The function reads either a matrix of measurement by variable (a 'data.frame'), or a table with the measured variable (an 'array').
readAssayTable(file = "AssayTable.txt", fun = NULL, log = FALSE, rows = "", columns = "", replicateNames = c("Repetition", "Replicate", "Block", "Plate", "Animal", "Group", "Litter"), doseNames = c("Dilution", "Dose"), sampleNames = c("Smp", "Sample"), otherNames = c("Outer", "Inner", "Assay"), responseName = "Response", combinedTreatment = FALSE, echoTitle = TRUE)TRUE, then the responses are log-transformed. TRUE then the dose and sample
does not have to be balanced.
Dose and sample is read as a combined treatment,
when they are given in columns, that is,
the levels are specified in the header line. TRUE then the project- and
assay-title is printed. reduced and selectFun.
With the boolean argument reduced the factor of sample
and factor of dilution are combined, with output similar to
using row.vars for ftable, but labels of dose
can then be different across samples.
The function selectFun should work on the array of all the read
factors.
For objects of class assayFrame-class also the arguments
which, FUN and responseName are available.
which gives the factors to determined the array, either by
indices, or names by text strings. The function is by default
mean, and is used to calculate the value of the cell in the
array from the available responses in the cell. Navigate to the folder .../pla/vignettes/'Source'/data/
or .../pla/scripts/data/ to see some examples of data-files.
The file .../pla/scripts/data/AssayTable.txt shows a very
simple example without "comments" for project- and assay-title,
design, etc.
If the function finds the text-string 'response' in the header line of the file, then the returned object is of class assayFrame-class, else a table with the measured variable (an 'array') is expected, and then the values are stored an object of class assayTable-class.
Her are some examples of the later: 1. form: Columns identified by the cross classification of sample and dose, and rows identified by replicate-, repetition-, plate-, or block-number, used for Erythromycin, Erythropoietin, and Turbidimetric:
Block S1 S2 S3 T1 T2 T3 Label
Block-1 185 192 210 172 201 214 "" Block-2 185 199 216 178 205 212 "" Block-3 174 200 211 185 196 216 "" Block-4 175 198 216 184 196 208 "" Block-5 185 201 211 175 201 218 "" Block-6 180 194 211 178 197 214 ""
# Model . . . . . "Parallel lines" # Design . . . . . "Randomized block" # DilutionRatio . . . . . "1.5" # ResultOfAssay . . . . . "Example 22: Erythromycin" # Project . . . . . "CombiStats - EDQM, Council of Europe" # Description . . . . . "Three-dose parallel line assay ..." ...
For data entered as this table for the measurements, the header line, first record or row, of the file gives the names of the treatments (samples and dilution-steps) together with the name of at least one factor variating over the rows.
Columns should then contain the replicates of responses for the individual treatments (cross classification of samples and dilution-steps), rows of the file the responses for different samples and doses for one replicate (plate or block). The first column is a label for the "replicate", "block", "plate", ...,
When the columns contains the responses for the individual treatments
(cross classification of samples and dilution-steps), and rows of the
file replicates of the response, samples are named by a single
letter ("S", "T", "U", ...) and dilution steps by a single digit,
and the header thus are text strings with two characters,
("S1", "S2", ..., "T1", "T2", ...) - then everything is very simple.
But sometimes the cross classification of samples and dilution-steps
have to be divided on more lines, or the replicates entered in the
individual lines determined by the doses of the samples, e.g. to make
proofreading against the source more easy.
But then the programming in this package is not so simple (though only
a few 'hacks' are necessary), but especially this documentation gets
complicated:
A column with label sample can be used when samples (for one
'replicate') requires more lines.
This is used for HepatitisB under the "2. alternative" below.
The table can also be transposed (transformed) relative to the above
"1. form", if rows contains the replicates of one treatment, i.e. one
cross-classification of sample and dose, and columns the responses for
the individual replicate numbers.
A column Dose and a column Sample is then used for naming
treatments.
Dose Sample R1 R2 R3 R4 Label Dose-0.05 S 0 0 1 0 "" Dose-0.1 S 2 3 2 2 "" Dose-0.2 S 4 4 3 4 "" Dose-0.02 T 1 1 0 1 "" Dose-0.04 T 2 2 2 2 "" Dose-0.08 T 2 4 4 4 ""
# Model . . . . "Parallel lines" # Design . . . . "Completely randomised" # ResultOfAssay . . . . "Example 1: Diphteria" # Project . . . . "CombiStats - EDQM, Council of Europe" # Description . . . . "Three-dose parallel line assay; explicit volume units" ...
1. alternative: Rows identified by the cross classification of sample and dose, and columns identified by replicate-, repetition-, plate-, or block-number:
If the keyword "Dose" is used in the first column (together with
a dose or step number) and a column is used to identify
"sample", then the lines are expected to have the replicates
for the cross-classification of dose and sample determined by the
these two columns.
This table is just a transposed version of the above "1. form".
This used for Diphteria, FactorIX, HepatitisBvaccine, HumanHepatitis, and IPV. 2. alternative: Rows identified by the cross classification of sample and replicate-, repetition-, plate-, or block-number and columns identified by dose:
This is similar to the above "1. form", but the lines are just too
long, and thus divided by sample.
Here also a column with name sample gives the sample.
Used for HepatitisB:
Replicate Sample D1 D2 D3 D4 D5 Label
Replicate-1 S 0.043 0.093 0.159 0.283 0.514 ""
Replicate-2 S 0.045 0.099 0.154 0.295 0.531 ""
Replicate-3 S 0.051 0.082 0.166 0.362 0.545 ""
Replicate-1 T 0.097 0.167 0.327 0.501 1.140 ""
Replicate-2 T 0.097 0.157 0.355 0.665 1.386 ""
Replicate-3 T 0.094 0.178 0.345 0.576 1.051 ""
Replicate-1 U 0.086 0.127 0.277 0.586 0.957 ""
Replicate-2 U 0.071 0.146 0.268 0.489 0.866 ""
Replicate-3 U 0.073 0.133 0.269 0.546 1.045 ""
Replicate-1 V 0.082 0.145 0.318 0.552 1.037 ""
Replicate-2 V 0.082 0.144 0.306 0.551 1.039 ""
Replicate-3 V 0.086 0.173 0.316 0.624 1.068 "" # Model . . . . . "Parallel lines" # Design . . . . . "Completely randomised" # ResultOfAssay . . . . . "Three hepatitis B vaccines against a standard" # Project . . . . . "Europ. Pharm., 5th Ed. (2005), Ch. 5.3, 5.1.4.a" # Description . . . . . "Five-Dose Multiple Assay" ...
For HeparinSodium a special form of the above is used with an "inner" factor:
Dose Sample R-1:I-y R-1:I-z R-2:I-y R-2:I-z Label Dose-1.044 S 78.9 79.8 79.0 79.1 "" Dose-1.321 S 108.2 113.1 119.1 122.5 "" Dose-1.670 S 176.9 180.1 173.6 169.8 "" Dose-1.000 T 79.8 76.4 78.9 78.7 "" Dose-1.265 T 107.2 106.5 117.7 117.3 "" Dose-1.600 T 168.2 171.4 179.3 162.7 ""
# Model . . . "Parallel lines" # Design . . . "Completely randomised" # Transformation . . . "y' = log((y+z)/2" # FUN . . . "function(x) log(apply(array(x, dim = c(6, 2, 2)), c(1, 3), mean))/log(10)" # ResultOfAssay . . . "Example 5: Heparin Sodium" # Project . . . "CombiStats - EDQM, Council of Europe" # Description . . . "Three-dose parallel line assay" ...
For the 1. and 2. alternative, the order of the dilution steps are
determined by the order of the columns (by assayTable2frame
when byOrder is TRUE, the default).
Please note that the labels of the dilution steps are then ignored.
These three above alternatives covers half of the six possibilities of the
layout of the table of responses: Three cases with columns determined
by one of the tree factors sample, dose and
replicate (block , plate, repetition,
...), and three cases with the columns determined by the cross
classification of two of the three factors.
The first found word of the header gives one of one or more factors
determination the rows. This word should also be a part of the first
word of the rows of the table (to separate the data-table from the
from the options).
Other names in the header not found in the 'Names' arguments
determinate the remaining factors and there levels, and are column
names of columns with responses.
Other interesting tested examples of headers are:
The pattern of special characters in the column names of columns with responses should be identical. 2. form: Matrix of measurement by variable: If the header of the file contains the text-string "Response", then a "data.frame" with rows of measurements and columns of variables and factors is expected. This is used for latin square designs.
Column Row Sample Dilution Response 1 1 S 1 161 1 2 T 1 151 1 3 T 2 162 1 4 S 3 194 1 5 S 2 176 1 6 T 3 193 2 1 T 1 160 2 2 T 3 192 2 3 S 3 195 2 4 S 2 184 2 5 T 2 181 2 6 S 1 166 3 1 T 2 178 3 2 S 1 150 3 3 S 2 174 3 4 T 3 199 3 5 S 3 201 3 6 T 1 161 4 1 S 3 187 4 2 S 2 172 4 3 S 1 161 4 4 T 1 160 4 5 T 3 202 4 6 T 2 186 5 1 S 2 171 5 2 T 2 170 5 3 T 3 193 5 4 S 1 163 5 5 T 1 154 5 6 S 3 198 6 1 T 3 194 6 2 S 3 192 6 3 T 1 151 6 4 T 2 171 6 5 S 1 151 6 6 S 2 182
# Model . . "Parallel lines" # Design . . "Latin square design"
# ResultOfAssay . . "Antibiotic agar diffusion assay (Example 15: Nystatin)" # Project . . "Europ. Pharm., 5th Ed. (2005), Ch. 5.3, 5.1.2" # Comment . . "Five-dose multiple assay" ...
Labels (text-lines for the "report"):
Labels as e.g. design, dilutionRatio, model,
Assay and Project, etc. are extracted from the
label-field in lines after the response values.
The first character in these lines has to be "#" when a "matrix"
of measurements by variables is entered. It is also suggested to start
the lines by "#", when a table of responses is entered.
## Not run:
# library (lattice)
# as.array(barley, 1:3, type = "counts", reduced = TRUE)
# as.array(barley, 1:3, reduced = TRUE)
# ## End(Not run)
as.array(data.frame(Titanic), 1:4)
as.array(data.frame(CO2), response = "conc",
factorNames = c("Plant", "Type", "Treatment"))
as.array(data.frame(CO2), response = "uptake",
factorNames = c("Plant", "Type", "Treatment"))
as.array(data.frame(ChickWeight), response = "weight",
factorNames = c("Time", "Chick", "Diet"))
as.array(npk)
data(Diphteria)
Data <- readAssayTable(paste(system.file(package = "pla"),
"vignettes/CombiStat/data/Diphteria.txt",
sep = "/"), fun = function(x) x^2,
rows = "Dilutions & Samples", columns = "Replicates")
Diphteria
print(Diphteria)
as.array(Diphteria)
as.data.frame(Diphteria)
as.table(Diphteria)
data(Nystatin)
Data <- readAssayTable(paste(system.file(package = "pla"),
"vignettes/CombiStat/data/Nystatin.txt",
sep = "/"),
rows = "Measurements",
columns = "All factors and variables")
Nystatin
print(Nystatin)
as.array(Nystatin)
as.array(Nystatin, which = 1:3, reduced = TRUE)
as.array(Nystatin, which = 1:2, reduced = TRUE)
as.array(Nystatin, which = 1:2, FUN = var, reduced = TRUE)
as.array(Nystatin, which = 1:2, FUN = length, reduced = TRUE)
as.data.frame(Nystatin)
as.table(Nystatin, which = 1:3, reduced = TRUE)
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