OLS.__init__() missing 1 required positional argument: 'endog'

这个错误通常是因为在初始化OLS模型时，没有正确传入模型的因变量（endogenous variable）。因此，您需要检查代码中是否正确指定了模型的因变量。

例如，如果您的因变量是y，您需要在初始化OLS模型时将其传递给endog参数，如下所示：

import statsmodels.api as sm

# 假设x是自变量，y是因变量
model = sm.OLS(endog=y, exog=x)

# 接下来，您可以拟合模型并进行预测等操作

请注意，这只是一个示例，具体的代码实现可能会因数据和模型的不同而有所不同。

相关问题

Traceback (most recent call last): File "D:\kelly\PycharmProjects\pythonProject8\大作业.py", line 145, in <module> model = smf.ols('ExRet ~ PEL1', data=datafit[['ExRet', 'PEL1']].iloc[:(n_in+i),:]) File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 226, in from_formula mod = cls(endog, exog, *args, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\regression\linear_model.py", line 906, in __init__ super(OLS, self).__init__(endog, exog, missing=missing, File "D:\python3.10\lib\site-packages\statsmodels\regression\linear_model.py", line 733, in __init__ super(WLS, self).__init__(endog, exog, missing=missing, File "D:\python3.10\lib\site-packages\statsmodels\regression\linear_model.py", line 190, in __init__ super(RegressionModel, self).__init__(endog, exog, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 267, in __init__ super().__init__(endog, exog, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 92, in __init__ self.data = self._handle_data(endog, exog, missing, hasconst, File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 132, in _handle_data data = handle_data(endog, exog, missing, hasconst, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\base\data.py", line 700, in handle_data return klass(endog, exog=exog, missing=missing, hasconst=hasconst, File "D:\python3.10\lib\site-packages\statsmodels\base\data.py", line 88, in __init__ self._handle_constant(hasconst) File "D:\python3.10\lib\site-packages\statsmodels\base\data.py", line 132, in _handle_constant exog_max = np.max(self.exog, axis=0) File "<__array_function__ internals>", line 180, in amax File "D:\python3.10\lib\site-packages\numpy\core\fromnumeric.py", line 2793, in amax return _wrapreduction(a, np.maximum, 'max', axis, None, out, File "D:\python3.10\lib\site-packages\numpy\core\fromnumeric.py", line 86, in _wrapreduction return ufunc.reduce(obj, axis, dtype, out, **passkwargs) ValueError: zero-size array to reduction operation maximum which has no identity报错如何四u该

根据报错信息，很可能是因为数据集中的某些列中没有数据，导致使用`np.max()`函数时出现了"zero-size array"的错误。可以在使用`np.max()`函数之前先检查数据集中是否存在空值或缺失值，或者可以使用`np.nanmax()`函数来处理数据，该函数可以忽略缺失值并返回最大值。同时，也可以检查一下数据的维度是否符合要求。如果无法解决问题，可以将报错信息和相关代码提供给更有经验的人士进行调试。

het_white() missing 1 required positional argument: 'exog'

`het_white()` is a function from the statsmodels library that tests for heteroscedasticity in a linear regression model. The function takes two arguments, `endog` and `exog`, where `endog` is the dependent variable and `exog` is a matrix of explanatory variables.

The error message you are seeing suggests that you are missing the `exog` argument when calling the `het_white()` function. You need to provide a matrix of explanatory variables as the second argument to the function. Here's an example of how to use the function:

import statsmodels.api as sm

# Load the data
data = sm.datasets.stackloss.load()

# Define the dependent and explanatory variables
y = data.endog
X = data.exog

# Fit a linear regression model
model = sm.OLS(y, X).fit()

# Test for heteroscedasticity
test_results = sm.stats.diagnostic.het_white(model.resid, X)

In this example, we first load a dataset of stack loss measurements and define the dependent variable `y` and explanatory variables `X`. We then fit a linear regression model using `sm.OLS()` and test for heteroscedasticity using `sm.stats.diagnostic.het_white()`, passing in the residuals from the model and the explanatory variables `X` as arguments.