regression

I'm using a data frame with many NA values. While I'm able to create a linear model, I am subsequently unable to line the fitted values of the model up with the original data due to the missing values and lack of indicator column. Here's a reproducible example: library(MASS) dat <- Aids2 # Add NA's dat[floor(runif(100, min = 1, max = nrow(dat))),3] <- NA # Create a model model <- lm(death ~ diag + age, data = dat) # Different Values length(fitted.values(model)) # 2745 nrow(dat) # 2843 There are actually three solutions here: pad NA to fitted values ourselves; use predict() to compute fitted

I'm going to write a program where the input is a data set of 2D points and the output is the regression coefficients of the line of best fit by minimizing the minimum MSE error. I have some sample points that I would like to process: X Y 1.00 1.00 2.00 2.00 3.00 1.30 4.00 3.75 5.00 2.25 How would I do this in MATLAB? Specifically, I need to get the following formula: y = A + Bx + e A is the intercept and B is the slope while e is the residual error per point. Judging from the link you provided, and my understanding of your problem, you want to calculate the line of best fit for a set of data

I want to do stepwise regression using AIC on a list of linear models. idea is to use e a list of linear models and then apply stepAIC on each list element. It fails. Hi guys I tried to track the problem down. I think I found the problem. However, I dont understand the cause. Try the code to see the difference between three cases. require(MASS) n<-30 x1<-rnorm(n, mean=0, sd=1) #create rv x1 x2<-rnorm(n, mean=1, sd=1) x3<-rnorm(n, mean=2, sd=1) epsilon<-rnorm(n,mean=0,sd=1) # random error variable dat<-as.data.frame(cbind(x1,x2,x3,epsilon)) # combine to a data frame dat$id<-c(rep(1,10),rep(2,10

I have many samples (y_i, (a_i, b_i, c_i)) where y is presumed to vary as a polynomial in a,b,c up to a certain degree. For example for a given set of data and degree 2 I might produce the model y = a^2 + 2ab - 3cb + c^2 +.5ac This can be done using least squares and is a slight extension of numpy's polyfit routine. Is there a standard implementation somewhere in the Python ecosystem? sklearn provides a simple way to do this. Building off an example posted here : #X is the independent variable (bivariate in this case) X = array([[0.44, 0.68], [0.99, 0.23]]) #vector is the dependent data vector

Is there an existing function to estimate fixed effect (one-way or two-way) from Pandas or Statsmodels. There used to be a function in Statsmodels but it seems discontinued. And in Pandas, there is something called plm , but I can't import it or run it using pd.plm() . As noted in the comments, PanelOLS has been removed from Pandas as of version 0.20.0. So you really have three options: If you use Python 3 you can use linearmodels as specified in the more recent answer: https://stackoverflow.com/a/44836199/3435183 Just specify various dummies in your statsmodels specification, e.g. using pd