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< h1 > Principal Component Analysis (for AMR)< / h1 >
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< small class = "dont-index" > Source: < a href = 'https://github.com/msberends/AMR/blob/master/R/pca.R' > < code > R/pca.R< / code > < / a > < / small >
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< div class = "hidden name" > < code > pca.Rd< / code > < / div >
< / div >
< div class = "ref-description" >
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< p > 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.< / p >
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< / div >
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< pre class = "usage" > < span class = 'fu' > pca< / span > < span class = 'op' > (< / span >
< span class = 'va' > x< / span > ,
< span class = 'va' > ...< / span > ,
retx < span class = 'op' > =< / span > < span class = 'cn' > TRUE< / span > ,
center < span class = 'op' > =< / span > < span class = 'cn' > TRUE< / span > ,
scale. < span class = 'op' > =< / span > < span class = 'cn' > TRUE< / span > ,
tol < span class = 'op' > =< / span > < span class = 'cn' > NULL< / span > ,
rank. < span class = 'op' > =< / span > < span class = 'cn' > NULL< / span >
< span class = 'op' > )< / span > < / pre >
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< h2 class = "hasAnchor" id = "arguments" > < a class = "anchor" href = "#arguments" > < / a > Arguments< / h2 >
< table class = "ref-arguments" >
< colgroup > < col class = "name" / > < col class = "desc" / > < / colgroup >
< tr >
< th > x< / th >
< td > < p > a < a href = 'https://rdrr.io/r/base/data.frame.html' > data.frame< / a > containing numeric columns< / p > < / td >
< / tr >
< tr >
< th > ...< / th >
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< td > < p > columns of < code > x< / code > to be selected for PCA, can be unquoted since it supports quasiquotation.< / p > < / td >
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< / tr >
< tr >
< th > retx< / th >
< td > < p > a logical value indicating whether the rotated variables
should be returned.< / p > < / td >
< / tr >
< tr >
< th > center< / th >
< td > < p > 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 < code > x< / code > can be supplied.
The value is passed to < code > scale< / code > .< / p > < / td >
< / tr >
< tr >
< th > scale.< / th >
< td > < p > a logical value indicating whether the variables should
be scaled to have unit variance before the analysis takes
place. The default is < code > FALSE< / code > for consistency with S, but
in general scaling is advisable. Alternatively, a vector of length
equal the number of columns of < code > x< / code > can be supplied. The
value is passed to < code > < a href = 'https://rdrr.io/r/base/scale.html' > scale< / a > < / code > .< / p > < / td >
< / tr >
< tr >
< th > tol< / th >
< td > < p > a value indicating the magnitude below which components
should be omitted. (Components are omitted if their
standard deviations are less than or equal to < code > tol< / code > times the
standard deviation of the first component.) With the default null
setting, no components are omitted (unless < code > rank.< / code > is specified
less than < code > < a href = 'https://rdrr.io/r/base/Extremes.html' > min(dim(x))< / a > < / code > .). Other settings for tol could be
< code > tol = 0< / code > or < code > tol = sqrt(.Machine$double.eps)< / code > , which
would omit essentially constant components.< / p > < / td >
< / tr >
< tr >
< th > rank.< / th >
< td > < p > 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 < code > tol< / code > , useful notably when the
desired rank is considerably smaller than the dimensions of the matrix.< / p > < / td >
< / tr >
< / table >
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< h2 class = "hasAnchor" id = "value" > < a class = "anchor" href = "#value" > < / a > Value< / h2 >
< p > An object of classes pca and < a href = 'https://rdrr.io/r/stats/prcomp.html' > prcomp< / a > < / p >
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< h2 class = "hasAnchor" id = "details" > < a class = "anchor" href = "#details" > < / a > Details< / h2 >
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< p > The < code > pca()< / code > function takes a < a href = 'https://rdrr.io/r/base/data.frame.html' > data.frame< / a > as input and performs the actual PCA with the < span style = "R" > R< / span > function < code > < a href = 'https://rdrr.io/r/stats/prcomp.html' > prcomp()< / a > < / code > .< / p >
< p > The result of the < code > pca()< / code > function is a < a href = 'https://rdrr.io/r/stats/prcomp.html' > prcomp< / a > object, with an additional attribute < code > non_numeric_cols< / code > 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 < code > < a href = 'ggplot_pca.html' > ggplot_pca()< / a > < / code > .< / p >
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< h2 class = "hasAnchor" id = "maturing-lifecycle" > < a class = "anchor" href = "#maturing-lifecycle" > < / a > Maturing lifecycle< / h2 >
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< p > < img src = 'figures/lifecycle_maturing.svg' style = margin-bottom:5px / > < br / >
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The < a href = 'lifecycle.html' > lifecycle< / a > of this function is < strong > maturing< / strong > . The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome < a href = 'https://github.com/msberends/AMR/issues' > to suggest changes at our repository< / a > or < a href = 'AMR.html' > write us an email (see section 'Contact Us')< / a > .< / p >
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< h2 class = "hasAnchor" id = "examples" > < a class = "anchor" href = "#examples" > < / a > Examples< / h2 >
< pre class = "examples" > < span class = 'co' > # `example_isolates` is a dataset available in the AMR package.< / span >
< span class = 'co' > # See ?example_isolates.< / span >
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< span class = 'co' > # \donttest{< / span >
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< span class = 'kw' > if< / span > < span class = 'op' > (< / span > < span class = 'kw' > < a href = 'https://rdrr.io/r/base/library.html' > require< / a > < / span > < span class = 'op' > (< / span > < span class = 'st' > < a href = 'https://dplyr.tidyverse.org' > "dplyr"< / a > < / span > < span class = 'op' > )< / span > < span class = 'op' > )< / span > < span class = 'op' > {< / span >
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< span class = 'co' > # calculate the resistance per group first < / span >
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< span class = 'va' > resistance_data< / span > < span class = 'op' > < -< / span > < span class = 'va' > example_isolates< / span > < span class = 'op' > %> %< / span >
< span class = 'fu' > < a href = 'https://dplyr.tidyverse.org/reference/group_by.html' > group_by< / a > < / span > < span class = 'op' > (< / span > order < span class = 'op' > =< / span > < span class = 'fu' > < a href = 'mo_property.html' > mo_order< / a > < / span > < span class = 'op' > (< / span > < span class = 'va' > mo< / span > < span class = 'op' > )< / span > , < span class = 'co' > # group on anything, like order< / span >
genus < span class = 'op' > =< / span > < span class = 'fu' > < a href = 'mo_property.html' > mo_genus< / a > < / span > < span class = 'op' > (< / span > < span class = 'va' > mo< / span > < span class = 'op' > )< / span > < span class = 'op' > )< / span > < span class = 'op' > %> %< / span > < span class = 'co' > # and genus as we do here< / span >
< span class = 'fu' > < a href = 'https://dplyr.tidyverse.org/reference/summarise_all.html' > summarise_if< / a > < / span > < span class = 'op' > (< / span > < span class = 'va' > is.rsi< / span > , < span class = 'va' > resistance< / span > < span class = 'op' > )< / span > < span class = 'co' > # then get resistance of all drugs< / span >
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< span class = 'co' > # now conduct PCA for certain antimicrobial agents< / span >
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< span class = 'va' > pca_result< / span > < span class = 'op' > < -< / span > < span class = 'va' > resistance_data< / span > < span class = 'op' > %> %< / span >
< span class = 'fu' > pca< / span > < span class = 'op' > (< / span > < span class = 'va' > AMC< / span > , < span class = 'va' > CXM< / span > , < span class = 'va' > CTX< / span > , < span class = 'va' > CAZ< / span > , < span class = 'va' > GEN< / span > , < span class = 'va' > TOB< / span > , < span class = 'va' > TMP< / span > , < span class = 'va' > SXT< / span > < span class = 'op' > )< / span >
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< span class = 'va' > pca_result< / span >
< span class = 'fu' > < a href = 'https://rdrr.io/r/base/summary.html' > summary< / a > < / span > < span class = 'op' > (< / span > < span class = 'va' > pca_result< / span > < span class = 'op' > )< / span >
< span class = 'fu' > < a href = 'https://rdrr.io/r/stats/biplot.html' > biplot< / a > < / span > < span class = 'op' > (< / span > < span class = 'va' > pca_result< / span > < span class = 'op' > )< / span >
< span class = 'fu' > < a href = 'ggplot_pca.html' > ggplot_pca< / a > < / span > < span class = 'op' > (< / span > < span class = 'va' > pca_result< / span > < span class = 'op' > )< / span > < span class = 'co' > # a new and convenient plot function< / span >
< span class = 'op' > }< / span >
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< span class = 'co' > # }< / span >
< / pre >
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< p > Developed by < a href = 'https://www.rug.nl/staff/m.s.berends/' > Matthijs S. Berends< / a > , < a href = 'https://www.rug.nl/staff/c.f.luz/' > Christian F. Luz< / a > , < a href = 'https://www.rug.nl/staff/a.w.friedrich/' > Alexander W. Friedrich< / a > , < a href = 'https://www.rug.nl/staff/b.sinha/' > Bhanu N. M. Sinha< / a > , < a href = 'https://www.rug.nl/staff/c.j.albers/' > Casper J. Albers< / a > , < a href = 'https://www.rug.nl/staff/c.glasner/' > Corinna Glasner< / a > .< / p >
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