lm

I am trying to create a function or even just work out how to run a loop using data.table syntax where I can subset the table by factor, in this case the id variable, then run a linear model on each subset and out the results. Sample data below. df <- data.frame(id = letters[1:3], cyl = sample(c("a","b","c"), 30, replace = TRUE), factor = sample(c(TRUE, FALSE), 30, replace = TRUE), hp = sample(c(20:50), 30, replace = TRUE)) dt=as.data.table(df) fit <- lm(hp ~ cyl + factor, data = df) #how do I get the [i] to work here to subset and iterate by each factor and also do it in data.table syntax?

I am trying to understand how R determines reference groups for interactions in a linear model. Consider the following: df <- structure(list(id = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), year = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("1", "2"), class = "factor"), treatment = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,

I have been trying to figure out how the subset argument in R's lm() function works. Especially the follwoing code seems dubious for me: data(mtcars) summary(lm(mpg ~ wt, data=mtcars)) summary(lm(mpg ~ wt, cyl, data=mtcars)) In every case the regression has 32 observations dim(lm(mpg ~ wt, cyl ,data=mtcars)$model) [1] 32 2 dim(lm(mpg ~ wt ,data=mtcars)$model) [1] 32 2 yet the coefficients change (along with the R²). The help doesn't provide too much information on this matter: subset an optional vector specifying a subset of observations to be used in the fitting process As a general principle

This is my data frame, with two columns Y (response) and X (covariate): ## Editor edit: use `dat` not `data` dat <- structure(list(Y = c(NA, -1.793, -0.642, 1.189, -0.823, -1.715, 1.623, 0.964, 0.395, -3.736, -0.47, 2.366, 0.634, -0.701, -1.692, 0.155, 2.502, -2.292, 1.967, -2.326, -1.476, 1.464, 1.45, -0.797, 1.27, 2.515, -0.765, 0.261, 0.423, 1.698, -2.734, 0.743, -2.39, 0.365, 2.981, -1.185, -0.57, 2.638, -1.046, 1.931, 4.583, -1.276, 1.075, 2.893, -1.602, 1.801, 2.405, -5.236, 2.214, 1.295, 1.438, -0.638, 0.716, 1.004, -1.328, -1.759, -1.315, 1.053, 1.958, -2.034, 2.936, -0.078, -0.676, -2

I used a linear regression on data I have, using the lm function. Everything works (no error message), but I'm somehow surprised by the result: I am under the impression R "misses" a group of points, i.e. the intercept and slope are not the best fit. For instance, I am referring to the group of points at coordinates x=15-25,y=0-20. My questions: is there a function to compare fit with "expected" coefficients and "lm-calculated" coefficients? have I made a silly mistake when coding, leading the lm to do that? Following some answers: additionnal information on x and y x and y are both visual

I've this programme dens <- read.table('DensPiu.csv', header = FALSE) fl <- read.table('FluxPiu.csv', header = FALSE) mydata <- data.frame(c(dens),c(fl)) dat = subset(mydata, dens>=3.15) colnames(dat) <- c("x", "y") attach(dat) and I want to do a least-square regression on the data contained in dat , the function has the form y ~ a + b*x and I want the regression line to pass through a specific point P(x0,y0) (which is not the origin). I'm trying to do it like this x0 <- 3.15 y0 <-283.56 regression <- lm(y ~ I(x-x0)-1, offset=y0) (I think that data = dat is not necessary in this case) but I

I am new to R and I am stuck with a problem. I am trying to read a set of data in a table and I want to perform linear modeling. Below is how I read my data and my variables names: >data =read.table(datafilename,header=TRUE) >names(data) [1] "price" "model" "size" "year" "color" What I want to do is create several linear models using different combinations of the variables (price being the target ), such as: > attach(data) > model1 = lm(price~model+size) > model2 = lm(price~model+year) > model3 = lm(price~model+color) > model4 = lm(price~model+size) > model4 = lm(price~size+year+color) #...