Deprecated functions in package RsSimulx.

Description

The functions listed below are deprecated and have been removed from the package. Alternative functions with similar functionality are given.

writeData

For writeData, use exportSimulatedData() in the lixoftConnectors package.

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).

wrapper for ggplot

Description

wrapper around ggplot with a custom theme

Usage

ggplotmlx(...)

Arguments

Value

see ggplot

Initialize RsSimulx library

Description

Initialize RsSimulx library

Usage

initRsSimulx(path = NULL, ...)

Arguments

Value

A list:

• software: the software that is used (should be simulx)

• 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: 
initRsSimulx(path = "/path/to/lixoftRuntime/")

## End(Not run)

Inline dataframe

Description

Convert a string in dataframe and save it in a temporary file

Usage

inlineDataFrame(str)

Arguments

Value

dataframe object

Examples

## Not run: 
occ <- inlineDataFrame("
 id time occ
 1   0    1
 1   12   2
 1   24   3
 2   0    1
 2   24   2
 3   0    1
")

## End(Not run)

Inline model

Description

Save a string in a temporary file to be used as a model file

Usage

inlineModel(srtIn, filename = NULL)

Arguments

Value

Name of the model file

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}
")

## 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

Value

a dataframe object

Percentiles of the empiricial distribution of longitudinal data

Description

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

Usage

prctilemlx(
  r = NULL,
  col = NULL,
  project = NULL,
  outputVariableName = NULL,
  number = 8,
  level = 80,
  plot = TRUE,
  color = NULL,
  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

Simulx project

Description

Model definition corresponding to rssimulxDemo.smlx project

Usage

rssimulxDemo.model

Format

A vector of string

Source

Simulx model

Simulx project

Description

rssimulxDemo.smlx is a Simulx project. In this demo three groups with different dose levels are simulated: low, medium and high. Groups have the same number of individuals, population parameters, distribution of covariates and outputs.

Usage

rssimulxDemo.project

Format

A vector of string

Source

Simulx project

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,
  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: 
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", appname=tempdir())

#------------------------------------------------------------------------
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, appname=tempdir())

## End(Not run)

Population parameters simulation

Description

Draw population parameters using the covariance matrix of the estimates

Usage

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

Arguments

Details

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

Value

dataframe object with generated population parameters

Examples

## Not run: 
param <- data.frame(pop.param=c(1.5, 0.5, 0.02, 0.4, 0.15, 0.2, 0.7),
                    sd=c(0.2, 0.05, 0.004, 0.05, 0.02, 0.02, 0.05),
                    trans=c('N','N','N','L','L','L','N'))
pop <- simpopmlx(n=3, parameter=param)

## 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,
  covariate = NULL,
  output = NULL,
  treatment = NULL,
  regressor = NULL,
  occasion = NULL,
  varlevel = NULL,
  group = NULL,
  project = NULL,
  nrep = 1,
  npop = NULL,
  fim = NULL,
  saveSmlxProject = NULL,
  result.file = NULL,
  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’. ‘⁠Mlxtran⁠’ models are automatically converted into C++ codes, compiled on the fly and linked to R using the ‘⁠RJSONIO⁠’ 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.lixoft.com 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))
parameter <- c(A=100, k_pop=6, omega=0.3, c=10, a=2)

res <- simulx(model     = myModel,
              parameter = parameter,
              occasion  = data.frame(time=c(0, 0), occ=c(1, 2)),
              output    = list(f,y),
              group     = list(size=4),
              saveSmlxProject = "./project.smlx")

res <- simulx(model     = myModel,
              parameter = parameter,
              occasion  = data.frame(time = c(0, 0, 0, 0),
                                     occ1 = c(1, 1, 2, 2),
                                     occ2 = c(1, 2, 3, 4)),
              output    = list(f,y),
              group     = list(size=4))
              
res <- simulx(model     = myModel,
              parameter = parameter,
              output    = list(f,y),
              group     = list(size=4))

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   <- list(out.y, out.e)
g <- list(size=100)
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)