regression

I have the data frame DF . I am using R and dplyr to analise it. DF contains: > glimpse(DF) Observations: 1244160 Variables: $ Channel (int) 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... $ Row (int) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,... $ Col (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1... $ mean (dbl) 776.0667, 786.6000, 833.4667, 752.3333, 831.6667, 772.9333... I fit it with: Fit <- DF %>% group_by(Channel) %>% do(fit = lm(mean ~ Col + poly(Row, 2), data = .)) How can I get another column in DF with the data (given Channel , Row and

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 use scipy.odr in order to make a fit with uncertainties on both x and y following this question Correct fitting with scipy curve_fit including errors in x? After the fit I would like to compute the uncertainties on the parameters. Thus I look at the square root of the diagonal elements of the covariance matrix. I get : >>> print(np.sqrt(np.diag(output.cov_beta))) [ 0.17516591 0.33020487 0.27856021] But in the Output there is also output.sd_beta which is, according to the doc on odr Standard errors of the estimated parameters, of shape (p,). But, it does not give me the same results : >>>

Given two variables, x and y , I run a dynlm regression on the variables and would like to plot the fitted model against one of the variables and the residual on the bottom showing how the actual data line differs from the predicting line. I've seen it done before and I've done it before, but for the life of me I can't remember how to do it or find anything that explains it. This gets me into the ballpark where I have a model and two variables, but I can't get the type of graph I want. library(dynlm) x <- rnorm(100) y <- rnorm(100) model <- dynlm(x ~ y) plot(x, type="l", col="red") lines(y,

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 have been experimenting with neural networks these days. I have come across a general question regarding the activation function to use. This might be a well known fact to but I couldn't understand properly. A lot of the examples and papers I have seen are working on classification problems and they either use sigmoid (in binary case) or softmax (in multi-class case) as the activation function in the out put layer and it makes sense. But I haven't seen any activation function used in the output layer of a regression model. So my question is that is it by choice we don't use any activation