The lm function is used to fit linear models in R. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance.
I would like to know how to constrain certain parameters in lm() to have positive coefficients. There are a few …
r regression linear-regression glm lmI would like to get the slope of a linear regression fit for 1M separate data sets (1M * 50 rows for …
r lmI have an example dataset below. train<-data.frame(x1 = c(4,5,6,4,3,5), x2 = c(4,2,4,0,5,4), x3 = c(1,1,1,0,0,1), x4 = c(1,0,1,1,0,0), x5 = c(0,0,0,1,1,1)) …
r regression linear-regression lm mlm> reg.len <- lm(chao1.ave ~ lg.std.len, b.div) # b.div is my data frame imported …
r linear-regression lm predictI'm using a set of points which go from (-5,5) to (0,0) and (5,5) in a "symmetric V-shape". I'm fitting a model …
r regression lm spline bsplineDoes anyone know how to get stargazer to display clustered SEs for lm models? (And the corresponding F-test?) If possible, …
r lm stargazer standard-errorI am using NeweyWest standard errors to correct my lm() / dynlm() output. E.g.: fit1<-dynlm(depvar~covariate1+covariate2) …
r summary lmI do not want main effect because it is collinear with a finer factor fixed effect, so it is annoying …
r regression linear-regression lm categorical-dataI'm using dlply() with a custom function that averages slopes of lm() fits on data that contain some NA values, …
r plyr lmI know there is a shortcut in Rto run an lm()regression on all a dataframe like this : reg<…
r regression lm