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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 Modeling Munic...

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 quantity y is observed at each time...

...ant 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-c_1_F))+exp...

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 model shoul...

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.

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 doing this? Would...

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 highl...

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.

...l 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 ; r10 = y_4 = *y++ SMLABT r8, r14, r11, r8 ; sum[2] = MAC16_16...

...l 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 ; r10 = y_4 = *y++ SMLABT r8, r14, r11, r8 ; sum[2] = MAC16_16...

...el 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 model shoul...

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 not totally su...

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 'magnitude' of spectrum and 'phase' of...

...spect 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 are given constants? I have googled around a bit (unsuccessfully) and have asked this question on crossvalidated a couple of days ago, with no response whatever. So I thought that I might...

...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="recursive", init=1:3) Time Series: Sta...

Dear R-Users, Dr. Wood replied to a similar topic before where confidence intervals were for a ratio of two treatments ( https://stat.ethz.ch/pipermail/r-help/2011-June/282190.html). But my question is more complicated than that one. In my case, log(E(y)) = s(x) where y is a smooth function of x. What I want is the confidence interval of a ratio of log[(E(y2))/E(y1)] given two fixed x values of

Hi everyone, Can anyone tell me if its possibility to extract the coefficients from the lm() command? For instance, imagine that we have the following data set (the number of observations for each company is actually larger than the one showed...): Company Y X1 X2 1 y_1 x1_1 x2_1 1 y_2 x1_2 x2_2 1 y_3 x1_3 x2_3 (...) 2 y_4 x1_4 x2_4 2 y_5 x1_5 x2_5 2 y_6 x1_6 x2_6 (...) n y_n x1_n x2_n n y_n1 x1_n1 x2_n1 n y_n2 x1_n2 x2_n2 (...) I need to run a regression of Y=b0+b1*X1+b2*X2 for EACH company in the dataset and then retrieve the coefficients for each regression obtained (...

I am using the lattice packge for its levelplot and contourplot. Is it possible to adjust the line thickness of the 'box' and tickmarks in these plots? Thanks for the attention, Matt Sundling

...N is of the order of 1000). Every object has its own label L_i, i=1...N, that is known. For each of these objects I measure some property in time (let's say I measure it Q times in a given time interval), i.e. the i-th object has an associated file {t, y}, where t=(t_1,t_2....t_Q) and y=(y_1,y_2,...y_Q). My problem is then to come up with an algorithm that after learning on the training dataset, can guess the labels of a testing dataset. The difference with respect to the datamining I have done so far is that I do not have a set of properties for every object (e.g. age, sex, income, etc...

Does anyone know of a literature reference, or a piece of code that can help me calculate the amount of variation explained (R2 value), in a regression constrained to have a slope of 1 and an intercept of 0? Thanks! Sebastian J. Sebastián Tello Department of Biological Sciences 285 Life Sciences Building Louisiana State University Baton Rouge, LA, 70803 (225) 578-4284 (office and lab.)