ci.cum {Epi}R Documentation

Compute cumulative sum of estimates.

Description

Computes the cumulative sum of parameter functions and the standard error of it. Optionally the exponential is applied to the parameter functions before it is cumulated.

Usage

ci.cum( obj,
    ctr.mat = NULL,
     subset = NULL,
       intl = 1,
      alpha = 0.05,
        Exp = TRUE )

Arguments

obj A model object.
ctr.mat Contrast matrix defining the parameter functions from the parameters of the model.
subset Subset of the parameters of the model to which ctr.mat should be applied.
intl Interval length for the cumulation. Either a constant or a numerical vector of length nrow(ctr.mat).
alpha Significance level used when computing confidence limits.
Exp Should the parameter function be exponentiated before it is cumulated?

Details

The purpose of this function is to compute cumulative rate based on a model for the rates. If the model is a multiplicative model for the rates, the purpose of ctr.mat is to return a vector of rates or log-rates when applied to the coefficients of the model. If log-rates are returned, the they should be exponentiated before cumulated, and the variances computed accordingly. Since log-linear models are the most common the Exp parameter defaults to TRUE.

Value

A matrix with 4 columns: Estimate, lower and upper c.i. and standard error.

Author(s)

Bendix Carstensen, http://www.pubhealth.ku.dk/~bxc

See Also

See also ci.lin

Examples

# Packages required for this example
library( splines )
library( survival )
data( lung )
par( mfrow=c(1,2) )

# Plot the Kaplan-meier-estimator
plot( survfit( Surv( time, status==2 ) ~ 1, data=lung ) )

# Declare data as Lexis
lungL <- Lexis( exit=list("tfd"=time),
                exit.status=(status==2)*1, data=lung )
summary( lungL )

# Cut the follow-up every 10 days
sL <- splitLexis( lungL, "tfd", breaks=seq(0,1100,10) )
str( sL )
summary( sL )

# Fit a Poisson model with a natural spline for the effect of time.
# Extract the variables needed
D <- status(sL, "exit")
Y <- dur(sL)
tB <- timeBand( sL, "tfd", "left" )
MM <- ns( tB, knots=c(50,100,200,400,700), intercept=TRUE )
mp <- glm( D ~ MM - 1 + offset(log(Y)),
           family=poisson, eps=10^-8, maxit=25 )

# Contrast matrix to extract effects, i.e. matrix to multiply with the
# coefficients to produce the log-rates: unique rows of MM, in time order.
T.pt <- sort( unique( tB ) )
T.wh <- match( T.pt, tB )
Lambda <- ci.cum( mp, ctr.mat=MM[T.wh,], intl=diff(c(0,T.pt)) )

# Put the estimated survival function on top of the KM-estimator
matlines( c(0,T.pt[-1]), exp(-Lambda[,1:3]), lwd=c(3,1,1), lty=1, col="Red" )

# Extract and plot the fitted intensity function
lambda <- ci.lin( mp, ctr.mat=MM[T.wh,], Exp=TRUE )
matplot( T.pt, lambda[,5:7]*10^3, type="l", lwd=c(3,1,1), col="black", lty=1,
         log="y", ylim=c(0.2,20) )

[Package Epi version 1.0.12 Index]