PK (version 1.3-6)

all.class: Different generic functions for class PK.

Description

Generic functions for summarizing an object of class PK

Usage

# S3 method for PK
print(x, digits=max(3, getOption("digits") - 4), ...)
# S3 method for PK
summary(object, ...)
# S3 method for PK
plot(x, bygroup=FALSE, col=NULL, pch=NULL, main=NULL, xlab="Time", 
         ylab="Concentration", ylim=NULL, xlim=NULL, add=FALSE, ...)

Value

Screen or graphics output.

Arguments

x

An output object of class PK.

digits

Number of significant digits to be printed.

object

An output object of class PK.

bygroup

A logical value indicating whether the plot should highlight the groups.

col

A specification for the default plotting color (default=NULL). See par for more details.

pch

Either an integer specifying a symbol or a single character to be used as the default in plotting points (default=NULL). See par for more details.

main

An overall title for the plot (default=NULL). The default setting produces "Concentration versus time plot (Design)".

xlab

A title for the x axis (default="time").

ylab

A title for the y axis (default="concentration").

xlim

Numeric vector of length 2, giving the x coordinates range. (default="NULL").

ylim

Numeric vector of length 2, giving the y coordinates range. (default="NULL").

add

A logical value indicating whether to add plot to current plot (default=FALSE).

...

Further (graphical) arguments to be passed to methods.

Author

Thomas Jaki and Martin J. Wolfsegger

Details

print.PK produces a minimal summary of an estimation object from class PK including point estimate, standard error and confidence interval. The confidence interval is the first of "boott", "fieller", "t" or "z" that was originally requested.

summary.PK prints a more detailed summary of an estimation object from class PK. Most notably all confidence intervals originally requested are printed.

plot.PK produces as concentration versus time plot of the data used of an estimation object from class PK.

References

Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis, Chapman and Hall, London.

Holder D. J., Hsuan F., Dixit R. and Soper K. (1999). A method for estimating and testing area under the curve in serial sacrifice, batch, and complete data designs. Journal of Biopharmaceutical Statistics, 9(3):451-464.

Jaki T. and Wolfsegger M. J. (2009). A theoretical framework for estimation of AUCs in complete and incomplete sampling designs. Statistics in Biopharmaceutical Research, 1(2):176-184.

Nedelman J. R., Gibiansky E. and Lau D. T. W. (1995). Applying Bailer's method for AUC confidence intervals to sparse sampling. Pharmaceutical Research, 12(1):124-128.

See Also

estimator, ci and test

Examples

## serial sampling desing: example from Nedelman et al. (1995)
conc <- c(2790, 3280, 4980, 7550, 5500, 6650, 2250, 3220, 213, 636)
time <- c(1, 1, 2, 2, 4, 4, 8, 8, 24, 24)

obj <- auc(conc=conc, time=time, method=c("z", "t"), design="ssd")

print(obj)

summary(obj)

## serial sampling design: example from Nedelman et al. (1995)
conc.m <- c(391, 396, 649, 1990, 3290, 3820, 844, 1650, 75.7, 288)
conc.f <- c(353, 384, 625, 1410, 1020, 1500, 933, 1030, 0, 80.5)
time <- c(1, 1, 2, 2, 4, 4, 8, 8, 24, 24)

res1 <- auc(conc=conc.m, time=time, method=c('t','z'), design='ssd')
res2 <- auc(conc=conc.f, time=time, method=c('t','z'), design='ssd')

plot(res1, pch=19, ylim=c(0,5000), xlim=c(0,25))
plot(res2, pch=21, col='red', add=TRUE)
legend(x=25, y=5000, xjust=1, pch=c(19,21), col=c('black','red'), 
       legend=c('Male', 'Female'))


## batch design: example from Jaki and Wolfsegger (2009),
## originally in Holder et al. (1999) using data for call
data(Rats)

data1 <- subset(Rats,Rats$dose==100)
data2 <- subset(Rats,Rats$dose==300)
res1 <- auc(data=data1,method='t', design='batch')
res2 <- auc(data=data2,method='t', design='batch')

plot(res1, col='black', ylim=c(0,8), xlim=c(0,25))
plot(res2, col='red', add=TRUE)
legend(x=0, y=8, xjust=0, lty=1, col=c('black','red'), 
       legend=c('Dose of 100', 'Dose of 300'))

data3 <- subset(Rats,Rats$dose==100 | Rats$dose==300)
data3$group <- data3$dose
res3 <- auc(data=data3,method='t', design='batch')
plot(res3,bygroup=TRUE)

## complete data design example
## originally in Hand and Crowler (1996)
data(Glucose)
data1 <- subset(Glucose, date==1)
data2 <- subset(Glucose, date==2)
res1 <- auc(conc=data1$conc, time=data1$time, design='complete', method='t')
res2 <- auc(conc=data2$conc, time=data2$time, design='complete', method='t')
plot(res1, pch=19, col='black', ylim=c(0,5))
plot(res2, pch=21, col='red', add=TRUE)

## more informative plot 
plot(x=c(0, 30), y=c(0, 5), type='n', main='Complete Data Design', xlab='Time', 
      ylab='Concentration')
for(i in unique(Glucose$id)){
   for(j in unique(Glucose$date)){
        temp <- subset(Glucose, id==i & date==j)
        col <- ifelse(j==1, 'black', 'red')
        lty <- ifelse(j==1, 1, 2)
        pch <- ifelse(j==1, 19, 21)
	points(x=temp$time, y=temp$conc, col=col, lty=lty, pch=pch, type='b')
   }
}
legend(x=30, y=5, xjust=1, pch=c(19,21), col=c('black','red'), lty=c(1,2), 
       legend=c('Date 1', 'Date 2'))