search for: chemineau

Hello, I have a basic problem but i can't figure it out with the table underneath. I would like to compute monthly averages. I would like to have the average measure for month #5 for the first three rows (the same number in the first three lines) and the average measure for month #6 for the last four rows ((the same number in the first three lines) in a separate vesctor (let's call

Hi, dear R developers, I've got a vector of monthly volatilities and i would like to get the position of the highest volatility of the vector without computing a loop. Is there a function that could give me such a result ? a<-c(1,2,4,100,3) the highest value is the fourth of the vector. how can i get "4" without a loop going through the vector ? Thanks ! Benoit.

Hi, dear R-users, I am computing a liner regression by rating category using the 'by' function as stated below: tmp <- by(projet, rating, function(x) lm(defaults ~ CGDP+CSAVE+SP500, data = x)) I would like to get not only the coefficients but also their p-values. I can't find the command in the help pages to get them. Does anyone have a suggestion ? Thank you, Benoit.

Hi R-programmers ! I would like to perform a linear model regression month by month using the 'lm' function and i don't know how to do it. The data is organised as below: Month ExcessReturn Return STO 8 0.047595875 0.05274292 0.854352503 8 0.016134874 0.049226941 4.399372005 8 -0.000443869 0.004357305 -1.04980297 9 0.002206554 -0.089068828 0.544809429 9 0.021296551

Hi R-programmers, I can't find find the White test to check the homoscedasticity of the residuals from a linear model. Could you please help me with this? Thank you ! BC [[alternative HTML version deleted]]

Hi R-programmers ! I would like to perform a linear model regressio using the 'lm' function and i don't know how to do it. The data is organised as below: Month ExcessReturn Return STO 8 0.047595875 0.05274292 0.854352503 8 0.016134874 0.049226941 4.399372005 8 -0.000443869 0.004357305 -1.04980297 9 0.002206554 -0.089068828 0.544809429 9 0.021296551 0.003795071