linear-regression

When we have a linear model with a factor variable X (with levels A , B , and C ) y ~ factor(X) + Var2 + Var3 The result shows the estimate XB and XC which is differences B - A and C - A . (suppose that the reference is A ). If we want to know the p-value of the difference between B and C : C - B , we should designate B or C as a reference group and re-run the model. Can we get the p-values of the effect B - A , C - A , and C - B at one time? 李哲源 You are looking for linear hypothesis test by check p-value of some linear combination of regression coefficients. Based on my answer: How to conduct

Simple regression of random normal on date fails, but identical data with small integers instead of dates works as expected. # Example dataset with 100 observations at 2 second intervals. set.seed(1) df <- data.frame(x=as.POSIXct("2017-03-14 09:00:00") + seq(0, 199, 2), y=rnorm(100)) #> head(df) # x y # 1 2017-03-14 09:00:00 -0.6264538 # 2 2017-03-14 09:00:02 0.1836433 # 3 2017-03-14 09:00:04 -0.8356286 # Simple regression model. m <- lm(y ~ x, data=df) The slope is missing due to singularities in the data. Calling the summary demonstrates this: summary(m) # Coefficients: (1 not defined

(reproducible example given) In the following, I get an abline line with y-intercept is about 30, but the regression says y-intercept should be 37.2851 Where am I wrong? mtcars$mpg # 21.0 21.0 22.8 ... 21.4 (32 obs) mtcars$wt # 2.620 2.875 2.320 ... 2.780 (32 obs) regression1 <- lm(mtcars$mpg ~ mtcars$wt) coef(regression1) # mpg ~ 37.2851 - 5.3445wt plot(mtcars$mpg ~ mtcars$wt, pch=19, col='gray50') # pch: shape of points abline(h=mean(mtcars$mpg), lwd=2, col ='darkorange') # The y-coordinate of hor'l line: 20,09062 abline(lm(mtcars$mpg ~ mtcars$wt), lwd=2, col ='sienna') I looked at all the

I have linear regression model that seems to be working fine, but I want to display the accuracy of the model. First, I initialize the variables and placeholders... X_train, X_test, Y_train, Y_test = train_test_split( X_data, Y_data, test_size=0.2 ) n_rows = X_train.shape[0] X = tf.placeholder(tf.float32, [None, 89]) Y = tf.placeholder(tf.float32, [None, 1]) W_shape = tf.TensorShape([89, 1]) b_shape = tf.TensorShape([1]) W = tf.Variable(tf.random_normal(W_shape)) b = tf.Variable(tf.random_normal(b_shape)) pred = tf.add(tf.matmul(X, W), b) cost = tf.reduce_sum(tf.pow(pred-Y, 2)/(2*n_rows-1))

I have a linear model: mod=lm(weight~age, data=f2) I would like to input an age value and have returned the corresponding weight from this model. This is probably simple, but I have not found a simple way to do this. If your purposes are related to just one prediction you can just grab your coefficient with coef(mod) Or you can just build a simple equation like this. coef(mod)[1] + "Your_Value"*coef(mod)[2] Its usually more robust to use the predict method of lm : f2<-data.frame(age=c(10,20,30),weight=c(100,200,300)) f3<-data.frame(age=c(15,25)) mod<-lm(weight~age,data=f2) pred3<-predict(mod

Short version : How to build separate models for each category (without splitting the data). (I am new to R) Long version: consider the following synthetic data housetype,ht1,ht2,age,price O,0,1,1,1000 O,0,1,2,2000 O,0,1,3,3000 N,1,0,1,10000 N,1,0,2,20000 N,1,0,3,30000 We can model the above using two separate models if(housetype=='o') price = 1000 * age else price = 10000 * age i.e. a separate model based on category type? This is what I have tried model=lm(price~housetype+age, data=datavar) and model=lm(price~ht1+ht2+age, data = datavar) Both the above models (which is essentially the same)