This function performs Poisson scaling on a numeric matrix or data frame. Poisson scaling scales each variable by its square root mean value, with an optional scaling offset to avoid over-scaling when a variable has a near-zero mean.
Usage
poisson_scale(x, sc = NULL, drop.na = TRUE, options = list())Arguments
- x
A numeric matrix or data frame.
- sc
A vector of previously calculated scales. If provided, these scales will be applied to the data.
- drop.na
A logical value indicating whether to remove rows containing missing values. If
TRUE(the default), such rows are removed. IfFALSE, missing values are kept and ignored when computing the means.- options
A list of options for Poisson scaling. See the 'Options' section below.
Value
If sc is not provided, the function returns a list with the following components:
xsThe Poisson-scaled data.
scA vector of scales calculated for the given data.
If sc is provided, the function returns the Poisson-scaled data xs.
Options
The options argument is a list with the following fields:
offset: A numeric value representing the percentage of the maximum mean value to be used as an offset on all scales. Avoids division by near-zero means. Default is 3.mode: An integer value (1 or 2) specifying the dimension of the data on which to calculate the mean value for scaling. 1 = mean over rows (to scale variables); 2 = mean over columns (to scale samples). Default is 1.
Examples
data(forageLIBS)
spectra <- forageLIBS[-(1:14)] # the spectral channels
res <- poisson_scale(spectra[1:300, ])
head(res$sc)
#> 199.3771616 199.4644141 199.5516666 199.6389192 199.7261717 199.8134242
#> 51.53800 51.59162 51.94261 51.63528 51.66161 51.70972
# the same scales for new spectra
poisson_scale(spectra[301:368, ], sc = res$sc)[1:3, 1:4]
#> 199.3771616 199.4644141 199.5516666 199.6389192
#> [1,] 13.27176 13.47118 14.74704 13.45979
#> [2,] 13.25236 13.23858 14.51602 13.24676
#> [3,] 13.46579 13.49056 14.49677 13.69219