similar to: Parameters of Weibull regression

If we capture transform feedback from n stream in (n-1) buffers we face a NULL buffer, use the buffer (n-1) to capture the output of stream n. This fixes one piglit test with nvc0: arb_gpu_shader5-xfb-streams-without-invocations Signed-off-by: Tobias Klausmann <tobias.johannes.klausmann at mni.thm.de> --- src/mesa/state_tracker/st_cb_xformfb.c | 5 +++++ 1 file changed, 5 insertions(+)

Can you elaborate a bit as to why that's the right thing to do? On Wed, Jan 7, 2015 at 1:52 PM, Tobias Klausmann <tobias.johannes.klausmann at mni.thm.de> wrote: > If we capture transform feedback from n stream in (n-1) buffers we face a > NULL buffer, use the buffer (n-1) to capture the output of stream n. > > This fixes one piglit test with nvc0: >

If a transform feedback buffer's size is 0, st_bufferobj_data doesn't end up creating a buffer for it. There's no point in trying to write to such a buffer, so just pretend as if it's not really there. This fixes arb_gpu_shader5-xfb-streams-without-invocations on nvc0. Signed-off-by: Ilia Mirkin <imirkin at alum.mit.edu> --- src/mesa/state_tracker/st_cb_xformfb.c | 2 +- 1

On Sun, Jan 11, 2015 at 7:43 PM, Tobias Klausmann <tobias.johannes.klausmann at mni.thm.de> wrote: > > > On 11.01.2015 06:05, Ilia Mirkin wrote: >> >> Can you elaborate a bit as to why that's the right thing to do? >> >> On Wed, Jan 7, 2015 at 1:52 PM, Tobias Klausmann >> <tobias.johannes.klausmann at mni.thm.de> wrote: >>>

Hi all, I'm looking for an efficient solution (speed and memory) for the following problem: Given - a data.frame x containing numbers of type double with nrow(x)>ncol(x) and unique row lables and - a character vector y containing a sorted order labels Now, I'd like to sort the rows of the data.frame x w.r.t. the order of labels in y. example: x <- data.frame(c(1:4),c(5:8))

On 12.01.2015 01:57, Ilia Mirkin wrote: > On Sun, Jan 11, 2015 at 7:43 PM, Tobias Klausmann > <tobias.johannes.klausmann at mni.thm.de> wrote: >> >> On 11.01.2015 06:05, Ilia Mirkin wrote: >>> Can you elaborate a bit as to why that's the right thing to do? >>> >>> On Wed, Jan 7, 2015 at 1:52 PM, Tobias Klausmann >>>

On 11.01.2015 06:05, Ilia Mirkin wrote: > Can you elaborate a bit as to why that's the right thing to do? > > On Wed, Jan 7, 2015 at 1:52 PM, Tobias Klausmann > <tobias.johannes.klausmann at mni.thm.de> wrote: >> If we capture transform feedback from n stream in (n-1) buffers we face a >> NULL buffer, use the buffer (n-1) to capture the output of stream n.

Dear List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

In the logistic regression model, there is no residual log (pi/(1-pi)) = beta_0 + beta_1*X_1 + ..... But glm model will return residuals What is that? How to understand this? Can we put some residual in the logistic regression model by replacing pi with pi' (the estimated pi)? log (pi'/(1-pi')) = beta_0 + beta_1*X_1 + .....+ ei Thanks! [[alternative HTML version deleted]]

Author: cwb Repository: /hg/zfs-crypto/gate Revision: 1363ff405550b8be8cd10a06075ba11178182f35 Log message: 6350363 anon_array_try_enter needs to initialise sobj->sync_cv Files: update: usr/src/uts/common/vm/vm_anon.c

Dear R users, I am using the function "constrOptim" to minimize the -1*log-likelihood where \beta_i>=0 i=1,...,p and \beta_0 is unconstrained. I construct u_i as 0 0 0 ... 0 0 1 0 ... 0 0 0 1 ... 0 . . . ... 0 . . . ... 0 .

Hi all I know about producing a minimal example to show my problem. But I'm having trouble producing a minimal example that displays this behaviour, so please bear with me to begin with. Observe: I create an array called model.mat. Some details on this: > str(model.mat) num [1:18, 1:4] -0.170 -0.304 -2.617 2.025 -1.610 ... - attr(*, "dimnames")=List of 2 ..$ : chr

Hello, I currently get anova results out of the aov function (see below) I use the model.tables and I believe it gives me back the model parameters of the fit (betas), however I don't see the intercept (beta_0) and don't understand what the "rep" output means and there is no description in the documentation. Another question: is there a function that outputs the results in a

Dear all, I have two highly correlated variables (y and x), and both of them depend on a third variable (A, for Area). Multiple regression (y=a+(b*x)+(c*A)) would have collinearity problems, so I decided to do a partial regression to predict y. I did it this way: - I regressed y to A, and calculated the residuals (e_y) (reg1) - I regressed x to A, and calculated the residuals (e_x) (reg2)

Hi all, I have run a ridge regression on a data set 'final' as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$u, lambda=seq(0,10,0.01)) Then I enter : select(reg) and it returns: modified HKB estimator is 19.3409 modified L-W estimator is 36.18617 smallest value of GCV at 10 I think it

Hola, Tengo una base de datos con estructura jerárquica en la que quiero clasificar observaciones en distintas categorías. En el caso más simple, tengo una variable con dos categorías (variable Y1) y dentro de cada una de ellas hay otras dos categorías (variable Y2). Además tengo una variable explicativa cuantitativa discreta X. El banco de datos sería de este tipo: X Y1 Y2 5 0 1 9 0 0 2

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Dear Dr.Goslee and anyone may intrested in matrix manipulate, I am using your ecodist to do mantel and partial mantel test, I have locality data and shape variation data, and the two distance matrixs are given as belowings. When I run the analysis, it is always report that the matrix is not square, but I didn't know what's wrong with my data. Would you please help me on this. I am quite

Hi Listers, seems I have forgotten some basics re offset in glm: data: counts (y) from locations off different size (area), explanatory variable: x Model: y ~ x+offset(area) Predictions (pred) using Poisson errors plot(x,y) and points(x,pred) gives neat "line" of estimated values. However, for ease of understanding graphs are better using plot(x,y/area). Question: How to display