regression

If memory servies me, in R there is a data type called factor which when used within a DataFrame can be automatically unpacked into the necessary columns of a regression design matrix. For example, a factor containing True/False/Maybe values would be transformed into: 1 0 0 0 1 0 or 0 0 1 for the purpose of using lower level regression code. Is there a way to achieve something similar using the pandas library? I see that there is some regression support within Pandas, but since I have my own customised regression routines I am really interested in the construction of the design matrix (a 2d

I've been digging into the API of statsmodels.regression.linear_model.RegressionResults and have found how to retrieve different flavors of heteroskedasticity corrected standard errors (via properties like HC0_se , etc.) However, I can't quite figure out how to get the t -tests on the coefficients to use these corrected standard errors. Is there a way to do this in the API, or do I have to do it manually? If the latter, can you suggest any guidance on how to do this with statsmodels results? The fit method of the linear models, discrete models and GLM, take a cov_type and a cov_kwds argument

I am using the ols.py code downloaded at scipy Cookbook (the download is in the first paragraph with the bold OLS) but I need to understand rather than using random data for the ols function to do a multiple linear regression. I have a specific dependent variable y , and three explanatory variables. Every time I try to put in my variables in place of the random variables, it gives me the error: TypeError: this constructor takes no arguments. Can anyone help? Is this possible to do? Here is a copy of the ols code I am trying to use along with the variables that I am trying to input from _

I'm trying to run a linear regression in Python that I have already done in R in order to find variables with 0 coefficients. The issue I'm running into is that the linear regression in R returns NAs for columns with low variance while the scikit learn regression returns the coefficients. In the R code, I find and save these variables by saving the variables with NAs as output from the linear regression, but I can't seem to figure out a way to mimic this behavior in python. The code I'm using can be found below. R Code: a <- c(23, 45, 546, 42, 68, 15, 47) b <- c(1, 2, 4, 6, 34, 2, 8) c <- c(22

I am trying to write a function for solving multiple regression using QR decomposition. Input: y vector and X matrix; output: b, e, R^2. So far I`ve got this and am terribly stuck; I think I have made everything way too complicated: QR.regression <- function(y, X) { X <- as.matrix(X) y <- as.vector(y) p <- as.integer(ncol(X)) if (is.na(p)) stop("ncol(X) is invalid") n <- as.integer(nrow(X)) if (is.na(n)) stop("nrow(X) is invalid") nr <- length(y) nc <- NCOL(X) # Householder for (j in seq_len(nc)) { id <- seq.int(j, nr) sigma <- sum(X[id, j]^2) s <- sqrt(sigma) diag_ej <- X[j, j] gamma <- 1.0 /

I have the following linear regression: import statsmodels.formula.api as sm model = sm.ols(formula = 'a ~ b + c', data = data).fit() I want to add a quadratic term for b in this model. Is there a simple way to do this with statsmodels.ols? Is there a better package I should be using to achieve this? Although the solution by Alexander is working, in some situations it is not very convenient. For example, each time you want to predict the outcome of the model for new values, you need to remember to pass both b**2 and b values which is cumbersome and should not be necessary. Although patsy does

There was already a question like this, but it was not answered, so I try to post it again. Does anyone know of an open-source implementation of a partial least squares algorithm in C++ (or C)? Or maybe a library that does it? FastPLS is a library that provides a C/C++ and MATLAB interface for speeding up partial least squares. Its author is Balaji Vasan Srinivasan. The author worked under the supervision of Professor Ramani Duraiswami at the University of Maryland, College Park, MD, USA. Partial Least Squares and Generalized Partial Least Squares models based on NIPALS algorithm . implement