nlr: Nonlinear Regression in R

In wnl: Minimization Tool for Pharmacokinetic-Pharmacodynamic Data Analysis

Nonlinear Regression in R

Description

It performs nonlinear regression usually for pharmacokinetic and pharmacodynamic models.

Usage

nlr(Fx, Data, pNames, IE, LB, UB, Error="A", ObjFx=ObjDef, SecNames, SecForms, 
    Method="L-BFGS-B", Sx, conf.level=0.95, k)

Arguments

Fx	Function for structural model. It should return a vector of the same length to observations.
Data	Data table which will be used in Fx. Fx should access this with e$DATA.
pNames	Parameter names in the order of Fx arguments
IE	Initial estimates of parameters
LB	Lower bound for optim function. The default value is 0.
UB	Upper bound for optim function. The default value is 1e+06.
Error	Error model. One of "A" for additive error, "POIS" for Poisson error, "P" for proportional error, "C" for combined error model, "S" for general error model. With Error="S", Sx should be provieded.
ObjFx	Objective function to be minimized. The default is maximum likelihood estimation function(-2 log likelihood).
SecNames	Names of secondary parameter estimates
SecForms	Formula to calculate the secondary parameter estimates
Method	"L-BFGS-B" is default. See optim for more detail.
Sx	Scale function. This is usually the inverse of weight. It should return the same length(nrow) of Y. When Error="S", Scale function should be provided as Sx.
conf.level	Confidence level for confidence interval
k	1/k likelihood interval(LI) will be provided. Currently recommended value is exp(qf(1 - alpha, 1, nRec-nPara)/2) + 1.

Details

This uses scaled transformed parameters and environment e internally.

Value

Est	Point estimate(PE) with standard error(SE) and relative standard error(RSE)
LI	1/k likelihood interval, at which likelihood drops to 1/k of maximum likelihood. This reflects asymmetry better than confidence interval. This is estimated likelihood interval, not profile likelihood interval.
Skewness	Hougaard's skewness measure. This is printed only with additive error model. See also hSkew
Cov	Variance-covariance matrix of the objective function at the value of point estimates
run$m	Count of positive residuals
run$n	Count of negative residuals
run$run	Count of runs of residuals
run$p.value	P value of run test with excluding zero points
Objective Function Value	Minimum value of the objective function
-2LL	-2 times log likelihood
AIC	Akaike Information Criterion
AICc	Corrected Akaike Information Criterion
BIC	Schwarz Bayesian Information Criterion
Convergence	Convergence code from optim
Message	Message from optim.
Prediction	Fitted(predicted) values
Residuals	Residuals
Scale	Scales with Error="S". Variances for each points are scale vector multiplied by ScaleErrVar in Est.
Elapsed Time	Consumed time by minimization

Author(s)

Kyun-Seop Bae <[e-mail]>

Examples

  tData = Theoph
  colnames(tData) = c("ID", "BWT", "DOSE", "TIME", "DV")

  fPK = function(THETA) # Prediction function
  {
    DOSE = 320000 # in microgram
    TIME = e$DATA[, "TIME"] # use data in e$DATA

    K    = THETA[1]
    Ka   = THETA[2]
    V    = THETA[3]

    P  = DOSE/V*Ka/(Ka - K) * (exp(-K*TIME) - exp(-Ka*TIME))
    return(P)
  }

  IDs = unique(tData[,"ID"])
  nID = length(IDs)
  for (i in 1:nID) {
    Data = tData[tData$ID == IDs[i],]
    Res = nlr(fPK, Data, pNames=c("k", "ka", "V"), IE=c(0.1, 3, 500), 
              SecNames=c("CL", "Thalf", "MRT"), SecForms=c(~V*k, ~log(2)/k, ~1/k))
    print(paste("## ID =", i, "##"))
    print(Res)
  }

wnl documentation built on April 25, 2023, 9:11 a.m.