regression

Consider the following table : DB <- data.frame( Y =rnorm(6), X1=c(T, T, F, T, F, F), X2=c(T, F, T, F, T, T) ) Y X1 X2 1 1.8376852 TRUE TRUE 2 -2.1173739 TRUE FALSE 3 1.3054450 FALSE TRUE 4 -0.3476706 TRUE FALSE 5 1.3219099 FALSE TRUE 6 0.6781750 FALSE TRUE I'd like to explain my quantitative variable Y by two binary variables (TRUE or FALSE) without intercept. The argument of this choice is that, in my study, we can't observe X1=FALSE and X2=FALSE at the same time, so it doesn't make sense to have a mean, other than 0, for this level. With intercept m1 <- lm(Y~X1+X2, data=DB) summary(m1)

I am trying to count objects in an image using Alexnet. I have currently images containing 1, 2, 3 or 4 objects per image. For initial checkup, I have 10 images per class. For example in training set I have: image label image1 1 image2 1 image3 1 ... image39 4 image40 4 I used imagenet create script to create a lmdb file for this dataset. Which successfully converted my set of images to lmdb. Alexnet, as an example is converted to a regression model for learning the number of objects in the image by introducing EucledeanLosslayer instead of Softmax Layer. As suggested by many. The rest of the

I need to run a multiple regression in R, with the variables X1, X2 and X3, where there is a variable θ = β2 + β3. So instead of β2, for the coefficient of X2 I need to use (θ - β3). How could I do this? Note that Y = b1 * x1 + (t - b3) * x2 + b3 * x3 is equivalent to Y = b1 * x1 + t * x2 - b3 * x2 + b3 * x3 = b1 * x1 + t * x2 + b3 * (x3 - x2) So, you can continue from there easily. 来源： https://stackoverflow.com/questions/53561699/linear-combination-of-regression-coefficients-in-r

I am looking to run Linear Regression on the below data frame. test<-data.frame(abc=c(2.4,3.2,8.9,9.8,10.0,3.2,5.4), city1_0=c(5.3,2.6,3,5.4,7.8,4.4,5.5), city1_1=c(2.3,5.6,3,2.4,3.6,2.4,6.5), city1_2=c(4.2,1.4,2.6,2,6,3.6,2.4), city1_3=c(2.4,2.6,9.4,4.6,2.5,1.2,7.5), city1_4=c(8.2,4.2,7.6,3.4,1.7,5.2,9.7), city2_0=c(4.3,8.6,6,3.7,7.8,4.7,5.8), city2_1=c(5.3,2.6,3,5.4,7.8,4.4,5.5)) Dataframe "test" is sample of the data. But original data frame contains 100 columns. I want to create a script for predicting values using linear regression. In this case, I want to build many models with different

So I have a data set that has 188 rows and 65 columns relating to World development indicators and Birth statistics. I am trying to do a purposeful selection method to create a regression model. The first step of this is to look at all of the individual simple linear models. my goal is to run regression models in R for for each of my variables against my response. I know I can run lm(x$v30 ~ x$v1) which would give the regression for one of the variables. however, i am hoping to be able to do this in one step and pull all of the p values into a table or write them to a CSV. I was following this