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Getting transformed X values from OLS model using statsmodels

I am trying to do a linear regression. With the results I want to multiply each x with its own estimated coefficient: xi·βi.
However, I am doing a lot of transformations on xi.
For example:
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
def log_plus_1(x):
return np.log(x + 1.0)
df = sm.datasets.get_rdataset("Guerry", "HistData").data
df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()
formule = 'Lottery ~ pow(Literacy,2) + log_plus_1(Wealth)'
mod = smf.ols(formula=formule, data=df)
res = mod.fit()
res.params
Now I would need pow(Literacy, 2) and log_plus_1(Wealth). But since they go into the model, I was hoping to get them out of there too. Instead of transforming the data from the original dataset.
In R I would use res$model to get it.

The data is stored as attributes of the model, e.g. the design matrix is mod.exog, the dependent or response variable is mod.endog.
(I'm not sure I remember correctly the details of the following: The data that patsy returns after creating the transformed design matrix should, in this case, be a pandas DataFrame, and should be stored in mod.data.orig_exog or something like that.)
res.predict automatically handles the transformation, i.e. patsy uses the formula information to transform the data for the explanatory variables in prediction in the same way as the data was transformed in creating the model.
predict only returns the prediction and not the internally transformed predict exog.

Output of a statsmodels regression

I would like to perform a simple linear regression using statsmodels and I've tried several different methods by now but I just don't get it to work. The code that I have constructed now doesn't give me any errors but it also doesn't show me the result
I am trying to create a model for the variable "Direction" which takes the value 0 if the return for the corresponding date was negative and 1 if it was positive. The explinatory variables are the (5) lags of the returns. The df13 contains the lags and also the direction for each observed date. I tried this code and as I mentioned it doesn't give an error but says " Optimization terminated successfully.
Current function value: 0.682314
Iterations 5
However, I would like to see the typical table with all the beta values, their significance etc.
Also, what would you say, since Direction is a binary variable may it be better to use a logit instead of a linear model? However, in the assignment it appeared as a linear model.
And lastly, I am sorry its not displayed here correctly but I don't know how to write as code or insert my dataframe
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import os
import itertools
from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
...
X = df13[['Lag1', 'Lag2', 'Lag3', 'Lag4', 'Lag5']]
Y = df13['Direction']
X = sm.add_constant(X)
model = sm.Logit(Y.astype(float), X.astype(float)).fit()
predictions = model.predict(X)
print_model = model.summary
print(print_model)
Edit: I'm sure it has to be a logit regression so I updated that part

I don't know if this is unintentional, but it looks like you need to define X and Y separately:
X = df13[['Lag1', 'Lag2', 'Lag3', 'Lag4', 'Lag5']]
Y = df13['Direction']
Secondly, I'm not familiar with statsmodel, but I would try converting your dataframes to numpy arrays. You can do this with
Xnum = X.to_numpy()
ynum = y.to_numpy()
And try passing those to the regressors.

Python: Random intercept model (have to replicate R code)

I'm trying to replicate code from R that estimates a random intercept model. The R code is:
fit=lmer(resid~-1+(1|groupid),data=df)
I'm using the lmer command of the lme4 package to estimate random intercepts for the variable resid for observations in different groups (defined by groupid). There is no 'fixed effects' part, therefore no variable before the (1|groupid). Moreover, I do not want a constant estimated so that I get an intercept for each group.
Not sure how to do similar estimation in Python. I tried something like:
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
np.random.seed(12345)
df = pd.DataFrame(np.random.randn(25, 4), columns=list('ABCD'))
df['groupid'] = [1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5]
df['groupid'] = df['groupid'].astype('category')
###Random intercepts models
md = smf.mixedlm('A~B-1',data=df,groups=df['groupid'])
mdf = md.fit()
print(mdf.random_effects)
A is resid from the earlier example, while groupid is the same.
1) I am not sure whether the mdf.random_effects are the random intercepts I am looking for
2) I cannot remove the variable B, which I understand is the fixed effects part. If I try:
md = smf.mixedlm('A~-1',data=df,groups=df['groupid'])
I get an error that "Arrays cannot be empty".
Just trying to estimate the exact same model as in the R code. Any advice will be appreciated.

Python statsmodels logit wald test input

I have fitted a logisitic regression model to some data, everything is working great. I need to calculate the wald statistic which is a function of the model result.
My problem is that I do not understand, from the documentation, what the wald test requires as input? Specifically what is the R matrix and how is it generated?
I tried simply inputting the data I used to train and test the model as the R matrix, but I do not think this is correct. The documentation suggest examining the examples however none give an example of this test. I also asked the same question on crossvalidated but got shot down.
Kind regards
http://statsmodels.sourceforge.net/0.6.0/generated/statsmodels.discrete.discrete_model.LogitResults.wald_test.html#statsmodels.discrete.discrete_model.LogitResults.wald_test

The Wald test is used to test if a predictor is significant or not, of the form:
W = (beta_hat - beta_0) / SE(beta_hat) ~ N(0,1)
So somehow you'll want to input the predictors into the test. Judging from the example of the t.test and f.test, it may be simpler to input a string or tuple to indicate what you are testing.
Here is their example using a string for the f.test:
from statsmodels.datasets import longley
from statsmodels.formula.api import ols
dta = longley.load_pandas().data
formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
results = ols(formula, dta).fit()
hypotheses = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)'
f_test = results.f_test(hypotheses)
print(f_test)
And here is their example using a tuple:
import numpy as np
import statsmodels.api as sm
data = sm.datasets.longley.load()
data.exog = sm.add_constant(data.exog)
results = sm.OLS(data.endog, data.exog).fit()
r = np.zeros_like(results.params)
r[5:] = [1,-1]
T_test = results.t_test(r)
If you're still struggling getting the wald test to work, include your code and I can try to help make it work.

Using weightings in a Poisson model using Statsmodels module

I'm trying to convert the following code from R to Python using the Statsmodels module:
model <- glm(goals ~ att + def + home - (1), data=df, family=poisson, weights=weight)
I've got a similar dataframe (named df) using pandas, and currently have the following line in Python (version 3.4 if it makes a difference):
model = sm.Poisson.from_formula("goals ~ att + def + home - 1", df).fit()
Or, using GLM:
smf.glm("goals ~ att + def + home - 1", df, family=sm.families.Poisson()).fit()
However, I can't get the weighting terms to work. Each record in the dataframe has a date, and I want more recent records to be more valuable for fitting the model than older ones. I've not seen an example of it being used, but surely if it can be done in R, it can be done on Statsmodels... right?

freq_weights is now supported on GLM Poisson, but unfortunately not on sm.Poisson
To use it, pass freq_weights when creating the GLM:
import statsmodels.api as sm
import statsmodels.formula.api as smf
formula = "goals ~ att + def + home - 1"
smf.glm(formula, df, family=sm.families.Poisson(), freq_weights=df['freq_weight']).fit()

I've encountered the same issue.
there is a workaround that should lead to same results. add the weight in logarithm scale (np.log(weight)) you need as one of the explanatory variables with beta equal to 1 (offset option).
I can see there is an option for the exposure which doing the same as I explained above.

There are two solutions for setting up weights for Poisson regression. The first is to use freq_weigths in the GLM function as mentioned by MarkWPiper. The second is to just go with Poisson regression and pass the weights to exposure. As documented here: "Log(exposure) is added to the linear prediction with coefficient equal to 1." This does the same mathematical trick as mentioned by Yaron, although the parameter has a different original meaning. A sample code is as follows:
import statsmodels.api as sm
# or: from statsmodels.discrete.discrete_model import Poisson
fitted = sm.Poisson.from_formula("goals ~ att + def + home - 1", data=df, exposure=df['weight']).fit()