biostats.lda_plot

biostats.lda_plot(data, x, y)

Perform a linear discriminant analysis and draw a scatter plot to show the transformed data.

Parameters:

datapandas.DataFrame

The input data. Must contain at least two numeric columns and one categorical column.

xlist

The list of numeric variables to be analyzed.

ystr

The categorical variable that specifies the groups to be distinguished. Maximum 20 groups.

Returns:

figmatplotlib.figure.Figure

The generated plot.

See also

fa_plot

Perform a factor analysis and draw a scatter plot to show the transformed data.

pca_plot

Perform a principle component analysis and draw a scatter plot to show the transformed data.

linear_discriminant_analysis

Find the linear combination of a set of variables to distinguish between groups.

Examples

>>> import biostats as bs
>>> import matplotlib.pyplot as plt
>>> data = bs.dataset("iris.csv")
>>> data
     sepal_length  sepal_width  petal_length  petal_width    species
0             5.1          3.5           1.4          0.2     setosa
1             4.9          3.0           1.4          0.2     setosa
2             4.7          3.2           1.3          0.2     setosa
3             4.6          3.1           1.5          0.2     setosa
4             5.0          3.6           1.4          0.2     setosa
..            ...          ...           ...          ...        ...
145           6.7          3.0           5.2          2.3  virginica
146           6.3          2.5           5.0          1.9  virginica
147           6.5          3.0           5.2          2.0  virginica
148           6.2          3.4           5.4          2.3  virginica
149           5.9          3.0           5.1          1.8  virginica

We want to perform a linear discriminant analysis and visualize the transformed data.

>>> fig = bs.lda_plot(data=data, x=["sepal_length", "sepal_width", "petal_length" ,"petal_width"], y="species")
>>> plt.show()