decostand in package vegan for several of the
available options. The formula method allows a convenient method for selecting or
excluding subsets of variables before applying the chosen
transformation.
## S3 method for class 'default':
tran(x, method, a = 1, b = 0, p = 2, base = exp(1),
na.rm = FALSE, na.value = 0, ...)## S3 method for class 'formula':
tran(formula, data = NULL, subset = NULL,
na.action = na.pass, \ldots)
x by. method = "log"
only. Can be a vector, in which case the vector of values to
multiply each column of x by.x before taking logs. method
= "log" only. Can be a vector, in which case the vector of values
to add to each column of x.log for further details. The default is
to compute natural logarithms.NA).decostand, or
other tran methods.~ foo + bar.model.frame for
details on these arguments. data will generally be the
object or environment within which the variables in the forumla are
searched for.x. If x
is a data frame, the returned value is like-wise a data frame. The
returned object also has an attribute "tran" giving the name of
applied transformation or standardization "method".sqrt: take the square roots of the observed values.cubert: take the cube root of the observed values.rootroot: take the fourthe root of the observed
values. This is also known as the root root transformation (Field et
al 1982).log: take the logarithms of the observed values. The
tansformation applied can be modified by constantsaandband thebaseof the logarithms. The transformation
applied is$x^* = \log_{\mathrm{base}}(ax + b)$reciprocal: returns the multiplicative inverse or
reciprocal,$1/x$, of the observed values.freq: divide by column (variable, species) maximum and
multiply by the number of non-zero items, so that the average of
non-zero entries is 1 (Oksanen 1983).center: centre all variables to zero mean.range: standardize values into range 0...1. If all
values are constant, they will be transformed to 0.percent: convert observed count values to percentages.proportion: convert observed count values to proportions.standardize: scalexto zero mean and unit
variance.pa: scalexto presence/absence scale (0/1).missing: replace missing values withna.value.chi.square: divide by row sums and square root of
column sums, and adjust for square root of matrix total
(Legendre & Gallagher 2001). When used with the Euclidean
distance, the distances should be similar to the the
Chi-square distance used in correspondence analysis. However, the
results fromcmdscalewould still differ, since
CA is a weighted ordination method.hellinger: square root of observed values that have
first been divided by row (site) sums (Legendre & Gallagher 2001).wisconsin: applies the Wisconsin double
standardization, where columns (species, variables) are first
standardized by maxima and then sites (rows) by site totals.pcent2prop: convert percentages to proportions.prop2pcent: convert proportions to percentages.logRatio: applies a log ransformation (seelogabove) to the data, then centres the data by rows (by subtraction of
the mean for rowifrom the observations in rowi). Using this transformation subsequent to PCA results in
Aitchison's Log Ratio Analysis (LRA), a means of dealing with closed
compositional data such as common in palaeoecology (Aitchison, 1983).power: applies a power tranformation.rowCenter: Centresxby rows through the
subtraction of the corresponding row mean from the observations in
the row.decostanddata(swapdiat)
## convert percentages to proportions
sptrans <- tran(swapdiat, "pcent2prop")
## apply Hellinger transformation
spHell <- tran(swapdiat, "hellinger")
## Dummy data to illustrate formula method
d <- data.frame(A = runif(10), B = runif(10), C = runif(10))
## simulate some missings
d[sample(10,3), 1] <- NA
## apply tran using formula
tran(~ . - B, data = d, na.action = na.pass,
method = "missing", na.value = 0)Run the code above in your browser using DataLab