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 was trying out linear regression with R using categorical attributes and observe that I don't get a coefficient value …
r linear-regression lmFor some reason, when I specify glms (and lm's too, it turns out), R is not predicting missing values of …
r prediction missing-data glm lmI have this data set: x <- c(0, 40, 80, 120, 160, 200) y <- c(6.52, 5.10, 4.43, 3.99, 3.75, 3.60) I calculated a linear model using lm(): …
r lm predict> age <- c(23,19,25,10,9,12,11,8) > steroid <- c(27.1,22.1,21.9,10.7,7.4,18.8,14.7,5.7) > sample <- data.frame(age,steroid) > fit2 &…
r linear-regression prediction lmI have two linear fits that I've gotten from lm calls in my R script. For instance... fit1 <- …
r intersection lmI wonder is there a way to output summary for ridge regression in R? It is a result of lm.…
r regression lm summaryI encountered a problem in R when trying to run a simple linear model with a categorical variable as predictor. …
r lmI am trying to fit a lineal model with some categorical variables model <- lm(price ~ carat+cut+color+…
r lm factorsBasically, I don't know how to plot a line of best fit on my data once it's a logarithmic scale. …
r plot lm