linear-regression

Let's say that I have a numeric data matrix with columns w, x, y, z and I also want to add in the columns that are equivalent to w*x, w*y, w*z, x*y, x*z, y*z since I want my covariate matrix to include all pairwise interactions. Is there a clean and effective way to do this? If you mean in a model formula , then the ^ operator does this. ## dummy data set.seed(1) dat <- data.frame(Y = rnorm(10), x = rnorm(10), y = rnorm(10), z = rnorm(10)) The formula is form <- Y ~ (x + y + z)^2 which gives (using model.matrix() - which is used internally by the standard model fitting functions) model.matrix

I'm trying to learn QR decomposition, but can't figure out how to get the variance of beta_hat without resorting to traditional matrix calculations. I'm practising with the iris data set, and here's what I have so far: y<-(iris$Sepal.Length) x<-(iris$Sepal.Width) X<-cbind(1,x) n<-nrow(X) p<-ncol(X) qr.X<-qr(X) b<-(t(qr.Q(qr.X)) %*% y)[1:p] R<-qr.R(qr.X) beta<-as.vector(backsolve(R,b)) res<-as.vector(y-X %*% beta) Thanks for your help! setup (copying in your code) y <- iris$Sepal.Length x <- iris$Sepal.Width X <- cbind(1,x) n <- nrow(X) p <- ncol(X) qr.X <- qr(X) b <- (t(qr.Q(qr.X)) %*% y)[1:p]

Does scikit-learn provide facility to perform regression using a gaussian or polynomial kernel? I looked at the APIs and I don't see any. Has anyone built a package on top of scikit-learn that does this? Either you use Support Vector Regression sklearn.svm.SVR and set the appropritate kernel (see here ). Or you install the latest master version of sklearn and use the recently added sklearn.preprocessing.PolynomialFeatures (see here ) and then OLS or Ridge on top of that. Salvador Dali Theory Polynomial regression is a special case of linear regression. With the main idea of how do you select

I got good use out of pandas' MovingOLS class (source here ) within the deprecated stats/ols module. Unfortunately, it was gutted completely with pandas 0.20. The question of how to run rolling OLS regression in an efficient manner has been asked several times ( here , for instance), but phrased a little broadly and left without a great answer, in my view. Here are my questions: How can I best mimic the basic framework of pandas' MovingOLS ? The most attractive feature of this class was the ability to view multiple methods/attributes as separate time series--i.e. coefficients, r-squared, t

This seems a simple question, so I hope its a simple answer. I am plotting my points and fitting a linear model, which I can do OK. I then want to plot some summary statistics, for example the R Squared value, on the plot also. I can only seem to get the R Squared value at the command line. Any advice; do I need to be looking at ggplot or anything else? Thanks in advance. #Does the plot plot(df$VAR1, df$VAR2) #Adds the line abline(lm(df$VAR2~df$VAR1), col="red") #Shows stats on command line summary(lm(df$VAR2~df$VAR1)) You can abuse legend() because it has the handy logical placement: R> DF <-

I am trying to implement multivariate linear regression in Python using TensorFlow, but have run into some logical and implementation issues. My code throws the following error: Attempting to use uninitialized value Variable Caused by op u'Variable/read' Ideally the weights output should be [2, 3] def hypothesis_function(input_2d_matrix_trainingexamples, output_matrix_of_trainingexamples, initial_parameters_of_hypothesis_function, learning_rate, num_steps): # calculate num attributes and num examples number_of_attributes = len(input_2d_matrix_trainingexamples[0]) number_of_trainingexamples =

I have figured out how to make a table in R with 4 variables, which I am using for multiple linear regressions. The dependent variable (Lung) for each regression is taken from one column of a csv table of 22,000 columns. One of the independent variables (Blood) is taken from a corresponding column of a similar table. Each column represents the levels of a particular gene, which is why there are so many of them. There are also two additional variables (Age and Gender of each patient). When I enter in the linear regression equation, I use lm(Lung[,1] ~ Blood[,1] + Age + Gender), which works for

With scipy.stats.linregress I am performing a simple linear regression on some sets of highly correlated x,y experimental data, and initially visually inspecting each x,y scatter plot for outliers. More generally (i.e. programmatically) is there a way to identify and mask outliers? The statsmodels package has what you need. Look at this little code snippet and its output: # Imports # import statsmodels.api as smapi import statsmodels.graphics as smgraphics # Make data # x = range(30) y = [y*10 for y in x] # Add outlier # x.insert(6,15) y.insert(6,220) # Make graph # regression = smapi.OLS(x, y