search for: 2ll

In accordance with Venables and Ripley, SAS documentation and other sources AIC with sigma^2 unknown is calculated as: AIC = -2LL + 2* #parameters = n log(RSS/n) + 2p For the fitness data: (http://support.sas.com/ctx/samples/index.jsp?sid=927), SAS gets an AIC of 64.534 with model oxygen = runtime. (SAS STAT User's Guide. Chapter 61. pp 3956, the REG Procedure). This value of AIC accords with p = 2. When I run the sa...

..., NULL, 0, &num, NULL) != LONGINT_OK) { + if (xstrtol (arg, NULL, 0, &num, "") != LONGINT_OK) { fprintf (stderr, _("%s: '%s' is not a numeric value.\n"), guestfs_int_program_name, arg); show_error (EXIT_FAILURE); } - if (num <= -(2LL<<30) || num >= ((2LL<<30)-1)) { + if (num < -(1<<30) || num > (1<<30)-1) { fprintf (stderr, _("%s: %s: integer out of range\n"), guestfs_int_program_name, arg); show_error (EXIT_FAILURE); -- 2.7.4

...%s: %s: integer out of range\n"), > > + guestfs_int_program_name, arg); > > + show_error (EXIT_FAILURE); > > These bounds are not tight enough. On 32 bit they should check the > range of a 31 bit signed int, and on 64 bit, a 63 bit signed int. Right, so -(2LL<<30) to (2LL<<30)-1 -- updating the patch accordingly. Thanks, -- Pino Toscano

Since we are using gnulib already, make use of xstrtol to parse the integer arguments to avoid extra suffixes, etc. Fixes commit 0f7bf8f714898c606e5d5015fff5b7803dcd1aee. --- mllib/getopt-c.c | 34 ++++++++++++++++++++++++---------- 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/mllib/getopt-c.c b/mllib/getopt-c.c index 1f129a7..2d3f9b6 100644 --- a/mllib/getopt-c.c +++

...re is a package) out there that calculates the different derivations of AIC (e.g. AIC, AICc, QAIC, etc.) along with AIC differences (delta's), model likelihoods, Akaike weights and evidence ratio's (from Burnham and Anderson 2002). Just in a "for instance" if someone had the -2LL, sample sizes, parameter counts, and estimates of c_hat output from a program, is there a function out there that calculates the above information. I did not see anything on the help pages (or in packages, but I could have missed it) and I didn't want to re-invent the wheel. TIA, Bret A....

Hi all, I constructed a GAM model with a linear term and two smooth terms, all of them statistically significant but the intercept was not significant. The adjusted r2 of this model is 0.572 and the deviance 65.3. I decided to run the model again without intercept, so I used in R the following instruction: regression= gam(dependent~ +linear_independent +s(smooth_independent_1)

...upo -2 25.6334407 146 941.0081 NA peso -1 0.7088892 145 940.2992 NA grupo:peso -2 1.0731248 143 939.2261 NA Older version of survival dont have this problem, look this printed result: > anova(glex8.m1) -2LL Df Deviance Resid. Df P(>|Chi|) NULL NA NA 2 966.6416 NA grupo 2 25.6334408 4 941.0081 2.714995e-06 peso 1 0.7088892 5 940.2992 3.998128e-01 grupo:peso 2 1.0731248 7 939.2261 5.847550e-01 This...

...upo -2 25.6334407 146 941.0081 NA peso -1 0.7088892 145 940.2992 NA grupo:peso -2 1.0731248 143 939.2261 NA Older version of survival dont have this problem, look this printed result: > anova(glex8.m1) -2LL Df Deviance Resid. Df P(>|Chi|) NULL NA NA 2 966.6416 NA grupo 2 25.6334408 4 941.0081 2.714995e-06 peso 1 0.7088892 5 940.2992 3.998128e-01 grupo:peso 2 1.0731248 7 939.2261 5.847550e-01 This...

...tion procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to each term, I will perform a likelihood test, in which the deviance of the (-2LL) of a model containing the specific term is compared to another model without it. In the case of the fixed effect terms I have no problem in the interpretation of the results. Each comparison returns a significance associated with the estimated coefficient of the term. Thus in this case: mod2.2 &l...

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

This map was originally included just for the java bindings, but is generally useful to any binding which uses camel case by requirement or convention. --- generator/generator_haskell.ml | 4 ++-- generator/generator_java.ml | 10 +++++----- generator/generator_main.ml | 2 +- generator/generator_structs.ml | 12 +++++------- generator/generator_structs.mli | 8 ++++---- 5

v1 -> v2: - Further fixes to Getopt int parsing. - Completed the L/S changes. - Fixed the test suite so it passes now. Also we don't need the special-case tests for 64 bit arch. Rich.

v2 -> v3: - Add M variant and test it. Rich.