regression

I am running the following source code: import statsmodels.formula.api as sm # Add one column of ones for the intercept term X = np.append(arr= np.ones((50, 1)).astype(int), values=X, axis=1) regressor_OLS = sm.OLS(endog=y, exog=X).fit() print(regressor_OLS.summary()) where X is an 50x5 (before adding the intercept term) numpy array which looks like this: [[0 1 165349.20 136897.80 471784.10] [0 0 162597.70 151377.59 443898.53]...] and y is a a 50x1 numpy array with float values for the dependent variable. The first two columns are for a dummy variable with three different values. The rest of

I am using following example from help pages of package VGAM library(VGAM) fit4 <- vgam(BMI ~ s(age, df = c(4, 2)), lms.bcn(zero = 1), data = bmi.nz, trace = TRUE) qtplot(fit4, percentiles = c(5,50,90,99), main = "Quantiles", las = 1, xlim = c(15, 90), ylab = "BMI", lwd = 2, lcol = 4) I am getting a proper graph with it: How can I avoid plotting points from the graph? Also I need to print out values for these percentiles at each of ages 20,30,40...80 (separately as a table). How can this be done? Can I use ggplot() format command rather than qtplot() command here? Thanks for your help. How

I really need your help regarding a problem which may seem easy to solve for you. Currently I work on a project which involves some panel-regressions. I have several large csv-files (up to 12 million entries per sheet) which are formatted as in the picture attached, whereas the columns (V1, V2) are individuals and the rows (1, 2, 3) are time identifiers. In order to use the plm() -function I need all these files to convert to the following data structure: ID Time X1 X2 1 1 x1 x2 1 2 x1 x2 1 ... ... ... 2 1 x1 x2 2 2 ... ... I really struggle with this transformation and I'm really frustrated

Consider the following example in R: x1 <- rnorm(100000) x2 <- rnorm(100000) g <- cbind(x1, x2, x1^2, x2^2) gg <- t(g) %*% g gginv <- solve(gg) bigmatrix <- outer(x1, x2, "<=") Gw <- t(g) %*% bigmatrix beta <- gginv %*% Gw w1 <- bigmatrix - g %*% beta If I try to run such a thing in my computer, it will throw a memory error (because the bigmatrix is too big). Do you know how can I achieve the same, without running into this problem? This is a least squares problem with 100,000 responses. Your bigmatrix is the response (matrix), beta is the coefficient (matrix), while w1 is the residual (matrix

I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from ‘nls’): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of ‘nls’ one can see that ‘nls’ generates a vector wts <- rep(1, n) . Now for a weighted fit: fm2DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1, weights = rep(1:8, each = 2)) in which I assign increasing weights for each of the 8 concentrations with 2 replicates. Now with deviance I