| Title: | Easy Interface to Effective Concentration Calculations |
|---|---|
| Description: | Because fungicide resistance is an important phenotypic trait for fungi and oomycetes, it is necessary to have a standardized method of statistically analyzing the Effective Concentration (EC) values. This package is designed for those who are not terribly familiar with R to be able to analyze and plot an entire set of isolates using the 'drc' package. |
| Authors: | Zhian N. Kamvar [cre, aut], Niklaus J. Grunwald [ths, ctb] |
| Maintainer: | Zhian N. Kamvar <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2024-09-12 07:34:06 UTC |
| Source: | https://github.com/grunwaldlab/ezec |
dummydata
data(dummydata)data(dummydata)
a data frame with 96 rows and 7 columns representing two isolates tested for Metalaxyl resistance over 6 concentrations with 8 replicates per concentration. Each rep number were conducted in separate weeks. The First sample is real and the second is fake.
Function to generate a table of EC values from a data frame of multiple isolates.
EC_table(x, form = NULL, model = "LL.3", response = c(10, 50, 90), idcol = "ID", result = "df", plot = TRUE, ...)EC_table(x, form = NULL, model = "LL.3", response = c(10, 50, 90), idcol = "ID", result = "df", plot = TRUE, ...)
x |
a data frame that has at least the columns listed in the |
form |
a formula specifying the column names for the response and dose. Defaults to NULL. |
model |
one of 4 options:
|
response |
a numeric vector specifying what EC values you want to calculate. |
idcol |
the name of the column that identifies the samples (case sensitive). |
result |
What result do you want returned? Default is "df" for a data frame of summary values. If you want the models returned, choose "model". If you want the summary output of the model, choose "summary". |
plot |
if |
... |
parameters passed on to |
a data frame that contains EC estimates and standard errors in columns and samples in rows.
Zhian N. Kamvar
data(dummydata) # Using 3 parameter Log-Logistic Model (default) EC_table(dummydata, form = response ~ dose) # Using 4 parameter Weibull Model. EC_table(dummydata, form = response ~ dose, model = "W2.4") # This function really only needs three columns. newdat <- dummydata[c("ID", "dose", "response")] EC_table(newdat, form = response ~ dose) # We can rename them, too. colnames(newdat) <- c("identity", "dosage", "growth") EC_table(newdat, form = growth ~ dosage, idcol = "identity")data(dummydata) # Using 3 parameter Log-Logistic Model (default) EC_table(dummydata, form = response ~ dose) # Using 4 parameter Weibull Model. EC_table(dummydata, form = response ~ dose, model = "W2.4") # This function really only needs three columns. newdat <- dummydata[c("ID", "dose", "response")] EC_table(newdat, form = response ~ dose) # We can rename them, too. colnames(newdat) <- c("identity", "dosage", "growth") EC_table(newdat, form = growth ~ dosage, idcol = "identity")
The ezec package for easy EC calculation.
The package ezec is not a revolutionary work. It
simply is a wrapper for the drc package that makes life a little
easier when it comes to calculating a simple EC 50. The main function of
the package is EC_table. This function will do as it says and
automatically produce a table to EC values for each isolate in your sample.
Data is expected to exist in a table with at least three columns:
Sample ID
Dosage
Response value (Growth)
Any other columns in your data are optional. An example data set
is dummydata.