Title: Basic Calculations for PK/PD Modeling
Version: 0.0.4
Maintainer: Philip Delff <philip@delff.dk>
Description: Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: stats, data.table
Suggests: testthat, ggplot2
BugReports: https://github.com/philipdelff/NMdata/issues
Language: en-US
NeedsCompilation: no
Packaged: 2024-08-26 17:07:33 UTC; philipde
Author: Philip Delff [aut, cre]
Repository: CRAN
Date/Publication: 2024-08-26 18:00:02 UTC

Calculate coefficient of variation of data

Description

Calculate coefficient of variation of data

Usage

CV(x, log = FALSE)

Arguments

x

The data

log

If TRUE, the geometric coefficient of variation is calculated. This is sqrt(exp(var(log(x))-1).

Details

This function is intended to be used on data. For a log-normal THETA1*EXP(ETA(1)) 'Nonmem' parameter, do CV=sqrt(exp(OMEGA[1,1])-1).

Value

A numeric

Examples

set.seed(139)
x1 <- rnorm(1000,mean=5)
CV(x1)
CV(x1,log=TRUE)
x2 <- exp(x1)
CV(x2)
CV(x2,log=TRUE)

CV of log-normal dist baed on omega parameters CV based variance like provided in Nonmem's OMEGA metrix.

Description

CV of log-normal dist baed on omega parameters CV based variance like provided in Nonmem's OMEGA metrix.

Usage

CVlnorm(omega)

Arguments

omega

A variance as provided in diagonal om the Nonmem OMEGA matrix

Details

This is a very simple function. All it does is sqrt(exp(omega)-1).

Value

CV of the distribution (numeric)

Inverse logit function

Description

Inverse logit function

Usage

invlogit(x)

Arguments

x

a number to transform

Value

A numeric

See Also

logit

Logit function

Description

Logit function

Usage

logit(x)

Arguments

x

a number to transform

Value

A numeric

See Also

invlogit

calculate arithmetic or geometric mean and confidence intervals

Description

calculate arithmetic or geometric mean and confidence intervals

Usage

means(
  x,
  type = "arithmetic",
  na.rm = FALSE,
  z.rm = FALSE,
  ci = FALSE,
  dist.ci = "t",
  p.ci = 0.95,
  colnames = c("est", "ll", "ul"),
  format = "df"
)

Arguments

x

vector to calculate the geometric mean of

type

type of mean or median. Default is arithmetic, geometric and median are available as well. Only first letters needed, so say "geo" or even "g" is enough.

na.rm

Remove NA's before doing calculations?

z.rm

removes zeros before calculation? Default is FALSE. Can only be TRUE if type="geometric".

ci

if TRUE, a data.frame including point estimate and confidence interval returned. If FALSE, a numeric representing the mean value returned.

dist.ci

The distribution to use for the confidence interval. Default and only supported is "t". If type=geometric, this is applied after transformation to gaussian.

p.ci

probability covered by confidence interval. Default is 0.95

colnames

If ci, this defines the column names of the resulting data frame. Default is c("est","ll","ul").

format

The format of the result. Possible values are df and num.

Value

If ci=FALSE, a numeric. If ci=TRUE, a data.frame.

Examples

x <- 1:100
means(x, type="arithmetic", ci=TRUE)
means(x, type="geometric", ci=TRUE)
means(x, type="median", ci=TRUE)
library(data.table)
## CRAN requires examples to run on a single thread
data.table::setDTthreads(1)
data.table(x=x)[,append(means(x,ci=TRUE),list(N=.N))]

Bin observations by quantiles. Label by bin number or by interval.

Description

This is simple stuff, but I can never remember the exact quantile and findInterval/cut commands to use. quantbin finds quantiles using quantile and then assigns bins using either findInterval or cut.

Usage

quantbin(x, nbins, probs, label = "num", ...)

Arguments

x

The observations

nbins

Number of bins to use

probs

Quantiles for construvtion of bins (optional). The default is to spread nbins quantiles equi-distantly across the observed values.

label

label="num" gives a numeric bin number (findInterval). label="interval" gives a character representation of the interval (cut).

...

additional arguments passed to quantile.

Details

quantbin uses stats::quantile for quantile estimation. Except for x and probs, all parameters can be controlled using na.rm and ... arguments. See ?stats::quantile for details.

na.rm na.rm=TRUE is needed for quantile to be able to estimate the distribution if x contains NA's. Notice, if na.rm=T, an NA element in x will still result in an NA element in return. If na.rm=F and there are NA's in x, all elements will be NA in result (quantiles cannot be determined, nor can the binning of x by those quantiles).

If data is not continuous, this method may not lead to balanced distributions.

Value

If label="num", integers. If label="interval", factors.

Examples

set.seed(134)
library(data.table)
## CRAN requires examples to run on a single thread
data.table::setDTthreads(1)
dt1 <- data.table(x=rnorm(n=1000))
dt1[,bin:=quantbin(x,nbins=4,label="num")]
dt1[,int:=quantbin(x,nbins=4,label="interval")]
## perfect - flat distribution
dt1[,.N,keyby=.(bin,int)]

dt2 <- data.table(x=c(rnorm(n=100000),NA))
dt2[,bin:=quantbin(x,nbins=4,label="num",na.rm=TRUE)]
dt2[,int:=quantbin(x,nbins=4,label="interval",na.rm=TRUE)]
## perfect - flat distribution
dt2[,.N,keyby=.(bin,int)]
unique(dt2[,.(bin,int)])[order(bin)]


## we may not get a flat distribution in case of discrete observations
dt3 <- data.table(x=c(sample(1:3,100,replace=TRUE)))
dt3[,bin:=quantbin(x,nbins=2,label="num",na.rm=TRUE)]
dt3[,int:=quantbin(x,nbins=2,label="interval",na.rm=TRUE)]
## Not a flat distribution
dt3[,.N,keyby=.(x,bin,int)]

Log-scale equidistant sequences

Description

Useful for generating sequences to be plotted on log scale. This is really simple - seq is run on from and to after log transformation, then the exponential is reported.

Usage

seqlog(from, to, length.out)

Arguments

from

start of sequence

to

end of sequence

length.out

length of sequence

Value

A numeric vector.

Examples

df <- data.frame(x=seqlog(1,100,100))
df <- transform(df, y=x/(10+x))
## Not run: 
library(ggplot2)
## the points are equidistant on the log x scale
ggplot(df,aes(x,y))+geom_point()+scale_x_log10()

## End(Not run)

round to fixed number of significant digits

Description

Even if theoretically correct, the built-in 'R' functions 'round' and 'signif' can be confusing (see examples). 'signif2' is a simple solution that can be used for reporting results consistently.

Usage

signif2(x, digits = 1, add, ...)

Arguments

x

a numeric vector.

digits

number of significant digits to round to. Must be an integer larger than 0.

add

pad with zeros where digits>nchar(x[i]). Currently not used.

...

additional arguments passed to formatC.

Value

A character vector.

Examples

x <- c(1.24e-4,1.1334e6,1.1,22.00000,10.00,1)
data.frame(x,s.3=signif(x,3),sc.3=as.character(signif(x,3)),s2.3=signif2(x,3))
signif2(c(.2,11.84),2)
## digits has no effect when x==0
signif2(0,1)
signif2(0,3)

trapezoidal area under the curve on linear scale

Description

This is a numerical integration of y with respect to x by the trapezoidal method on linear scale.

Usage

trapez(x, y, cum = FALSE, na.rm = FALSE)

Arguments

x

The vector to integrate y with respect to (typically TIME to get area under the curve).

y

The variable to integrate.

cum

Return the cumulative trapezoidal area under the curve? If false (default) a single number is returned. If true, a vector is returned. Notice, the vector is one element shorter than x and y.

na.rm

Remove indexes in x and y wherever x or y are NA.

Value

a numeric