linear-regression

I am running a linear regression on a number of attributes including two categorical attributes, B and F , and I don't get a coefficient value for every factor level I have. B has 9 levels and F has 6 levels. When I initially ran the model (with intercepts), I got 8 coefficients for B and 5 for F which I understood as the first level of each being included in the intercept. I want ranking the levels within B and F based on their coefficient so I added -1 after each factor to lock the intercept at 0 so that I could get coefficients for all levels. Call: lm(formula = dependent ~ a + B-1 + c + d

I have seen pairwise or general paired simple linear regression many times on Stack Overflow. Here is a toy dataset for this kind of problem. set.seed(0) X <- matrix(runif(100), 100, 5, dimnames = list(1:100, LETTERS[1:5])) b <- c(1, 0.7, 1.3, 2.9, -2) dat <- X * b[col(X)] + matrix(rnorm(100 * 5, 0, 0.1), 100, 5) dat <- as.data.frame(dat) pairs(dat) So basically we want to compute 5 * 4 = 20 regression lines: ----- A ~ B A ~ C A ~ D A ~ E B ~ A ----- B ~ C B ~ D B ~ E C ~ A C ~ B ----- C ~ D C ~ E D ~ A D ~ B D ~ C ----- D ~ E E ~ A E ~ B E ~ C E ~ D ----- Here is a poor man's strategy: poor <

def gradient(X_norm,y,theta,alpha,m,n,num_it): temp=np.array(np.zeros_like(theta,float)) for i in range(0,num_it): h=np.dot(X_norm,theta) #temp[j]=theta[j]-(alpha/m)*( np.sum( (h-y)*X_norm[:,j][np.newaxis,:] ) ) temp[0]=theta[0]-(alpha/m)*(np.sum(h-y)) temp[1]=theta[1]-(alpha/m)*(np.sum((h-y)*X_norm[:,1])) theta=temp return theta X_norm,mean,std=featureScale(X) #length of X (number of rows) m=len(X) X_norm=np.array([np.ones(m),X_norm]) n,m=np.shape(X_norm) num_it=1500 alpha=0.01 theta=np.zeros(n,float)[:,np.newaxis] X_norm=X_norm.transpose() theta=gradient(X_norm,y,theta,alpha,m,n,num_it)