API reference / Statistics and utilities

You are viewing the documentation for metbit 8.4.3. Change release context

metbit.utility

Statistics and utilities module in metbit 8.4.3.

import metbit.utility

Classes

lazypair

Methods

__init__(self, dataset, column_name)

get_index(self)

get_name(self)

get_meta(self)

get_column_name(self)

get_dataset(self)

gen_page

Methods

__init__(self, data_path)

This function takes in the path to the data folder and returns the HTML files for the OPLS-DA plots.

Parameters

data_pathstr

The path to the data folder. gen_page(data_path).get_files()

get_files(self)

oplsda_path

Methods

__init__(self, data_path)

make_path(self)

get_path(self)

Normality_distribution

Methods

__init__(self, data: pd.DataFrame)

plot_distribution(self, feature)

pca_distributions(self)

Normalise

Methods

__init__(self, data: pd.DataFrame, compute_missing: bool=True)

pqn_normalise(self, ref_index: list=None, plot: bool=True)

decimal_place_normalisation(self, decimals: int=2)

This function returns the dataframe with values rounded to a specified number of decimal places.

Parameters

decimalsint

The number of decimal places to round to.

z_score_normalisation(self)

This function returns the dataframe normalized using Z-Score.

linear_normalisation(self)

This function returns the dataframe normalized using Min-Max (linear normalization).

normalize_to_100(self)

This function returns the dataframe with values normalized to 100.

clipping_normalisation(self, lower: float, upper: float)

This function returns the dataframe with values clipped to the specified range.

Parameters

lowerfloat

The lower bound for clipping.

upperfloat

The upper bound for clipping.

standard_deviation_normalisation(self)

This function returns the dataframe normalized using Standard Deviation.

UnivarStats

Perform univariate statistical analysis and visualization using Plotly.

Parameters

dfpd.DataFrame

Input DataFrame containing the measurement and group columns.

x_colstr

Column name for the grouping variable.

y_colstr

Column name for the measurement variable.

group_orderlist of str, optional

Custom group plotting order.

custom_colorsdict of str -> str, optional

Mapping from group name to color.

stats_optionslist of str, optional

Supported["t-test", "anova", "nonparametric", "effect-size"].

p_value_thresholdfloat, default=0.05

Significance threshold.

annotate_style{'value', 'symbol'}, default='value'

Annotation stylenumeric or stars.

y_offset_factorfloat, default=0.35

Vertical spacing factor for annotations.

show_non_significantbool, default=True

Whether to display 'ns'.

correct_pstr or None, default='bonferroni'

Method for multiple testing correction. Supported: - 'bonferroni', 'holm', 'hochberg', 'hommel' - 'fdr_bh', 'fdr_by', 'fdr_tsbh', 'fdr_tsbky' - None or 'none' = no correction

title_str, optional

Plot title.

y_labelstr, optional

Y-axis label.

x_labelstr, optional

X-axis label.

fig_heightint, default=800

Figure height.

fig_widthint, default=600

Figure width.

plot_type{'box', 'violin'}, default='box'

Plot type.

show_axis_linesbool, default=True

Whether to show axis lines.

Methods

__init__(self, df: pd.DataFrame, x_col: str, y_col: str, group_order: Optional[List[str]]=None, custom_colors: Optional[Dict[str, str]]=None, stats_options: Optional[List[str]]=None, p_value_threshold: float=0.05, annotate_style: str='value', y_offset_factor: float=0.35, show_non_significant: bool=True, correct_p: Optional[str]='bonferroni', title_: Optional[str]=None, y_label: Optional[str]=None, x_label: Optional[str]=None, fig_height: int=800, fig_width: int=600, plot_type: str='box', show_axis_lines: bool=True)

compute_effsize(a, b, eftype: str='cohen')

plot(self, show_description: bool=True)

get_stats_table(self)

Return a DataFrame of statistical results.

Functions

project_name_generator()

Source

metbit/utility.py at v8.4.3