regression

How do you calculate a best fit line in python, and then plot it on a scatterplot in matplotlib? I was I calculate the linear best-fit line using Ordinary Least Squares Regression as follows: from sklearn import linear_model clf = linear_model.LinearRegression() x = [[t.x1,t.x2,t.x3,t.x4,t.x5] for t in self.trainingTexts] y = [t.human_rating for t in self.trainingTexts] clf.fit(x,y) regress_coefs = clf.coef_ regress_intercept = clf.intercept_ This is multivariate (there are many x-values for each case). So, X is a list of lists, and y is a single list. For example: x = [[1,2,3,4,5], [2,2,4,4,5

I'm looking for a way to produce a non-linear (preferably quadratic) curve, based on a 2D data set, for predictive purposes. Right now I'm using my own implementation of ordinary least squares (OLS) to produce a linear trend, but my trends are much more suited to a curve model. The data I'm analysing is system load over time. Here's the equation that I'm using to produce my linear coefficients: I've had a look at Math.NET Numerics and a few other libs, but they either provide interpolation instead of regression (which is of no use to me), or the code just doesn't work in some way. Anyone know

These are questions on how to calculate & reduce overfitting in machine learning. I think many new to machine learning will have the same questions, so I tried to be clear with my examples and questions in hope that answers here can help others. I have a very small sample of texts and I'm trying to predict values associated with them. I've used sklearn to calculate tf-idf, and insert those into a regression model for prediction. This gives me 26 samples with 6323 features - not a lot.. I know: >> count_vectorizer = CountVectorizer(min_n=1, max_n=1) >> term_freq = count_vectorizer.fit_transform

I hope that this one is not going to be "ask-and-answer" question... here goes: (multi)collinearity refers to extremely high correlations between predictors in the regression model. How to cure them... well, sometimes you don't need to "cure" collinearity, since it doesn't affect regression model itself, but interpretation of an effect of individual predictors. One way to spot collinearity is to put each predictor as a dependent variable, and other predictors as independent variables, determine R 2 , and if it's larger than .9 (or .95), we can consider predictor redundant. This is one "method"

I want to do a regression in R using glm , but is there a way to do it since I get the contrasts error. mydf <- data.frame(Group=c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12), WL=rep(c(1,0),12), New.Runner=c("N","N","N","N","N","N","Y","N","N","N","N","N","N","Y","N","N","N","Y","N","N","N","N","N","Y"), Last.Run=c(1,5,2,6,5,4,NA,3,7,2,4,9,8,NA,3,5,1,NA,6,10,7,9,2,NA)) mod <- glm(formula = WL~New.Runner+Last.Run, family = binomial, data = mydf) #Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : # contrasts can be applied only to factors with 2 or more levels Using

This question already has an answer here: Linear Regression and group by in R 10 answers I'm trying to run anova() in R and running into some difficulty. This is what I've done up to now to help shed some light on my question. Here is the str() of my data to this point. str(mhw) 'data.frame': 500 obs. of 5 variables: $ r : int 1 2 3 4 5 6 7 8 9 10 ... $ c : int 1 1 1 1 1 1 1 1 1 1 ... $ grain: num 3.63 4.07 4.51 3.9 3.63 3.16 3.18 3.42 3.97 3.4 ... $ straw: num 6.37 6.24 7.05 6.91 5.93 5.59 5.32 5.52 6.03 5.66 ... $ Quad : Factor w/ 4 levels "NE","NW","SE",..: 2 2 2 2 2 2 2 2 2 2 ... Column r

I am currently trying to extract the points of multiple crossings of series (a,b,c,d) of a specific series (x). I can't seem to find any function that can aid me in this task. My best bet is to measure the distance of every single point in x with every single point in a,b,c,d... and assume when the distance reaches under some threshold, the point must be a crossing point. I think this approach is far too computational heavy and seems "dirty". I believe there must be easier or better ways, even perhaps functions within highcharts own API. I have searched various sources and sites, but I can't

Based on the example here Adding Regression Line Equation and R2 on graph , I am struggling to include the regression line equation for my model in each facet. However, I don't figure why is changing the limits of my x axis. library(ggplot2) library(reshape2) df <- data.frame(year = seq(1979,2010), M02 = runif(32,-4,6), M06 = runif(32, -2.4, 5.1), M07 = runif(32, -2, 7.1)) df <- melt(df, id = c("year")) ggplot(data = df, mapping = aes(x = year, y = value)) + geom_point() + scale_x_continuous() + stat_smooth_func(geom = 'text', method = 'lm', hjust = 0, parse = T) + geom_smooth(method = 'lm',