knitr::opts_chunk$set(message = FALSE, warning = FALSE, tidy = FALSE) knitr::opts_chunk$set(fig.align = "center", fig.show = 'asis', fig.height = 5, fig.width = 5) options(out.width = 100)
The package EZEC stands for Easy Effective Concentration. This package was
designed to be a simple wrapper for the functions drm()
and ED()
in the
drc package. It provides the function
EC_table()
that will first fit a model to your data and then calculate
effective concentrations from that model over several samples arranged in a data
frame. In this tutorial, I will give a couple of short examples of how you can
analyze your data. To get help with any functions in the package, simply type
help("function.name")
and replace function.name with the name of the function
you want help for.
As this package is simply a wrapper to two functions from the drc package, please cite the drc package and indicate the version number you used:
packageVersion("drc") citation("drc")
Your data must be in a format that encodes one observation per row. You can see
an example of this in the dummydata
data set:
library("ezec") data("dummydata", package = "ezec") head(dummydata) # the function head means "look at the top of the object"
You can see that this contains the columns r paste(names(dummydata), collapse =
", ")
. The only important columns here are the ID, dose, and
response columns.
To import your own data from csv text file, you should use the function
read.table()
to import it into R. If you want to import data from an xlsx
file, you can use the function read_excel()
from the readxl package.
You can analyze your data simply by running EC_table()
with your data and
supplying a formula to describe what you want to analyze in the form of
response_variable ~ explanitory_variable
. In our example of "dummydata", our
response variable is "response" and our explanitory variable is "dose".
library("ezec") data("dummydata", package = "ezec") res <- EC_table(dummydata, form = response ~ dose) print(res)
If you want to have all the plots plotted in one window, you can use the par()
function:
par(mfrow = c(1, 2)) # set window to have 1 row and two columns EC_table(dummydata, form = response ~ dose) par(mfrow = c(1, 1)) # reset the window
If you want to save your results to a file that you can format for a manuscript,
you can use the write.table()
functon:
write.table(res, file = "dummy_results.csv", row.names = FALSE, sep = ",")
You can also have the output be a summary of the model for each sample as a list
EC_table(dummydata, form = response ~ dose, plot = FALSE, result = "summary")
You can also choose to have the output be the model itself
EC_table(dummydata, form = response ~ dose, plot = FALSE, result = "model")
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