similar to: Model formula

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

Dear R users, I have a response variable in a csv file called "y" and a matrix of predictor variables in a csv file called "mat". I have used the function "nls" I have specified the nonlinear relation between these variable.The code I have witten is called Rprog which begins with the phrase: L.minor.m1<-nls(Y~a ....etc.. The program when I execute the program, I

This is required in order to build using the built-in assembler in clang. --- I squashed the two changes since it would break the normal gcc build otherwise. --- celt/arm/arm2gnu.pl | 2 ++ celt/arm/celt_pitch_xcorr_arm.s | 18 +++++++++--------- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/celt/arm/arm2gnu.pl b/celt/arm/arm2gnu.pl index eab42ef..5c24758 100755 ---

This is required in order to build using the built-in assembler in clang. --- celt/arm/celt_pitch_xcorr_arm.s | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/celt/arm/celt_pitch_xcorr_arm.s b/celt/arm/celt_pitch_xcorr_arm.s index 09917b1..3c4b950 100644 --- a/celt/arm/celt_pitch_xcorr_arm.s +++ b/celt/arm/celt_pitch_xcorr_arm.s @@ -309,7 +309,7 @@

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

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

Hello everyone, I want to define a function that receives the name of two variables (may include Greek letters and subscripts) and uses them into the title of a plot. My best attempt is the following: myplot <- function(var1, var2) { v=paste(var1,"==1 & ",var2,"==2"); plot(1:10, main=parse(,,v)) } But when I call it with something like myplot("Q[i]",

Hi - I am fitting a probit model using glm(), and the deviance and residual degrees of freedom are different depending on whether I use a binary response vector of length 80 or a two-column matrix response (10 rows) with the number of success and failures in each column. I would think that these would be just two different ways of specifying the same model, but this does not appear to be the case.

Hi, I have two groups of data of different size: group A: x1, x2, ...., x_n; group B: y1, y2, ...., y_m; (m is not equal to n) The two groups are independent but observations within each group are not independent, i.e., x1, x2, ..., x_n are not independent; but x's are independent from y's I wonder if randomization test is still applicable to this case. Does R have any function

Dear R-Team, Am I right supposing that the "breakpoints()" function in the strucchange package is an implementation of the pure structural change model proposed by Bai and Perron (1997, 2003)? My question relates to a partial structural change model that Bai and Perron formulate in their 2003 paper, e.g. formulated as y = x' beta + z' delta_j + epsilon, where beta and delta

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.

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).

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)??

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 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

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"

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

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

Dear All, I have already posted before on the list about data mining and it has proved very useful. I have now a training dataset consisting of N objects of M<<N different kinds (actually, M is usually 3 to 5, whereas 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

Dear people, I'm running RedHat 9.0 and R : Version 1.7.1 (2003-06-16) from the help file # Usage: # # filter(x, filter, method = c("convolution", "recursive"), # sides = 2, circular = FALSE, init) # init: for recursive filters only. Specifies the initial values of # the time series just prior to the start value, in reverse # time