pca
Principal Component Analysis (for AMR)
Description
Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
Usage
pca( x, ..., retx = TRUE, center = TRUE, scale. = TRUE, tol = NULL, rank. = NULL )
Arguments
x |
a data.frame containing numeric columns |
... |
columns of |
retx |
a logical value indicating whether the rotated variables should be returned. |
center |
a logical value indicating whether the variables
should be shifted to be zero centered. Alternately, a vector of
length equal the number of columns of |
scale. |
a logical value indicating whether the variables should
be scaled to have unit variance before the analysis takes
place. The default is |
tol |
a value indicating the magnitude below which components
should be omitted. (Components are omitted if their
standard deviations are less than or equal to |
rank. |
optionally, a number specifying the maximal rank, i.e.,
maximal number of principal components to be used. Can be set as
alternative or in addition to |
Details
The pca() function takes a data.frame as input and performs the actual PCA with the R function prcomp().
The result of the pca() function is a prcomp object, with an additional attribute non_numeric_cols which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by ggplot_pca().
Value
Stable Lifecycle
The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
Read more on Our Website!
On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.
if (require("dplyr")) {
# calculate the resistance per group first
resistance_data <- example_isolates %>%
group_by(order = mo_order(mo), # group on anything, like order
genus = mo_genus(mo)) %>% # and genus as we do here;
summarise_if(is.rsi, resistance) # then get resistance of all drugs
# now conduct PCA for certain antimicrobial agents
pca_result <- resistance_data %>%
pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
pca_result
summary(pca_result)
biplot(pca_result)
ggplot_pca(pca_result) # a new and convenient plot function
}