pheur325: Potency with confidence intervals

Description

Estimation of potency and confidence limits as described at page 480 in Ph.Eur (but with the Sums Of Squares computed by lm).

If complete data analyses is performed, then the slope and contrasts of intercepts are returned form the lm-fit, and confidence intervals for potency computed by Fieller's theorem.

Usage

pheur325(data,
         lmInteraction = NULL,
         lmRestricted = NULL,
         dr = 2,
         response = "Response",
         sampleLabels = levels(unlist(data["Sample"])),
         indexOfReference = 1,
         StdName = sampleLabels[indexOfReference],
         sampleStepName = "SampleStep",
         dfAdj = 0,
         factor = 1.0, 
         alpha = 0.05)

Arguments

data

data.frame with the columns response and sampleStepName. Note that the slope then is determined by only the additional dilution ratio dr.

lmInteraction

Linear model for ANOVA table - with Sums of Squares.

lmRestricted

Linear model with estimates of regression parameters for complete analysis - when missing values are present.

The dilution ratio of the equally spaced dilutions, see data2assayFrame.

response

A character string giving the name of the response column of data.

sampleLabels

A vector of character strings giving the labels of the samples. If data["Sample"] has one of these values, then these rows are used. These labels are also used for labels of the returned values.

indexOfReference

Index of the reference among the samples given by sampleLabels. If data["Sample"] has this value, then these rows are from the 'reference'.

StdName

A character string identifying the reference in the columns "Sample" of the input data.

sampleStepName

A character string giving the name of the sampleStepName column of data. This variable should identify both the preparation and the dilution step.

dfAdj

The needed adjustment of the number of degrees of freedom, if e.g. data are corrected for blocks, rows or columns, or if values are imputed before applying the model of the completely randomized design on block designs.

factor

A numeric (vector) to multiply on the estimated potency.

alpha

A real number, preferable between 0 and 1 - for the confidence interval.

Value

A list of lists with variables named as at page 480 in Ph.Eur.

Details

See page 480 in Ph.Eur.

References

Chapter 5.3. Statistical analysis. In EUROPEAN PHARMACOPOEIA 5.0, 2004; 475-504. Fieller, E.C.: The biological standardization of insulin. Supplement to the Journal of the Royal Statistical Society. 1940; Vol. VII., No. 1.