Description Usage Arguments Examples
pcaBootPlot draws a 2D PCA plot using the first 2 principal components using the original and bootstrapped data to give some sense of variability.
1 2 3 4 5 6 7 8 | pcaBootPlot(data = NULL, groups = NULL, min.value = 1,
all.min.value = FALSE, num.boot.samples = 100, log2.transform = TRUE,
pdf.filename = NULL, pdf.width = 6, pdf.height = 6,
draw.legend = FALSE, legend.names = NULL, legend.x = NULL,
legend.y = NULL, transparency = 77, min.x = NULL, max.x = NULL,
min.y = NULL, max.y = NULL, correct.inversions = TRUE,
confidence.regions = FALSE, confidence.size = 0.95, step.size = 0.1,
trim.proportion = 0, return.samples = FALSE, use.prcomp = FALSE)
|
data |
A data.frame where the first column is named "ID" and contains IDs for each item measured. Measurements for each sample are in subsequent columns. |
groups |
The default value is |
min.value |
The default value is 1. |
all.min.value |
This parameter, set to either |
num.boot.samples |
The default value is 100. The number of bootstrap iterations to be performed. |
log2.transform |
The default value is |
pdf.filename |
If you wish to save the the graph as a PDF, you may use this argument to specify the filename. |
pdf.width |
If you specify a value for |
pdf.height |
If you specify a value for |
draw.legend |
The default value is |
legend.names |
If |
legend.x,legend.y |
If |
transparency |
The default value is 77. This allow you to set how transparent the bootstrapped symbols are in the graph. Values range from 00 to FF. |
min.x,min.y,max.x,max.y |
By default, pcaBootPlot automatically determines limits for the x and y axes. Use this option to override this behavior. |
correct.inversions |
The default value is |
confidence.regions |
The default value is |
confidence.size |
The default value is 0.95. A value betweeo 0 and 1 - the proportion of bootstrapped points that need to be within the confidence regions. |
step.size |
The default value is 0.1. This option determines how the radii for confidence regions are increased each iteration when trying to contain confidence.size of the bootstrapped samples. |
trim.proportion |
The default value is 0.0. This is the proportion of entries that should be removed from the plot based on the size of the the confidence regions. This should be a value between 0 and 1. For example, if you set it to 0.1, then the top 10 regions will be removed from the plot. |
return.samples |
The default value is |
use.prcomp |
The default value is |
1 2 3 4 5 6 7 |
Performing PCA on 3 samples
Using FactoMineR for analysis. However you may be able to speed up
computation by setting use.prcomp to TRUE
There are 0 duplicated entries
Filtering entries based on min.val and groups...
100 entries had at least one sample with value > 1
Using FactoMineR for analysis
Bootstrapping the PCA at the entry level...
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Done! Hooray!
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