linear-regression

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'm trying to find a minimal adequate model using AIC in R. I keep getting the following error: Error in step(model) : number of rows in use has changed: remove missing values? My data: data<-structure(list(ID = c(1L, 2L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 27L, 28L, 29L, 30L, 31L, 33L, 34L, 35L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,

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 want to perform a moving window regression on every pixel of two raster stacks representing Band3 and Band4 of Landsat data. The result should be two additional stacks, one representing the Intercept and the other one representing the slope of the regression. So layer 1 of stack "B3" and stack "B4" result in layer 1 of stack "intercept" and stack "slope". Layer 2 of stack B3 and stack B4 result in layer 2,.... and so on. I already came along the gwr function, but want to stay in the raster package. I somehow know that focal must be included in order to set my moving window (which should be

I was trying to solve a linear system Xc=y that was square. The methods I know to solve this are: using inverse c=<X^-1,y> using Gaussian elimination using the pseudo-inverse It seems as far as I can tell that these don't match what I thought would be the ground truth. First generate the truth parameters by fitting a polynomial of degree 30 to a cosine with frequency 5. So I have y_truth = X*c_truth . Then I check if the above three methods match the truth I tried it but the methods don't seem to match and I don't see why that should be the case. I produced fully runnable reproducible code: