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...      8 7      2       7 8      2       7 9      5       2 10    5       2 11    6       4 unique(mydat$x) will give me 1, 2, 5, 6  i.e. 4 values and unique(mydat$y) will give me 10, 8, 7, 2, 4. What I need is a data frame where I will get a vector (say) x_new as (1, 2, 2, 5, 6) and corresponding y_new as (10, 8, 7, 2, 4). I need to use these two vectors viz. x_new and y_new seperately for further processing. They may be under same data frame say mydat_new but I should be able to access them as mydat_new$x_new and similarly for y. I tried following way. pp = paste(mydat$x, mydat$y) pp = > p...

I have some data fit to a smooth.spline object as follows: (x=vector of data for the predictor variable, y=vector of data for the response variable) fit <- smooth.spline(x,y) Now, given a spline fit point y_new, I want to be able to find out what value of x_new yielded this fit value. How to do so? (This problem is the inverse of the predict.smooth.spline function, which takes x_new as input and yields the corresponding y_new fit value) Any insight is much appreciated! Thanks, Kavitha [[alternative HT...

hi all a technical question for those bright statisticians. my question involves ridge regression. definition: n=sample size of a data set X is the matrix of data with , say p variables Y is the y matrix i.e the response variable Z(i,j) = ( X(i,j)- xbar(j) / [ (n-1)^0.5* std(x(j))] Y_new(i)=( Y(i)- ybar(j) ) / [ (n-1)^0.5* std(Y(i))] (note that i have scaled the Y matrix as well) k is the ridge constant the ridge estimate for the betas is = inverse(Z'Z+kI)*Z'Y_new=W*Z'Y_new the associated variance covariance matrix sigma*W*(Z'Z)*W where sigma is the residual vari...

...me(x=c(10,20)),se.fit=T,interval="prediction")$se.fit 1 2 0.2708064 0.7254615 I was surprised to find that the standard errors returned were in fact the standard errors of the sampling distribution of Y_hat: sqrt(MSE(1/n + (x-x_bar)^2/SS_x)), not the standard errors of Y_new (predicted value): sqrt(MSE(1 + 1/n + (x-x_bar)^2/SS_x)). Is there a reason this quantity is called the "standard error of predicted means" if it doesn't relate to the prediction distribution? Turning to Neter et al.'s Applied Linear Statistical Models, I note that if we have m...