| analyzeSingleLab {POD} | R Documentation |
Analyze Single Lab Qualitative PCR Outcomes
Description
Compute the POD curve and the LOD value to validate a qualitative PCR method of a single laboratory.
Usage
analyzeSingleLab(x = NULL, X = NULL, S = NULL, N = NULL,
qLOD = 95, b = 1)
Arguments
x |
A matrix or dataframe with columns 'X', 'S' and 'N'. |
X |
Nominal DNA concentration. |
S |
Number of successfull PCR outcomes. |
N |
Total number of PCR experiments. |
qLOD |
The quantile(s) for the Limit Of Detection (LOD). Divided by |
b |
Fixed value for the corrective parameter |
Details
According to the suggestion of Uhlig et al. (2015), the corrective parameter b is set to 1 if it is close to 1 (simplified fit). However, if sensitivity is better than achievable according to the theoretical POD curve or average amplification probability is higher at higher dilution levels than at lower dilution levels, the b is estimated from the data (full fit).
The value of b can be changed by the user. However, it is not recommended to do so.
In particular unexperienced users struggle with decimal commas and decimal dots, transforming digits from strings into numeric values etc. To lower the burden, beginning with package version 1.2.0 this function automatically and only where necessary
adds column names (with warning)
transforms values in all columns from factor or character into numeric values
thereby substituting decimal commas by decimal dots
transforms columns 'S' and 'N' to integer (
link{as.integer})
Value
A list with following items
- x
Input data plus extra columns
- b
The parameter
b, as provided by the user- fit.glm.simple
Results for the simplified GLM
- fit.glm.full
Results for the full GLM
where "fit.glm.simple" and "fit.glm.full" are lists with the following parameters
- b
The parameter
b(estimated from the model)- lambda
The parameter
\lambda(estimated from the model)- model
The generalized linear model (GLM) fit to the data
- lod
A named vector of LOD values
- lodci
The 95% confidence interval of the LOD
- warn
A character vector containing warnings that appeared during GLM fit
References
Uhlig et al. Accred Qual Assur (2015) 20: 75. https://doi.org/10.1007/s00769-015-1112-9
Examples
x <- cbind(
X=c(0.1,1,2,5,10,20),
S=c( 0,5,6,6,6,6 ),
N=c( 6,6,6,6,6,6 )
)
obj <- analyzeSingleLab(x=x)