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Well, I could argue that it's not *completely* OT since my question is motivated by an enquiry that I received in respect of a CRAN package "Iso" that I wrote and maintain. The question is this: Given observations y_1, ..., y_n, what is the solution to the problem: minimise \sum_{i=1}^n (y_i - y_i^*)^2 with respect to y_1^*, ..., y_n^* subject to the "isotonic" constraint y_1^* <= y_2^* <= ... <= y_n^* and the *additional8 bound constraint a <= y_1^* and y_n^* <= b, where a and b a...

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)?? Thanks. Sincerely, Yanwei Zhang Department of Actuarial Research and...

...is is not what it looks like in LLVM. > > In LLVM, it looks like this: > > std::vector<int> A(100); >> int* x,y; >> > >> > x_1=GEP A, 0, 0 >> > for(int i=0; i<100; i++) { >> > i_2 = phi (0, i_3) > x_2 = phi(x_1, x_3) > y_1 = GEP A, 0, i_2 > temp = load x_2 > store y_1, temp > temp2 = add i_2, 1 > x_3 = GEP A, 0, temp2 > i_3 = add i_2, 1 > } > > As you can see, every time you redefine the value of the pointer x to a > new value, it gets a new name. > > alias(x_1, y_...

...very important for my phd thesis. Thanks in advance. Hammadi. -------------- next part -------------- mat<- read.csv(file="mat.csv",head=FALSE,sep=";") y<-read.csv(file="y.csv",head=FALSE,sep=";") Y=y[,1] D_1=mat[,1] D_2=mat[,2] D_3=mat[,3] D_4=mat[,4] Y_1=mat[,5] Y_2=mat[,6] Y_3=mat[,7] Y_4=mat[,8] s_F=mat[,9] s_G=mat[,10] L.minor=data.frame(Y=Y,D_1=D_1,D_2=D_2,D_3=D_3,D_4=D_4,Y_1=Y_1,Y_2=Y_2,Y_3=Y_3,Y_4=Y_4,s_F=s_F,s_G=s_G) stdS=1/(sd(s_F)) stdG=1/(sd(s_G)) L.minor.m1<-nls(Y~a_1_1*D_1+a_2_1*D_2+a_3_1*D_3+a_4_1*D_4+a_1_2*(exp(-gamma_1_F*stdS*(s_F...

Dear All, Here is what I am trying to achieve: I would like to plot some data in 3D. Usually, one has a matrix of the kind y_1(x_1) , y_1(x_2).....y_1(x_i) y_2(x_1) , y_2(x_2).....y_2(x_i) ........................................... y_n(x_1) , y_n(x_2)......y_n(x_i) where e.g. y_2(x_1) is the value of y at time 2 at point x_1 (see that the grid in x is the same for the y values at all times). Instead, in my case, the qu...

Hi all, I'm not familiar with R programming and I'm trying to reproduce a result from a paper. Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta j = y_j - y_(j-1). In order to estimate y-values, I'm assuming that delta j is approximately equal to kj**u, such that my regression...

On Thu, Aug 14, 2014 at 6:37 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > On Wed, Aug 13, 2014 at 8:35 PM, Jeremy Salwen <jeremysalwen at gmail.com> wrote: >> Hey Daniel, >> >> Thanks again for the help. I'm still a bit confused about the interface to >> the alias analysis. It seems like we are talking about different >> interfaces. >

...ector<int> A(100); > > > > int* x,y; > > > > > > > > > > > > > > > > > > x_1=GEP A, 0, 0 > > > > > > > > for(int i=0; i<100; i++) { > > i_2 = phi (0, i_3) > > x_2 = phi(x_1, x_3) > > y_1 = GEP A, 0, i_2 > > temp = load x_2 > > store y_1, temp > > temp2 = add i_2, 1 > > x_3 = GEP A, 0, temp2 > > i_3 = add i_2, 1 > > } > > > > > > As you can see, every time you redefine the value of the pointer x to > > a new value, it gets...

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes estimators? Any relevant work and codes in R (or S+) is...

Hi, Can anyone tell me how to construct a simple VAR(1) time series with two variables using the dse package? I would like to end up with two time series y_1t = \phi_11 y_1,t-1 + \phi_12 y_2,t-1 + e_1t y_2t = \phi_21 y_1,t-1 + \phi_22 y_2,t-1 + e_2t Best regards, Sam.

On Fri, 7 Feb 2014, Timothy B. Terriberry wrote: > Martin Storsjo wrote: >> This is required in order to build using the built-in assembler >> in clang. > > These patches break the gcc build (with "Error: bad instruction"). Ah, right, sorry about that. > Documentation I've seen is contradictory on which order ({cond}{size} or > {size}{cond}) is correct.

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I compared the mean from both the Gibbs sample and the arms sample for several y and x. Surprisingly, both means always di...

...edsp_process4_done SUBS r2, r2, #1 ; j-- ; Stall SMLABB r6, r12, r10, r6 ; sum[0] = MAC16_16(sum[0],x,y_0) - LDRGTH r14, [r4], #2 ; r14 = *x++ + LDRHGT r14, [r4], #2 ; r14 = *x++ SMLABT r7, r12, r10, r7 ; sum[1] = MAC16_16(sum[1],x,y_1) SMLABB r8, r12, r11, r8 ; sum[2] = MAC16_16(sum[2],x,y_2) SMLABT r9, r12, r11, r9 ; sum[3] = MAC16_16(sum[3],x,y_3) @@ -319,7 +319,7 @@ xcorr_kernel_edsp_process4_done SMLABB r7, r14, r11, r7 ; sum[1] = MAC16_16(sum[1],x,y_2) LDRH r10, [r5], #2 ; r1...

...edsp_process4_done SUBS r2, r2, #1 ; j-- ; Stall SMLABB r6, r12, r10, r6 ; sum[0] = MAC16_16(sum[0],x,y_0) - LDRGTH r14, [r4], #2 ; r14 = *x++ + LDRHGT r14, [r4], #2 ; r14 = *x++ SMLABT r7, r12, r10, r7 ; sum[1] = MAC16_16(sum[1],x,y_1) SMLABB r8, r12, r11, r8 ; sum[2] = MAC16_16(sum[2],x,y_2) SMLABT r9, r12, r11, r9 ; sum[3] = MAC16_16(sum[3],x,y_3) @@ -319,7 +319,7 @@ xcorr_kernel_edsp_process4_done SMLABB r7, r14, r11, r7 ; sum[1] = MAC16_16(sum[1],x,y_2) LDRH r10, [r5], #2 ; r1...

...nction(x) { x+y } test2=function(y1) { y=y1 test1(2,y1) } Running the second function results in an error: > test2(1) Error in test1(2) : object 'y' not found I see 2 possible solutions to this, but neither of them is preferred in my more complex situation: 1. Setting y<<-y_1 globally in test2-function 2. making test1 a function of both x and y. Is there any other way to do this except from the 2 above? I hope someone can help me with this. -- View this message in context: http://r.789695.n4.nabble.com/How-to-define-a-variable-in-a-function-that-another-function-uses-...

...ubject: Model formula question To: r-help at stat.math.ethz.ch Hi all, I'm not familiar with R programming and I'm trying to reproduce a result from a paper. Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta j = y_j - y_(j-1). In order to estimate y-values, I'm assuming that delta j is approximately equal to kj**u, such that my regression...

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the logit of Y_3. Is there an existing facility or package in R for doi...

Hi, I have two (different types of) measurements, say X and Y, resulting from the same set of experiments. So X and Y are paired: (x_1, y_1), (x_2, y_2), ... I am trying to calculate the standard deviation of Y with respect to X. In other words, in terms of the scatter plot of X and Y, I would like to divide it into bins along the X-axis and for each bin calculate the standard deviation along the Y results in that bin. (Though I am no...

I did a fourier transform on a function in time domain to get the following functions in frequency domain (in latex): $Y_1[\omega] = \frac{1}{1-\phi_1 e^{-jw}}$ $Y_2[\omega] = \frac{1}{1-(\phi_1 + \phi_2)e^{-jw} +\phi_1\phi_2e^{-2jw}}$ How do I find the spectrum of this function for given $\phi_1$ and $\phi_2$ coefficients and in the discretization interval $w = [-\pi:.1*\pi: \pi]$? Then, how do I find the 'magni...

...s only. Specifies the initial values of # the time series just prior to the start value, in reverse # time order. The default is a set of zeros. but looks as if it should be in usual order as x is e.g. init y_0 y_-1 y_-2: 3, 2, 1 filter f_1 f_2 f_3: 1, .5, .25 x: 4, 5, 6, 7, 8 y_1 = 4 + 1*3 + .5*2 + .25*1 = 8.25 y_2 = 5 + 1*8.25 + .5*3 + .25*2 = 15.25 ... but > filter(4:8,c(1,.5,.25),method="recursive", init=3:1) Time Series: Start = 1 End = 5 Frequency = 1 [1] 6.7500 12.7500 22.3750 37.4375 59.8125 whereas > filter(4:8,c(1,.5,.25),method="recursi...