regression

I am trying to fit some data with a Gaussian (and more complex) function(s). I have created a small example below. My first question is, am I doing it right? My second question is, how do I add an error in the x-direction, i.e. in the x-position of the observations/data? It is very hard to find nice guides on how to do this kind of regression in pyMC. Perhaps because its easier to use some least squares, or similar approach, I however have many parameters in the end and need to see how well we can constrain them and compare different models, pyMC seemed like the good choice for that. import

I want to calculate a linear regression using the lm() function in R. Additionally I want to get the slope of a regression, where I explicitly give the intercept to lm() . I found an example on the internet and I tried to read the R-help "?lm" (unfortunately I'm not able to understand it), but I did not succeed. Can anyone tell me where my mistake is? lin <- data.frame(x = c(0:6), y = c(0.3, 0.1, 0.9, 3.1, 5, 4.9, 6.2)) plot (lin$x, lin$y) regImp = lm(formula = lin$x ~ lin$y) abline(regImp, col="blue") # Does not work: # Use 1 as intercept explicitIntercept = rep(1, length(lin$x)) regExp = lm

I have a pandas data frame and I would like to able to predict the values of column A from the values in columns B and C. Here is a toy example: import pandas as pd df = pd.DataFrame({"A": [10,20,30,40,50], "B": [20, 30, 10, 40, 50], "C": [32, 234, 23, 23, 42523]}) Ideally, I would have something like ols(A ~ B + C, data = df) but when I look at the examples from algorithm libraries like scikit-learn it appears to feed the data to the model with a list of rows instead of columns. This would require me to reformat the data into lists inside lists, which seems to defeat the purpose of using

I am building a quadratic model with lm in R: y <- data[[1]] x <- data[[2]] x2 <- x^2 quadratic.model = lm(y ~ x + x2) Now I want to display both the predicted values and the actual values on a plot. I tried this: par(las=1,bty="l") plot(y~x) P <- predict(quadratic.model) lines(x, P) but the line comes up all squiggely. Maybe it has to do with the fact that it's quadratic? Thanks for any help. 李哲源 You need order() : P <- predict(quadratic.model) plot(y~x) reorder <- order(x) lines(x[reorder], P[reorder]) My answer here is related: Problems displaying LOESS regression line and confidence

So I am having some issues with some NA values in the residuals of a lm cross sectional regression in R. The issue isn't the NA values themselves, it's the way R presents them. For example: test$residuals # 1 2 4 5 # 0.2757677 -0.5772193 -5.3061303 4.5102816 test$residuals[3] # 4 # -5.30613 In this simple example a NA value will make one of the residuals go missing. When I extract the residuals I can clearly see the third index missing. So far so good, no complaints here. The problem is that the corresponding numeric vector is now one item shorter so the third index is actually the fourth. How

My understanding of orthogonal polynomials is that they take the form y(x) = a1 + a2(x - c1) + a3(x - c2)(x - c3) + a4(x - c4)(x - c5)(x - c6)... up to the number of terms desired where a1 , a2 etc are coefficients to each orthogonal term (vary between fits), and c1 , c2 etc are coefficients within the orthogonal terms, determined such that the terms maintain orthogonality (consistent between fits using the same x values) I understand poly() is used to fit orthogonal polynomials. An example x = c(1.160, 1.143, 1.126, 1.109, 1.079, 1.053, 1.040, 1.027, 1.015, 1.004, 0.994, 0.985, 0.977) #