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• Boot stuck at blinking cursor before GRUB - only works via BIOS boot menu

  - by delta1

  I have a new box running Debian Squeeze. Grub is installed on /dev/sda, but when booting up I just get a blinking cursor, before the Grub menu. I can only boot to grub successfully when I choose boot options (during post) and select that specific drive! I have made sure the correct drive is set to boot first in the BIOS. So Grub works, but the system won't boot to that drive automatically? Any ideas on what could cause this? Drives sda/b/c are all 2TB (sda runs the system with b/c as raid device md0) with the following partitions: $ cat /proc/partitions major minor #blocks name 8 0 1953514584 sda 8 1 977 sda1 8 2 9765625 sda2 8 3 6445313 sda3 8 4 1937302627 sda4 8 32 1953514584 sdc 8 16 1953514584 sdb 9 0 1953513424 md0 but # fdisk -l /dev/sda gives WARNING: GPT (GUID Partition Table) detected on '/dev/sda'! The util fdisk doesn't support GPT. Use GNU Parted. Disk /dev/sda: 2000.4 GB, 2000398934016 bytes 255 heads, 63 sectors/track, 243201 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x00000000 Device Boot Start End Blocks Id System /dev/sda1 1 243202 1953514583+ ee GPT Any insight into this strange behaviour would be greatly appreciated.

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• Error using `loess.smooth` but not `loess` or `lowess`

  - by Sandy

  I need to smooth some simulated data, but occasionally run into problems when the simulated ordinates to be smoothed are mostly the same value. Here is a small reproducible example of the simplest case. > x <- 0:50 > y <- rep(0,51) > loess.smooth(x,y) Error in simpleLoess(y, x, w, span, degree, FALSE, FALSE, normalize = FALSE, : NA/NaN/Inf in foreign function call (arg 1) loess(y~x), lowess(x,y), and their analogue in MATLAB produce the expected results without error on this example. I am using loess.smooth here because I need the estimates evaluated at a set number of points. According to the documentation, I believe loess.smooth and loess are using the same estimation functions, but the former is an "auxiliary function" to handle the evaluation points. The error seems to come from a C function: > traceback() 3: .C(R_loess_raw, as.double(pseudovalues), as.double(x), as.double(weights), as.double(weights), as.integer(D), as.integer(N), as.double(span), as.integer(degree), as.integer(nonparametric), as.integer(order.drop.sqr), as.integer(sum.drop.sqr), as.double(span * cell), as.character(surf.stat), temp = double(N), parameter = integer(7), a = integer(max.kd), xi = double(max.kd), vert = double(2 * D), vval = double((D + 1) * max.kd), diagonal = double(N), trL = double(1), delta1 = double(1), delta2 = double(1), as.integer(0L)) 2: simpleLoess(y, x, w, span, degree, FALSE, FALSE, normalize = FALSE, "none", "interpolate", control$cell, iterations, control$trace.hat) 1: loess.smooth(x, y) loess also calls simpleLoess, but with what appears to be different arguments. Of course, if you vary enough of the y values to be nonzero, loess.smooth runs without error, but I need the program to run in even the most extreme case. Hopefully, someone can help me with one and/or all of the following: Understand why only loess.smooth, and not the other functions, produces this error and find a solution for this problem. Find a work-around using loess but still evaluating the estimate at a specified number of points that can differ from the vector x. For example, I might want to use only x <- seq(0,50,10) in the smoothing, but evaluate the estimate at x <- 0:50. As far as I know, using predict with a new data frame will not properly handle this situation, but please let me know if I am missing something there. Handle the error in a way that doesn't stop the program from moving onto the next simulated data set. Thanks in advance for any help on this problem.

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