View source: R/continuousanchoringdata.R
continuousanchoringdata | R Documentation |
Continuous anchoring apical data are imported from a .txt file
(internally imported using the function read.table
)
and checked or from a R object of class data.frame
(see the description
of argument file
for the required format
of data).
No transformation is provided in this function.
If needed the pretreatment of data must be done before importation of data,
so that they can be directly modelled using a normal
error model. This strong hypothesis is required both for selection of responsive endpoints
and for dose-reponse modelling.
continuousanchoringdata(file, backgrounddose, check = TRUE)
## S3 method for class 'continuousanchoringdata'
print(x, ...)
## S3 method for class 'continuousanchoringdata'
plot(x, dose_log_transfo = TRUE, ...)
file |
The name of the .txt file (e.g. |
backgrounddose |
This argument must be used when there is no dose at zero in the data, to prevent the calculation of the BMD by extrapolation. All doses below or equal to the value given in backgrounddose will be fixed at 0, so as to be considered at the background level of exposition. |
check |
If TRUE the format of the input file is checked. |
x |
An object of class |
dose_log_transfo |
If TRUE a log transformation of the dose is used in the plot. |
... |
further arguments passed to print or plot functions. |
This function imports the data, checks their format
(see the description
of argument file
for the required format
of data) and gives in the print
information
that should help the user to check that the coding of data is correct : the tested doses (or concentrations)
the number of replicates for each dose, the number of endpoints.
continuousanchoringdata
returns an object of class "continuousanchoringdata", a list with 7 components:
data |
the numeric matrix of responses of each item in each replicate (one line per item, one column per replicate) |
dose |
the numeric vector of the tested doses or concentrations corresponding to each column of data |
item |
the character vector of the identifiers of the endpoints, corresponding to each line of data |
design |
a table with the experimental design (tested doses and number of replicates for each dose) for control by the user |
data.mean |
the numeric matrix of mean responses of each item per dose (mean of the corresponding replicates) (one line per item, one column per unique value of the dose |
data.sd |
the numeric matrix of standard deviations of the response of each item per dose (sd of the corresponding replicates, NA if no replicate) (one line per item, one column per unique value of the dose) |
containsNA |
TRUE if the data set contains NA values |
The print of a continuousanchoringdata
object gives the tested doses (or concentrations)
and number of replicates for each dose, the number of items, the identifiers
of the first 20 items (for check of good coding of data) and the normalization method.
The plot of a continuousanchoringdata
object shows the data distribution for each dose or concentration and replicate.
Marie-Laure Delignette-Muller
See read.table
the function used to import data, and
microarraydata
, RNAseqdata
and continuousomicdata
for other types of data.
# (1) import and check of continuous anchoring data
# (an example with two apical endpoints of an example given in the package (see ?Scenedesmus))
#
datafilename <- system.file("extdata", "apical_anchoring.txt", package = "DRomics")
o <- continuousanchoringdata(datafilename, backgrounddose = 0.1, check = TRUE)
# It is here necessary to define the background dose as there is no dose at 0 in the data
# The BMD cannot be computed without defining the background level
print(o)
plot(o)
# If you want to use your own data set just replace datafilename,
# the first argument of continuousanchoringdata(),
# by the name of your data file (e.g. "mydata.txt")
#
# You should take care that the field separator of this data file is one
# of the default field separators recognised by the read.table() function
# when it is used with its default field separator (sep argument)
# Tabs are recommended.
# Use of an R object of class data.frame
# on the same example (see ?Scenedesmus for details)
data(Scenedesmus_apical)
o <- continuousanchoringdata(Scenedesmus_apical, backgrounddose = 0.1)
print(o)
plot(o)
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