pca
2.0.1
Background
Background
Installation
Installation
Uninstalling
Methods
Algorithm
Standardization
Explained Variance
Loadings
Examination of the loadings
Best Performing Features
Outlier detection
Hotelling T2
SPE/Dmodx
Selection of the Outliers
Detect new unseen outliers
Detection of outliers without PCA
Plots
Load dataset
Scatter plot
Biplot
Biplot (only arrows)
Explained variance plot
Alpha Transparency
Markers
Control color/marker/size per sample
3D plots
Toggle visible status
Control Arrows
Examples
Quickstart
Demonstration of feature importance
Analyzing Discrete datasets
Map unseen datapoints into fitted space
Normalizing out PCs
Colors in plots
Notebook
Documentation
Sponsor
Blog
Github
Colab Notebook
Citing
Additional Information
Coding quality
API References
Index
pca
Index
Index
B
|
C
|
D
|
F
|
H
|
I
|
M
|
N
|
P
|
S
|
T
|
W
B
biplot() (pca.pca.pca method)
biplot3d() (pca.pca.pca method)
C
compute_outliers() (pca.pca.pca method)
compute_topfeat() (pca.pca.pca method)
D
download() (pca.pca.wget method)
F
filename_from_url() (pca.pca.wget method)
fit_transform() (pca.pca.pca method)
H
hotellingsT2() (in module pca.pca)
I
import_example() (in module pca.pca)
(pca.pca.pca method)
M
module
pca.pca
multitest_correction() (in module pca.pca)
N
norm() (pca.pca.pca method)
normalize_size() (in module pca.pca)
P
pca (class in pca.pca)
pca.pca
module
plot() (pca.pca.pca method)
S
scatter() (pca.pca.pca method)
scatter3d() (pca.pca.pca method)
spe_dmodx() (in module pca.pca)
T
transform() (pca.pca.pca method)
W
wget (class in pca.pca)