Source code for statsmodels.multivariate.pca
"""Principal Component Analysis
Author: josef-pktd
Modified by Kevin Sheppard
"""
import numpy as np
import pandas as pd
from statsmodels.tools.sm_exceptions import (ValueWarning,
EstimationWarning)
from statsmodels.tools.validation import (string_like,
array_like,
bool_like,
float_like,
int_like,
)
def _norm(x):
return np.sqrt(np.sum(x * x))
[docs]
class PCA:
"""
Principal Component Analysis
Parameters
----------
data : array_like
Variables in columns, observations in rows.
ncomp : int, optional
Number of components to return. If None, returns the as many as the
smaller of the number of rows or columns in data.
standardize : bool, optional
Flag indicating to use standardized data with mean 0 and unit
variance. standardized being True implies demean. Using standardized
data is equivalent to computing principal components from the
correlation matrix of data.
demean : bool, optional
Flag indicating whether to demean data before computing principal
components. demean is ignored if standardize is True. Demeaning data
but not standardizing is equivalent to computing principal components
from the covariance matrix of data.
normalize : bool , optional
Indicates whether to normalize the factors to have unit inner product.
If False, the loadings will have unit inner product.
gls : bool, optional
Flag indicating to implement a two-step GLS estimator where
in the first step principal components are used to estimate residuals,
and then the inverse residual variance is used as a set of weights to
estimate the final principal components. Setting gls to True requires
ncomp to be less then the min of the number of rows or columns.
weights : ndarray, optional
Series weights to use after transforming data according to standardize
or demean when computing the principal components.
method : str, optional
Sets the linear algebra routine used to compute eigenvectors:
* 'svd' uses a singular value decomposition (default).
* 'eig' uses an eigenvalue decomposition of a quadratic form
* 'nipals' uses the NIPALS algorithm and can be faster than SVD when
ncomp is small and nvars is large. See notes about additional changes
when using NIPALS.
missing : {str, None}
Method for missing data. Choices are:
* 'drop-row' - drop rows with missing values.
* 'drop-col' - drop columns with missing values.
* 'drop-min' - drop either rows or columns, choosing by data retention.
* 'fill-em' - use EM algorithm to fill missing value. ncomp should be
set to the number of factors required.
* `None` raises if data contains NaN values.
tol : float, optional
Tolerance to use when checking for convergence when using NIPALS.
max_iter : int, optional
Maximum iterations when using NIPALS.
tol_em : float
Tolerance to use when checking for convergence of the EM algorithm.
max_em_iter : int
Maximum iterations for the EM algorithm.
svd_full_matrices : bool, optional
If the 'svd' method is selected, this flag is used to set the parameter
'full_matrices' in the singular value decomposition method. Is set to
False by default.
Attributes
----------
factors : array or DataFrame
nobs by ncomp array of principal components (scores)
scores : array or DataFrame
nobs by ncomp array of principal components - identical to factors
loadings : array or DataFrame
ncomp by nvar array of principal component loadings for constructing
the factors
coeff : array or DataFrame
nvar by ncomp array of principal component loadings for constructing
the projections
projection : array or DataFrame
nobs by var array containing the projection of the data onto the ncomp
estimated factors
rsquare : array or Series
ncomp array where the element in the ith position is the R-square
of including the fist i principal components. Note: values are
calculated on the transformed data, not the original data
ic : array or DataFrame
ncomp by 3 array containing the Bai and Ng (2003) Information
criteria. Each column is a different criteria, and each row
represents the number of included factors.
eigenvals : array or Series
nvar array of eigenvalues
eigenvecs : array or DataFrame
nvar by nvar array of eigenvectors
weights : ndarray
nvar array of weights used to compute the principal components,
normalized to unit length
transformed_data : ndarray
Standardized, demeaned and weighted data used to compute
principal components and related quantities
cols : ndarray
Array of indices indicating columns used in the PCA
rows : ndarray
Array of indices indicating rows used in the PCA
Notes
-----
The default options perform principal component analysis on the
demeaned, unit variance version of data. Setting standardize to False
will instead only demean, and setting both standardized and
demean to False will not alter the data.
Once the data have been transformed, the following relationships hold when
the number of components (ncomp) is the same as tne minimum of the number
of observation or the number of variables.
.. math:
X' X = V \\Lambda V'
.. math:
F = X V
.. math:
X = F V'
where X is the `data`, F is the array of principal components (`factors`
or `scores`), and V is the array of eigenvectors (`loadings`) and V' is
the array of factor coefficients (`coeff`).
When weights are provided, the principal components are computed from the
modified data
.. math:
\\Omega^{-\\frac{1}{2}} X
where :math:`\\Omega` is a diagonal matrix composed of the weights. For
example, when using the GLS version of PCA, the elements of :math:`\\Omega`
will be the inverse of the variances of the residuals from
.. math:
X - F V'
where the number of factors is less than the rank of X
References
----------
.. [*] J. Bai and S. Ng, "Determining the number of factors in approximate
factor models," Econometrica, vol. 70, number 1, pp. 191-221, 2002
Examples
--------
Basic PCA using the correlation matrix of the data
>>> import numpy as np
>>> from statsmodels.multivariate.pca import PCA
>>> x = np.random.randn(100)[:, None]
>>> x = x + np.random.randn(100, 100)
>>> pc = PCA(x)
Note that the principal components are computed using a SVD and so the
correlation matrix is never constructed, unless method='eig'.
PCA using the covariance matrix of the data
>>> pc = PCA(x, standardize=False)
Limiting the number of factors returned to 1 computed using NIPALS
>>> pc = PCA(x, ncomp=1, method='nipals')
>>> pc.factors.shape
(100, 1)
"""
def __init__(self, data, ncomp=None, standardize=True, demean=True,
normalize=True, gls=False, weights=None, method='svd',
missing=None, tol=5e-8, max_iter=1000, tol_em=5e-8,
max_em_iter=100, svd_full_matrices=False):
self._index = None
self._columns = []
if isinstance(data, pd.DataFrame):
self._index = data.index
self._columns = data.columns
self.data = array_like(data, "data", ndim=2)
# Store inputs
self._gls = bool_like(gls, "gls")
self._normalize = bool_like(normalize, "normalize")
self._svd_full_matrices = bool_like(svd_full_matrices, "svd_fm")
self._tol = float_like(tol, "tol")
if not 0 < self._tol < 1:
raise ValueError('tol must be strictly between 0 and 1')
self._max_iter = int_like(max_iter, "int_like")
self._max_em_iter = int_like(max_em_iter, "max_em_iter")
self._tol_em = float_like(tol_em, "tol_em")
# Prepare data
self._standardize = bool_like(standardize, "standardize")
self._demean = bool_like(demean, "demean")
self._nobs, self._nvar = self.data.shape
weights = array_like(weights, "weights", maxdim=1, optional=True)
if weights is None:
weights = np.ones(self._nvar)
else:
weights = np.array(weights).flatten()
if weights.shape[0] != self._nvar:
raise ValueError('weights should have nvar elements')
weights = weights / np.sqrt((weights ** 2.0).mean())
self.weights = weights
# Check ncomp against maximum
min_dim = min(self._nobs, self._nvar)
self._ncomp = min_dim if ncomp is None else ncomp
if self._ncomp > min_dim:
import warnings
warn = 'The requested number of components is more than can be ' \
'computed from data. The maximum number of components is ' \
'the minimum of the number of observations or variables'
warnings.warn(warn, ValueWarning)
self._ncomp = min_dim
self._method = method
# Workaround to avoid instance methods in __dict__
if self._method not in ('eig', 'svd', 'nipals'):
raise ValueError(f'method {method} is not known.')
if self._method == 'svd':
self._svd_full_matrices = True
self.rows = np.arange(self._nobs)
self.cols = np.arange(self._nvar)
# Handle missing
self._missing = string_like(missing, "missing", optional=True)
self._adjusted_data = self.data
self._adjust_missing()
# Update size
self._nobs, self._nvar = self._adjusted_data.shape
if self._ncomp == np.min(self.data.shape):
self._ncomp = np.min(self._adjusted_data.shape)
elif self._ncomp > np.min(self._adjusted_data.shape):
raise ValueError('When adjusting for missing values, user '
'provided ncomp must be no larger than the '
'smallest dimension of the '
'missing-value-adjusted data size.')
# Attributes and internal values
self._tss = 0.0
self._ess = None
self.transformed_data = None
self._mu = None
self._sigma = None
self._ess_indiv = None
self._tss_indiv = None
self.scores = self.factors = None
self.loadings = None
self.coeff = None
self.eigenvals = None
self.eigenvecs = None
self.projection = None
self.rsquare = None
self.ic = None
# Prepare data
self.transformed_data = self._prepare_data()
# Perform the PCA
self._pca()
if gls:
self._compute_gls_weights()
self.transformed_data = self._prepare_data()
self._pca()
# Final calculations
self._compute_rsquare_and_ic()
if self._index is not None:
self._to_pandas()
def _adjust_missing(self):
"""
Implements alternatives for handling missing values
"""
def keep_col(x):
index = np.logical_not(np.any(np.isnan(x), 0))
return x[:, index], index
def keep_row(x):
index = np.logical_not(np.any(np.isnan(x), 1))
return x[index, :], index
if self._missing == 'drop-col':
self._adjusted_data, index = keep_col(self.data)
self.cols = np.where(index)[0]
self.weights = self.weights[index]
elif self._missing == 'drop-row':
self._adjusted_data, index = keep_row(self.data)
self.rows = np.where(index)[0]
elif self._missing == 'drop-min':
drop_col, drop_col_index = keep_col(self.data)
drop_col_size = drop_col.size
drop_row, drop_row_index = keep_row(self.data)
drop_row_size = drop_row.size
if drop_row_size > drop_col_size:
self._adjusted_data = drop_row
self.rows = np.where(drop_row_index)[0]
else:
self._adjusted_data = drop_col
self.weights = self.weights[drop_col_index]
self.cols = np.where(drop_col_index)[0]
elif self._missing == 'fill-em':
self._adjusted_data = self._fill_missing_em()
elif self._missing is None:
if not np.isfinite(self._adjusted_data).all():
raise ValueError("""\
data contains non-finite values (inf, NaN). You should drop these values or
use one of the methods for adjusting data for missing-values.""")
else:
raise ValueError('missing method is not known.')
if self._index is not None:
self._columns = self._columns[self.cols]
self._index = self._index[self.rows]
# Check adjusted data size
if self._adjusted_data.size == 0:
raise ValueError('Removal of missing values has eliminated '
'all data.')
def _compute_gls_weights(self):
"""
Computes GLS weights based on percentage of data fit
"""
projection = np.asarray(self.project(transform=False))
errors = self.transformed_data - projection
if self._ncomp == self._nvar:
raise ValueError('gls can only be used when ncomp < nvar '
'so that residuals have non-zero variance')
var = (errors ** 2.0).mean(0)
weights = 1.0 / var
weights = weights / np.sqrt((weights ** 2.0).mean())
nvar = self._nvar
eff_series_perc = (1.0 / sum((weights / weights.sum()) ** 2.0)) / nvar
if eff_series_perc < 0.1:
eff_series = int(np.round(eff_series_perc * nvar))
import warnings
warn = f"""\
Many series are being down weighted by GLS. Of the {nvar} series, the GLS
estimates are based on only {eff_series} (effective) series."""
warnings.warn(warn, EstimationWarning)
self.weights = weights
def _pca(self):
"""
Main PCA routine
"""
self._compute_eig()
self._compute_pca_from_eig()
self.projection = self.project()
def __repr__(self):
string = self.__str__()
string = string[:-1]
string += ', id: ' + hex(id(self)) + ')'
return string
def __str__(self):
string = 'Principal Component Analysis('
string += 'nobs: ' + str(self._nobs) + ', '
string += 'nvar: ' + str(self._nvar) + ', '
if self._standardize:
kind = 'Standardize (Correlation)'
elif self._demean:
kind = 'Demean (Covariance)'
else:
kind = 'None'
string += 'transformation: ' + kind + ', '
if self._gls:
string += 'GLS, '
string += 'normalization: ' + str(self._normalize) + ', '
string += 'number of components: ' + str(self._ncomp) + ', '
string += 'method: ' + 'Eigenvalue' if self._method == 'eig' else 'SVD'
string += ')'
return string
def _prepare_data(self):
"""
Standardize or demean data.
"""
adj_data = self._adjusted_data
if np.all(np.isnan(adj_data)):
return np.empty(adj_data.shape[1]).fill(np.nan)
self._mu = np.nanmean(adj_data, axis=0)
self._sigma = np.sqrt(np.nanmean((adj_data - self._mu) ** 2.0, axis=0))
if self._standardize:
data = (adj_data - self._mu) / self._sigma
elif self._demean:
data = (adj_data - self._mu)
else:
data = adj_data
return data / np.sqrt(self.weights)
def _compute_eig(self):
"""
Wrapper for actual eigenvalue method
This is a workaround to avoid instance methods in __dict__
"""
if self._method == 'eig':
return self._compute_using_eig()
elif self._method == 'svd':
return self._compute_using_svd()
else: # self._method == 'nipals'
return self._compute_using_nipals()
def _compute_using_svd(self):
"""SVD method to compute eigenvalues and eigenvecs"""
x = self.transformed_data
u, s, v = np.linalg.svd(x, full_matrices=self._svd_full_matrices)
self.eigenvals = s ** 2.0
self.eigenvecs = v.T
def _compute_using_eig(self):
"""
Eigenvalue decomposition method to compute eigenvalues and eigenvectors
"""
x = self.transformed_data
self.eigenvals, self.eigenvecs = np.linalg.eigh(x.T.dot(x))
def _compute_using_nipals(self):
"""
NIPALS implementation to compute small number of eigenvalues
and eigenvectors
"""
x = self.transformed_data
if self._ncomp > 1:
x = x + 0.0 # Copy
tol, max_iter, ncomp = self._tol, self._max_iter, self._ncomp
vals = np.zeros(self._ncomp)
vecs = np.zeros((self._nvar, self._ncomp))
for i in range(ncomp):
max_var_ind = np.argmax(x.var(0))
factor = x[:, [max_var_ind]]
_iter = 0
diff = 1.0
while diff > tol and _iter < max_iter:
vec = x.T.dot(factor) / (factor.T.dot(factor))
vec = vec / np.sqrt(vec.T.dot(vec))
factor_last = factor
factor = x.dot(vec) / (vec.T.dot(vec))
diff = _norm(factor - factor_last) / _norm(factor)
_iter += 1
vals[i] = (factor ** 2).sum()
vecs[:, [i]] = vec
if ncomp > 1:
x -= factor.dot(vec.T)
self.eigenvals = vals
self.eigenvecs = vecs
def _fill_missing_em(self):
"""
EM algorithm to fill missing values
"""
non_missing = np.logical_not(np.isnan(self.data))
# If nothing missing, return without altering the data
if np.all(non_missing):
return self.data
# 1. Standardized data as needed
data = self.transformed_data = np.asarray(self._prepare_data())
ncomp = self._ncomp
# 2. Check for all nans
col_non_missing = np.sum(non_missing, 1)
row_non_missing = np.sum(non_missing, 0)
if np.any(col_non_missing < ncomp) or np.any(row_non_missing < ncomp):
raise ValueError('Implementation requires that all columns and '
'all rows have at least ncomp non-missing values')
# 3. Get mask
mask = np.isnan(data)
# 4. Compute mean
mu = np.nanmean(data, 0)
# 5. Replace missing with mean
projection = np.ones((self._nobs, 1)) * mu
projection_masked = projection[mask]
data[mask] = projection_masked
# 6. Compute eigenvalues and fit
diff = 1.0
_iter = 0
while diff > self._tol_em and _iter < self._max_em_iter:
last_projection_masked = projection_masked
# Set transformed data to compute eigenvalues
self.transformed_data = data
# Call correct eig function here
self._compute_eig()
# Call function to compute factors and projection
self._compute_pca_from_eig()
projection = np.asarray(self.project(transform=False,
unweight=False))
projection_masked = projection[mask]
data[mask] = projection_masked
delta = last_projection_masked - projection_masked
diff = _norm(delta) / _norm(projection_masked)
_iter += 1
# Must copy to avoid overwriting original data since replacing values
data = self._adjusted_data + 0.0
projection = np.asarray(self.project())
data[mask] = projection[mask]
return data
def _compute_pca_from_eig(self):
"""
Compute relevant statistics after eigenvalues have been computed
"""
# Ensure sorted largest to smallest
vals, vecs = self.eigenvals, self.eigenvecs
indices = np.argsort(vals)
indices = indices[::-1]
vals = vals[indices]
vecs = vecs[:, indices]
if (vals <= 0).any():
# Discard and warn
num_good = vals.shape[0] - (vals <= 0).sum()
if num_good < self._ncomp:
import warnings
warnings.warn('Only {num:d} eigenvalues are positive. '
'This is the maximum number of components '
'that can be extracted.'.format(num=num_good),
EstimationWarning)
self._ncomp = num_good
vals[num_good:] = np.finfo(np.float64).tiny
# Use ncomp for the remaining calculations
vals = vals[:self._ncomp]
vecs = vecs[:, :self._ncomp]
self.eigenvals, self.eigenvecs = vals, vecs
# Select correct number of components to return
self.scores = self.factors = self.transformed_data.dot(vecs)
self.loadings = vecs
self.coeff = vecs.T
if self._normalize:
self.coeff = (self.coeff.T * np.sqrt(vals)).T
self.factors /= np.sqrt(vals)
self.scores = self.factors
def _compute_rsquare_and_ic(self):
"""
Final statistics to compute
"""
# TSS and related calculations
# TODO: This needs careful testing, with and without weights,
# gls, standardized and demean
weights = self.weights
ss_data = self.transformed_data * np.sqrt(weights)
self._tss_indiv = np.sum(ss_data ** 2, 0)
self._tss = np.sum(self._tss_indiv)
self._ess = np.zeros(self._ncomp + 1)
self._ess_indiv = np.zeros((self._ncomp + 1, self._nvar))
for i in range(self._ncomp + 1):
# Projection in the same space as transformed_data
projection = self.project(ncomp=i, transform=False, unweight=False)
indiv_rss = (projection ** 2).sum(axis=0)
rss = indiv_rss.sum()
self._ess[i] = self._tss - rss
self._ess_indiv[i, :] = self._tss_indiv - indiv_rss
self.rsquare = 1.0 - self._ess / self._tss
# Information Criteria
ess = self._ess
invalid = ess <= 0 # Prevent log issues of 0
if invalid.any():
last_obs = (np.where(invalid)[0]).min()
ess = ess[:last_obs]
log_ess = np.log(ess)
r = np.arange(ess.shape[0])
nobs, nvar = self._nobs, self._nvar
sum_to_prod = (nobs + nvar) / (nobs * nvar)
min_dim = min(nobs, nvar)
penalties = np.array([sum_to_prod * np.log(1.0 / sum_to_prod),
sum_to_prod * np.log(min_dim),
np.log(min_dim) / min_dim])
penalties = penalties[:, None]
ic = log_ess + r * penalties
self.ic = ic.T
[docs]
def project(self, ncomp=None, transform=True, unweight=True):
"""
Project series onto a specific number of factors.
Parameters
----------
ncomp : int, optional
Number of components to use. If omitted, all components
initially computed are used.
transform : bool, optional
Flag indicating whether to return the projection in the original
space of the data (True, default) or in the space of the
standardized/demeaned data.
unweight : bool, optional
Flag indicating whether to undo the effects of the estimation
weights.
Returns
-------
array_like
The nobs by nvar array of the projection onto ncomp factors.
Notes
-----
"""
# Projection needs to be scaled/shifted based on inputs
ncomp = self._ncomp if ncomp is None else ncomp
if ncomp > self._ncomp:
raise ValueError('ncomp must be smaller than the number of '
'components computed.')
factors = np.asarray(self.factors)
coeff = np.asarray(self.coeff)
projection = factors[:, :ncomp].dot(coeff[:ncomp, :])
if transform or unweight:
projection *= np.sqrt(self.weights)
if transform:
# Remove the weights, which do not depend on transformation
if self._standardize:
projection *= self._sigma
if self._standardize or self._demean:
projection += self._mu
if self._index is not None:
projection = pd.DataFrame(projection,
columns=self._columns,
index=self._index)
return projection
def _to_pandas(self):
"""
Returns pandas DataFrames for all values
"""
index = self._index
# Principal Components
num_zeros = np.ceil(np.log10(self._ncomp))
comp_str = 'comp_{0:0' + str(int(num_zeros)) + 'd}'
cols = [comp_str.format(i) for i in range(self._ncomp)]
df = pd.DataFrame(self.factors, columns=cols, index=index)
self.scores = self.factors = df
# Projections
df = pd.DataFrame(self.projection,
columns=self._columns,
index=index)
self.projection = df
# Weights
df = pd.DataFrame(self.coeff, index=cols,
columns=self._columns)
self.coeff = df
# Loadings
df = pd.DataFrame(self.loadings,
index=self._columns, columns=cols)
self.loadings = df
# eigenvals
self.eigenvals = pd.Series(self.eigenvals)
self.eigenvals.name = 'eigenvals'
# eigenvecs
vec_str = comp_str.replace('comp', 'eigenvec')
cols = [vec_str.format(i) for i in range(self.eigenvecs.shape[1])]
self.eigenvecs = pd.DataFrame(self.eigenvecs, columns=cols)
# R2
self.rsquare = pd.Series(self.rsquare)
self.rsquare.index.name = 'ncomp'
self.rsquare.name = 'rsquare'
# IC
self.ic = pd.DataFrame(self.ic, columns=['IC_p1', 'IC_p2', 'IC_p3'])
self.ic.index.name = 'ncomp'
[docs]
def plot_scree(self, ncomp=None, log_scale=True,
cumulative=False, ax=None):
"""
Plot of the ordered eigenvalues
Parameters
----------
ncomp : int, optional
Number of components ot include in the plot. If None, will
included the same as the number of components computed
log_scale : boot, optional
Flag indicating whether ot use a log scale for the y-axis
cumulative : bool, optional
Flag indicating whether to plot the eigenvalues or cumulative
eigenvalues
ax : AxesSubplot, optional
An axes on which to draw the graph. If omitted, new a figure
is created
Returns
-------
matplotlib.figure.Figure
The handle to the figure.
"""
import statsmodels.graphics.utils as gutils
fig, ax = gutils.create_mpl_ax(ax)
ncomp = self._ncomp if ncomp is None else ncomp
vals = np.asarray(self.eigenvals)
vals = vals[:self._ncomp]
if cumulative:
vals = np.cumsum(vals)
if log_scale:
ax.set_yscale('log')
ax.plot(np.arange(ncomp), vals[: ncomp], 'bo')
ax.autoscale(tight=True)
xlim = np.array(ax.get_xlim())
sp = xlim[1] - xlim[0]
xlim += 0.02 * np.array([-sp, sp])
ax.set_xlim(xlim)
ylim = np.array(ax.get_ylim())
scale = 0.02
if log_scale:
sp = np.log(ylim[1] / ylim[0])
ylim = np.exp(np.array([np.log(ylim[0]) - scale * sp,
np.log(ylim[1]) + scale * sp]))
else:
sp = ylim[1] - ylim[0]
ylim += scale * np.array([-sp, sp])
ax.set_ylim(ylim)
ax.set_title('Scree Plot')
ax.set_ylabel('Eigenvalue')
ax.set_xlabel('Component Number')
fig.tight_layout()
return fig
[docs]
def plot_rsquare(self, ncomp=None, ax=None):
"""
Box plots of the individual series R-square against the number of PCs.
Parameters
----------
ncomp : int, optional
Number of components ot include in the plot. If None, will
plot the minimum of 10 or the number of computed components.
ax : AxesSubplot, optional
An axes on which to draw the graph. If omitted, new a figure
is created.
Returns
-------
matplotlib.figure.Figure
The handle to the figure.
"""
import statsmodels.graphics.utils as gutils
fig, ax = gutils.create_mpl_ax(ax)
ncomp = 10 if ncomp is None else ncomp
ncomp = min(ncomp, self._ncomp)
# R2s in rows, series in columns
r2s = 1.0 - self._ess_indiv / self._tss_indiv
r2s = r2s[1:]
r2s = r2s[:ncomp]
ax.boxplot(r2s.T)
ax.set_title('Individual Input $R^2$')
ax.set_ylabel('$R^2$')
ax.set_xlabel('Number of Included Principal Components')
return fig
[docs]
def pca(data, ncomp=None, standardize=True, demean=True, normalize=True,
gls=False, weights=None, method='svd'):
"""
Perform Principal Component Analysis (PCA).
Parameters
----------
data : ndarray
Variables in columns, observations in rows.
ncomp : int, optional
Number of components to return. If None, returns the as many as the
smaller to the number of rows or columns of data.
standardize : bool, optional
Flag indicating to use standardized data with mean 0 and unit
variance. standardized being True implies demean.
demean : bool, optional
Flag indicating whether to demean data before computing principal
components. demean is ignored if standardize is True.
normalize : bool , optional
Indicates whether th normalize the factors to have unit inner
product. If False, the loadings will have unit inner product.
gls : bool, optional
Flag indicating to implement a two-step GLS estimator where
in the first step principal components are used to estimate residuals,
and then the inverse residual variance is used as a set of weights to
estimate the final principal components
weights : ndarray, optional
Series weights to use after transforming data according to standardize
or demean when computing the principal components.
method : str, optional
Determines the linear algebra routine uses. 'eig', the default,
uses an eigenvalue decomposition. 'svd' uses a singular value
decomposition.
Returns
-------
factors : {ndarray, DataFrame}
Array (nobs, ncomp) of principal components (also known as scores).
loadings : {ndarray, DataFrame}
Array (ncomp, nvar) of principal component loadings for constructing
the factors.
projection : {ndarray, DataFrame}
Array (nobs, nvar) containing the projection of the data onto the ncomp
estimated factors.
rsquare : {ndarray, Series}
Array (ncomp,) where the element in the ith position is the R-square
of including the fist i principal components. The values are
calculated on the transformed data, not the original data.
ic : {ndarray, DataFrame}
Array (ncomp, 3) containing the Bai and Ng (2003) Information
criteria. Each column is a different criteria, and each row
represents the number of included factors.
eigenvals : {ndarray, Series}
Array of eigenvalues (nvar,).
eigenvecs : {ndarray, DataFrame}
Array of eigenvectors. (nvar, nvar).
Notes
-----
This is a simple function wrapper around the PCA class. See PCA for
more information and additional methods.
"""
pc = PCA(data, ncomp=ncomp, standardize=standardize, demean=demean,
normalize=normalize, gls=gls, weights=weights, method=method)
return (pc.factors, pc.loadings, pc.projection, pc.rsquare, pc.ic,
pc.eigenvals, pc.eigenvecs)
Last update:
Dec 16, 2024