lm

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 <

I was trying to automate a piece of my code so that programming become less tedious. Basically I was trying to do a stepwise selection of variables using fastbw() in the rms package. I would like to pass the list of variables selected by fastbw() into a formula as y ~ x1+x2+x3 , "x1" "x2" "x3" being the list of variables selected by fastbw() Here is the code I tried and did not work olsOAW0.r060 <- ols(roll_pct~byoy+trans_YoY+change18m, subset= helper=="POPNOAW0_r060", na.action = na.exclude, data = modelready) OAW0 <- fastbw(olsOAW0.r060, rule="p", type="residual", sls= 0.05) vec <- as.vector