Package 'mlxR' reference manual

Title:	Simulation of Longitudinal Data
Description:	Simulation and visualization of complex models for longitudinal data. The models are encoded using the model coding language 'Mlxtran' and automatically converted into C++ codes. That allows one to implement very easily complex ODE-based models and complex statistical models, including mixed effects models, for continuous, count, categorical, and time-to-event data.
Authors:	Marc Lavielle [aut, cre], Esther Ilinca [ctb], Raphael Kuate [ctb]
Maintainer:	Marc Lavielle <[email protected]>
License:	BSD_2_clause + file LICENSE
Version:	4.2.2
Built:	2024-11-01 03:08:51 UTC
Source:	https://github.com/marclavielle/mlxr

Plot Categorical Longitudinal Data

Description

Plot the empirical distribution of categorical longitudinal data.

Usage

catplotmlx(
  r,
  col = NULL,
  breaks = NULL,
  plot = TRUE,
  color = "#194280",
  group = NULL,
  facet = TRUE,
  labels = NULL
)
catplotmlx(
  r,
  col = NULL,
  breaks = NULL,
  plot = TRUE,
  color = "#194280",
  group = NULL,
  facet = TRUE,
  labels = NULL
)

Arguments

`r`	a data frame with a column ‘⁠id⁠’, a column ‘⁠time⁠’, a column with values and possibly Hk[ja column ‘⁠group⁠’.
`col`	a vector of 3 column numbers: (‘⁠id⁠’, ‘⁠time/x⁠’, ‘⁠y⁠’. Default = c(1, 2,3).
`breaks`	one of: a vector giving the breakpoints, a single number giving the number of segments.
`plot`	if `TRUE` the empirical distribution is displayed, if `FALSE` the values are returned
`color`	a color to be used for the plots (default="#194280")
`group`	variable to be used for defining groups (by default, ‘⁠group⁠’ is used when it exists)
`facet`	makes subplots for different groups if `TRUE`
`labels`	vector of strings

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

a ggplot object if plot=TRUE ; otherwise, a list with fields:

color a vector of colors used for the plot
y a data frame with the values of the empirical distribution computed at each time point

Examples

## Not run: 
  catModel <- inlineModel("
  [LONGITUDINAL]
  input =  {a,b}
  EQUATION:
  lp1=a-b*t
  lp2=a-b*t/2
  DEFINITION:
  y = {type=categorical, categories={1,2,3}, 
  logit(P(y<=1))=lp1, logit(P(y<=2))=lp2}
  ")
  
  y.out  <- list(name='y', time=seq(0, 100, by=4))

  Ng  <- 1000
  g1 <- list(size=Ng, parameter=c(a=6,b=0.2))
  res <- simulx(model=catModel, output=y.out, group=g1)
  catplotmlx(res$y)
  catplotmlx(res$y, breaks=seq(-2,102,by=8), color="purple") 
  catplotmlx(res$y, breaks=5, color="#490917") 
  
  g2 <- list(size=Ng, parameter=c(a=10,b=0.2))
  res <- simulx(model=catModel, output=y.out, group=list(g1,g2))
  catplotmlx(res$y) 
  catplotmlx(res$y, group="none")
  
  g3 <- list(size=Ng, parameter=c(a=6,b=0.4))
  g4 <- list(size=Ng, parameter=c(a=10,b=0.4))
  res <- simulx(model=catModel, output=y.out, group=list(g1,g2,g3,g4))
  catplotmlx(res$y)
   
  cov <- data.frame(id=levels(res$y$id), a=rep(c(6,10,6,10),each=Ng), 
                    b=rep(c(0.2,0.2,0.4,0.4),each=Ng))
  catplotmlx(res$y, group=cov) 

## End(Not run)
## Not run: 
  catModel <- inlineModel("
  [LONGITUDINAL]
  input =  {a,b}
  EQUATION:
  lp1=a-b*t
  lp2=a-b*t/2
  DEFINITION:
  y = {type=categorical, categories={1,2,3}, 
  logit(P(y<=1))=lp1, logit(P(y<=2))=lp2}
  ")
  
  y.out  <- list(name='y', time=seq(0, 100, by=4))

  Ng  <- 1000
  g1 <- list(size=Ng, parameter=c(a=6,b=0.2))
  res <- simulx(model=catModel, output=y.out, group=g1)
  catplotmlx(res$y)
  catplotmlx(res$y, breaks=seq(-2,102,by=8), color="purple") 
  catplotmlx(res$y, breaks=5, color="#490917") 
  
  g2 <- list(size=Ng, parameter=c(a=10,b=0.2))
  res <- simulx(model=catModel, output=y.out, group=list(g1,g2))
  catplotmlx(res$y) 
  catplotmlx(res$y, group="none")
  
  g3 <- list(size=Ng, parameter=c(a=6,b=0.4))
  g4 <- list(size=Ng, parameter=c(a=10,b=0.4))
  res <- simulx(model=catModel, output=y.out, group=list(g1,g2,g3,g4))
  catplotmlx(res$y)
   
  cov <- data.frame(id=levels(res$y$id), a=rep(c(6,10,6,10),each=Ng), 
                    b=rep(c(0.2,0.2,0.4,0.4),each=Ng))
  catplotmlx(res$y, group=cov) 

## End(Not run)

Computation of AUC, Cmax and Cmin

Description

Compute the area under the curve, the maximum and minimum values of a function of time over a given interval or at steady state

Usage

exposure(
  model = NULL,
  output = NULL,
  group = NULL,
  treatment = NULL,
  parameter = NULL,
  data = NULL,
  project = NULL,
  settings = NULL,
  regressor = NULL,
  varlevel = NULL
)
exposure(
  model = NULL,
  output = NULL,
  group = NULL,
  treatment = NULL,
  parameter = NULL,
  data = NULL,
  project = NULL,
  settings = NULL,
  regressor = NULL,
  varlevel = NULL
)

Arguments

`model`	a `Mlxtran` model used for the simulation
`output`	a list with fields: `name`: a vector of output names `time`: = 'steady.state' `ntp`: number of time points used for computing the exposure (default=100) `tol`: tolerance number, between 0 and 1, for approximating steaty-state (default=0.01) `ngc`: number of doses used for estimating the convergence rate to steaty-state (default=5)
`group`	a list, or a list of lists, with fields: `size` : size of the group (default=1), `level` : level(s) of randomization, `parameter` : if different parameters per group are defined, `output` : if different outputs per group are defined, `treatment` : if different treatements per group are defined, `regressor` : if different regression variables per group are defined.
`treatment`	a list with fields `tfd` : time of first dose (default=0), `ii` : inter dose interval (mandatory), `amount` : the amount for each dose, `rate` : the infusion rate (default=`Inf`), `tinf` : the time of infusion (default=0), `type` : the type of input (default=1), `target` : the target compartment (default=NULL).
`parameter`	a vector of parameters with their names and values
`data`	a list
`project`	the name of a Monolix project
`settings`	a list of optional settings `result.file` : name of the datafile where the simulated data is written (string), `seed` : initialization of the random number generator (integer), `load.design` : TRUE/FALSE (if load.design is not defined, a test is automatically performed to check if a new design has been defined), `data.in` : TRUE/FALSE (default=FALSE) `id.out` : add columns id (when N=1) and group (when #group=1), TRUE/FALSE (default=FALSE) `Nmax` : maximum group size used in a single call of mlxCompute (default=100)
`regressor`	a list, or a list of lists, with fields `name` : a vector of regressor names, `time` : a vector of times, `value` : a vector of values.
`varlevel`	a list, or a list of lists, with fields `name` : a vector of names of variability levels, `time` : a vector of times that define the occasions.

Details

Input arguments are the input arguments of Simulx (http://simulx.webpopix.org)

Specific input arguments can be also used for computing the exposure at steady state, i.e. after the administration of an "infinite" number of doses. See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

A list of data frames. One data frame per output is created with columns id (if number of subject >1), group (if number of groups >1), t1 (beginning of time interval), t2 (end of time interval), step (time step), auc (area under the curve), tmax (time of maximum value), cmax (maximum value), tmin (time of minimum value), cmin (minimum value).

mlxR wrapper for ggplot

Description

mlxR wrapper around ggplot with a custom theme

Usage

ggplotmlx(...)
ggplotmlx(...)

Arguments

...

parameters passed to ggplot

Value

see ggplot

Initialize mlxR library

Description

Initialize mlxR library

Usage

initMlxR(path = NULL, ...)
initMlxR(path = NULL, ...)

Arguments

path

(character) [optional] Path to installation directory of the Lixoft suite. If mlxR library is not already loaded and no path is given, the directory written in the lixoft.ini file is used for initialization.

...

[optional] Extra arguments passed to lixoftConnectors package when mlxR is used with a version of Lixoft(/@) software suite higher or equal to 2019R1.

force (bool) [optional] Should mlxR initialization overpass lixoftConnectors software switch security or not. Equals FALSE by default.

Value

A list:

software: the software that is used (should be monolix with Rsmlx)
path: the path to MonolixSuite
version: the version of MonolixSuite that is used
status: boolean equaling TRUE if the initialization has been successful.

Examples

## Not run: 
initMlxR(path = "/path/to/lixoftRuntime/")

## End(Not run)
## Not run: 
initMlxR(path = "/path/to/lixoftRuntime/")

## End(Not run)

inline data frame

Description

utility function to inline creation of a data frame

Usage

inlineDataFrame(str, header = TRUE, colClasses = NA, ...)
inlineDataFrame(str, header = TRUE, colClasses = NA, ...)

Arguments

`str`	text representation of the data frame
`header`	see `read.table`
`colClasses`	see `read.table`
`...`	see `read.table`

inline model

Description

Define a model "inline"

Usage

inlineModel(str, filename = NULL)
inlineModel(str, filename = NULL)

Arguments

`str`	model
`filename`	name of the temporary model file

Details

A temporary model file filename is created. Default name is "tempModel.txt". filename="random" generates a random name.

Value

A Shiny app with files ui.R, server.R and model.txt

Examples

## Not run: 
myModel1 <- inlineModel("
[LONGITUDINAL]
EQUATION:
f = 10*exp(-0.2*t)
")

print(myModel1)

r <- simulx(model=myModel1, output=list(name="f", time=0:100))

myModel2 <- inlineModel("
[LONGITUDINAL]
EQUATION:
f = 10*exp(-0.2*t)
", filename="random")

print(myModel2)

## End(Not run)
## Not run: 
myModel1 <- inlineModel("
[LONGITUDINAL]
EQUATION:
f = 10*exp(-0.2*t)
")

print(myModel1)

r <- simulx(model=myModel1, output=list(name="f", time=0:100))

myModel2 <- inlineModel("
[LONGITUDINAL]
EQUATION:
f = 10*exp(-0.2*t)
", filename="random")

print(myModel2)

## End(Not run)

Kaplan Meier plot

Description

Plot empirical survival functions using the Kaplan Meier estimate.

Usage

kmplotmlx(
  r,
  index = 1,
  level = NULL,
  time = NULL,
  cens = TRUE,
  plot = TRUE,
  color = "#e05969",
  group = NULL,
  facet = TRUE,
  labels = NULL
)
kmplotmlx(
  r,
  index = 1,
  level = NULL,
  time = NULL,
  cens = TRUE,
  plot = TRUE,
  color = "#e05969",
  group = NULL,
  facet = TRUE,
  labels = NULL
)

Arguments

`r`	a data frame with a column ‘⁠id⁠’, a column ‘⁠time⁠’, a column with values and possibly a column ‘⁠group⁠’.
`index`	an integer: `index=k` means that the survival function for the k-th event is displayed. Default is `index=1`.
`level`	a number between 0 and 1: confidence interval level.
`time`	a vector of time points where the survival function is evaluated.
`cens`	if `TRUE` right censoring times are diplayed.
`plot`	if `TRUE` the estimated survival function is displayed, if `FALSE` the values are returned
`color`	color to be used for the plots (default="#e05969")
`group`	variable to be used for defining groups (by default, ‘⁠group⁠’ is used when it exists)
`facet`	makes subplots for different groups if `TRUE`
`labels`	vector of strings

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

a ggplot object if plot=TRUE ; otherwise, a list with fields:

surv a data frame with columns T (time), S (survival), possibly (S1, S2) (confidence interval) and possibly group
cens a data frame with columns T0 (time), S0 (survival) and possibly group

Examples

## Not run: 
tteModel1 <- inlineModel("
  [LONGITUDINAL]
  input = {beta,lambda}  
  EQUATION:
  h=(beta/lambda)*(t/lambda)^(beta-1)
  DEFINITION:
  e = {type=event, maxEventNumber=1, rightCensoringTime=70, hazard=h}
  ")

  p1   <- c(beta=2.5,lambda=50)
  e    <- list(name='e', time=0)
  res1 <- simulx(model=tteModel1, parameter=p1, output=e, group=list(size=100))
  pl1  <- kmplotmlx(res1$e,level=0.95)
  print(pl1)

  p2   <- c(beta=2,lambda=45)
  g1   <- list(size=50, parameter=p1)
  g2   <- list(size=100, parameter=p2)
  res2 <- simulx(model=tteModel1, output=e, group=list(g1,g2))
  pl2  <- kmplotmlx(res2$e)
  print(pl2)

## End(Not run)
## Not run: 
tteModel1 <- inlineModel("
  [LONGITUDINAL]
  input = {beta,lambda}  
  EQUATION:
  h=(beta/lambda)*(t/lambda)^(beta-1)
  DEFINITION:
  e = {type=event, maxEventNumber=1, rightCensoringTime=70, hazard=h}
  ")

  p1   <- c(beta=2.5,lambda=50)
  e    <- list(name='e', time=0)
  res1 <- simulx(model=tteModel1, parameter=p1, output=e, group=list(size=100))
  pl1  <- kmplotmlx(res1$e,level=0.95)
  print(pl1)

  p2   <- c(beta=2,lambda=45)
  g1   <- list(size=50, parameter=p1)
  g2   <- list(size=100, parameter=p2)
  res2 <- simulx(model=tteModel1, output=e, group=list(g1,g2))
  pl2  <- kmplotmlx(res2$e)
  print(pl2)

## End(Not run)

Read Lixoft@ files

Description

Utility function to read Lixoft@ formated input/output files

Usage

lixoft.read.table(file, sep = "", ...)
lixoft.read.table(file, sep = "", ...)

Arguments

`file`	file path of the file to read
`sep`	separator
`...`	see `read.table`

Explore and visualize models

Description

Explore and visualize ‘⁠Mlxtran⁠’ models with the ‘⁠Mlxplore⁠’ software.

Usage

mlxplore(
  model,
  parameter = NULL,
  output = NULL,
  group = NULL,
  treatment = NULL
)
mlxplore(
  model,
  parameter = NULL,
  output = NULL,
  group = NULL,
  treatment = NULL
)

Arguments

`model`	a `Mlxtran` model
`parameter`	a vector of parameters with their names and values
`output`	a list with fields: `name`: a vector of output names `time`: a vector of times
`group`	a list with unique field: `treatment` : a list,
`treatment`	a list with fields `time` : a vector of input times, `amount` : a scalar or a vector of amounts, `rate` : a scalar or a vector of infusion rates (default=`Inf`), `type` : the type of input (default=1), `target` : the target compartment (default=NULL).

Details

See http://simulx.webpopix.org/mlxr/mlxplore/ for more details.

Convert a Monolix Project into an executable for the simulator Simulx

Description

Convert a Monolix Project into an executable for the simulator Simulx

Usage

monolix2simulx(
  project,
  parameter = NULL,
  group = NULL,
  open = FALSE,
  r.data = TRUE,
  fim = NULL
)
monolix2simulx(
  project,
  parameter = NULL,
  group = NULL,
  open = FALSE,
  r.data = TRUE,
  fim = NULL
)

Arguments

`project`	: the name of a Monolix project
`parameter`	: string $(NameOfTypeOfParameter), the type of specific parameters to use example: "mode", "mean"...
`group`	: a list with the number of subjects
`open`	: load the R script created if `open=TRUE`
`r.data`	: read the data if `r.data=TRUE`
`fim`	: Fisher information matrix

Value

creates a folder projectNameR containing files :

projectName.R : executable R code for the simulator,
treatment.txt : contains the treatment informations,
populationParameter.txt : contains the population parameters estimated from Monolix,
individualParameter.txt : contains the individual parameters (mode/mean) estimated from Monolix (if used for the simulation),
individualCovariate.txt : contains the individual covariates,
originalId.txt : contains the original id's when group is used with a different size than the original one,
outputi.txt : contains the output number i informations (time, id),
$(NameOfTypeOfParameter)s.txt : contains the specific parameter used.

Examples

## Not run: 
project.file <- 'monolixRuns/theophylline1_project.mlxtran'  #relative path
monolix2simulx(project=project.file,open=TRUE)
monolix2simulx(project=project.file,parameter=list("mean",c(a=0, b=0)),open=TRUE)

## End(Not run)
## Not run: 
project.file <- 'monolixRuns/theophylline1_project.mlxtran'  #relative path
monolix2simulx(project=project.file,open=TRUE)
monolix2simulx(project=project.file,parameter=list("mean",c(a=0, b=0)),open=TRUE)

## End(Not run)

Easy simulation of PK models Easy simulation of basic PK models See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Description

Easy simulation of PK models

Easy simulation of basic PK models

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Usage

pkmodel(time, treatment, parameter)
pkmodel(time, treatment, parameter)

Arguments

time

a vector

treatment

a list with fields

time : a vector of input times,
amount : a scalar or a vector of amounts,
rate : a scalar or a vector of infusion rates (default=Inf),
tinf : a scalar or a vector of infusion times (default=0),

parameter

vector of parameters with their names and values

Examples

   
## Not run: 
  adm <- list(time=c(2,14,20), amount=40)
  p   <- c(V=8, Cl=0.5,k12=0.3, k21=0.2)
  t   <- seq(0, 30, by=0.1)
  
  res   <- pkmodel(time = t, treatment = adm, parameter = p)
  
  print(ggplot(data=res, aes(x=time, y=cc)) + geom_line(size=1) +
    xlab("time (h)") + ylab("concentration (mg/L)"))
  
  adm <- list(time = c(1,23,37,45), amount = c(1,0.5,2,0.3))
  p <- c(Mtt=5, Ktr=1, ka=0.5, V=10, Vm=1, Km=0.6, p=0.5)
  t <- seq(0, 80, by=0.1)
  
  res <- pkmodel(t,adm,p)
  
  print(ggplot(data=res, aes(x=time, y=cc)) + geom_line(size=1) +
    xlab("time (h)") + ylab("concentration (mg/L)"))
  
  adm <- list( time = 2, amount = 40)
  
  p <- inlineDataFrame("
  id   ka   V    Cl
  1   0.5   4     1
  2     1   6     1
  3   1.5   6   1.5
  ")
  
  t <- seq(0, 30, by=0.1)
  
  res <- pkmodel(t,adm,p)
  
  print(ggplot(data=res, aes(x=time, y=cc, colour=id)) + geom_line(size=1) +
    xlab("time (h)") + ylab("concentration (mg/L)"))   
  adm <- list(time=seq(2, 100, by=24), amount=40, rate=5)
  p <- c(V=8, Cl=0.5, k12=0.3, k21=0.2, ke0=0.2)
  t <- seq(0, 50, by=0.1)
  
  res <- pkmodel(t,adm,p)
  
  if( require("reshape2") ){
    r <- melt(res, id='time', variable.name='c')
    print(ggplot(r, aes(time,value)) + geom_line(aes(colour = c),size=1) +
            ylab('concentration') + guides(colour=guide_legend(title=NULL)) +
            theme(legend.position=c(.9, .8)))
  } 

## End(Not run)
## Not run: 
  adm <- list(time=c(2,14,20), amount=40)
  p   <- c(V=8, Cl=0.5,k12=0.3, k21=0.2)
  t   <- seq(0, 30, by=0.1)
  
  res   <- pkmodel(time = t, treatment = adm, parameter = p)
  
  print(ggplot(data=res, aes(x=time, y=cc)) + geom_line(size=1) +
    xlab("time (h)") + ylab("concentration (mg/L)"))
  
  adm <- list(time = c(1,23,37,45), amount = c(1,0.5,2,0.3))
  p <- c(Mtt=5, Ktr=1, ka=0.5, V=10, Vm=1, Km=0.6, p=0.5)
  t <- seq(0, 80, by=0.1)
  
  res <- pkmodel(t,adm,p)
  
  print(ggplot(data=res, aes(x=time, y=cc)) + geom_line(size=1) +
    xlab("time (h)") + ylab("concentration (mg/L)"))
  
  adm <- list( time = 2, amount = 40)
  
  p <- inlineDataFrame("
  id   ka   V    Cl
  1   0.5   4     1
  2     1   6     1
  3   1.5   6   1.5
  ")
  
  t <- seq(0, 30, by=0.1)
  
  res <- pkmodel(t,adm,p)
  
  print(ggplot(data=res, aes(x=time, y=cc, colour=id)) + geom_line(size=1) +
    xlab("time (h)") + ylab("concentration (mg/L)"))   
  adm <- list(time=seq(2, 100, by=24), amount=40, rate=5)
  p <- c(V=8, Cl=0.5, k12=0.3, k21=0.2, ke0=0.2)
  t <- seq(0, 50, by=0.1)
  
  res <- pkmodel(t,adm,p)
  
  if( require("reshape2") ){
    r <- melt(res, id='time', variable.name='c')
    print(ggplot(r, aes(time,value)) + geom_line(aes(colour = c),size=1) +
            ylab('concentration') + guides(colour=guide_legend(title=NULL)) +
            theme(legend.position=c(.9, .8)))
  } 

## End(Not run)

Percentiles of the empiricial distribution of longitudinal data

Description

Compute and display percentiles of the empiricial distribution of longitudinal data.

Usage

prctilemlx(
  r,
  col = NULL,
  number = 8,
  level = 80,
  plot = TRUE,
  color = "#9a35ff",
  group = NULL,
  facet = TRUE,
  labels = NULL,
  band = NULL
)
prctilemlx(
  r,
  col = NULL,
  number = 8,
  level = 80,
  plot = TRUE,
  color = "#9a35ff",
  group = NULL,
  facet = TRUE,
  labels = NULL,
  band = NULL
)

Arguments

`r`	a data frame with a column ‘⁠id⁠’, a column ‘⁠time⁠’ and a column with values. The times should be the same for each individual.
`col`	a vector with the three column indexes for ‘⁠id⁠’, ‘⁠time/x⁠’ and ‘⁠y⁠’. Default = c(1, 2,3).
`number`	the number of intervals (i.e. the number of percentiles minus 1).
`level`	the largest interval (i.e. the difference between the lowest and the highest percentile).
`plot`	if `TRUE` the empirical distribution is displayed, if `FALSE` the values are returned
`color`	a color to be used for the plots (default="#9a35ff")
`group`	variable to be used for defining groups (by default, ‘⁠group⁠’ is used when it exists)
`facet`	makes subplots for different groups if `TRUE`
`labels`	vector of strings
`band`	is deprecated (use number and level instead) ; a list with two fields `number` the number of intervals (i.e. the number of percentiles minus 1). `level` the largest interval (i.e. the difference between the lowest and the highest percentile).

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

a ggplot object if plot=TRUE ; otherwise, a list with fields:

proba a vector of probabilities of length band$number+1
color a vector of colors used for the plot of length band$number
y a data frame with the values of the empirical percentiles computed at each time point

Examples

## Not run: 
  myModel <- inlineModel("
  [LONGITUDINAL]
  input = {ka, V, Cl}
  EQUATION:
  C = pkmodel(ka,V,Cl)
  
  [INDIVIDUAL]
  input = {ka_pop, V_pop, Cl_pop, omega_ka, omega_V, omega_Cl}
  DEFINITION:
  ka = {distribution=lognormal, reference=ka_pop, sd=omega_ka}
  V  = {distribution=lognormal, reference=V_pop,  sd=omega_V }
  Cl = {distribution=lognormal, reference=Cl_pop, sd=omega_Cl}
  ")
  
  N=2000
  
  pop.param   <- c(
    ka_pop  = 1,    omega_ka  = 0.5,
    V_pop   = 10,   omega_V   = 0.4,
    Cl_pop  = 1,    omega_Cl  = 0.3)
    
  res <- simulx(model     = myModel,
                parameter = pop.param,
                treatment = list(time=0, amount=100),
                group     = list(size=N, level='individual'),
                output    = list(name='C', time=seq(0,24,by=0.1)))
  # res$C is a data.frame with 2000x241=482000 rows and 3 columns
  head(res$C)
  # we can display the empirical percentiles of C using the default 
  # settings (i.e. percentiles of order 10%, 20%, ... 90%)
  prctilemlx(res$C)
  # The 3 quartiles (i.e. percentiles of order 25%, 50% and 75%) are displayed by 
  # selecting a 50% interval splitted into 2 subintervals
  prctilemlx(res$C, number=2, level=50)
  # A one 90% interval can be displayed using only one interval
  prctilemlx(res$C, number=1, level=90)
  # or 75 subintervals in order to better represent the continuous distribution 
  # of the data within this interval
  prctilemlx(res$C, number=75, level=90)
  # prctilemlx produces a ggplot object that can be modified
  pl <- prctilemlx(res$C, number=4, level=80) 
  pl + ylab("concentration") + ggtitle("predictive distribution")
  # The percentiles are not plotted by setting plot=FALSE
  pr.out <- prctilemlx(res$C, number=4, level=80, plot=FALSE)
  print(pr.out$proba)
  print(pr.out$color)
  print(pr.out$y[1:5,])

## End(Not run)
## Not run: 
  myModel <- inlineModel("
  [LONGITUDINAL]
  input = {ka, V, Cl}
  EQUATION:
  C = pkmodel(ka,V,Cl)
  
  [INDIVIDUAL]
  input = {ka_pop, V_pop, Cl_pop, omega_ka, omega_V, omega_Cl}
  DEFINITION:
  ka = {distribution=lognormal, reference=ka_pop, sd=omega_ka}
  V  = {distribution=lognormal, reference=V_pop,  sd=omega_V }
  Cl = {distribution=lognormal, reference=Cl_pop, sd=omega_Cl}
  ")
  
  N=2000
  
  pop.param   <- c(
    ka_pop  = 1,    omega_ka  = 0.5,
    V_pop   = 10,   omega_V   = 0.4,
    Cl_pop  = 1,    omega_Cl  = 0.3)
    
  res <- simulx(model     = myModel,
                parameter = pop.param,
                treatment = list(time=0, amount=100),
                group     = list(size=N, level='individual'),
                output    = list(name='C', time=seq(0,24,by=0.1)))
  # res$C is a data.frame with 2000x241=482000 rows and 3 columns
  head(res$C)
  # we can display the empirical percentiles of C using the default 
  # settings (i.e. percentiles of order 10%, 20%, ... 90%)
  prctilemlx(res$C)
  # The 3 quartiles (i.e. percentiles of order 25%, 50% and 75%) are displayed by 
  # selecting a 50% interval splitted into 2 subintervals
  prctilemlx(res$C, number=2, level=50)
  # A one 90% interval can be displayed using only one interval
  prctilemlx(res$C, number=1, level=90)
  # or 75 subintervals in order to better represent the continuous distribution 
  # of the data within this interval
  prctilemlx(res$C, number=75, level=90)
  # prctilemlx produces a ggplot object that can be modified
  pl <- prctilemlx(res$C, number=4, level=80) 
  pl + ylab("concentration") + ggtitle("predictive distribution")
  # The percentiles are not plotted by setting plot=FALSE
  pr.out <- prctilemlx(res$C, number=4, level=80, plot=FALSE)
  print(pr.out$proba)
  print(pr.out$color)
  print(pr.out$y[1:5,])

## End(Not run)

Reads a table into a vector

Description

Reads a table into a vector

Usage

read.vector(f, header = FALSE, sep = "", quote = "\"'")
read.vector(f, header = FALSE, sep = "", quote = "\"'")

Arguments

`f`	: the table
`header`	: bool, use the header or not
`sep`	: the separator
`quote`	: the quote character

Value

the vector

Read formatted data file

Description

Read data in a Monolix/NONMEM format

Usage

readDatamlx(
  project = NULL,
  data = NULL,
  out.data = FALSE,
  nbSSDoses = 10,
  obs.rows = FALSE,
  error.iov = FALSE,
  filter = NULL,
  datafile = NULL,
  header = NULL,
  infoProject = NULL,
  addl.ss = NULL
)
readDatamlx(
  project = NULL,
  data = NULL,
  out.data = FALSE,
  nbSSDoses = 10,
  obs.rows = FALSE,
  error.iov = FALSE,
  filter = NULL,
  datafile = NULL,
  header = NULL,
  infoProject = NULL,
  addl.ss = NULL
)

Arguments

`project`	a Monolix project
`data`	a list with fields `dataFile`: path of a formatted data file `headerTypes`: a vector of strings
`out.data`	TRUE/FALSE (default=FALSE) returns the original data as a table and some information about the Monolix project
`nbSSDoses`	number of additional doses to use for steady-state (default=10)
`obs.rows`	a list of observation indexes
`error.iov`	TRUE/FALSE (default=TRUE) returns an error message if occasions are overlapping
`filter`	filter to apply to the data (string)
`datafile`	(deprecated) a formatted data file
`header`	(deprecated) a vector of strings
`infoProject`	(deprecated) an xmlfile
`addl.ss`	(deprecated) number of additional doses to use for steady-state (default=10)

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

A list of data frames

Examples

## Not run: 
# using a Monolix project:
d <- readDatamlx(project='projects/warfarinPK.mlxtran')


# using a data file:
warfarinPK <- list(dataFile = "data/warfarinPK.csv",
                   headerTypes = c("id", "time", "observation", "amount", 
                                   "contcov", "contcov", "catcov"),
                   administration = "oral")
d <- readDatamlx(data=warfarinPK)


## End(Not run)
## Not run: 
# using a Monolix project:
d <- readDatamlx(project='projects/warfarinPK.mlxtran')


# using a data file:
warfarinPK <- list(dataFile = "data/warfarinPK.csv",
                   headerTypes = c("id", "time", "observation", "amount", 
                                   "contcov", "contcov", "catcov"),
                   administration = "oral")
d <- readDatamlx(data=warfarinPK)


## End(Not run)

Automatic code generation for Shiny applications

Description

Creates a Shiny application for longitudinal data model

Usage

shinymlx(
  model,
  parameter = NULL,
  output = NULL,
  treatment = NULL,
  regressor = NULL,
  group = NULL,
  data = NULL,
  appname = "shinymlxApp",
  style = "basic",
  settings = NULL,
  title = " "
)
shinymlx(
  model,
  parameter = NULL,
  output = NULL,
  treatment = NULL,
  regressor = NULL,
  group = NULL,
  data = NULL,
  appname = "shinymlxApp",
  style = "basic",
  settings = NULL,
  title = " "
)

Arguments

`model`	a `Mlxtran` model used for the simulation
`parameter`	a vector, or a list of shiny widgets
`output`	a list - or a list of lists - with fields: `name`: a vector of output names `time`: a vector of times, or a vector (min, max, step)
`treatment`	a list with fields `tfd` : first time of dose, `amount` : amount, `nd` : number of doses, `ii` : interdose interval, `type` : the type of input, Input argument of Simulx can also be used, i.e. a list with fields `time`, `amount`, `rate`, `tinf`, `type`, `target`.
`regressor`	a list, or a list of lists, with fields `name` : a vector of regressor names, `time` : a vector of times, `value` : a vector of values.
`group`	a list, or a list of lists, with fields: `size` : size of the group (default=1), `level` : level(s) of randomization, `parameter` : if different parameters per group are defined, `output` : if different outputs per group are defined, `treatment` : if different treatements per group are defined, `regressor` : if different regression variables per group are defined.
`data`	data to display with the plot (either a data frame or the name of a file)
`appname`	the name of the application (and possibly its path)
`style`	the style of the Shiny app `"basic"`: basic Shiny app with a single side bar (default) `"navbar1"`: navigation bar and tabPanels (including outputs) `"navbar2"`: navigation bar and tabPanels (outputs separated) `"dashboard1"` : Shiny dashboard,
`settings`	a list of settings `"tabstyle"` : look of the tabs c("tabs","pills"), `"select.x"` : display the list of variables available for the x-axis c(TRUE,FALSE), `"select.y"` : display the list of variables available for the y-axis c(TRUE,FALSE), `"select.log"` : log scale option c(TRUE,FALSE), `"select.ref"` : reference curves option c(TRUE,FALSE)
`title`	the title of the application

Details

shinymlx automatically generates files ui.R and server.R required for a Shiny application.

Elements of parameters and treatment can be either scalars or lists. A scalar automatically generates a slider with default minimum and maximum values and default step. A list may contain the type of widget ("slider", "select", "numeric") and the settings defining the widget: (value, min, max, step) for slider, (selected, choices) for select and value for numeric.

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

A Shiny app with files ui.R, server.R and model.txt

Examples

## Not run: 
library(mlxR)
PKPDmodel <- inlineModel("
[LONGITUDINAL] 
input={ka,V,Cl,Imax,IC50,S0,kout}
EQUATION:
C     = pkmodel(ka, V, Cl)
E_0   = S0 
ddt_E = kout*((1-Imax*C/(C+IC50))*S0- E)  
")

p1  <- c(ka=0.5, V=10, Cl=1)
p2  <- c(Imax=0.5, IC50=0.03, S0=100, kout=0.1)
adm <- list(tfd=5, nd=15, ii=12, amount=1)
f1  <- list(name = 'C', time = seq(0, 250, by=1))
f2  <- list(name = 'E', time = seq(0, 250, by=1))
f   <- list(f1, f2)

shinymlx(model=PKPDmodel, treatment=adm, parameter=list(p1,p2), output=f,
         style="dashboard1")

#------------------------------------------------------------------------
p1 <- list(
  ka    = list(widget="slider", value=0.5, min=0.1, max=2,  step=0.1),
  V     = list(widget="slider", value=10,  min=2,   max=20, step=2),
  Cl    = list(widget="slider", value=1,   min=0.1, max=2,  step=0.1)
)  
adm <- list(
  tfd    = list(widget="slider", value=5,  min=0, max=100, step=5),
  nd     = list(widget="numeric", value=15),
  ii     = list(widget="select", selected=12, choices=c(3,6,12,18,24)),
  amount = list(widget="slider", value=40, min=0, max=50, step=5)
)
s <- list(select.x=FALSE, select.y=FALSE)
shinymlx(model=PKPDmodel, treatment=adm, parameter=list(p1,p2), output=f, 
         style="navbar1", settings=s)

## End(Not run)
## Not run: 
library(mlxR)
PKPDmodel <- inlineModel("
[LONGITUDINAL] 
input={ka,V,Cl,Imax,IC50,S0,kout}
EQUATION:
C     = pkmodel(ka, V, Cl)
E_0   = S0 
ddt_E = kout*((1-Imax*C/(C+IC50))*S0- E)  
")

p1  <- c(ka=0.5, V=10, Cl=1)
p2  <- c(Imax=0.5, IC50=0.03, S0=100, kout=0.1)
adm <- list(tfd=5, nd=15, ii=12, amount=1)
f1  <- list(name = 'C', time = seq(0, 250, by=1))
f2  <- list(name = 'E', time = seq(0, 250, by=1))
f   <- list(f1, f2)

shinymlx(model=PKPDmodel, treatment=adm, parameter=list(p1,p2), output=f,
         style="dashboard1")

#------------------------------------------------------------------------
p1 <- list(
  ka    = list(widget="slider", value=0.5, min=0.1, max=2,  step=0.1),
  V     = list(widget="slider", value=10,  min=2,   max=20, step=2),
  Cl    = list(widget="slider", value=1,   min=0.1, max=2,  step=0.1)
)  
adm <- list(
  tfd    = list(widget="slider", value=5,  min=0, max=100, step=5),
  nd     = list(widget="numeric", value=15),
  ii     = list(widget="select", selected=12, choices=c(3,6,12,18,24)),
  amount = list(widget="slider", value=40, min=0, max=50, step=5)
)
s <- list(select.x=FALSE, select.y=FALSE)
shinymlx(model=PKPDmodel, treatment=adm, parameter=list(p1,p2), output=f, 
         style="navbar1", settings=s)

## End(Not run)

Population parameters simulation

Description

Draw population parameters using the covariance matrix of the estimates.

Usage

simpopmlx(
  n = 1,
  project = NULL,
  fim = "needed",
  parameter = NULL,
  corr = NULL,
  kw.max = 100
)
simpopmlx(
  n = 1,
  project = NULL,
  fim = "needed",
  parameter = NULL,
  corr = NULL,
  kw.max = 100
)

Arguments

`n`	the number of vectors of population parameters (default = 1),
`project`	a Monolix project, assuming that the Fisher information Matrix was estimated by Monolix.
`fim`	the Fisher Information Matrix estimated by Monolix. fim="sa", "lin" (default="sa")
`parameter`	a data frame with a column ‘⁠pop.param⁠’ (no default), a column ‘⁠sd⁠’ (no default), possibly a column ‘⁠trans⁠’ (default ='N') and possibly columns ‘⁠lim.a⁠’ (default=0) and ‘⁠lim.b⁠’ (default=1). Only when project is not used.
`corr`	correlation matrix of the population parameters (default = identity). Only when project is not used.
`kw.max`	maximum number of trials for generating a positive definite covariance matrix (default = 100)

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Examples

## Not run: 
project.file <- 'monolixRuns/theophylline1_project.mlxtran'  #relative path
pop1 <- simpopmlx(n=3, project=project.file)

## End(Not run)
## Not run: 
project.file <- 'monolixRuns/theophylline1_project.mlxtran'  #relative path
pop1 <- simpopmlx(n=3, project=project.file)

## End(Not run)

Simulation of mixed effects models and longitudinal data

Description

Compute predictions and sample data from Mlxtran and R models

Usage

simulx(
  model = NULL,
  parameter = NULL,
  output = NULL,
  treatment = NULL,
  regressor = NULL,
  varlevel = NULL,
  group = NULL,
  data = NULL,
  project = NULL,
  nrep = 1,
  npop = NULL,
  fim = NULL,
  result.folder = NULL,
  result.file = NULL,
  stat.f = "statmlx",
  addlines = NULL,
  settings = NULL
)
simulx(
  model = NULL,
  parameter = NULL,
  output = NULL,
  treatment = NULL,
  regressor = NULL,
  varlevel = NULL,
  group = NULL,
  data = NULL,
  project = NULL,
  nrep = 1,
  npop = NULL,
  fim = NULL,
  result.folder = NULL,
  result.file = NULL,
  stat.f = "statmlx",
  addlines = NULL,
  settings = NULL
)

Arguments

`model`	a `Mlxtran`, or `R` model used for the simulation
`parameter`	a vector of parameters with their names and values
`output`	a list (or list of lists) with fields: `name`: a vector of output names `time`: a vector of times (only for the longitudinal outputs) `lloq`: lower limit of quantification (only for the longitudinal outputs) `uloq`: upper limit of quantification (only for the longitudinal outputs) `limit`: lower bound of the censoring interval (only for the longitudinal outputs)
`treatment`	a list with fields `time` : a vector of input times, `amount` : a scalar or a vector of amounts, `rate` : a scalar or a vector of infusion rates (default=`Inf`), `tinf` : a scalar or a vector of infusion times (default=0), `type` : the type of input (default=1), `target` : the target compartment (default=NULL).
`regressor`	a list, or a list of lists, with fields `name` : a vector of regressor names, `time` : a vector of times, `value` : a vector of values.
`varlevel`	(IOV supported by mlxR >= 3.2.2) a list (or a dataframe) with fields: `name` : name of the variable which defines the occasions, `time` : a vector of times (beginnings of occasions) ,
`group`	a list, or a list of lists, with fields: `size` : size of the group (default=1), `level` : level(s) of randomization, `parameter` : if different parameters per group are defined, `output` : if different outputs per group are defined, `treatment` : if different treatements per group are defined, `regressor` : if different regression variables per group are defined.
`data`	a list (output of simulx when settings$data.in==TRUE)
`project`	the name of a Monolix project
`nrep`	number of replicates
`npop`	number of population parameters to draw randomly
`fim`	a string with the Fisher Information Matrix to be used
`result.folder`	the name of the folder where the outputs of simulx should be stored
`result.file`	the name of the single file where the outputs of simulx should be saved
`stat.f`	a R function for computing some summary (mean, quantiles, survival,...) of the simulated data. Default = "statmlx".
`addlines`	a list with fields: `section`: a string (default = "[LONGITUDINAL]"), `block`: a string (default = "EQUATION:"), `formula`: string, or vector of strings, to be inserted .
`settings`	a list of optional settings `seed` : initialization of the random number generator (integer), `load.design` : TRUE/FALSE (if load.design is not defined, a test is automatically performed to check if a new design has been defined), `data.in` : TRUE/FALSE (default=FALSE) `id.out` : add columns id (when N=1) and group (when #group=1), TRUE/FALSE (default=FALSE) `kw.max` : maximum number of trials for generating a positive definite covariance matrix (default = 100) `sep` : the field separator character (default = ",") `digits` : number of decimal digits in output files (default = 5) `disp.iter` : TRUE/FALSE (default = FALSE) display replicate and population numbers `replacement` : TRUE/FALSE (default = FALSE) sample id's with/without replacement `out.trt` : TRUE/FALSE (default = TRUE) output of simulx includes treatment `format.original` : TRUE/FALSE (default = FALSE) with a Monolix project, write data in result.file using the original format of the data file

Details

simulx takes advantage of the modularity of hierarchical models for simulating different components of a model: models for population parameters, individual covariates, individual parameters and longitudinal data.

Furthermore, simulx allows to draw different types of longitudinal data, including continuous, count, categorical, and time-to-event data.

The models are encoded using either the model coding language ‘⁠Mlxtran⁠’, or ‘⁠R⁠’. ‘⁠Mlxtran⁠’ models are automatically converted into C++ codes, compiled on the fly and linked to R using the ‘⁠Rcpp⁠’ package. That allows one to implement very easily complex models and to take advantage of the numerical sovers used by the C++ ‘⁠mlxLibrary⁠’.

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

A list of data frames. Each data frame is an output of simulx

Examples

## Not run: 
myModel <- inlineModel("
[LONGITUDINAL]
input = {A, k, c, a}
EQUATION:
t0    = 0 
f_0   = A
ddt_f = -k*f/(c+f)
DEFINITION:
y = {distribution=normal, prediction=f, sd=a}
[INDIVIDUAL]
input = {k_pop, omega}
DEFINITION:
k = {distribution=lognormal, prediction=k_pop, sd=omega}
")
f <- list(name='f', time=seq(0, 30, by=0.1))
y <- list(name='y', time=seq(0, 30, by=2))
res <- simulx(model     = 'model/home.txt', 
              parameter = c(A=100, k_pop=6, omega=0.3, c=10, a=2), 
              output    = list(f,y,"k"),
              group     = list(size=4, level='individual'))

plot(ggplotmlx() + geom_line(data=res$f, aes(x=time, y=f, colour=id)) +
     geom_point(data=res$y, aes(x=time, y=y, colour=id)))
print(res$parameter)

## End(Not run)

## Not run: 
myModel <- inlineModel("
[LONGITUDINAL]
input = {A, k, c, a}
EQUATION:
t0    = 0 
f_0   = A
ddt_f = -k*f/(c+f)
DEFINITION:
y = {distribution=normal, prediction=f, sd=a}
[INDIVIDUAL]
input = {k_pop, omega}
DEFINITION:
k = {distribution=lognormal, prediction=k_pop, sd=omega}
")
f <- list(name='f', time=seq(0, 30, by=0.1))
y <- list(name='y', time=seq(0, 30, by=2))
res <- simulx(model     = 'model/home.txt', 
              parameter = c(A=100, k_pop=6, omega=0.3, c=10, a=2), 
              output    = list(f,y,"k"),
              group     = list(size=4, level='individual'))

plot(ggplotmlx() + geom_line(data=res$f, aes(x=time, y=f, colour=id)) +
     geom_point(data=res$y, aes(x=time, y=y, colour=id)))
print(res$parameter)

## End(Not run)

Summary of data

Description

Compute statistical summaries (mean, quantile, variance, survival rate,...)

Usage

statmlx(r, FUN = "mean", probs = c(0.05, 0.5, 0.95), surv.time = NULL)
statmlx(r, FUN = "mean", probs = c(0.05, 0.5, 0.95), surv.time = NULL)

Arguments

`r`	a data frame
`FUN`	a string, or a vector of strings, with the name of the functions to apply to the result of the simulation
`probs`	a vector of quantiles between 0 and 1. Only used when "quantile" has been defined in `FUN`
`surv.time`	a scalar or a vector of times. Only used when "event" has been defined in `type`

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

A data frame.

Examples

## Not run: 
modelPK <- inlineModel("
[LONGITUDINAL] 
input={V,Cl,alpha, beta,b}

EQUATION:
C = pkmodel(V, Cl)
h = alpha*exp(beta*C)
g = b*C

DEFINITION:
y = {distribution=normal, prediction=C, sd=g}
e = {type=event, maxEventNumber=1, rightCensoringTime=30, hazard=h}

[INDIVIDUAL]
input={V_pop,Cl_pop,omega_V,omega_Cl}

DEFINITION:
V     = {distribution=lognormal,   prediction=V_pop,    sd=omega_V}
Cl    = {distribution=lognormal,   prediction=Cl_pop,   sd=omega_Cl}
")

adm  <- list(amount=100, time=0)
p <- c(V_pop=10, Cl_pop=1, omega_V=0.2, omega_Cl=0.2, alpha=0.02, beta=0.1, b=0.1)
out.y <- list(name=c('y'), time=seq(0,to=25,by=5))
out.e <- list(name=c('e'), time=0)
out.p <- c("V", "Cl")
out   <- list(out.y, out.e, out.p)
g <- list(size=100, level='individual')
res1 <- simulx(model=modelPK, treatment=adm, parameter=p, output=out, group=g)

statmlx(res1$parameter, FUN = "mean", probs = c(0.05, 0.5, 0.95))
statmlx(res1$parameter, FUN = "quantile", probs = c(0.05, 0.5, 0.95))
statmlx(res1$parameter, FUN = c("sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res1$y, FUN = c("mean", "sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res1$e, surv.time=c(10,20))

res2 <- simulx(model=modelPK, treatment=adm, parameter=p, output=out, group=g, nrep=3)
statmlx(res2$parameter, FUN = c("sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res2$y, FUN = c("mean", "sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res2$e, surv.time=c(10,20,30))

## End(Not run)
## Not run: 
modelPK <- inlineModel("
[LONGITUDINAL] 
input={V,Cl,alpha, beta,b}

EQUATION:
C = pkmodel(V, Cl)
h = alpha*exp(beta*C)
g = b*C

DEFINITION:
y = {distribution=normal, prediction=C, sd=g}
e = {type=event, maxEventNumber=1, rightCensoringTime=30, hazard=h}

[INDIVIDUAL]
input={V_pop,Cl_pop,omega_V,omega_Cl}

DEFINITION:
V     = {distribution=lognormal,   prediction=V_pop,    sd=omega_V}
Cl    = {distribution=lognormal,   prediction=Cl_pop,   sd=omega_Cl}
")

adm  <- list(amount=100, time=0)
p <- c(V_pop=10, Cl_pop=1, omega_V=0.2, omega_Cl=0.2, alpha=0.02, beta=0.1, b=0.1)
out.y <- list(name=c('y'), time=seq(0,to=25,by=5))
out.e <- list(name=c('e'), time=0)
out.p <- c("V", "Cl")
out   <- list(out.y, out.e, out.p)
g <- list(size=100, level='individual')
res1 <- simulx(model=modelPK, treatment=adm, parameter=p, output=out, group=g)

statmlx(res1$parameter, FUN = "mean", probs = c(0.05, 0.5, 0.95))
statmlx(res1$parameter, FUN = "quantile", probs = c(0.05, 0.5, 0.95))
statmlx(res1$parameter, FUN = c("sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res1$y, FUN = c("mean", "sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res1$e, surv.time=c(10,20))

res2 <- simulx(model=modelPK, treatment=adm, parameter=p, output=out, group=g, nrep=3)
statmlx(res2$parameter, FUN = c("sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res2$y, FUN = c("mean", "sd", "quantile"), probs = c(0.05, 0.95))
statmlx(res2$e, surv.time=c(10,20,30))

## End(Not run)

Write formatted data file

Description

Write data contained in a list of dataframes in a single file (NONMEM/Monolix format) or in several files as tables

Usage

writeDatamlx(
  r,
  result.file = NULL,
  result.folder = NULL,
  sep = ",",
  ext = NULL,
  digits = 5,
  app.file = F,
  app.dir = F,
  project = NULL
)
writeDatamlx(
  r,
  result.file = NULL,
  result.folder = NULL,
  sep = ",",
  ext = NULL,
  digits = 5,
  app.file = F,
  app.dir = F,
  project = NULL
)

Arguments

`r`	a list of dataframes
`result.file`	a string with the name of the file
`result.folder`	a string with the name of the folder
`sep`	(default = ",")
`ext`	a string with the extension of the file names
`digits`	(default = 5)
`app.file`	TRUE/FALSE (default=FALSE) append to file
`app.dir`	TRUE/FALSE (default=FALSE) append to dir
`project`	A Monolix project

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Examples

## Not run: 
modelPK <- inlineModel("
[LONGITUDINAL]
input = {V, Cl, a1}
EQUATION:
Cc = pkmodel(V, Cl)
DEFINITION:
y1 ={distribution=lognormal, prediction=Cc, sd=a1}
")
adm  <- list(amount=100, time=seq(0,50,by=12))
p <- c(V=10, Cl=1, a1=0.1)
y1 <- list(name=c('y1'), time=seq(5,to=50,by=5))
res <- simulx(model=modelPK, treatment=adm, parameter=p, output=y1)
writeDatamlx(res, result.file="res.csv")
writeDatamlx(res, result.file="res.txt", sep="\t")
writeDatamlx(res, result.folder="res")

## End(Not run)
## Not run: 
modelPK <- inlineModel("
[LONGITUDINAL]
input = {V, Cl, a1}
EQUATION:
Cc = pkmodel(V, Cl)
DEFINITION:
y1 ={distribution=lognormal, prediction=Cc, sd=a1}
")
adm  <- list(amount=100, time=seq(0,50,by=12))
p <- c(V=10, Cl=1, a1=0.1)
y1 <- list(name=c('y1'), time=seq(5,to=50,by=5))
res <- simulx(model=modelPK, treatment=adm, parameter=p, output=y1)
writeDatamlx(res, result.file="res.csv")
writeDatamlx(res, result.file="res.txt", sep="\t")
writeDatamlx(res, result.folder="res")

## End(Not run)

Package 'mlxR'

Help Index

Plot Categorical Longitudinal Data

Description

Usage

Arguments

Details

Value

Examples

Computation of AUC, Cmax and Cmin

Description

Usage

Arguments

Details

Value

mlxR wrapper for ggplot

Description

Usage

Arguments

Value

Initialize mlxR library

Description

Usage

Arguments

Value

Examples

inline data frame

Description

Usage

Arguments

inline model

Description

Usage

Arguments

Details

Value

Examples

Kaplan Meier plot

Description

Usage

Arguments

Details

Value

Examples

Read Lixoft@ files

Description

Usage

Arguments

Explore and visualize models

Description

Usage

Arguments

Details

Convert a Monolix Project into an executable for the simulator Simulx

Description

Usage

Arguments

Value

Examples

Easy simulation of PK models Easy simulation of basic PK models See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Description

Usage

Arguments

Examples

Percentiles of the empiricial distribution of longitudinal data

Description

Usage

Arguments

Details

Value

Examples

Reads a table into a vector

Description

Usage

Arguments

Value

Read formatted data file

Description

Usage

Arguments