regression

How to perform stepwise regression in python ? There are methods for OLS in SCIPY but I am not able to do stepwise. Any help in this regard would be a great help. Thanks. Edit: I am trying to build a linear regression model. I have 5 independent variables and using forward stepwise regression, I aim to select variables such that my model has the lowest p-value. Following link explains the objective: https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&ved=0CEAQFjAD&url=http%3A%2F%2Fbusiness.fullerton.edu%2Fisds%2Fjlawrence%2FStat-On-Line%2FExcel%2520Notes%2FExcel%2520Notes%2520-

i am trying to do hyperparemeter search with using scikit-learn's GridSearchCV on XGBoost. During gridsearch i'd like it to early stop, since it reduce search time drastically and (expecting to) have better results on my prediction/regression task. I am using XGBoost via its Scikit-Learn API. model = xgb.XGBRegressor() GridSearchCV(model, paramGrid, verbose=verbose ,fit_params={'early_stopping_rounds':42}, cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX, trainY]), n_jobs=n_jobs, iid=iid).fit(trainX,trainY) I tried to give early stopping parameters with using fit_params, but then it throws

I have a bunch of data, generally in the form a, b, c, ..., y where y = f(a, b, c...) Most of them are three and four variables, and have 10k - 10M records. My general assumption is that they are algebraic in nature, something like: y = P1 a^E1 + P2 b^E2 + P3 c^E3 Unfortunately, my last statistical analysis class was 20 years ago. What is the easiest way to get a good approximation of f? Open source tools with a very minimal learning curve (i.e. something where I could get a decent approximation in an hour or so) would be ideal. Thanks! David Z In case it's useful, here's a Numpy/Scipy (Python

I have a regression model with binary outcome. I fitted the model with glmnet and got the selected variables and their coefficients. Since glmnet doesn't calculate variable importance, I would like to feed the exact output (selected variables and their coefficients) to glm to get the information (Standard errors, etc). I searched r documents, it seems I can use "method" option in glm to specify user defined function. But I failed to do so, could someone help me with this? "It is a very natural question to ask for standard errors of regression coefficients or other estimated quantities. In

I have data frame as: df=data.frame(x=rnorm(100),y1=rnorm(100),y2=rnorm(100),y3=...) I want to run a loop which regresses each column starting from the second column on the first column: for(i in names(df[,-1])){ model = lm(i~x, data=df) } But I failed. The point is that I want to do a loop of regression for each column and some column names is just a number (e.g. 404.1). I cannot find a way to run a loop for each column using the above command. Your code looks fine except when you call i within lm , R will read i as a string, which you can't regress things against. Using get will allow you to

I want to use aggregate with this custom function: #linear regression f-n CalculateLinRegrDiff = function (sample){ fit <- lm(value~ date, data = sample) diff(range(fit$fitted)) } dataset2 = aggregate(value ~ id + col, dataset, CalculateLinRegrDiff(dataset)) I receive the error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'FUN' of mode 'function' was not found What is wrong? Your syntax on using aggregate is wrong in the first place. Pass function CalculateLinRegrDiff not an evaluated one CalculateLinRegrDiff(dataset) to FUN argument. Secondly, you've chosen the

I wrote a code for multivariate polynomial regression, I used polynomial features and transformation function from sklearn. Is it possible to make multivariate logarithmic regression? Does sklearn have some kind of logarithmic transformation, like it has for polynomial features? How can I write multivariate logarithmic regression in python? This is my code for multivariate polynomial features: import numpy as np import pandas as pd import math import xlrd from sklearn import linear_model from sklearn.model_selection import train_test_split from sklearn.preprocessing import PolynomialFeatures

I have three datasets: response - matrix of 5(samples) x 10(dependent variables) predictors - matrix of 5(samples) x 2(independent variables) test_set - matrix of 10(samples) x 10(dependent variables defined in response) response <- matrix(sample.int(15, size = 5*10, replace = TRUE), nrow = 5, ncol = 10) colnames(response) <- c("1_DV","2_DV","3_DV","4_DV","5_DV","6_DV","7_DV","8_DV","9_DV","10_DV") predictors <- matrix(sample.int(15, size = 7*2, replace = TRUE), nrow = 5, ncol = 2) colnames(predictors) <- c("1_IV","2_IV") test_set <- matrix(sample.int(15, size = 10*2, replace = TRUE), nrow =