| testassay {testassay} | R Documentation |
Hypothesis testing procedure for assay validation for precision
Description
Does an m:n:q procedure for assay validation for precision.
Returns an object of class 'assaytest'. There is are predict
and print methods for that class.
Usage
testassay(x, m, n, q = 0.9, model = "normal", constant = "SD", data = NULL)
Arguments
x |
The vector of assay values |
m |
The vector of values indicating sample membership |
n |
The vector of values indicating replicate membership |
q |
The confidence level, typically 0.8 or 0.9 |
model |
String specifying the distribution for the assay values. Valid values are "normal" or "lognormal" |
constant |
String specifying whether the standard deviation is assumed to be constant over the levels ("SD") or the coefficient of variation is assumed constant over the levels ("CV"). The values "sd", "var", or "variance" may be used for "SD", and "cv" may be used for "CV". |
data |
Data frame or environment in which to look for x |
Details
The m:n:q procedure uses m different samples that have different levels of the true value
with n replicates for each sample. The output is a 100q percent upper limit of the bound on the
precision parameter when the true values within the range of values for the m samples all follow either a
a constant coefficient of variation model or a constant
standard deviation model (same as a constant variance model) (see constant argument).
For example, if the 4:4:90 percent procedure using a normal model with a constant variance model returns a bound on the standard deviation (the Umax element of the assaytest class) of 7.9 then under the assumptions we have 90 percent confidence that the true SD is less than 7.9.
The predict method gives effective standard deviation intervals (i.e., 68.27 pct CIs)
for the expected response from subsequent observed values from the assay.
Value
An object of class "assaytest", which is a list of components including a data frame of the relevant statistics calculated on x. Print, summary, predict, and plot methods are available. The list has the following components
sumtabTable summarizing the experiment, includes mean values, SD or CV estimates, and upper confidence limits on those.
UmaxThe maximum of the upper limits on the SD or CV, used in the effective SD interval calculation
nThe number of samples per level
mThe number of levels
qThe confidence level
modelThe assumed model
constantThe parameter assumed to be constant (either 'SD' or 'CV').
alphaThe alpha level, calculated as (1 - q)^(1 / m)
xThe data vector supplied by the user
References
Fay, MP, Sachs, MC, and Miura, K (2016). A Hypothesis Testing Framework for Validating and Assay for Precision (unpublished manuscript).
Examples
# reproduce Table 3 of Fay, Sachs and Miura
I<- gia$parasite=="3D7" & gia$meanAAgia<80
treD7.test<-testassay(x=gia, m=sample, n=assay, q=.9,
data=subset(gia, parasite=="3D7" & meanAAgia<80))
treD7.test
# get estimated effective standard deviation intervals (68.27 percent CIs)
# for observed values 21.4 and 65.9
# using results from testassay
predict(treD7.test,c(21.4,65.9))