R Dataset / Package car / LoBD
On this R-data statistics page, you will find information about the LoBD data set which pertains to Cancer drug data use to provide an example of the use of the skew power distributions.. The LoBD data set is found in the car R package. You can load the LoBD data set in R by issuing the following command at the console data("LoBD"). This will load the data into a variable called LoBD. If R says the LoBD data set is not found, you can try installing the package by issuing this command install.packages("car") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the LoBD R data set. The size of this file is about 2,983 bytes.
Cancer drug data use to provide an example of the use of the skew power distributions.
Description
A portion of an experiment to determine the limit of blank/limit of detection in a biochemical assay.
Usage
LoBD
Format
A data frame with 84 observations on the following 9 variables.
pool-
a factor with levels
123456789101112denoting the 12 pools used in the experiment; each pool had a different level of drug. I1L1-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
I1L2-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
I2L1-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
I2L2-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
I3L1-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
I3L2-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
I4L1-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
I4L2-
a numeric vector giving the measured concentration in pmol/L of drug in the assay
Details
Important characteristics of a clinical chemistry assay are its limit of blank (LoB), and its limit of detection (LoD). The LoB, conceptually the highest reading likely to be obtained from a zero-concentration sample, is defined operationally by the upper 95% point of readings obtained from samples that do not contain the analyte. The LoD, conceptually the lowest level of analyte that can be reliably determined not to be blank, is defined operationally as true value at which there is a 95% chance of the reading being above the LoB.
These data are from a portion of a LoB/D study of an assay for a drug used to treat certain cancers. Twelve pools were used, four of them blanks of different types, and eight with successively increasing drug levels. The 8 columns of the data set refer to measurements made using different instruments I and reagent lots L.
Source
Used as an illustrative example for Box-Cox type transformations with negative readings in Hawkins and Weisberg (2015). For examples of its use, see skewPower.
References
Hawkins, D. and Weisberg, S. (2015) Combining the Box-Cox Power and Generalized Log Transformations to Accommodate Negative Responses, submitted for publication.
Examples
LoBD
Dataset imported from https://www.r-project.org.
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