Modeling of Scientific Data. Chapman & | ||
| Hall, pp. 229, 238-240. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{cprocess}}. | ||
| } | ||
| \examples{ | ||
| times <- rgamma(100,2,scale=4) | ||
| autointensity(times, window=3) | ||
| } | ||
| \keyword{hplot} |
| @@ -1,31 +1,37 @@ | ||
| \name{bp} | ||
| \title{Create a Vector of Cumulative Numbers of Previous Events | ||
| for a Point Process (Birth Processes)} | ||
| \alias{bp} | ||
| \usage{ | ||
| bp(y, id, censor=1) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{id}{Vector of corresponding individual identifiers for who had | ||
| which sequence of times.} | ||
| \item{censor}{Vector of censoring indicators.} | ||
| } | ||
| \value{ | ||
| \code{bp} creates a vector of length \code{sum(y)} of cumulative | ||
| numbers of previous events for each individual for use in fitting | ||
| birth processes with \code{\link{ehr}}. Add one if the process starts | ||
| at an event. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{ehr}}, \code{\link{ident}}, \code{\link{pp}}, \code{\link{tccov}}, | ||
| \code{\link{tpast}}, \code{\link{ttime}}, \code{\link{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| i <- c(1,1,2,2) | ||
| birth <- bp(y, i) | ||
| birth | ||
| } | ||
| \keyword{manip} | ||
| \name{bp} | ||
| \title{Create a Vector of Cumulative Numbers of Previous Events | ||
| for a Point Process (Birth Processes)} | ||
| \alias{bp} | ||
| \usage{ | ||
| bp(y, id, censor=1) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{id}{Vector of corresponding individual identifiers for who had | ||
| which sequence of times.} | ||
| \item{censor}{Vector of censoring indicators.} | ||
| } | ||
| \description{ | ||
| \code{bp} creates a vector of length \code{sum(y)} of cumulative | ||
| numbers of previous events for each individual for use in fitting | ||
| birth processes with \code{\link{ehr}}. Add one if the process starts | ||
| at an event. | ||
| } | ||
| \value{ | ||
| \code{bp} creates a vector of length \code{sum(y)} of cumulative | ||
| numbers of previous events for each individual for use in fitting | ||
| birth processes with \code{\link{ehr}}. Add one if the process starts | ||
| at an event. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{ehr}}, \code{\link{ident}}, \code{\link{pp}}, \code{\link{tccov}}, | ||
| \code{\link{tpast}}, \code{\link{ttime}}, \code{\link{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| i <- c(1,1,2,2) | ||
| birth <- bp(y, i) | ||
| birth | ||
| } | ||
| \keyword{manip} |
| @@ -1,66 +1,74 @@ | ||
| \name{coxre} | ||
| \title{Cox Proportional Hazards Model with Random Effect} | ||
| \alias{coxre} | ||
| \usage{ | ||
| coxre(response, censor, nest=NULL, cov=NULL, stratified=FALSE, | ||
| cumul=FALSE,estimate=1, iter=10, print.level=0, ndigit=10, | ||
| gradtol=0.00001, steptol=0.00001, iterlim=100, fscale=1, | ||
| typsiz=abs(estimate), stepmax=estimate) | ||
| } | ||
| \description{ | ||
| \code{coxre} fits a Cox proportional hazards model to event history | ||
| data using a gamma distribution random effect. The parameter, gamma, | ||
| is the variance of this mixing distribution. | ||
|
|
||
| If a matrix of response times is supplied, the model can be stratified | ||
| by columns, i.e. a different intensity function is fitted for each | ||
| column. To fit identical intensity functions to all response types, | ||
| give the times as a vector. | ||
| } | ||
| \arguments{ | ||
| \item{response}{Vector or matrix of times to events, with one column | ||
| per type of response (or subunit).} | ||
| \item{censor}{Corresponding vector or matrix of censoring indicators. | ||
| If NULL all values are set to one.} | ||
| \item{nest}{Vector indicating to which unit each observation belongs.} | ||
| \item{cov}{One covariate} | ||
| \item{stratified}{If TRUE, a model stratified on type of response | ||
| (the columns of response) is fitted instead of proportional intensities.} | ||
| \item{cumul}{Set to TRUE if response times are from a common origin | ||
| instead of times to (or between) events.} | ||
| \item{estimate}{Initial estimate of the frailty parameter.} | ||
| \item{iter}{Maximum number of iterations allowed for the inner EM loop.} | ||
| \item{others}{Plotting control options.} | ||
| } | ||
| \author{D.G. Clayton and J.K. Lindsey} | ||
| \references{ | ||
| Clayton, D. (1987) The analysis of event history data: a review of | ||
| progress and outstanding problems. Statistics in Medicine 7: 819-841 | ||
| } | ||
| \seealso{ | ||
| \code{\link[event]{kalsurv}}. | ||
| } | ||
| \examples{ | ||
| # 11 individuals, each with 5 responses | ||
| y <- matrix(c(51,36,50,35,42, | ||
| 27,20,26,17,27, | ||
| 37,22,41,37,30, | ||
| 42,36,32,34,27, | ||
| 27,18,33,14,29, | ||
| 43,32,43,35,40, | ||
| 41,22,36,25,38, | ||
| 38,21,31,20,16, | ||
| 36,23,27,25,28, | ||
| 26,31,31,32,36, | ||
| 29,20,25,26,25),ncol=5,byrow=TRUE) | ||
| # Different intensity functions | ||
| coxre(response=y, censor=matrix(rep(1,55),ncol=5), nest=1:11, | ||
| est=0.7, stratified=TRUE) | ||
| # Proportional intensity functions for the five responses | ||
| coxre(response=y, censor=matrix(rep(1,55),ncol=5), nest=1:11, | ||
| est=0.7, stratified=FALSE) | ||
| # Identical intensity functions | ||
| coxre(response=as.vector(t(y)), censor=rep(1,55), | ||
| nest=rep(1:11,rep(5,11)), est=0.7) | ||
| } | ||
| \keyword{models} | ||
| \name{coxre} | ||
| \title{Cox Proportional Hazards Model with Random Effect} | ||
| \alias{coxre} | ||
| \alias{print.llrf} | ||
| \usage{ | ||
| coxre(response, censor, nest=NULL, cov=NULL, stratified=FALSE, | ||
| cumul=FALSE,estimate=1, iter=10, print.level=0, ndigit=10, | ||
| gradtol=0.00001, steptol=0.00001, iterlim=100, fscale=1, | ||
| typsize=abs(estimate), stepmax=estimate) | ||
| } | ||
| \description{ | ||
| \code{coxre} fits a Cox proportional hazards model to event history | ||
| data using a gamma distribution random effect. The parameter, gamma, | ||
| is the variance of this mixing distribution. | ||
|
|
||
| If a matrix of response times is supplied, the model can be stratified | ||
| by columns, i.e. a different intensity function is fitted for each | ||
| column. To fit identical intensity functions to all response types, | ||
| give the times as a vector. | ||
| } | ||
| \arguments{ | ||
| \item{response}{Vector or matrix of times to events, with one column | ||
| per type of response (or subunit).} | ||
| \item{censor}{Corresponding vector or matrix of censoring indicators. | ||
| If NULL all values are set to one.} | ||
| \item{nest}{Vector indicating to which unit each observation belongs.} | ||
| \item{cov}{One covariate} | ||
| \item{stratified}{If TRUE, a model stratified on type of response | ||
| (the columns of response) is fitted instead of proportional intensities.} | ||
| \item{cumul}{Set to TRUE if response times are from a common origin | ||
| instead of times to (or between) events.} | ||
| \item{estimate}{Initial estimate of the frailty parameter.} | ||
| \item{iter}{Maximum number of iterations allowed for the inner EM loop.} | ||
| \item{print.level}{\code{nlm} control options.} | ||
| \item{ndigit}{\code{nlm} control options.} | ||
| \item{gradtol}{\code{nlm} control options.} | ||
| \item{steptol}{\code{nlm} control options.} | ||
| \item{iterlim}{\code{nlm} control options.} | ||
| \item{fscale}{\code{nlm} control options.} | ||
| \item{typsize}{\code{nlm} control options.} | ||
| \item{stepmax}{\code{nlm} control options.} | ||
| } | ||
| \author{D.G. Clayton and J.K. Lindsey} | ||
| \references{ | ||
| Clayton, D. (1987) The analysis of event history data: a review of | ||
| progress and outstanding problems. Statistics in Medicine 7: 819-841 | ||
| } | ||
| \seealso{ | ||
| \code{\link[event]{kalsurv}}. | ||
| } | ||
| \examples{ | ||
| # 11 individuals, each with 5 responses | ||
| y <- matrix(c(51,36,50,35,42, | ||
| 27,20,26,17,27, | ||
| 37,22,41,37,30, | ||
| 42,36,32,34,27, | ||
| 27,18,33,14,29, | ||
| 43,32,43,35,40, | ||
| 41,22,36,25,38, | ||
| 38,21,31,20,16, | ||
| 36,23,27,25,28, | ||
| 26,31,31,32,36, | ||
| 29,20,25,26,25),ncol=5,byrow=TRUE) | ||
| # Different intensity functions | ||
| coxre(response=y, censor=matrix(rep(1,55),ncol=5), nest=1:11, | ||
| est=0.7, stratified=TRUE) | ||
| # Proportional intensity functions for the five responses | ||
| coxre(response=y, censor=matrix(rep(1,55),ncol=5), nest=1:11, | ||
| est=0.7, stratified=FALSE) | ||
| # Identical intensity functions | ||
| coxre(response=as.vector(t(y)), censor=rep(1,55), | ||
| nest=rep(1:11,rep(5,11)), est=0.7) | ||
| } | ||
| \keyword{models} |
| @@ -1,30 +1,35 @@ | ||
| \name{cprocess} | ||
| \title{Plot Counting Process Data} | ||
| \alias{cprocess} | ||
| \usage{ | ||
| cprocess(times=NULL, events=NULL, number=FALSE, lty=NULL, xlim=NULL, | ||
| ylim=NULL, xlab="Time", ylab="Counts", ...) | ||
| } | ||
| \description{ | ||
| \code{cprocess} plots the cumulative number of events (the counting | ||
| process) over time for each vector in the list. One or both of | ||
| \code{times} and \code{events} can be supplied. | ||
| } | ||
| \arguments{ | ||
| \item{times}{Vector of times between events, a list of such | ||
| vectors, or a \code{repeated} object.} | ||
| \item{events}{Vector of counts of events, a list of such vectors, or a | ||
| \code{repeated} object.} | ||
| \item{number}{If TRUE, the counting processes are numbered | ||
| consecutively on the graph.} | ||
| \item{others}{Plotting control options.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{read.list}}, \code{\link[rmutil]{rmna}}. | ||
| } | ||
| \examples{ | ||
| times <- rgamma(20,2,scale=4) | ||
| cprocess(times) | ||
| } | ||
| \keyword{hplot} | ||
| \name{cprocess} | ||
| \title{Plot Counting Process Data} | ||
| \alias{cprocess} | ||
| \usage{ | ||
| cprocess(times=NULL, events=NULL, number=FALSE, lty=NULL, xlim=NULL, | ||
| ylim=NULL, xlab="Time", ylab="Counts", ...) | ||
| } | ||
| \description{ | ||
| \code{cprocess} plots the cumulative number of events (the counting | ||
| process) over time for each vector in the list. One or both of | ||
| \code{times} and \code{events} can be supplied. | ||
| } | ||
| \arguments{ | ||
| \item{times}{Vector of times between events, a list of such | ||
| vectors, or a \code{repeated} object.} | ||
| \item{events}{Vector of counts of events, a list of such vectors, or a | ||
| \code{repeated} object.} | ||
| \item{number}{If TRUE, the counting processes are numbered | ||
| consecutively on the graph.} | ||
| \item{ylab}{Plotting control options.} | ||
| \item{xlab}{Plotting control options.} | ||
| \item{xlim}{Plotting control options.} | ||
| \item{ylim}{Plotting control options.} | ||
| \item{lty}{Plotting control options.} | ||
| \item{...}{Plotting control options.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{read.list}}, \code{\link[rmutil]{rmna}}. | ||
| } | ||
| \examples{ | ||
| times <- rgamma(20,2,scale=4) | ||
| cprocess(times) | ||
| } | ||
| \keyword{hplot} |
| @@ -1,100 +1,110 @@ | ||
| \name{ehr} | ||
| \title{Regression Models for Event History Intensity Functions} | ||
| \alias{ehr} | ||
| \usage{ | ||
| ehr(point, lambda=NULL, linear=NULL, plambda=NULL, delta=1, | ||
| envir=parent.frame(), print.level=0, typsiz=rep(1,length(plambda)), | ||
| ndigit=10, gradtol=0.00001, iterlim=100, fscale=1, | ||
| stepmax=max(10*sqrt(plambda\%*\%plambda),10), steptol=0.0004) | ||
| } | ||
| \description{ | ||
| \code{ehr} fits an intensity function to event histories, where point is | ||
| produced by \code{point <- pp(y)} and \code{lambda} is the user-defined | ||
| log intensity function. | ||
|
|
||
| Nonlinear regression models for \code{lambda} can be supplied as | ||
| formulae where parameters are unknowns. Factor variables cannot be | ||
| used and parameters must be scalars. (See \code{\link[rmutil]{finterp}}.) | ||
| } | ||
| \arguments{ | ||
| \item{point}{A point process vector produced by \code{\link[event]{pp}}.} | ||
| \item{lambda}{User-specified function of \code{p}, and possibly | ||
| \code{linear}, giving the regression equation for the intensity or a | ||
| formula beginning with ~, specifying either a linear regression | ||
| function in the Wilkinson and Rogers notation or a general function | ||
| with named unknown parameters. The function may contain a | ||
| linear part that must simply be given the name, \code{linear}, in the | ||
| function. If no function is supplied, the intensity is taken to be | ||
| constant (a homogeneous Poisson process).} | ||
| \item{linear}{A formula beginning with ~ specifying the linear part of | ||
| the regression function.} | ||
| \item{plambda}{Vector of initial parameter estimates. If \code{lambda} | ||
| is a formula with unknown parameters, their estimates must be supplied | ||
| either in their order of appearance in the expression or in a named list.} | ||
| \item{delta}{If any time intervals are different from unity, a vector | ||
| of time intervals.} | ||
| \item{envir}{Environment in which model formulae are to be | ||
| interpreted or a data object of class, repeated, tccov, or tvcov. | ||
| If \code{point} has class \code{repeated}, it is used as the | ||
| environment.} | ||
| \item{others}{Arguments controlling \code{\link{nlm}}.} | ||
| } | ||
| \references{ | ||
| Lindsey, J.K. (1995) Fitting parametric counting processes by | ||
| using log linear models. Journal of the Royal Statistical | ||
| Society C44, 201-212. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{bp}}, \code{\link[rmutil]{finterp}}, | ||
| \code{\link[event]{ident}}, \code{\link[event]{pp}}, | ||
| \code{\link[event]{tccov}}, \code{\link[event]{tpast}}, | ||
| \code{\link[event]{ttime}}, \code{\link[event]{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| # event indicator | ||
| py <- pp(y) | ||
| # time since previous event | ||
| ptime <- tpast(y) | ||
| # individual ID | ||
| i <- c(1,1,2,2) | ||
| id <- ident(y, i) | ||
| # times and corresponding covariate values | ||
| tx <- c(2,3,1,2,2,2,2) | ||
| x <- c(1,2,2,1,2,2,1) | ||
| zcov <- tvcov(y, x, tx) | ||
| # Poisson process | ||
| ehr(py, plambda=1) | ||
| # Weibull process | ||
| lambda1 <- function(p) p[1]+p[2]*log(ptime) | ||
| ehr(py, lambda=lambda1, plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~log(ptime), plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~b0+b1*log(ptime), plambda=list(b0=1,b1=1)) | ||
| # Poisson process with time-varying covariate | ||
| lambda2 <- function(p) p[1]+p[2]*zcov | ||
| ehr(py, lambda=lambda2, plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~zcov, plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~c0+c1*zcov, plambda=list(c0=1,c1=1)) | ||
| # Weibull process with time-varying covariate | ||
| lambda3 <- function(p) p[1]+p[2]*log(ptime)+p[3]*zcov | ||
| ehr(py, lambda=lambda3, plambda=c(1,1,1)) | ||
| # or | ||
| ehr(py, lambda=~log(ptime)+zcov, plambda=c(1,1,1)) | ||
| # or | ||
| ehr(py, lambda=~c0+b1*log(ptime)+c1*zcov, plambda=list(c0=1,c1=1,b1=1)) | ||
| # gamma process with time-varying covariate | ||
| lambda4 <- function(p) hgamma(ptime, p[1], exp(p[2]+p[3]*zcov)) | ||
| ehr(py, lambda=lambda4, plambda=c(1,1,1)) | ||
| # or | ||
| ehr(py, lambda=~hgamma(ptime, b1, exp(c0+c1*zcov)), | ||
| plambda=list(c0=1,c1=1,b1=1)) | ||
| # or | ||
| lambda5 <- function(p, linear) hgamma(ptime, p[1], exp(linear)) | ||
| ehr(py, lambda=lambda5, linear=~zcov, plambda=c(1,1,1)) | ||
| } | ||
| \keyword{models} | ||
| \name{ehr} | ||
| \title{Regression Models for Event History Intensity Functions} | ||
| \alias{ehr} | ||
| \alias{print.intensity} | ||
| \alias{deviance.intensity} | ||
| \alias{vdm} | ||
| \usage{ | ||
| ehr(point, lambda=NULL, linear=NULL, plambda=NULL, delta=1, | ||
| envir=parent.frame(), print.level=0, typsize=rep(1,length(plambda)), | ||
| ndigit=10, gradtol=0.00001, iterlim=100, fscale=1, | ||
| stepmax=max(10*sqrt(plambda\%*\%plambda),10), steptol=0.0004) | ||
| } | ||
| \description{ | ||
| \code{ehr} fits an intensity function to event histories, where point is | ||
| produced by \code{point <- pp(y)} and \code{lambda} is the user-defined | ||
| log intensity function. | ||
|
|
||
| Nonlinear regression models for \code{lambda} can be supplied as | ||
| formulae where parameters are unknowns. Factor variables cannot be | ||
| used and parameters must be scalars. (See \code{\link[rmutil]{finterp}}.) | ||
| } | ||
| \arguments{ | ||
| \item{point}{A point process vector produced by \code{\link[event]{pp}}.} | ||
| \item{lambda}{User-specified function of \code{p}, and possibly | ||
| \code{linear}, giving the regression equation for the intensity or a | ||
| formula beginning with ~, specifying either a linear regression | ||
| function in the Wilkinson and Rogers notation or a general function | ||
| with named unknown parameters. The function may contain a | ||
| linear part that must simply be given the name, \code{linear}, in the | ||
| function. If no function is supplied, the intensity is taken to be | ||
| constant (a homogeneous Poisson process).} | ||
| \item{linear}{A formula beginning with ~ specifying the linear part of | ||
| the regression function.} | ||
| \item{plambda}{Vector of initial parameter estimates. If \code{lambda} | ||
| is a formula with unknown parameters, their estimates must be supplied | ||
| either in their order of appearance in the expression or in a named list.} | ||
| \item{delta}{If any time intervals are different from unity, a vector | ||
| of time intervals.} | ||
| \item{envir}{Environment in which model formulae are to be | ||
| interpreted or a data object of class, repeated, tccov, or tvcov. | ||
| If \code{point} has class \code{repeated}, it is used as the | ||
| environment.} | ||
| \item{print.level}{\code{nlm} control options.} | ||
| \item{ndigit}{\code{nlm} control options.} | ||
| \item{gradtol}{\code{nlm} control options.} | ||
| \item{steptol}{\code{nlm} control options.} | ||
| \item{iterlim}{\code{nlm} control options.} | ||
| \item{fscale}{\code{nlm} control options.} | ||
| \item{typsize}{\code{nlm} control options.} | ||
| \item{stepmax}{\code{nlm} control options.} | ||
| } | ||
| \references{ | ||
| Lindsey, J.K. (1995) Fitting parametric counting processes by | ||
| using log linear models. Journal of the Royal Statistical | ||
| Society C44, 201-212. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{bp}}, \code{\link[rmutil]{finterp}}, | ||
| \code{\link[event]{ident}}, \code{\link[event]{pp}}, | ||
| \code{\link[event]{tccov}}, \code{\link[event]{tpast}}, | ||
| \code{\link[event]{ttime}}, \code{\link[event]{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| # event indicator | ||
| py <- pp(y) | ||
| # time since previous event | ||
| ptime <- tpast(y) | ||
| # individual ID | ||
| i <- c(1,1,2,2) | ||
| id <- ident(y, i) | ||
| # times and corresponding covariate values | ||
| tx <- c(2,3,1,2,2,2,2) | ||
| x <- c(1,2,2,1,2,2,1) | ||
| zcov <- tvcov(y, x, tx) | ||
| # Poisson process | ||
| ehr(py, plambda=1) | ||
| # Weibull process | ||
| lambda1 <- function(p) p[1]+p[2]*log(ptime) | ||
| ehr(py, lambda=lambda1, plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~log(ptime), plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~b0+b1*log(ptime), plambda=list(b0=1,b1=1)) | ||
| # Poisson process with time-varying covariate | ||
| lambda2 <- function(p) p[1]+p[2]*zcov | ||
| ehr(py, lambda=lambda2, plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~zcov, plambda=c(1,1)) | ||
| # or | ||
| ehr(py, lambda=~c0+c1*zcov, plambda=list(c0=1,c1=1)) | ||
| # Weibull process with time-varying covariate | ||
| lambda3 <- function(p) p[1]+p[2]*log(ptime)+p[3]*zcov | ||
| ehr(py, lambda=lambda3, plambda=c(1,1,1)) | ||
| # or | ||
| ehr(py, lambda=~log(ptime)+zcov, plambda=c(1,1,1)) | ||
| # or | ||
| ehr(py, lambda=~c0+b1*log(ptime)+c1*zcov, plambda=list(c0=1,c1=1,b1=1)) | ||
| # gamma process with time-varying covariate | ||
| lambda4 <- function(p) hgamma(ptime, p[1], exp(p[2]+p[3]*zcov)) | ||
| ehr(py, lambda=lambda4, plambda=c(1,1,1)) | ||
| # or | ||
| ehr(py, lambda=~hgamma(ptime, b1, exp(c0+c1*zcov)), | ||
| plambda=list(c0=1,c1=1,b1=1)) | ||
| # or | ||
| lambda5 <- function(p, linear) hgamma(ptime, p[1], exp(linear)) | ||
| ehr(py, lambda=lambda5, linear=~zcov, plambda=c(1,1,1)) | ||
| } | ||
| \keyword{models} |
| @@ -1,77 +1,77 @@ | ||
| \name{event} | ||
| \title{Event History Analysis Library} | ||
| \alias{event} | ||
| \description{ | ||
| \code{\link[event]{autointensity}} Plot Autointensity Function of a Point Process | ||
|
|
||
| \code{\link[event]{bp}} Create a Vector of Cumulative Numbers of Previous Events for a Point Process | ||
|
|
||
| \code{\link[event]{coxre}} Cox Proportional Hazards Model with Random Effect | ||
|
|
||
| \code{\link[event]{cprocess}} Counting Process Plot | ||
|
|
||
| \code{\link[event]{ehr}} Fit an Intensity Function to Event Histories | ||
|
|
||
| \code{\link[event]{hboxcox}} Log Hazard Function for a Box-Cox Process | ||
|
|
||
| \code{\link[event]{hburr}} Log Hazard Function for a Burr Process | ||
|
|
||
| \code{\link[event]{hcauchy}} Log Hazard Function for a Cauchy Process | ||
|
|
||
| \code{\link[event]{hexp}} Log Hazard Function for an Exponential (Poisson) Process | ||
|
|
||
| \code{\link[event]{hgamma}} Log Hazard Function for a Gamma Process | ||
|
|
||
| \code{\link[event]{hgextval}} Log Hazard Function for an Extreme Value Process | ||
|
|
||
| \code{\link[event]{hggamma}} Log Hazard Function for a Generalized Gamma Process | ||
|
|
||
| \code{\link[event]{hglogis}} Log Hazard Function for a Generalized Logistic Process | ||
|
|
||
| \code{\link[event]{hgweibull}} Log Hazard Function for a Generalized Weibull Process | ||
|
|
||
| \code{\link[event]{hhjorth}} Log Hazard Function for a Hjorth Process | ||
|
|
||
| \code{\link[event]{hinvgauss}} Log Hazard Function for a Inverse Gauss Process | ||
|
|
||
| \code{\link[event]{hlaplace}} Log Hazard Function for a Laplace Process | ||
|
|
||
| \code{\link[event]{hlnorm}} Log Hazard Function for a Log Normal Process | ||
|
|
||
| \code{\link[event]{hlogis}} Log Hazard Function for a Logistic Process | ||
|
|
||
| \code{\link[event]{hnorm}} Log Hazard Function for a Normal Process | ||
|
|
||
| \code{\link[event]{hpareto}} Log Hazard Function for a Pareto Process | ||
|
|
||
| \code{\link[event]{hskewlaplace}} Log Hazard Function for a Skew Laplace Process | ||
|
|
||
| \code{\link[event]{hstudent}} Log Hazard Function for a Student Process | ||
|
|
||
| \code{\link[event]{hweibull}} Log Hazard Function for a Weibull Process | ||
|
|
||
| \code{\link[event]{ident}} Create an Individual Identification Vector for a Point Process | ||
|
|
||
| \code{\link[event]{kalsurv}} Generalized Repeated Measurements Models for Event Histories | ||
|
|
||
| \code{\link[event]{km}} Kaplan-Meier Survival Curves | ||
|
|
||
| \code{\link[event]{pbirth}} Fit Overdispersed Count Data as a Birth Process | ||
|
|
||
| \code{\link[event]{pp}} Create a Point Process Vector from Times between Events | ||
|
|
||
| \code{\link[event]{read.list}} Read a List of Matrices of Unbalanced Repeated Measurements from a File | ||
|
|
||
| \code{\link[event]{read.surv}} Read a List of Vectors of Event Histories from a File | ||
|
|
||
| \code{\link[event]{survkit}} Weibull and Cox Models with Random Effects | ||
|
|
||
| \code{\link[event]{tccov}} Create a Vector of Time-constant Covariates for a Point Process | ||
|
|
||
| \code{\link[event]{tpast}} Create a Vector of Times Past since Previous Events for a Point Process | ||
|
|
||
| \code{\link[event]{ttime}} Create a Vector of Total Time Elapsed for each Individual for a Point Process | ||
|
|
||
| \code{\link[event]{tvcov}} Create a Vector of Time-varying Covariates for a Point Process | ||
| } | ||
| \keyword{documentation} | ||
| \name{event} | ||
| \title{Event History Analysis Library} | ||
| \alias{event} | ||
| \description{ | ||
| \code{\link[event]{autointensity}} Plot Autointensity Function of a Point Process | ||
|
|
||
| \code{\link[event]{bp}} Create a Vector of Cumulative Numbers of Previous Events for a Point Process | ||
|
|
||
| \code{\link[event]{coxre}} Cox Proportional Hazards Model with Random Effect | ||
|
|
||
| \code{\link[event]{cprocess}} Counting Process Plot | ||
|
|
||
| \code{\link[event]{ehr}} Fit an Intensity Function to Event Histories | ||
|
|
||
| \code{\link[event]{hboxcox}} Log Hazard Function for a Box-Cox Process | ||
|
|
||
| \code{\link[event]{hburr}} Log Hazard Function for a Burr Process | ||
|
|
||
| \code{\link[event]{hcauchy}} Log Hazard Function for a Cauchy Process | ||
|
|
||
| \code{\link[event]{hexp}} Log Hazard Function for an Exponential (Poisson) Process | ||
|
|
||
| \code{\link[event]{hgamma}} Log Hazard Function for a Gamma Process | ||
|
|
||
| \code{\link[event]{hgextval}} Log Hazard Function for an Extreme Value Process | ||
|
|
||
| \code{\link[event]{hggamma}} Log Hazard Function for a Generalized Gamma Process | ||
|
|
||
| \code{\link[event]{hglogis}} Log Hazard Function for a Generalized Logistic Process | ||
|
|
||
| \code{\link[event]{hgweibull}} Log Hazard Function for a Generalized Weibull Process | ||
|
|
||
| \code{\link[event]{hhjorth}} Log Hazard Function for a Hjorth Process | ||
|
|
||
| \code{\link[event]{hinvgauss}} Log Hazard Function for a Inverse Gauss Process | ||
|
|
||
| \code{\link[event]{hlaplace}} Log Hazard Function for a Laplace Process | ||
|
|
||
| \code{\link[event]{hlnorm}} Log Hazard Function for a Log Normal Process | ||
|
|
||
| \code{\link[event]{hlogis}} Log Hazard Function for a Logistic Process | ||
|
|
||
| \code{\link[event]{hnorm}} Log Hazard Function for a Normal Process | ||
|
|
||
| \code{\link[event]{hpareto}} Log Hazard Function for a Pareto Process | ||
|
|
||
| \code{\link[event]{hskewlaplace}} Log Hazard Function for a Skew Laplace Process | ||
|
|
||
| \code{\link[event]{hstudent}} Log Hazard Function for a Student Process | ||
|
|
||
| \code{\link[event]{hweibull}} Log Hazard Function for a Weibull Process | ||
|
|
||
| \code{\link[event]{ident}} Create an Individual Identification Vector for a Point Process | ||
|
|
||
| \code{\link[event]{kalsurv}} Generalized Repeated Measurements Models for Event Histories | ||
|
|
||
| \code{\link[event]{km}} Kaplan-Meier Survival Curves | ||
|
|
||
| \code{\link[event]{pbirth}} Fit Overdispersed Count Data as a Birth Process | ||
|
|
||
| \code{\link[event]{pp}} Create a Point Process Vector from Times between Events | ||
|
|
||
| \code{\link[rmutil]{read.list}} Read a List of Matrices of Unbalanced Repeated Measurements from a File | ||
|
|
||
| \code{\link[rmutil]{read.surv}} Read a List of Vectors of Event Histories from a File | ||
|
|
||
| \code{\link[event]{survkit}} Weibull and Cox Models with Random Effects | ||
|
|
||
| \code{\link[event]{tccov}} Create a Vector of Time-constant Covariates for a Point Process | ||
|
|
||
| \code{\link[event]{tpast}} Create a Vector of Times Past since Previous Events for a Point Process | ||
|
|
||
| \code{\link[event]{ttime}} Create a Vector of Total Time Elapsed for each Individual for a Point Process | ||
|
|
||
| \code{\link[event]{tvcov}} Create a Vector of Time-varying Covariates for a Point Process | ||
| } | ||
| \keyword{documentation} |
| @@ -0,0 +1,40 @@ | ||
| \name{hboxcox} | ||
| \title{Log Hazard Function for a Box-Cox Process} | ||
| \usage{ | ||
| hboxcox(y, m, s, f) | ||
| } | ||
| \alias{hboxcox} | ||
| \description{ | ||
| This function provides information about the Box-Cox | ||
| distribution with location parameter equal to \code{m}, dispersion | ||
| equal to \code{s}, and power transformation equal to \code{f}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The Box-Cox distribution has density | ||
| \deqn{ | ||
| f(y) = | ||
| \frac{1}{\sqrt{2 \pi \sigma^2}} \exp(-((y^\nu/\nu-\mu)^2/(2 \sigma^2)))/ | ||
| (1-I(\nu<0)-sign(\nu)*pnorm(0,\mu,sqrt(\sigma)))}{ | ||
| f(y) = 1/sqrt(2 pi s^2) exp(-((y^f/f - mu)^2/(2 s^2)))/ | ||
| (1-I(f<0)-sign(f)*pnorm(0,m,sqrt(s)))} | ||
| where \eqn{\mu}{m} is the location parameter of the distribution, | ||
| \eqn{\sigma}{s} is the dispersion, \eqn{\nu}{f} is the family | ||
| parameter, \eqn{I()} is the indicator function, and \eqn{y>0}. | ||
|
|
||
| \eqn{\nu=1}{f=1} gives a truncated normal distribution. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of power parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dnorm}} for the normal or Gaussian distribution. | ||
| } | ||
| \examples{ | ||
| hboxcox(2, 5, 5, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,33 @@ | ||
| \name{hburr} | ||
| \title{Log Hazard Function for a Burr Process} | ||
| \usage{ | ||
| hburr(y, m, s, f) | ||
| } | ||
| \alias{hburr} | ||
| \description{ | ||
| These functions provide information about the Burr distribution with | ||
| location parameter equal to \code{m}, dispersion equal to | ||
| \code{s}, and family parameter equal to \code{f}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The Burr distribution has density | ||
| \deqn{ | ||
| f(y) = \frac{\nu \sigma (y / \mu)^{\sigma-1}} | ||
| {\mu (1+(y/\mu)^\sigma)^{\nu+1}}}{ | ||
| f(y) = f s (y/m)^(s-1)/(m (1+(y/m)^s)^(f+1))} | ||
| where \eqn{\mu}{m} is the location parameter of the distribution, | ||
| \eqn{\sigma}{s} is the dispersion, and \eqn{\nu}{f} is the family | ||
| parameter. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of family parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \examples{ | ||
| hburr(2, 5, 1, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,30 +1,31 @@ | ||
| \name{hcauchy} | ||
| \title{Hazard Function for a Cauchy Process} | ||
| \alias{hcauchy} | ||
| \usage{ | ||
| hcauchy(y, m, s) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Location parameter.} | ||
| \item{s}{Dispersion parameter.} | ||
| } | ||
| \value{ | ||
| \code{hcauchy} returns the log hazard function for a Cauchy process with | ||
| the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{hboxcox}}, \code{\link[rmutil]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[rmutil]{hgextval}}, | ||
| \code{\link[event]{hgamma}}, \code{\link[rmutil]{hggamma}}, | ||
| \code{\link[rmutil]{hhjorth}}, \code{\link[rmutil]{hinvgauss}}, | ||
| \code{\link[rmutil]{hlaplace}}, \code{\link[event]{hlnorm}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[rmutil]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[rmutil]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hcauchy(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hcauchy} | ||
| \title{ Log Hazard Function for a Cauchy Process} | ||
| \alias{hcauchy} | ||
| \usage{ | ||
| hcauchy(y, m, s) | ||
| } | ||
| \description{ Log Hazard Function for a Cauchy Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Location parameter.} | ||
| \item{s}{Dispersion parameter.} | ||
| } | ||
| \value{ | ||
| \code{hcauchy} returns the log hazard function for a Cauchy process with | ||
| the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{hboxcox}}, \code{\link[event]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[event]{hgextval}}, | ||
| \code{\link[event]{hgamma}}, \code{\link[event]{hggamma}}, | ||
| \code{\link[event]{hhjorth}}, \code{\link[event]{hinvgauss}}, | ||
| \code{\link[event]{hlaplace}}, \code{\link[event]{hlnorm}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[event]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[event]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hcauchy(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,29 +1,30 @@ | ||
| \name{hexp} | ||
| \title{Hazard Function for a Poisson Process} | ||
| \alias{hexp} | ||
| \usage{ | ||
| hexp(y, rate) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{rate}{Vector of rates.} | ||
| } | ||
| \value{ | ||
| \code{hexp} returns the log hazard function for a Poisson process with | ||
| the given parameter value. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{hboxcox}}, \code{\link[rmutil]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[rmutil]{hgextval}}, | ||
| \code{\link[event]{hgamma}}, \code{\link[rmutil]{hggamma}}, | ||
| \code{\link[rmutil]{hhjorth}}, \code{\link[rmutil]{hinvgauss}}, | ||
| \code{\link[rmutil]{hlaplace}}, \code{\link[event]{hlnorm}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[rmutil]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[rmutil]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hexp(1:10, 3) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hexp} | ||
| \title{Log Hazard Function for a Poisson Process} | ||
| \alias{hexp} | ||
| \usage{ | ||
| hexp(y, rate) | ||
| } | ||
| \description{Log Hazard Function for a Poisson Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{rate}{Vector of rates.} | ||
| } | ||
| \value{ | ||
| \code{hexp} returns the log hazard function for a Poisson process with | ||
| the given parameter value. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{hboxcox}}, \code{\link[event]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[event]{hgextval}}, | ||
| \code{\link[event]{hgamma}}, \code{\link[event]{hggamma}}, | ||
| \code{\link[event]{hhjorth}}, \code{\link[event]{hinvgauss}}, | ||
| \code{\link[event]{hlaplace}}, \code{\link[event]{hlnorm}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[event]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[event]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hexp(1:10, 3) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,31 +1,32 @@ | ||
| \name{hgamma} | ||
| \title{Hazard Function for a Gamma Process} | ||
| \alias{hgamma} | ||
| \usage{ | ||
| hgamma(y, shape, rate=1, scale=1/rate) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{shape}{Shape parameter.} | ||
| \item{rate}{Rate parameter.} | ||
| \item{scale}{Scale parameter.} | ||
| } | ||
| \value{ | ||
| \code{hgamma} returns the log hazard function for a gamma process with | ||
| the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{hboxcox}}, \code{\link[rmutil]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[rmutil]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[rmutil]{hggamma}}, | ||
| \code{\link[rmutil]{hhjorth}}, \code{\link[rmutil]{hinvgauss}}, | ||
| \code{\link[rmutil]{hlaplace}}, \code{\link[event]{hlnorm}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[rmutil]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[rmutil]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hgamma(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hgamma} | ||
| \title{Log Hazard Function for a Gamma Process} | ||
| \alias{hgamma} | ||
| \usage{ | ||
| hgamma(y, shape, rate=1, scale=1/rate) | ||
| } | ||
| \description{Log Hazard Function for a Gamma Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{shape}{Shape parameter.} | ||
| \item{rate}{Rate parameter.} | ||
| \item{scale}{Scale parameter.} | ||
| } | ||
| \value{ | ||
| \code{hgamma} returns the log hazard function for a gamma process with | ||
| the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{hboxcox}}, \code{\link[event]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[event]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hggamma}}, | ||
| \code{\link[event]{hhjorth}}, \code{\link[event]{hinvgauss}}, | ||
| \code{\link[event]{hlaplace}}, \code{\link[event]{hlnorm}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[event]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[event]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hgamma(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,43 @@ | ||
| \name{hgextval} | ||
| \title{ Log Hazard Function for an Extreme Value Process} | ||
| \usage{ | ||
| hgextval(y, s, m, f) | ||
| } | ||
| \alias{hgextval} | ||
| \description{ | ||
| These functions provide information about the generalized extreme | ||
| value distribution with location parameter equal to \code{m}, dispersion | ||
| equal to \code{s}, and family parameter equal to \code{f}:log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The generalized extreme value distribution has density | ||
| \deqn{ | ||
| f(y) = | ||
| y^{\nu-1} \exp(y^\nu/\nu) \frac{\sigma}{\mu} | ||
| \frac{\exp(y^\nu/\nu)}{\mu^{\sigma-1}/(1-I(\nu>0)+sign(\nu) | ||
| exp(-\mu^-\sigma))}\exp(-(\exp(y^\nu\nu)/\mu)^\sigma)}{ | ||
| f(y) = y^(f-1) exp(y^f/f) (s/m) (exp(y^f/f)/m)^(s-1) | ||
| exp(-(exp(y^f/f)/m)^s)/(1-I(f>0)+sign(f) exp(-m^-s))} | ||
|
|
||
| where \eqn{\mu}{m} is the location parameter of the distribution, | ||
| \eqn{\sigma}{s} is the dispersion, \eqn{\nu}{f} is the family | ||
| parameter, \eqn{I()} is the indicator function, and \eqn{y>0}. | ||
|
|
||
| \eqn{\nu=1}{f=1} a truncated extreme value distribution. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of family parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
|
|
||
| \seealso{ | ||
| \code{\link{dweibull}} for the Weibull distribution. | ||
| } | ||
| \examples{ | ||
| hgextval(1, 2, 1, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,44 @@ | ||
| \name{hggamma} | ||
| \title{Log Hazard Function for a Generalized Gamma Process} | ||
| \usage{ | ||
| hggamma(y, s, m, f) | ||
| } | ||
| \alias{hggamma} | ||
| \description{ | ||
| These functions provide information about the generalized gamma | ||
| distribution with scale parameter equal to \code{m}, shape equal | ||
| to \code{s}, and family parameter equal to \code{f}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The generalized gamma distribution has density | ||
| \deqn{ | ||
| f(y) = \frac{\nu y^{\nu-1}} | ||
| {(\mu/\sigma)^{\nu\sigma} Gamma(\sigma)} y^{\nu(\sigma-1)} | ||
| \exp(-(y \sigma/\mu)^\nu)}{ | ||
| f(y) = fy^(f-1)/((m/s)^(fs) Gamma(s)) y^(f(s-1)) exp(-(y s/m)^f)} | ||
|
|
||
| where \eqn{\mu}{m} is the scale parameter of the distribution, | ||
| \eqn{\sigma}{s} is the shape, and \eqn{\nu}{f} is the family | ||
| parameter. | ||
|
|
||
| \eqn{\nu=1}{f=1} yields a gamma distribution, \eqn{\sigma=1}{s=1} a | ||
| Weibull distribution, and \eqn{\sigma=\infty}{s=infinity} a | ||
| log normal distribution. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of family parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dgamma}} for the gamma distribution, | ||
| \code{\link{dweibull}} for the Weibull distribution, \code{\link{dlnorm}} | ||
| for the log normal distribution. | ||
| } | ||
| \examples{ | ||
| hggamma(2, 5, 4, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,41 @@ | ||
| \name{hglogis} | ||
| \title{Log Hazard Function for a Generalized Logistic Process} | ||
| \usage{ | ||
| hglogis(y, m, s, f) | ||
| } | ||
| \alias{hglogis} | ||
| \description{ | ||
| These functions provide information about the generalized logistic | ||
| distribution with location parameter equal to \code{m}, dispersion equal | ||
| to \code{s}, and family parameter equal to \code{f}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The generalized logistic distribution has density | ||
| \deqn{ | ||
| f(y) = | ||
| \frac{\nu \sqrt{3} \exp(-\sqrt{3} (y-\mu)/(\sigma \pi))}{ | ||
| \sigma \pi (1+\exp(-\sqrt{3} (y-\mu)/(\sigma \pi)))^{\nu+1}}}{ | ||
| f(y) = f sqrt(3) exp(-sqrt(3) (y-m)/(s pi))/ | ||
| (s pi (1+exp(-sqrt(3) (y-m)/(s pi)))^(f+1))} | ||
|
|
||
| where \eqn{\mu}{m} is the location parameter of the distribution, | ||
| \eqn{\sigma}{s} is the dispersion, and \eqn{\nu}{f} is the family | ||
| parameter. | ||
|
|
||
| \eqn{\nu=1}{f=1} gives a logistic distribution. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of family parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dlogis}} for the logistic distribution. | ||
| } | ||
| \examples{ | ||
| hglogis(5, 5, 1, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,44 @@ | ||
| \name{hgweibull} | ||
| \title{Log Hazard Function for a Generalized Weibull Process} | ||
| \usage{ | ||
| hgweibull(y, s, m, f) | ||
| } | ||
| \alias{hgweibull} | ||
| \description{ | ||
| These functions provide information about the generalized Weibull | ||
| distribution, also called the exponentiated Weibull, with scale | ||
| parameter equal to \code{m}, shape equal to \code{s}, and family | ||
| parameter equal to \code{f}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The generalized Weibull distribution has density | ||
| \deqn{ | ||
| f(y) = \frac{\sigma \nu y^{\sigma-1} (1-\exp(-(y/\mu)^\sigma))^{\nu-1} | ||
| \exp(-(y/\mu)^\sigma)}{\mu^\sigma}}{ | ||
| f(y) = s f y^(s-1) (1-exp(-(y/m)^s))^(f-1) exp(-(y/m)^s)/m^s} | ||
|
|
||
| where \eqn{\mu}{m} is the scale parameter of the distribution, | ||
| \eqn{\sigma}{s} is the shape, and \eqn{\nu}{f} is the family | ||
| parameter. | ||
|
|
||
| \eqn{\nu=1}{f=1} gives a Weibull distribution, for | ||
| \eqn{\sigma=1}{s=1}, \eqn{\nu<0}{f<0} a generalized F distribution, | ||
| and for \eqn{\sigma>0}{s>0}, \eqn{\nu\leq0}{f<=0} a Burr type XII distribution. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of family parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dweibull}} for the Weibull distribution, | ||
| \code{\link{df}} for the F distribution, | ||
| \code{\link[rmutil]{dburr}} for the Burr distribution. | ||
| } | ||
| \examples{ | ||
| hgweibull(5, 1, 3, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,35 @@ | ||
| \name{hhjorth} | ||
| \title{Log Hazard Function for a Hjorth Process} | ||
| \usage{ | ||
| hhjorth(y, m, s, f) | ||
| } | ||
| \alias{hhjorth} | ||
| \description{ | ||
| These functions provide information about the Hjorth | ||
| distribution with location parameter equal to \code{m}, dispersion equal | ||
| to \code{s}, and family parameter equal to \code{f}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
|
|
||
| The Hjorth distribution has density | ||
| \deqn{ | ||
| f(y) = (1+\sigma y)^{-\nu/\sigma} \exp(-(y/\mu)^2/2) | ||
| (\frac{y}{\mu^2}+\frac{\nu}{1+\sigma y})}{ | ||
| f(y) = (1+s y)^(-f/s) exp(-(y/m)^2/2) (y/m^2+f/(1+s y))} | ||
|
|
||
| where \eqn{\mu}{m} is the location parameter of the distribution, | ||
| \eqn{\sigma}{s} is the dispersion, and \eqn{\nu}{f} is the family | ||
| parameter. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of family parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \examples{ | ||
| hhjorth(5, 5, 5, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,35 @@ | ||
| \name{hinvgauss} | ||
| \title{Log Hazard Function for a Inverse Gauss Process} | ||
| \usage{ | ||
| hinvgauss(y, m, s) | ||
| } | ||
| \alias{hinvgauss} | ||
| \description{ | ||
| These functions provide information about the inverse Gaussian | ||
| distribution with mean equal to \code{m} and dispersion equal to | ||
| \code{s}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The inverse Gaussian distribution has density | ||
| \deqn{ | ||
| f(y) = | ||
| \frac{1}{\sqrt{2\pi\sigma y^3}} e^{-(y-\mu)^2/(2 y \sigma m^2)}}{ | ||
| f(y) = 1/sqrt(2 pi s y^3) e^-((y - m)^2/(2 y s m^2))} | ||
| where \eqn{\mu}{m} is the mean of the distribution and | ||
| \eqn{\sigma}{s} is the dispersion. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of means.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dnorm}} for the normal distribution and | ||
| \code{\link{dlnorm}} for the \emph{Log}normal distribution. | ||
| } | ||
| \examples{ | ||
| hinvgauss(5, 5, 1) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,34 @@ | ||
| \name{hlaplace} | ||
| \title{Log Hazard Function for a Laplace Process} | ||
| \usage{ | ||
| hlaplace(y, m=0, s=1) | ||
| } | ||
| \alias{hlaplace} | ||
| \description{ | ||
| These functions provide information about the Laplace distribution | ||
| with location parameter equal to \code{m} and dispersion equal to | ||
| \code{s}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The Laplace distribution has density | ||
| \deqn{ | ||
| f(y) = \frac{\exp(-abs(y-\mu)/\sigma)}{(2\sigma)}}{ | ||
| f(y) = exp(-abs(y-m)/s)/(2*s)} | ||
| where \eqn{\mu}{m} is the location parameter of the distribution and | ||
| \eqn{\sigma}{s} is the dispersion. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dexp}} for the exponential distribution and | ||
| \code{\link{dcauchy}} for the Cauchy distribution. | ||
| } | ||
| \examples{ | ||
| hlaplace(5, 2, 1) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,30 +1,31 @@ | ||
| \name{hlnorm} | ||
| \title{Hazard Function for a Log Normal Process} | ||
| \alias{hlnorm} | ||
| \usage{ | ||
| hlnorm(y, m, s) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Mean parameter.} | ||
| \item{s}{Variance parameter.} | ||
| } | ||
| \value{ | ||
| \code{hlnorm} returns the log hazard function for a log normal | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{hboxcox}}, \code{\link[rmutil]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[rmutil]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[rmutil]{hggamma}}, \code{\link[rmutil]{hhjorth}}, | ||
| \code{\link[rmutil]{hinvgauss}}, \code{\link[rmutil]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[rmutil]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[rmutil]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hlnorm(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hlnorm} | ||
| \title{Log Hazard Function for a Log Normal Process} | ||
| \alias{hlnorm} | ||
| \usage{ | ||
| hlnorm(y, m, s) | ||
| } | ||
| \description{Log Hazard Function for a Log Normal Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Mean parameter.} | ||
| \item{s}{Variance parameter.} | ||
| } | ||
| \value{ | ||
| \code{hlnorm} returns the log hazard function for a log normal | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{hboxcox}}, \code{\link[event]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[event]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[event]{hggamma}}, \code{\link[event]{hhjorth}}, | ||
| \code{\link[event]{hinvgauss}}, \code{\link[event]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[event]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[event]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hlnorm(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,28 +1,29 @@ | ||
| \name{hlogis} | ||
| \title{Hazard Function for a Logistic Process} | ||
| \alias{hlogis} | ||
| \usage{ | ||
| hlogis(y, m, s) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Location parameter.} | ||
| \item{s}{Scale parameter.} | ||
| } | ||
| \value{ | ||
| \code{hlogis} returns the log hazard function for a logistic | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{hboxcox}}, \code{\link{hburr}}, \code{\link{hexp}}, | ||
| \code{\link{hgextval}}, \code{\link{hcauchy}}, \code{\link{hgamma}}, | ||
| \code{\link{hggamma}}, \code{\link{hhjorth}}, \code{\link{hinvgauss}}, | ||
| \code{\link{hlaplace}}, \code{\link{hlnorm}}, \code{\link{hglogis}}, | ||
| \code{\link{hnorm}}, \code{\link{hstudent}}, \code{\link{hweibull}}, | ||
| \code{\link{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hlogis(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hlogis} | ||
| \title{Log Hazard Function for a Logistic Process} | ||
| \alias{hlogis} | ||
| \usage{ | ||
| hlogis(y, m, s) | ||
| } | ||
| \description{Log Hazard Function for a Logistic Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Location parameter.} | ||
| \item{s}{Scale parameter.} | ||
| } | ||
| \value{ | ||
| \code{hlogis} returns the log hazard function for a logistic | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{hboxcox}}, \code{\link{hburr}}, \code{\link{hexp}}, | ||
| \code{\link{hgextval}}, \code{\link{hcauchy}}, \code{\link{hgamma}}, | ||
| \code{\link{hggamma}}, \code{\link{hhjorth}}, \code{\link{hinvgauss}}, | ||
| \code{\link{hlaplace}}, \code{\link{hlnorm}}, \code{\link{hglogis}}, | ||
| \code{\link{hnorm}}, \code{\link{hstudent}}, \code{\link{hweibull}}, | ||
| \code{\link{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hlogis(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,30 +1,31 @@ | ||
| \name{hnorm} | ||
| \title{Hazard Function for a Normal Process} | ||
| \alias{hnorm} | ||
| \usage{ | ||
| hnorm(y, m, s) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Mean parameter.} | ||
| \item{s}{Variance parameter.} | ||
| } | ||
| \value{ | ||
| \code{hnorm} returns the log hazard function for a normal | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{hboxcox}}, \code{\link[rmutil]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[rmutil]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[rmutil]{hggamma}}, \code{\link[rmutil]{hhjorth}}, | ||
| \code{\link[rmutil]{hinvgauss}}, \code{\link[rmutil]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[rmutil]{hglogis}}, | ||
| \code{\link[event]{hlnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[rmutil]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hnorm(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hnorm} | ||
| \title{Log Hazard Function for a Normal Process} | ||
| \alias{hnorm} | ||
| \usage{ | ||
| hnorm(y, m, s) | ||
| } | ||
| \description{Log Hazard Function for a Normal Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Mean parameter.} | ||
| \item{s}{Variance parameter.} | ||
| } | ||
| \value{ | ||
| \code{hnorm} returns the log hazard function for a normal | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{hboxcox}}, \code{\link[event]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[event]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[event]{hggamma}}, \code{\link[event]{hhjorth}}, | ||
| \code{\link[event]{hinvgauss}}, \code{\link[event]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[event]{hglogis}}, | ||
| \code{\link[event]{hlnorm}}, \code{\link[event]{hstudent}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[event]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hnorm(1:10, 3, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,36 @@ | ||
| \name{hpareto} | ||
| \title{Log Hazard Function for a Pareto Process} | ||
| \usage{ | ||
| hpareto(y, m, s) | ||
| } | ||
| \alias{hpareto} | ||
| \description{ | ||
| These functions provide information about the Pareto distribution | ||
| with location parameter equal to \code{m} and dispersion equal to | ||
| \code{s}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
|
|
||
| The Pareto distribution has density | ||
| \deqn{ | ||
| f(y) = \frac{\sigma }{\mu (\sigma-1)(1 + y/(\mu (\sigma-1)))^{\sigma+1}}}{ | ||
| f(y) = s (1 + y/(m (s-1)))^(-s-1)/(m (s-1))} | ||
| where \eqn{\mu}{m} is the mean parameter of the distribution and | ||
| \eqn{\sigma}{s} is the dispersion. | ||
|
|
||
| This distribution can be obtained as a mixture distribution from the | ||
| exponential distribution using a gamma mixing distribution. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dexp}} for the exponential distribution. | ||
| } | ||
| \examples{ | ||
| hpareto(5, 2, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -0,0 +1,50 @@ | ||
| \name{hskewlaplace} | ||
| \title{Log Hazard Function for a Skew Laplace Process} | ||
| \usage{ | ||
| hskewlaplace(y, m=0, s=1, f=1) | ||
| } | ||
| \alias{hskewlaplace} | ||
| \description{ | ||
| These functions provide information about the skew Laplace distribution | ||
| with location parameter equal to \code{m}, dispersion equal to | ||
| \code{s}, and skew equal to \code{f}: log hazard. | ||
| (See `rmutil` for the d/p/q/r boxcox functions density, | ||
| cumulative distribution, quantiles, and random generation). | ||
| For \code{f=1}, this is an ordinary (symmetric) Laplace distribution. | ||
|
|
||
| The skew Laplace distribution has density | ||
| \deqn{ | ||
| f(y) = \frac{\nu\exp(-\nu(y-\mu)/\sigma)}{(1+\nu^2)\sigma}}{ | ||
| f(y) = f*exp(-f*(y-m)/s)/((1+f^2)*s)} | ||
| if \eqn{y\ge\mu}{y>=m} and else | ||
| \deqn{ | ||
| f(y) = \frac{\nu\exp((y-\mu)/(\nu\sigma))}{(1+\nu^2)\sigma}}{ | ||
| f(y) = f*exp((y-m)/(f*s))/((1+f^2)*s)} | ||
| where \eqn{\mu}{m} is the location parameter of the distribution, | ||
| \eqn{\sigma}{s} is the dispersion, and \eqn{\nu}{f} is the skew. | ||
|
|
||
| The mean is given by \eqn{\mu+\frac{\sigma(1-\nu^2)}{\sqrt{2}\nu}}{m | ||
| + (s * (1 - f^2)) / (sqrt(2) * f)} | ||
| and the variance by \eqn{\frac{\sigma^2(1+\nu^4)}{2\nu^2}}{(s^2 | ||
| * (1 + f^4)) / (2 * f^2)}. | ||
|
|
||
| Note that this parametrization of the skew (family) parameter is | ||
| different than that used for the multivariate skew Laplace | ||
| distribution in 'growth::elliptic'. | ||
| } | ||
| \arguments{ | ||
| \item{y}{vector of responses.} | ||
| \item{m}{vector of location parameters.} | ||
| \item{s}{vector of dispersion parameters.} | ||
| \item{f}{vector of skew parameters.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link{dexp}} for the exponential distribution, | ||
| \code{\link{dcauchy}} for the Cauchy distribution, and | ||
| \code{\link[rmutil]{dlaplace}} for the Laplace distribution. | ||
| } | ||
| \examples{ | ||
| hskewlaplace(5, 2, 1, 0.5) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,31 +1,32 @@ | ||
| \name{hstudent} | ||
| \title{Hazard Function for a Student t Process} | ||
| \alias{hstudent} | ||
| \usage{ | ||
| hstudent(y, m, s, f) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Location parameter.} | ||
| \item{s}{Scale parameter.} | ||
| \item{f}{Degrees of freedom.} | ||
| } | ||
| \value{ | ||
| \code{hstudent} returns the log hazard function for a Student t | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{hboxcox}}, \code{\link[rmutil]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[rmutil]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[rmutil]{hggamma}}, \code{\link[rmutil]{hhjorth}}, | ||
| \code{\link[rmutil]{hinvgauss}}, \code{\link[rmutil]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[rmutil]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hnorm}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[rmutil]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hstudent(1:10, 3, 2, 5) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hstudent} | ||
| \title{Log Hazard Function for a Student t Process} | ||
| \alias{hstudent} | ||
| \usage{ | ||
| hstudent(y, m, s, f) | ||
| } | ||
| \description{Log Hazard Function for a Student t Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{m}{Location parameter.} | ||
| \item{s}{Scale parameter.} | ||
| \item{f}{Degrees of freedom.} | ||
| } | ||
| \value{ | ||
| \code{hstudent} returns the log hazard function for a Student t | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{hboxcox}}, \code{\link[event]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[event]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[event]{hggamma}}, \code{\link[event]{hhjorth}}, | ||
| \code{\link[event]{hinvgauss}}, \code{\link[event]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[event]{hglogis}}, | ||
| \code{\link[event]{hnorm}}, \code{\link[event]{hnorm}}, | ||
| \code{\link[event]{hweibull}}, \code{\link[event]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hstudent(1:10, 3, 2, 5) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,30 +1,31 @@ | ||
| \name{hweibull} | ||
| \title{Hazard Function for a Weibull Process} | ||
| \alias{hweibull} | ||
| \usage{ | ||
| hweibull(y, s, m) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{s}{Shape parameter.} | ||
| \item{m}{Scale parameter.} | ||
| } | ||
| \value{ | ||
| \code{hweibull} returns the log hazard function for a Weibull | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[rmutil]{hboxcox}}, \code{\link[rmutil]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[rmutil]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[rmutil]{hggamma}}, \code{\link[rmutil]{hhjorth}}, | ||
| \code{\link[rmutil]{hinvgauss}}, \code{\link[rmutil]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[rmutil]{hglogis}}, | ||
| \code{\link[event]{hlnorm}}, \code{\link[event]{hnorm}}, | ||
| \code{\link[event]{hstudent}}, \code{\link[rmutil]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hweibull(1:10, 1.5, 2) | ||
| } | ||
| \keyword{distribution} | ||
| \name{hweibull} | ||
| \title{Log Hazard Function for a Weibull Process} | ||
| \alias{hweibull} | ||
| \usage{ | ||
| hweibull(y, s, m) | ||
| } | ||
| \description{Log Hazard Function for a Weibull Process} | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{s}{Shape parameter.} | ||
| \item{m}{Scale parameter.} | ||
| } | ||
| \value{ | ||
| \code{hweibull} returns the log hazard function for a Weibull | ||
| process with the given parameter values. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{hboxcox}}, \code{\link[event]{hburr}}, | ||
| \code{\link[event]{hexp}}, \code{\link[event]{hgextval}}, | ||
| \code{\link[event]{hcauchy}}, \code{\link[event]{hgamma}}, | ||
| \code{\link[event]{hggamma}}, \code{\link[event]{hhjorth}}, | ||
| \code{\link[event]{hinvgauss}}, \code{\link[event]{hlaplace}}, | ||
| \code{\link[event]{hlogis}}, \code{\link[event]{hglogis}}, | ||
| \code{\link[event]{hlnorm}}, \code{\link[event]{hnorm}}, | ||
| \code{\link[event]{hstudent}}, \code{\link[event]{hgweibull}}. | ||
| } | ||
| \examples{ | ||
| hweibull(1:10, 1.5, 2) | ||
| } | ||
| \keyword{distribution} |
| @@ -1,30 +1,35 @@ | ||
| \name{ident} | ||
| \title{Create an Individual Identification Vector for a Point Process} | ||
| \alias{ident} | ||
| \usage{ | ||
| ident(y, id) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{id}{Vector of corresponding individual identifiers for who had | ||
| which sequence of times.} | ||
| } | ||
| \value{ | ||
| \code{ident} creates a vector of length \code{sum(y)} by repeating the | ||
| values of individual identifiers for the times for use with | ||
| \code{\link[event]{ehr}}. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{bp}}, \code{\link[event]{ehr}}, | ||
| \code{\link[event]{pp}}, \code{\link[event]{tccov}}, | ||
| \code{\link[event]{tpast}}, \code{\link[event]{ttime}}, | ||
| \code{\link[event]{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| i <- c(1,1,2,2) | ||
| id <- ident(y, i) | ||
| id | ||
| } | ||
| \keyword{manip} | ||
| \name{ident} | ||
| \title{Create an Individual Identification Vector for a Point Process} | ||
| \alias{ident} | ||
| \usage{ | ||
| ident(y, id) | ||
| } | ||
| \description{ | ||
| \code{ident} creates a vector of length \code{sum(y)} by repeating the | ||
| values of individual identifiers for the times for use with | ||
| \code{\link[event]{ehr}}. | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{id}{Vector of corresponding individual identifiers for who had | ||
| which sequence of times.} | ||
| } | ||
| \value{ | ||
| \code{ident} creates a vector of length \code{sum(y)} by repeating the | ||
| values of individual identifiers for the times for use with | ||
| \code{\link[event]{ehr}}. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{bp}}, \code{\link[event]{ehr}}, | ||
| \code{\link[event]{pp}}, \code{\link[event]{tccov}}, | ||
| \code{\link[event]{tpast}}, \code{\link[event]{ttime}}, | ||
| \code{\link[event]{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| i <- c(1,1,2,2) | ||
| id <- ident(y, i) | ||
| id | ||
| } | ||
| \keyword{manip} |
| @@ -1,50 +1,60 @@ | ||
| \name{km} | ||
| \title{Kaplan-Meier Survivor Curves} | ||
| \usage{ | ||
| km(times, censor, group=1, freq=1, cdf=FALSE) | ||
| plot.km(z, add=FALSE, xlim, ylim=c(0,1), main=NULL, xlab="Time", | ||
| ylab=NULL, lty=NULL, ...) | ||
| plot.intensity.km(z, add=FALSE, xlab="Time", ylab="Intensity", type="l", | ||
| lty=NULL, ...) | ||
| plot.dist.km(z) | ||
| } | ||
| \alias{km} | ||
| \alias{plot.km} | ||
| \alias{plot.intensity.km} | ||
| \alias{plot.dist} | ||
| \alias{plot.dist.km} | ||
| \description{ | ||
| \code{km} calculates the Kaplan-Meier estimates for survival. | ||
|
|
||
| To plot the survivor curve, use \code{plot()}; for the empirical | ||
| intensity curve, use \code{plot.intensity()}; for diagnostic curves to | ||
| choose a distribution to which the data might belong, use \code{plot.dist()}. | ||
| } | ||
| \arguments{ | ||
| \item{times}{Vector of times to events or a list of vectors of such | ||
| times for different individuals.} | ||
| \item{censor}{Vector of censoring indicators corresponding to the | ||
| vector of times or to the last time in each vector of a list.} | ||
| \item{group}{Vector indicating to which group each individual belongs.} | ||
| \item{freq}{Vector of frequencies for grouped data.} | ||
| \item{cdf}{If TRUE, calculate the cdf instead of the survivor curve.} | ||
| \item{z}{An object produced by \code{km}.} | ||
| } | ||
| \value{ | ||
| A matrix with class, \code{km}, containing the Kaplan-Meier estimates | ||
| is returned. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{plot.intensity}}, \code{\link[event]{plot.survivor}} | ||
| } | ||
| \examples{ | ||
| surv <- rgamma(40,2,scale=5) | ||
| cens <- rbinom(40,1,0.9) | ||
| treat <- gl(2,20) | ||
| plot(km(surv, cens, group=treat), main="",xlab="Months", | ||
| ylab="Probability of deterioration") | ||
| plot.dist(km(surv, cens, group=treat)) | ||
| plot.intensity(km(surv, cens, group=treat),ylab="Risk of deterioration") | ||
| } | ||
| \keyword{hplot} | ||
| \name{km} | ||
| \title{Kaplan-Meier Survivor Curves} | ||
| \usage{ | ||
| km(times, censor, group=1, freq=1, cdf=FALSE) | ||
| \method{plot}{km}(x, add=FALSE, xlim=NULL, ylim=c(0,1), | ||
| main=NULL, xlab="Time", ylab=NULL, lty=NULL, ...) | ||
| \method{plot.intensity}{km}(x, add=FALSE, xlab="Time", ylab="Hazard", type="l", lty=NULL, ...) | ||
| \method{plot.dist}{km}(x, ...) | ||
| } | ||
| \alias{km} | ||
| \alias{plot.surv} | ||
| \alias{plot.km} | ||
| \alias{print.km} | ||
| \alias{plot.intensity.km} | ||
| \alias{plot.dist} | ||
| \alias{plot.dist.km} | ||
| \description{ | ||
| \code{km} calculates the Kaplan-Meier estimates for survival. | ||
|
|
||
| To plot the survivor curve, use \code{plot()}; for the empirical | ||
| intensity curve, use \code{plot.intensity()}; for diagnostic curves to | ||
| choose a distribution to which the data might belong, use \code{plot.dist()}. | ||
| } | ||
| \arguments{ | ||
| \item{times}{Vector of times to events or a list of vectors of such | ||
| times for different individuals.} | ||
| \item{censor}{Vector of censoring indicators corresponding to the | ||
| vector of times or to the last time in each vector of a list.} | ||
| \item{group}{Vector indicating to which group each individual belongs.} | ||
| \item{freq}{Vector of frequencies for grouped data.} | ||
| \item{cdf}{If TRUE, calculate the cdf instead of the survivor curve.} | ||
| \item{x}{An object produced by \code{km}.} | ||
| \item{add}{Plotting control options.} | ||
| \item{main}{Plotting control options.} | ||
| \item{type}{Plotting control options.} | ||
| \item{ylab}{Plotting control options.} | ||
| \item{xlab}{Plotting control options.} | ||
| \item{xlim}{Plotting control options.} | ||
| \item{ylim}{Plotting control options.} | ||
| \item{lty}{Plotting control options.} | ||
| \item{...}{Plotting control options.} | ||
| } | ||
| \value{ | ||
| A matrix with class, \code{km}, containing the Kaplan-Meier estimates | ||
| is returned. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{plot.intensity}}, \code{\link[event]{plot.surv}} | ||
| } | ||
| \examples{ | ||
| surv <- rgamma(40,2,scale=5) | ||
| cens <- rbinom(40,1,0.9) | ||
| treat <- gl(2,20) | ||
| plot(km(surv, cens, group=treat), main="",xlab="Months", | ||
| ylab="Probability of deterioration") | ||
| plot.dist(km(surv, cens, group=treat)) | ||
| plot.intensity(km(surv, cens, group=treat),ylab="Risk of deterioration") | ||
| } | ||
| \keyword{hplot} |
| @@ -1,43 +1,52 @@ | ||
| \name{pbirth} | ||
| \title{Fit Overdispersed Count Data as a Birth Process} | ||
| \alias{pbirth} | ||
| \usage{ | ||
| pbirth(frequencies, p, intensity="negative binomial", | ||
| type="spectral decomposition", print.level=0, ndigit=10, | ||
| gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, | ||
| typsiz=abs(p), stepmax=10*sqrt(p\%*\%p)) | ||
| } | ||
| \description{ | ||
| \code{pbirth} fits binomial, binomial exponential, binomial logistic, | ||
| binomial total, Poisson, Poisson exponential, negative binomial, | ||
| gen(eralized) negative binomial, and generalized negative binomial | ||
| processes as a birth process. | ||
| } | ||
| \arguments{ | ||
| \item{frequencies}{Vector of frequencies or a matrix with each row a | ||
| different series of frequencies.} | ||
| \item{p}{Vector of initial estimates.} | ||
| \item{intensity}{The intensity function of the process: binomial, | ||
| binomial exdponential, binomial logistic, binomial total, | ||
| Poisson, Poisson exponential, negative binomial, or gen(eralized) | ||
| negative binomial.} | ||
| \item{type}{Algorithm used for matrix exponentiation: spectral | ||
| decomposition or series approximation.} | ||
| \item{others}{Arguments controlling \code{\link{nlm}}.} | ||
| } | ||
| \references{ | ||
| Faddy, M.J. and Fenlon, J.S. (1999) Stochastic modelling of the | ||
| invasion process of nematodes in fly larvae. Applied Statistics 48: | ||
| 31-37. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \examples{ | ||
| y <- rnbinom(100,2,0.6) | ||
| fr <- tabulate(y) | ||
| pbirth(fr, p=log(-log(0.7)), intensity="Poisson", type="series") | ||
| pbirth(fr, p=c(log(-log(0.7)),log(5)), | ||
| intensity="negative binomial", type="series") | ||
| pbirth(fr, p=c(log(-log(0.7)),log(5),-1), | ||
| intensity="gen negative binomial", type="series") | ||
| } | ||
| \keyword{models} | ||
| \name{pbirth} | ||
| \title{Fit Overdispersed Count Data as a Birth Process} | ||
| \alias{pbirth} | ||
| \alias{deviance.pbirth} | ||
| \alias{print.pbirth} | ||
| \usage{ | ||
| pbirth(frequencies, p, intensity="negative binomial", | ||
| type="spectral decomposition", print.level=0, ndigit=10, | ||
| gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, | ||
| typsize=abs(p), stepmax=10*sqrt(p\%*\%p)) | ||
| } | ||
| \description{ | ||
| \code{pbirth} fits binomial, binomial exponential, binomial logistic, | ||
| binomial total, Poisson, Poisson exponential, negative binomial, | ||
| gen(eralized) negative binomial, and generalized negative binomial | ||
| processes as a birth process. | ||
| } | ||
| \arguments{ | ||
| \item{frequencies}{Vector of frequencies or a matrix with each row a | ||
| different series of frequencies.} | ||
| \item{p}{Vector of initial estimates.} | ||
| \item{intensity}{The intensity function of the process: binomial, | ||
| binomial exdponential, binomial logistic, binomial total, | ||
| Poisson, Poisson exponential, negative binomial, or gen(eralized) | ||
| negative binomial.} | ||
| \item{type}{Algorithm used for matrix exponentiation: spectral | ||
| decomposition or series approximation.} | ||
| \item{print.level}{\code{nlm} control options.} | ||
| \item{ndigit}{\code{nlm} control options.} | ||
| \item{gradtol}{\code{nlm} control options.} | ||
| \item{steptol}{\code{nlm} control options.} | ||
| \item{iterlim}{\code{nlm} control options.} | ||
| \item{fscale}{\code{nlm} control options.} | ||
| \item{typsize}{\code{nlm} control options.} | ||
| \item{stepmax}{\code{nlm} control options.} | ||
| } | ||
| \references{ | ||
| Faddy, M.J. and Fenlon, J.S. (1999) Stochastic modelling of the | ||
| invasion process of nematodes in fly larvae. Applied Statistics 48: | ||
| 31-37. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \examples{ | ||
| y <- rnbinom(100,2,0.6) | ||
| fr <- tabulate(y) | ||
| pbirth(fr, p=log(-log(0.7)), intensity="Poisson", type="series") | ||
| pbirth(fr, p=c(log(-log(0.7)),log(5)), | ||
| intensity="negative binomial", type="series") | ||
| pbirth(fr, p=c(log(-log(0.7)),log(5),-1), | ||
| intensity="gen negative binomial", type="series") | ||
| } | ||
| \keyword{models} |
| @@ -1,35 +1,42 @@ | ||
| \name{plot.intensity} | ||
| \title{Plot Intensity Functions} | ||
| \alias{plot.intensity} | ||
| \usage{ | ||
| plot.intensity(z, ...) | ||
| plot.intensity.default(times, censor=1, group=1, colour=TRUE, mix=1, | ||
| ylim=c(0,1), ylab="p", xlab="Time", | ||
| main="Empirical Intensity Function(s)") | ||
| } | ||
| \description{ | ||
| Plot the empirical intensity curve for given times between events. | ||
| } | ||
| \arguments{ | ||
| \item{times}{Vector of times to events or a list of vectors of such | ||
| times for different individuals.} | ||
| \item{censor}{Vector of censoring indicators corresponding to the | ||
| vector of times or to the last time in each vector of a list.} | ||
| \item{group}{Vector indicating to which group each individual | ||
| belongs.} | ||
| \item{colour}{Use a different colour for each curve.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{km}}, \code{\link[event]{plot.survivor}} | ||
| } | ||
| \examples{ | ||
| surv <- rgamma(40,2,scale=5) | ||
| cens <- rbinom(40,1,0.9) | ||
| treat <- gl(2,20) | ||
| plot(km(surv, cens, group=treat), main="",xlab="Months", | ||
| ylab="Probability of deterioration") | ||
| plot.dist(km(surv, cens, group=treat)) | ||
| plot.intensity(km(surv, cens, group=treat),ylab="Risk of deterioration") | ||
| } | ||
| \keyword{hplot} | ||
| \name{plot.intensity} | ||
| \title{Plot Intensity Functions} | ||
| \alias{plot.intensity} | ||
| \alias{plot.intensity.default} | ||
| \usage{ | ||
| \method{plot}{intensity}(x, ...) | ||
| \method{plot.intensity}{default}(x, censor=1, group=1, colour=TRUE, mix=1, | ||
| ylim=c(0,1), ylab="p", xlab="Time", | ||
| main="Empirical Hazard Function(s)", ...) | ||
| } | ||
| \description{ | ||
| Plot the empirical intensity curve for given times between events. | ||
| } | ||
| \arguments{ | ||
| \item{censor}{Vector of censoring indicators corresponding to the | ||
| vector of times or to the last time in each vector of a list.} | ||
| \item{group}{Vector indicating to which group each individual | ||
| belongs.} | ||
| \item{colour}{Use a different colour for each curve.} | ||
| \item{x}{An object produced by \code{km} for \code{plot.intensity}; for \code{plot.intensity.default} it is \code{times} (Vector of times to events or a list of vectors of such | ||
| times for different individuals.) These changes were made for S3 methods compatability.} | ||
| \item{mix}{...} | ||
| \item{main}{Plotting control options.} | ||
| \item{ylab}{Plotting control options.} | ||
| \item{xlab}{Plotting control options.} | ||
| \item{ylim}{Plotting control options.} | ||
| \item{...}{Plotting control options.} | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{km}}, \code{\link[event]{plot.surv}} | ||
| } | ||
| \examples{ | ||
| surv <- rgamma(40,2,scale=5) | ||
| cens <- rbinom(40,1,0.9) | ||
| treat <- gl(2,20) | ||
| plot(km(surv, cens, group=treat), main="",xlab="Months", | ||
| ylab="Probability of deterioration") | ||
| plot.dist(km(surv, cens, group=treat)) | ||
| plot.intensity(km(surv, cens, group=treat),ylab="Risk of deterioration") | ||
| } | ||
| \keyword{hplot} |
| @@ -1,28 +1,33 @@ | ||
| \name{pp} | ||
| \title{Create a Point Process Vector from Times between Events} | ||
| \alias{pp} | ||
| \usage{ | ||
| pp(y, censor=1) | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{cens}{Vector of censoring indicators.} | ||
| } | ||
| \value{ | ||
| \code{pp} creates a vector of length \code{sum(y)} of zeroes with a one | ||
| at the end of each uncensored time interval for use with | ||
| \code{\link[event]{ehr}}. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{bp}}, \code{\link[event]{ehr}}, | ||
| \code{\link[event]{ident}}, \code{\link[event]{tccov}}, | ||
| \code{\link[event]{tpast}}, \code{\link[event]{ttime}}, | ||
| \code{\link[event]{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| py <- pp(y) | ||
| py | ||
| } | ||
| \keyword{manip} | ||
| \name{pp} | ||
| \title{Create a Point Process Vector from Times between Events} | ||
| \alias{pp} | ||
| \usage{ | ||
| pp(y, censor=1) | ||
| } | ||
| \description{ | ||
| \code{pp} creates a vector of length \code{sum(y)} of zeroes with a one | ||
| at the end of each uncensored time interval for use with | ||
| \code{\link[event]{ehr}}. | ||
| } | ||
| \arguments{ | ||
| \item{y}{Vector of times.} | ||
| \item{censor}{Vector of censoring indicators.} | ||
| } | ||
| \value{ | ||
| \code{pp} creates a vector of length \code{sum(y)} of zeroes with a one | ||
| at the end of each uncensored time interval for use with | ||
| \code{\link[event]{ehr}}. | ||
| } | ||
| \author{J.K. Lindsey} | ||
| \seealso{ | ||
| \code{\link[event]{bp}}, \code{\link[event]{ehr}}, | ||
| \code{\link[event]{ident}}, \code{\link[event]{tccov}}, | ||
| \code{\link[event]{tpast}}, \code{\link[event]{ttime}}, | ||
| \code{\link[event]{tvcov}}. | ||
| } | ||
| \examples{ | ||
| y <- c(5,3,2,4) | ||
| py <- pp(y) | ||
| py | ||
| } | ||
| \keyword{manip} |