similar to: trouble getting table of coeffs with quantreg with fixed effects

Dear all, This is off-topic, however I hope someone can give me useful suggestion.. Given the regression model y = b0 + b1*x + e I am interested in testing for positive coeffs, namely H0: b0>0 AND b1>0 H1: b0,b1 unconstrained It is simple to estimate the model under H0 and H1 (there are several suggestions on the Rlist about estimation but nothing about testing..) perform a likelihood

Is there any way in R to do a linear regression with positive coefficients only? Thanks for any help! Sandor Lehoczky -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

Hi, I am a new R user. I am using it develop regression models with categorical variables. Is there a way to force some regression coefficients to be zero for some of the values in a categorical variable (with 12 factor levels)? I am recoding the values to the default value (1st in the order of dummy's). But I am not sure if this is the correct approach if I want to force coefficients to be

Am I correct in assuming that the output below essentially translates to "Males have a mean time that is significantly lower than Females"? Is this the correct way to interpret the fact that the coefficient is negative? Assume the variale sex is treated as a factor with Female =0 and Male=1. survmodel<-survreg(survobj~sex,data=data1, dist="weibull")

Brian Willoughby wrote: > Ok, I just did a comparison of 1.2.1 with 1.3.2, and the change you're > suggesting was already there before. So, now the question becomes: why > was the code changed in the first place? There should be some indication of why in the git history. Erik -- ---------------------------------------------------------------------- Erik de Castro Lopo

Martijn van Beurden wrote: > Yes, but that MAX value is used to loop over the > qlp_coeff_precision values between MIN and MAX. So, if the > qlp_coeff_precision value is limited in the loop but MAX is not > limited, the loop does the exact same thing multiple times: a > waste of time. Therefore, only the MAX should be limited. > > I don't think the logic is needed

Martijn van Beurden wrote: > Yes, indeed. I removed the 17-bits part because I just matched > the code in evaluate_lpc_subframe_ with the process_subframe_ > code. It appears it only makes sense that those two pieces code > are the same. A bit of history: 1) The conditional "if(subframe_bps <= 16)" was added to evaluate_lpc_subframe_() in the commit

Erik de Castro Lopo wrote: > There should be some indication of why in the git history. http://git.xiph.org/?p=flac.git;a=commitdiff;h=27846708fe6271e5e3965a4bbad99baa1ca24c49 Now I remember a discussion about a bug in -p switch: the old code substracts lpc_order instead of FLAC__bitmath_ilog2(lpc_order), and this commit fixes this. It seems that the logic in process_subframe_() and in

Martijn van Beurden wrote: > Or maybe the question is: why is this code in evaluate_lpc_subframe anyway, > i.e, why is this code duplicated? If process_subframe_ sets the > qlp_precision for evaluate_lpc_subframe, why should the latter ignore that? > > We can only speculate about this, but I think this code has been duplicated > by accident, and therefore it wasn't changed

stream_encoder.c has the following code: /* try to keep qlp coeff precision such that only 32-bit math is required for decode of <=16bps streams */ if(subframe_bps <= 16) { ... But FLAC can convert 16-bit input to 17-bit if mid-side coding is used. So, does it make sense to compare subframe_bps with 17? (The patch is attached. What do you think about it?) -------------- next part

Ok, I just did a comparison of 1.2.1 with 1.3.2, and the change you're suggesting was already there before. So, now the question becomes: why was the code changed in the first place? Was there a bug that was fixed by changing 17 to 16, or did someone just get overzealous in a code review and thought that 17 was a bad choice? Perhaps 32 bits isn't actually large enough to handle the

Dear UseRs, I built varying coefficient models (in mgcv) for two groups separately, with one explanatory and one moderator variable (see the example below). # ------- #  Example: # ------ # generate moderator variable (can the same for both groups) modvar <- c(1:1000) # generate group1 values x1 <- rnorm(1000) y1 <- scale(cbind(1,poly(modvar,2))%*%c(1,2,1)*x1 + rnorm(1000,0,0.3)) #

Hi, Being a R-newbie I am wondering how to calculate a correlation coefficient (preferably with an associated p-value) for data like: > d[,1] [1] 25.5 25.3 25.1 NA 23.3 21.5 23.8 23.2 24.2 22.7 27.6 24.2 ... > d[,2] [1] 0.0 11.1 0.0 NA 0.0 10.1 10.6 9.5 0.0 57.9 0.0 0.0 ... Apparently corr(d) from the boot-library fails with NAs in the data, also cor.test cannot cope with a

Dear All, have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

I have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni