pla-package: Parallel Line Assays
In pla: Parallel Line Assays
Description
Details
Author(s)
References
Examples
Description
Parallel Line Models:
Completely Randomized Design,
Randomized Block Design, and Latin Squares Design.
Balanced data are fitted as described in the Ph.Eur.
In the presence of missing values complete data analysis can be
performed (with computation of Fieller's confidence intervals for
the estimated potency), or imputation of values can be applied.
The package contains a script such that a pdf-document with a
report of an analysis of an assay can be produced from an input file
with data of the assay just by entering the name of the input-file.
Here no knowledge of R is needed by the user.
This tool requires R and Tex
(e.g. MikTex) to be installed.
The .BAT- and .csh-files for this is found in
.../pla/scripts/ of your installed pla package.
You can of course modify the "skeleton"
(.../pla/scripts/Skeleton/Skeleton-xtable.Rnw)
Sweave program of this script.
You should not (only) place your data in .../pla/scripts/data,
but copy the content of the directory .../pla/scripts/ to some
place not effected by updates of R and the pla-package.
Details
Package: pla
Type: Package
Version: 0.2
Date: 2015-09-09
License: GPL (>= 2)
Contents:
-
readAssayTable: Reads (.txt) files with
responses of assays, entered as matrices with rows of measurements
by columns of factors and variables, or entered as tables (arrays)
of responses. The result has methods as.data.frame,
as.array, and as.table.
Many examples of input data are found in the folder
.../pla/vignettes/'Source'/data/.
-
assayTable2frame: An array with the responses of
an assay is transformed to a data.frame for pla.fit and
pla.plots.
-
data2assayFrame: Check and prepare a data.frame for
pla.fit and pla.plots.
-
pla: Functions for defining and creating parallel line
assay models. Imputation of missing values is performed, as an option.
-
pla.fit or fit on pla-model: Estimation in
parallel line models, with listing of results.
-
pheur325: Estimation of potency and confidence limits
as described at page 480 in the Ph.Eur.
-
pla.plots or plot on pla-model: Make plots for
parallel line models.
-
plotSamples: One scatter plot for parallel line models.
-
jitterSteps: Compute a perturbed version of the
concentration-variable.
Examples from Ph.Eur.:
5.1.1. Two-dose multiple assay with completely randomized design;
An assay of corticotrophin by subcutaneous injection in rats:
Corticotrophin
5.1.2. Three-dose latin square design;
Antibiotic agar diffusion assay using a rectangular tray:
AgarDiffusionAssay.
5.1.3. Four-dose randomized block design;
Antibiotic turbidimetric assay:
Turbidimetric.
5.1.4. Five-dose multiple assay with completely randomized design;
An in-vitro assay of three hepatitis B vaccines against a standard:
HepatitisB.
From CombiStats - EDQM, Council of Europe
http://combistats.edqm.eu:
Example 1 - Three-dose parallel line assay; completely randomized;
square transformation; explicit volume units;
Diphteria,
Example 2 - Three-dose parallel line assay; randomized block;
explicit content notation;
Erythropoietin,
Example 3 - Four-dose parallel line assay; completely randomized;
logarithmic transformation; explicit ratio notation;
FactorIX,
Example 5 - Three-dose parallel line assay; completely randomized;
custom transformation; explicit content notation;
HeparinSodium,
Example 7 - Five-dose parallel line assay; completely randomized;
logarithmic transformation; explicit ratio notation;
HepatitisBvaccine,
Example 8 - Four-dose parallel line assay; completely randomized;
square root transformation; explicit content notation;
HumanHepatitis
(Human Hepatitis A immunoglobulin),
Example 10 - Four-dose parallel line assay; completely randomized;
logarithmic transformation; explicit ratio notation;
IPV (Inactivated Poliomyelitis Vaccine).
Example 15 - Five-dose multiple assay; randomized block design;
explicit ratio notation
Nystatin,
Example 22 - Three-dose parallel line assay at three independent occasions;
randomized block; symbolic notation;
Erythromycin,
Other example: Vancomycin.
The two main functions are pla.fit and pla.plots,
which expects data in the format as returned by
assayTable2frame or data2assayFrame.
But the function readAssayTable is also very useful.
One way to understand (and to reproduce) the structure of the expected
input of pla.fit and pla.plots is to look into
data2assayFrame and AgarDiffusionAssay.
The column names Response, Dilution, Sample,
Replicate, Row, and Column of the input for
data2assayFrame cannot be changed.
Row and Column are used for "Latin squares".
Replicate is used for "blocks" and completely random
designs.
pheur325 is designed to be called from pla.fit, and
plotSamples is designed to be called from pla.plots.
The input dataframe for pla.plots and pla.fit should be
ordered by Sample and "DilutionStep".
The output listing is designed for "R CMD Sweave" and "pdflatex".
Acknowledgment: Thanks to CombiStats
for permitting the presentation of the data of CombiStats in this package.
The statistical analyses of this data are also performed by
the CombiStats program, and the results can be found on
http://combistats.edqm.eu/.
Author(s)
Jens Henrik Badsberg <pla2015@badsberg.eu>
References
Ph.Eur.: Chapter 5.3. Statistical analysis.
In EUROPEAN PHARMACOPOEIA version 8.0, 2014;
607–635 (475-504 in version 5.0, 2004).
Coward, Katrine Hope, Kassner, Elsie Woodward (1941):
A Comparison between Interlitter and Intralitter variation in rats
with respect to the healing of rachitic bones by vitamin D.
Pharmaceutical Society, London.
Fieller, E.C.: The biological standardization of insulin. Supplement to the
Journal of the Royal Statistical Society. 1940; Vol. VII., No. 1.
Bliss, C.I. (1952):
The Statistics of Bioassay - with special reference to the vitamin.
Academic Press, New York.
Arthur Linder, Genova, Switzerland (1964):
Statistics of Bioassays,
Notes on lectures held during the spring semester (1964)
at the Statistics Department, University of North Carolina, Chapel Hill, N. C.
Finney, David J. (1978): Statistical Method in Biological Assay.
Charles Griffin & Company Ltd. Third Edition.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
data(Corticotrophin); Data <- Corticotrophin
Design <- "crd"
Data <- readAssayTable(paste(system.file(package = "pla"),
"vignettes/PhEur/data/Corticotrophin.txt",
sep = "/"))
Frame <- as.data.frame(Data)
fits <- pla.fit(Frame, design = Design, sampleLabels = c("S", "T", "U"),
dr = 4, returnPotencyEstimates = TRUE)
plots <- pla.plots(Frame, design = Design, sampleLabels = c("S", "T", "U"),
colTst = c("blue", "red"), showRho = FALSE,
main = "PhEur: Corticotrophin; Subcutaneous Injection In Rats",
tests = fits@tests,)
## Alternative on object of class 'pla':
plaModel <- plaCRD(Data,
assayTitle = "PhEur: Corticotrophin; Subcutaneous Injection In Rats")
Fits <- fit(plaModel)
Example output
Note: Two factors without labels: Sample, Dose .
================================================================================
Project: Europ. Pharm., 5th Ed. (2005), Ch. 5.3, 5.1.1
Assay: Subcutaneous Injection In Rats
================================================================================
================================================================================
Data values:
--------------------------------------------------------------------------------
S:1:1 S:2:2 T:1:1 T:2:2 U:1:1 U:2:2
1 300.0 289.0 310.0 230 250 236
2 310.0 221.0 290.0 210 268 213
3 330.0 267.0 360.0 280 273 283
4 290.0 236.0 341.0 261 240 269
5 364.0 250.0 321.0 241 307 251
6 328.0 231.0 370.0 290 270 294
7 390.0 229.0 303.0 223 317 223
8 360.0 269.0 334.0 254 312 250
9 342.0 233.0 295.0 216 320 216
0 306.0 259.0 315.0 235 265 265
Mean 332.0 248.4 323.9 244 282 250
SD 32.0 22.0 26.9 27 29 28
CV 9.7 8.9 8.3 11 10 11
================================================================================
================================================================================
Data values:
--------------------------------------------------------------------------------
S:1:1 S:2:2 T:1:1 T:2:2 U:1:1 U:2:2
1 300.0 289.0 310.0 230 250 236
2 310.0 221.0 290.0 210 268 213
3 330.0 267.0 360.0 280 273 283
4 290.0 236.0 341.0 261 240 269
5 364.0 250.0 321.0 241 307 251
6 328.0 231.0 370.0 290 270 294
7 390.0 229.0 303.0 223 317 223
8 360.0 269.0 334.0 254 312 250
9 342.0 233.0 295.0 216 320 216
0 306.0 259.0 315.0 235 265 265
Mean 332.0 248.4 323.9 244 282 250
SD 32.0 22.0 26.9 27 29 28
CV 9.7 8.9 8.3 11 10 11
================================================================================
pla documentation built on May 2, 2019, 11:12 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References Examples
Description
Parallel Line Models:
Completely Randomized Design, Randomized Block Design, and Latin Squares Design.
Balanced data are fitted as described in the Ph.Eur.
In the presence of missing values complete data analysis can be performed (with computation of Fieller's confidence intervals for the estimated potency), or imputation of values can be applied.
The package contains a script such that a pdf-document with a
report of an analysis of an assay can be produced from an input file
with data of the assay just by entering the name of the input-file.
Here no knowledge of R is needed by the user.
This tool requires R and Tex
(e.g. MikTex) to be installed.
The .BAT- and .csh-files for this is found in
.../pla/scripts/ of your installed pla package.
You can of course modify the "skeleton"
(.../pla/scripts/Skeleton/Skeleton-xtable.Rnw)
Sweave program of this script.
You should not (only) place your data in .../pla/scripts/data,
but copy the content of the directory .../pla/scripts/ to some
place not effected by updates of R and the pla-package.
Details
| Package: | pla |
| Type: | Package |
| Version: | 0.2 |
| Date: | 2015-09-09 |
| License: | GPL (>= 2) |
Contents:
-
readAssayTable: Reads (
.txt) files with responses of assays, entered as matrices with rows of measurements by columns of factors and variables, or entered as tables (arrays) of responses. The result has methods as.data.frame, as.array, and as.table. Many examples of input data are found in the folder.../pla/vignettes/'Source'/data/.-
assayTable2frame: An array with the responses of an assay is transformed to a data.frame for pla.fit and pla.plots.
-
data2assayFrame: Check and prepare a data.frame for pla.fit and pla.plots.
-
-
pla: Functions for defining and creating parallel line assay models. Imputation of missing values is performed, as an option.
-
pla.fit or fit on pla-model: Estimation in parallel line models, with listing of results.
-
pheur325: Estimation of potency and confidence limits as described at page 480 in the Ph.Eur.
-
-
pla.plots or plot on pla-model: Make plots for parallel line models.
-
plotSamples: One scatter plot for parallel line models.
-
jitterSteps: Compute a perturbed version of the concentration-variable.
-
Examples from Ph.Eur.:
5.1.1. Two-dose multiple assay with completely randomized design; An assay of corticotrophin by subcutaneous injection in rats: Corticotrophin
5.1.2. Three-dose latin square design; Antibiotic agar diffusion assay using a rectangular tray: AgarDiffusionAssay.
5.1.3. Four-dose randomized block design; Antibiotic turbidimetric assay: Turbidimetric.
5.1.4. Five-dose multiple assay with completely randomized design; An in-vitro assay of three hepatitis B vaccines against a standard: HepatitisB.
From CombiStats - EDQM, Council of Europe http://combistats.edqm.eu:
Example 1 - Three-dose parallel line assay; completely randomized; square transformation; explicit volume units; Diphteria,
Example 2 - Three-dose parallel line assay; randomized block; explicit content notation; Erythropoietin,
Example 3 - Four-dose parallel line assay; completely randomized; logarithmic transformation; explicit ratio notation; FactorIX,
Example 5 - Three-dose parallel line assay; completely randomized; custom transformation; explicit content notation; HeparinSodium,
Example 7 - Five-dose parallel line assay; completely randomized; logarithmic transformation; explicit ratio notation; HepatitisBvaccine,
Example 8 - Four-dose parallel line assay; completely randomized; square root transformation; explicit content notation; HumanHepatitis (Human Hepatitis A immunoglobulin),
Example 10 - Four-dose parallel line assay; completely randomized; logarithmic transformation; explicit ratio notation; IPV (Inactivated Poliomyelitis Vaccine).
Example 15 - Five-dose multiple assay; randomized block design; explicit ratio notation Nystatin,
Example 22 - Three-dose parallel line assay at three independent occasions; randomized block; symbolic notation; Erythromycin,
Other example: Vancomycin.
The two main functions are pla.fit and pla.plots, which expects data in the format as returned by assayTable2frame or data2assayFrame. But the function readAssayTable is also very useful.
One way to understand (and to reproduce) the structure of the expected
input of pla.fit and pla.plots is to look into
data2assayFrame and AgarDiffusionAssay.
The column names Response, Dilution, Sample,
Replicate, Row, and Column of the input for
data2assayFrame cannot be changed.
Row and Column are used for "Latin squares".
Replicate is used for "blocks" and completely random
designs.
pheur325 is designed to be called from pla.fit, and plotSamples is designed to be called from pla.plots.
The input dataframe for pla.plots and pla.fit should be
ordered by Sample and "DilutionStep".
The output listing is designed for "R CMD Sweave" and "pdflatex".
Acknowledgment: Thanks to CombiStats for permitting the presentation of the data of CombiStats in this package. The statistical analyses of this data are also performed by the CombiStats program, and the results can be found on http://combistats.edqm.eu/.
Author(s)
Jens Henrik Badsberg <pla2015@badsberg.eu>
References
Ph.Eur.: Chapter 5.3. Statistical analysis. In EUROPEAN PHARMACOPOEIA version 8.0, 2014; 607–635 (475-504 in version 5.0, 2004).
Coward, Katrine Hope, Kassner, Elsie Woodward (1941): A Comparison between Interlitter and Intralitter variation in rats with respect to the healing of rachitic bones by vitamin D. Pharmaceutical Society, London.
Fieller, E.C.: The biological standardization of insulin. Supplement to the Journal of the Royal Statistical Society. 1940; Vol. VII., No. 1.
Bliss, C.I. (1952): The Statistics of Bioassay - with special reference to the vitamin. Academic Press, New York.
Arthur Linder, Genova, Switzerland (1964): Statistics of Bioassays, Notes on lectures held during the spring semester (1964) at the Statistics Department, University of North Carolina, Chapel Hill, N. C.
Finney, David J. (1978): Statistical Method in Biological Assay. Charles Griffin & Company Ltd. Third Edition.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(Corticotrophin); Data <- Corticotrophin
Design <- "crd"
Data <- readAssayTable(paste(system.file(package = "pla"),
"vignettes/PhEur/data/Corticotrophin.txt",
sep = "/"))
Frame <- as.data.frame(Data)
fits <- pla.fit(Frame, design = Design, sampleLabels = c("S", "T", "U"),
dr = 4, returnPotencyEstimates = TRUE)
plots <- pla.plots(Frame, design = Design, sampleLabels = c("S", "T", "U"),
colTst = c("blue", "red"), showRho = FALSE,
main = "PhEur: Corticotrophin; Subcutaneous Injection In Rats",
tests = fits@tests,)
## Alternative on object of class 'pla':
plaModel <- plaCRD(Data,
assayTitle = "PhEur: Corticotrophin; Subcutaneous Injection In Rats")
Fits <- fit(plaModel)
|
Example output
Note: Two factors without labels: Sample, Dose .
================================================================================
Project: Europ. Pharm., 5th Ed. (2005), Ch. 5.3, 5.1.1
Assay: Subcutaneous Injection In Rats
================================================================================
================================================================================
Data values:
--------------------------------------------------------------------------------
S:1:1 S:2:2 T:1:1 T:2:2 U:1:1 U:2:2
1 300.0 289.0 310.0 230 250 236
2 310.0 221.0 290.0 210 268 213
3 330.0 267.0 360.0 280 273 283
4 290.0 236.0 341.0 261 240 269
5 364.0 250.0 321.0 241 307 251
6 328.0 231.0 370.0 290 270 294
7 390.0 229.0 303.0 223 317 223
8 360.0 269.0 334.0 254 312 250
9 342.0 233.0 295.0 216 320 216
0 306.0 259.0 315.0 235 265 265
Mean 332.0 248.4 323.9 244 282 250
SD 32.0 22.0 26.9 27 29 28
CV 9.7 8.9 8.3 11 10 11
================================================================================
================================================================================
Data values:
--------------------------------------------------------------------------------
S:1:1 S:2:2 T:1:1 T:2:2 U:1:1 U:2:2
1 300.0 289.0 310.0 230 250 236
2 310.0 221.0 290.0 210 268 213
3 330.0 267.0 360.0 280 273 283
4 290.0 236.0 341.0 261 240 269
5 364.0 250.0 321.0 241 307 251
6 328.0 231.0 370.0 290 270 294
7 390.0 229.0 303.0 223 317 223
8 360.0 269.0 334.0 254 312 250
9 342.0 233.0 295.0 216 320 216
0 306.0 259.0 315.0 235 265 265
Mean 332.0 248.4 323.9 244 282 250
SD 32.0 22.0 26.9 27 29 28
CV 9.7 8.9 8.3 11 10 11
================================================================================
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