inst/Examples/Example_5/stdin.r

In PFIM: Pharmacokinetic/Pharmacodynamic Fisher Information Matrix

#########################################################################
##					        		                                       ##
##	              INPUT FILE FOR PFIM 3.2                          ##
#########################################################################


#Name of the project
#-------------------- 
project <- "Example 5 "


#Name of the file containing the PK or PD model
#----------------------------------------------
file.model <- "model.r"

#Name of the output file for the results
#---------------------------------------
output <- "Stdout.r"

#RUN:  Evaluation (EVAL) or Optimisation (OPT) 
#-------------------------------------------------------
#run<-"OPT"
run <- "EVAL"

#Block diagonal Fisher information matrix (option == 1) or complete Information matrix (option == 2)
#----------------------------------------------------------
option <- 1L

#Number of responses
#--------------------------------------------------------------------
nr <- 1L

################### MODEL OPTION ###########################

#Model form: Differential equations (DE) or analytical form (AF)
#---------------------------------------------------------------
modelform <- "AF"

###### ANALYTICAL MODEL OPTION #############################
############################################################

#Identical dose in each elementary design (Yes=T, No=F)
#-------------------------------------------------------------
dose.identical <- T

# If 'Yes', enter the value of the dose, 
# else, enter the vector of the dose values for each elementary design
#--------------------------------------------------------------------
dose <- 30L

#Vector of the times intervals of each expression  
#-----------------------------------------------------------
boundA <- list(c(0,Inf))

###### END ANALYTICAL MODEL OPTION ########################

###### DIFFERENTIAL EQUATION OPTION ##################################
######################################################################

#Initial time for which initial conditions are given
#---------------------------------------------------

#time.condinit<-0

#Identical initial conditions in each elementary design (Yes=T, No=F)
#-------------------------------------------------------------
#condinit.identical<-T

# If 'Yes', enter once the expression of the initial values of the system at the initial time
# else, enter the vectors of the initial conditions for each elementary design
# If initial values depend on parameters to be estimated, 
# enter this parameter into the expression without any quotation marks 
#---------------------------------------------------------

#condinit<-expression(c(100))


# Error tolerance for solving differential equations
#----------------------------------------------------
#RtolEQ <- 1e-08
#AtolEQ <- 1e-08
#Hmax <- 0.01                            # Default value 

###### END DIFFERENTIAL EQUATION OPTION #################################


#Name of the fixed effects parameters
#-------------------------------------
parameters <- c("ka","V","Cl")

#Fixed effects parameters values
#--------------------------------
beta <- c(1,3.5,2)


#Number of occasions
#--------------------------------------------------------------------------------
#n_occ<-2
n_occ <- 1L

#Random effect model (1) = additive  (2) = exponential 
#------------------------------------------------------------------

Trand <- 2L


#Diagonal Matrix of variance for inter-subject random effects:
#---------------------------------------------------
omega <- diag(c(0.09,0.09,0.09))

#Diagonal Matrix of variance for inter-occasion random effects:
#---------------------------------------------------
gamma <- diag(c(0.0225,0.0225,0.0225))

#Standard deviation of residual error (sig.inter+sig.slope*f)^2:
#------------------------------------------------------------------
sig.interA <- 0.1
sig.slopeA <- 0


#List of the vectors of sampling times for each elementary design 
#you can specify any sampling times for a group by writing NULL 
#ONLY if you have several responses
#-----------------------------------------------------------------

protA <- list(c(0.5,2,4,8)) 

#Vector of initial proportions or numbers of subjects for each elementary design 
#--------------------------------------------------------------

subjects <- 40L

#Subjects input: (1) for number of subjects (2) for proportions of subjects
#---------------------------------------------------------------------------
subjects.input <- 1

#If 'proportions of subjects' give the total number of samples
#-------------------------------------------------------------
#Ntot <- 1000L


###################################################################
#                                                                 #
#                        Covariate model                          #
#                                                                 #
###################################################################

##########################################
# Covariates not changing with occasion  # 
##########################################

#Add covariate to the model
#---------------------------------------------------------------------------
covariate.model <- FALSE

#Vector of covariates
#---------------------------------------------------------------------
#covariate.name <- list("Sex")

#Categories for each covariate (the first category is the reference)
#-----------------------------------------------------------------------
#covariate.category <- list(Sex=c("M","F"))

#Proportions of subjects in each category
#-------------------------------------------------------------------------
#covariate.proportions <- list(Sex=c(0.5, 0.5))

#Parameter(s) associated with each covariate
#-------------------------------------------------------------------------
#parameter.associated <- list(Sex ="V")

# Values of covariate parameters in covariate model 
# (values of parameters for all other categories than the reference category (for which beta=0) 
# covariate is additive on parameter if additive random effect model (Trand=1)
# covariate is additive on log parameters if exponential random effect model (Trand=2)
#-----------------------------------------------------------------------
#beta.covariate <- list(Sex=list(log(1.2)))

#####################################
#Covariates changing with occasion  # 
#####################################


#Add covariate to the model   (Yes==T No==F)
#-------------------------------------------------------------------
covariate_occ.model <- FALSE

#Vector of covariates depending on the occasion
#-------------------------------------------------------------------
#covariate_occ.name <- list("Treat")

#Categories for each covariate (the first category is the reference)
#-------------------------------------------------------------------
#covariate_occ.category <- list(Treat=c("A","B"))

#Sequences of values of covariates at each occasion 
#Specify as many values in each sequence as number of occasions (n_occ) for each covariate
#-------------------------------------------------------------------
 
covariate_occ.sequence<-list(
Treat=list(c("A","B"),c("B","A")))

#Proportions of elementary designs corresponding to each sequence of
#covariate values. Specify as many values of proportion as number of
#sequences defined in covariate_occ.sequence for each covariate 
#-------------------------------------------------------------------
#covariate_occ.proportions <- list(Treat=c(0.5,0.5))

#Parameter(s) associated with each covariate
#-------------------------------------------------------------------
#parameter_occ.associated <- list(Treat="Cl")


# Values of covariate parameters in covariate model (values of
# parameters for all other categories than the reference category (for
# which beta=0) covariate is additive on parameter if additive random
# effect model (Trand=1) covariate is additive on log parameters if
# exponential random effect model (Trand=2)
#-------------------------------------------------------------------
#beta.covariate_occ <- list(Treat=list(log(1.1)))

#############################################
# Power and number of subjects              #
#############################################

#Type one error alpha 
#-------------------------------------------------------------------
alpha <- 0.05

#Compute expected power for comparison test
#-------------------------------------------------------------------
compute.power <- FALSE

#Compute the number of subjects needed for a given power for
#comparison test
#-------------------------------------------------------------------
compute.nni <- FALSE

#Equivalence interval
interval_eq <- c(log(0.8), log(1.25))

#Compute expected power for equivalence test
#-------------------------------------------------------------------
compute.power_eq <- FALSE

#Compute the number of subjects needed for a given power for
#equivalence test 
#-------------------------------------------------------------------
compute.nni_eq <- FALSE

#Set value the given power
#-------------------------------------------------------------------
given.power<-0.9



############ONLY FOR OPTIMISATION ###############################

#Identical sampling times for each response
# (only if you do not have sampling times==NULL)
#-------------------------------------------------------------------
identical.times<-T

######## OPTIMISATION ALGORITHM OPTION ###############

#Character string for thoice of the optimisation algorithm: 
#	"FW" for the Fedorov-Wynn algorithm 
#	"SIMP" for the Simplex algorithm
#-------------------------------------------------------------------
algo.option <- "FW"

########################
#SIMPLEX SPECIFICATION #
########################

#Optimisation of the proportions of subjects:
#-------------------------------------------------------------------
subjects.opt <- TRUE

#Vector of lower and upper admissible sampling times
#-------------------------------------------------------------------
lowerA <-  0
upperA <- 24

lowerB <-  0
upperB <- 24

#Minimum delay between two sampling times
#-------------------------------------------------------------------
delta.time <- 0

#Print iteration step
#-------------------------------------------------------------------
iter.print <- TRUE

#Parameter for initial simplex building (%)
#-------------------------------------------------------------------
simplex.parameter <- 20

#Maximum number of iterations
#-------------------------------------------------------------------
Max.iter <- 5000L

#Relative convergence tolerance
#-------------------------------------------------------------------
Rctol <- 1e-6

#############################
#FEDOROV-WYNN SPECIFICATION #
#############################

#Number of sampling windows
#-------------------------------------------------------------------
nwindA <- 1

#List of vector of the allowed sampling times for each sampling window
#-------------------------------------------------------------------
sampwinA <- list(c(0.5,1,1.5,2,4,6,8))

#List of vector of allowed number of points to be taken from each sampling window
#-------------------------------------------------------------------
nsampA <- list(4)

#Maximum total number of sampling times per subject
#-------------------------------------------------------------------
nmaxptsA <- 4

#Minimum total number of sampling times per subject
#-------------------------------------------------------------------
nminptsA <- 4

############# END OF OPTIMISATION ALGORITHM OPTION ###############

############## GRAPH SPECIFICATION OPTION ###############

#graphical representation
#-------------------------------------------------------------------
graph.logical <- TRUE

#Vector of Names on Y axes for each response
#-------------------------------------------------------------------
names.datax <- "t"

#Vector of Names on Y axes for each response
#-------------------------------------------------------------------
names.datay <- "Concentration"

#Controls logarithmic axes for the graphical representation.
#Values "xy", "x", or "y" produce log-log or log-x or log-y axes.
#(For standard graphic, log.logical<-F)
#--------------------------------------------------------------
log.logical <- FALSE

#Vector of lower and upper sampling times for the graphical representation
#-------------------------------------------------------------------------
graph.infA <-  0
graph.supA <- 12

#Vector of lower and upper concentration for the graphical representation
#------------------------------------------------------------------------
y.rangeA <- NULL # default range
#y.range <- c(0,10)

############# END OF GRAPH SPECIFICATION OPTION ###############