statsmobel 提取输出的回归结果
作者：互联网
直接看return部分，举例
model = statsmodel.WLS(Y,X)
model = model.fit()
# 打印所有的结果
print(model.summary)
# 打印你想提取的结果，比如p值
print(model.pvalues)
class RegressionResults(base.LikelihoodModelResults):
r"""
This class summarizes the fit of a linear regression model.
It handles the output of contrasts, estimates of covariance, etc.
Returns
-------
**Attributes**
aic
Akaike's information criteria. For a model with a constant
:math:`-2llf + 2(df\_model + 1)`. For a model without a constant
:math:`-2llf + 2(df\_model)`.
bic
Bayes' information criteria. For a model with a constant
:math:`-2llf + \log(n)(df\_model+1)`. For a model without a constant
:math:`-2llf + \log(n)(df\_model)`
bse
The standard errors of the parameter estimates.
pinv_wexog
See specific model class docstring
centered_tss
The total (weighted) sum of squares centered about the mean.
cov_HC0
Heteroscedasticity robust covariance matrix. See HC0_se below.
cov_HC1
Heteroscedasticity robust covariance matrix. See HC1_se below.
cov_HC2
Heteroscedasticity robust covariance matrix. See HC2_se below.
cov_HC3
Heteroscedasticity robust covariance matrix. See HC3_se below.
cov_type
Parameter covariance estimator used for standard errors and t-stats
df_model
Model degrees of freedom. The number of regressors `p`. Does not
include the constant if one is present
df_resid
Residual degrees of freedom. `n - p - 1`, if a constant is present.
`n - p` if a constant is not included.
ess
Explained sum of squares. If a constant is present, the centered
total sum of squares minus the sum of squared residuals. If there is
no constant, the uncentered total sum of squares is used.
fvalue
F-statistic of the fully specified model. Calculated as the mean
squared error of the model divided by the mean squared error of the
residuals.
f_pvalue
p-value of the F-statistic
fittedvalues
The predicted values for the original (unwhitened) design.
het_scale
adjusted squared residuals for heteroscedasticity robust standard
errors. Is only available after `HC#_se` or `cov_HC#` is called.
See HC#_se for more information.
history
Estimation history for iterative estimators
HC0_se
White's (1980) heteroskedasticity robust standard errors.
Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1)
where e_i = resid[i]
HC0_se is a cached property.
When HC0_se or cov_HC0 is called the RegressionResults instance will
then have another attribute `het_scale`, which is in this case is just
resid**2.
HC1_se
MacKinnon and White's (1985) alternative heteroskedasticity robust
standard errors.
Defined as sqrt(diag(n/(n-p)*HC_0)
HC1_see is a cached property.
When HC1_se or cov_HC1 is called the RegressionResults instance will
then have another attribute `het_scale`, which is in this case is
n/(n-p)*resid**2.
HC2_se
MacKinnon and White's (1985) alternative heteroskedasticity robust
standard errors.
Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1)
where h_ii = x_i(X.T X)^(-1)x_i.T
HC2_see is a cached property.
When HC2_se or cov_HC2 is called the RegressionResults instance will
then have another attribute `het_scale`, which is in this case is
resid^(2)/(1-h_ii).
HC3_se
MacKinnon and White's (1985) alternative heteroskedasticity robust
standard errors.
Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1)
where h_ii = x_i(X.T X)^(-1)x_i.T
HC3_see is a cached property.
When HC3_se or cov_HC3 is called the RegressionResults instance will
then have another attribute `het_scale`, which is in this case is
resid^(2)/(1-h_ii)^(2).
model
A pointer to the model instance that called fit() or results.
mse_model
Mean squared error the model. This is the explained sum of
squares divided by the model degrees of freedom.
mse_resid
Mean squared error of the residuals. The sum of squared
residuals divided by the residual degrees of freedom.
mse_total
Total mean squared error. Defined as the uncentered total sum
of squares divided by n the number of observations.
nobs
Number of observations n.
normalized_cov_params
See specific model class docstring
params
The linear coefficients that minimize the least squares
criterion. This is usually called Beta for the classical
linear model.
pvalues
The two-tailed p values for the t-stats of the params.
resid
The residuals of the model.
resid_pearson
`wresid` normalized to have unit variance.
rsquared
R-squared of a model with an intercept. This is defined here
as 1 - `ssr`/`centered_tss` if the constant is included in the
model and 1 - `ssr`/`uncentered_tss` if the constant is
omitted.
rsquared_adj
Adjusted R-squared. This is defined here as 1 -
(`nobs`-1)/`df_resid` * (1-`rsquared`) if a constant is
included and 1 - `nobs`/`df_resid` * (1-`rsquared`) if no
constant is included.
scale
A scale factor for the covariance matrix. Default value is
ssr/(n-p). Note that the square root of `scale` is often
called the standard error of the regression.
ssr
Sum of squared (whitened) residuals.
uncentered_tss
Uncentered sum of squares. Sum of the squared values of the
(whitened) endogenous response variable.
wresid
The residuals of the transformed/whitened regressand and
regressor(s)
标签：statsmobel,输出,constant,cov,squared,resid,提取,model,se 来源： https://blog.csdn.net/qq_37890276/article/details/93016165