similar to: title expressions

Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

Dear All, I am wondering on how to use the abline.xyplot with xyplot such that I will have different vertical lines for each panel. More sepcifically, suppose that the xyplot generates 4 panels defined by the combination of two binary variables: X_1 and X_2. i.e. xyplot(Y ~ Z | X_1*X_2, data = df) I want something like: abline(v = 5) if X_1=0 and X_2 = 0 abline(v =

Hi All Apologies if this is not the correct list for this question. The Rglpk package offers the following example in its documentation library(Rglpk) ## Simple mixed integer linear program. ## maximize: 3 x_1 + 1 x_2 + 3 x_3 ## subject to: -1 x_1 + 2 x_2 + x_3 <= 4 ## 4 x_2 - 3 x_3 <= 2 ## x_1 - 3 x_2 + 2 x_3 <= 3 ## x_1, x_3 are non-negative integers ## x_2 is a non-negative real

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)??

Dear R-users, i try to solve to following linear programm in R 0 * x_1 + 2/3 * x_2 + 1/3 * x_3 + 1/3 * x_4 = 0.3 x_1 + x_2 + x_3 + x_4 = 1 x_1, x_2, x_3, x_4 > 0, x_1, x_2, x_3, x_4 < 1 as you can see i have no objective function here besides that i use the following code. library(lpSolve) f.obj<-c(1,1,1,1) f.con<-matrix(c(0,2/3,1/3,1/3, 1,1,1,1,

Hi all; I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error  in calling gradient  function; The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector  ‘h’ is

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).

Dear List: I cannot seem to find a way to plot my data correctly. I have a small data frame with 6 total variables (x_1 ... x_6). I am trying to plot x_1 against x_2 and x_3. I have tried plot(x_2, x_1) #obviously works fine plot(x_3, x_1, add=TRUE) # Does not work. I keep getting error messages. I would also like to add ablines to this plot. I have experimented with a number of other

Dear R experts, I was wondering if there are any R functions that give the tail area of a sum of chisquare distributions of the type: a_1 X_1 + a_2 X_2 where a_1 and a_2 are constants and X_1 and X_2 are independent chi-square variables with different degrees of freedom. Thanks, Klaus -- "Feel free" - 5 GB Mailbox, 50 FreeSMS/Monat ...

Dear R-Users, I need to find a smooth function f() and coefficients a_i that give the best fit to y ~ a_0 + a_1*f(x_1) + a_2*f(x_2) Note that it is the same non-linear transformation f() that is applied to both x_1 and x_2. So my first question is how can I do it in R? A more general question is this: suppose I have a utility function U(a_i, f()), where f() is say a spline. Is there a general

Hi,   I have a system in which I analyze 2 subjects and 1 variable, so I have 2 models as follow:   y ~ x_1[, 1] + x_2[, 1] + x_1[, 2] + x_2[, 2]   Where   x_1[, i] = cos(2 * pi * t / T_i) x_2[, i] = sin(2 * pi * t / T_i)   i = 1, 2   Data have two columns: t and y.   As you can see, I have a multiple components model, with rithm and without trends, and I have a fundamental

Hi there! Today I tried to estimate models using both plm and pgmm functions, with an interaction between X1 and lag(X2, 1). And I notice two issues. Let "Y=b_1 * X_1 + b_2 * X_2 + b_3 * X_1 * x_2 + e" be our model. 1) When using plm, I got different results when I coded the interaction term with I(X1 * lag(X2, 1)) and when I just saved this multiplication X1 * lag(X2, 1) in a

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

I have a simple system of linear equations to solve for X, aX=b: > a [,1] [,2] [,3] [,4] [1,] 1 2 1 1 [2,] 3 0 0 4 [3,] 1 -4 -2 -2 [4,] 0 0 0 0 > b [,1] [1,] 0 [2,] 2 [3,] 2 [4,] 0 (This is ex Ch1, 2.2 of Artin, Algebra). So, 3 eqs in 4 unknowns. One can easily use row-reductions to find a homogeneous solution(b=0) of: X_1

Hello folks, I am on learning phase of R. I have developed Regression Model over six predictor variables. while development, i found my all data are not very linear. So, may because of this the prediction of my model is not exact. Here is the summary of model : Call: lm(formula = y ~ x_1 + x_2 + x_3 + x_4 + x_5 + x_6) Residuals: Min 1Q Median 3Q Max -125.302

Hello all, I am relatively new to R, but enjoying it very much. I am hoping that someone on this list can help me with an issue I am having. I am having issues with iterations over nleqslv, in that the solver does not appear to clean up memory used in previous iterations. I believe I've isolated the/my issue in a small sample of code: library(nleqslv) cons_ext_test <- function(x){

Hi Hadley, Thanks for the counterpoint. Response below. On Thu, May 16, 2019 at 1:59 PM Hadley Wickham <h.wickham at gmail.com> wrote: > The existing behaviour seems inutitive to me. I would consider these > invariants for n vector x_i's each with size m: > > * nrow(rbind(x_1, x_2, ..., x_n)) equals n > Personally, no I wouldn't. I would consider m==0 a degenerate

Hi Daniel, Sorry for taking so long to respond. I spoke with a colleague more familiar with llvm who thought he could clear up my confusion, but we both came out of the conversation confused. I will try my best to explain the ambiguity. In an DAG, alias queries would be completely unambiguous. Every instruction would only be executed once, and every SSA value really would have a single static

Dear All, Suppose I have a vector X = (x_1, x_2, ...., x_n), X_sort = sort(X) = (x_(1), x_(2), ... , x(n) ), and I would like to know the original position of these ordered x_(i) in X, how can I do it? case 1: all values are unique x <- c( 3, 5, 4, 6) x.sort <- sort(x) # # I would like to obtain a vector (1, 3, 2, 4) which indicates that 3 in x is still the 1st element in x.sort, 5 is at