search for: y_fit

...t as lambda. I'm a bit confused because I was expecting an optimization of this parameter to gain a good fit... If I would use mle() of stats4 package or mle2() of bbmle package, I would have to write the function by myself which should be optimized. But what shall I return? -sum((y_observed - y_fitted)^2) ? Any other suggestions or comments on my solution? Antje

...per following. y = c(15.91,9.80,17.16,16.68,15.53,22.66,31.01,8.62,45.82,10.97,45.46,28.69,36.75,37.75, 41.18,42.67,46.05, 43.70,53.08,47.56) t = c(1:20) # defining time variable. Fitting y = a + bt + e, I get the estimates of a and b as a = 9.12 and b = 2.07. So using these estimates I obtain y_fit = c(11.19,13.26,15.33,17.40,19.47,21.54,23.61,25.68,27.75,29.82,31.89,33.96, 36.03,38.10, 40.17,42.24,44.31,46.38,48.45,50.52)  # these are fitted values. e_t = (y - y_fit)   # dif between the observed y and fitted value of corresponding y > e_t  [1]   4.72  -3.46   1.83  -0.72  -3.94   1.12 ...

Dear list, I've got a question concerning difference between loess and locpoly. I have to use a plug-in method to chose a bandwith so I take locpoly method to fit a curve. My problem is:I know how to get predicted values in loess: m=loess(y~x) y_fitted=predict(m). But how to get the same in locpoly? I computed like this: bw=dpill(x, y, blockmax = 5, divisor = 20,trim = 0.01, proptrun = 0.05, gridsize = n, range(x), truncate = FALSE) m=locpoly(x, y, drv = 0, degree = 1, gridsize= n, bandwidth=bw, bwdisc = 25,...