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
)

Arguments

Details

See http://simulx.webpopix.org/mlxr/catplotmlx/ 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)

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
)

Arguments

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 http://simulx.webpopix.org/exposure/ 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(...)

Arguments

Value

see ggplot

Initialize mlxR library

Description

Initialize mlxR library

Usage

initMlxR(path = NULL, ...)

Arguments

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)

inline data frame

Description

utility function to inline creation of a data frame

Usage

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

Arguments

inline model

Description

Define a model "inline"

Usage

inlineModel(str, filename = NULL)

Arguments

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)

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
)

Arguments

Details

See http://simulx.webpopix.org/mlxr/kmplotmlx/ 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)

Read Lixoft@ files

Description

Utility function to read Lixoft@ formated input/output files

Usage

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

Arguments

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
)

Arguments

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
)

Arguments

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)

Easy simulation of PK models Easy simulation of basic PK models See http://simulx.webpopix.org/mlxr/pkmodel/ for more details.

Description

Easy simulation of PK models

Easy simulation of basic PK models

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

Usage

pkmodel(time, treatment, parameter)

Arguments

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)

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
)

Arguments

Details

See http://simulx.webpopix.org/mlxr/prctilemlx/ 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)

Reads a table into a vector

Description

Reads a table into a vector

Usage

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

Arguments

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
)

Arguments

Details

See http://simulx.webpopix.org/mlxr/readdatamlx/ 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)

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 = " "
)

Arguments

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 http://simulx.webpopix.org/mlxr/shinymlx/ 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)

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
)

Arguments

Details

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

Examples

## 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
)

Arguments

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 http://simulx.webpopix.org 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)

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)

Arguments

Details

See http://simulx.webpopix.org/stamlx 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)

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
)

Arguments

Details

See http://simulx.webpopix.org/mlxr/writedatamlx/ 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)