search for: x_2

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 = 10) if X_1=0 and X_2 = 1 abline(v = 15) if X_1=1 and X_2 = 0 abline(v = 31) if X_1=1 and X_2 = 1 when I used : xyplot(Y ~ Z | X_1*X_2, data=df, panel = f...

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 number obj <- c(3, 1, 3) mat <- matrix(c(-1, 0, 1, 2, 4, -3, 1, -3, 2), nrow = 3) dir <- c("<=", &quo...

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...

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 optimizer that could find an ex...

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,...

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 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 obs...

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 plotting functions and I cannot seem to get this to work. The data are...

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 different variable of the dataset and then used it. in the regression. 2) With pgmm it is n...

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 period (T_1 = 24 hour; T_2 =...

...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 = 0, X_2 = -c/2, X_3 = c, X_4 = 0 and solutions of the above system are: X_1 = 2/3, X_2 = -1/3-c/2, X_3 = c, X_4 = 0. So the Kernel is 1-D spanned by X_2 = -X_3 /2, (nulliity=1), rank is 3. In R I use solve(): > solve(a,b) Error in solve.default(a, b) : Lapack routine dgesv: system is exactly sin...

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 multinomRob fit the bill? Many thank...

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 -26.210 0.702 26.261 111.511 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 48.62944 0.27999 173.684 < 2e-16 *** x_1 -0.67831 0.08053 -8.423 < 2e-...

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 * ncol(rbind(x_1, x_2, ..., x_n)) equals m Additionally, wouldn't you expect rbind(x_1[i], x_2[i]) to equal rbind(x_1, x_2)[, i, drop = FALSE] ? Hadley On Thu, May 16, 2019 at 3:26 PM Gabriel Becker <gabembecker at gmail.com> wrote: > > Hi all, > > Apol...

...> } > > > > This 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...

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions I report below, and I have tried to implement them with different values of ?n?. BUT I have encountered 2 problems: 1) the result of the maximization is the same of the minimization, i.e. the maximum...

I noticed that there is no generalised additive model functions in R (1.1.1) ... is there a package that implements them? Thanks Prasad ***************************************************************** Mr. Anantha Prasad, Ecologist/GIS Specialist USDA Forest Service, 359 Main Rd. Delaware OHIO 43015 USA Ph: 740-368-0103 Email: aprasad at fs.fed.us Web:

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. >

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...

...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){ rows_x <- length(x)/2 x_1 <- x[1:rows_x] x_2 <- x[(rows_x+1):(rows_x*2)] eq1<- x_1-100 eq2<-x_2*10-40 return(c(eq1,eq2)) } model_test <- function() { reserves<-(c(0:200)/200)^(2)*2000 lambda <- numeric(NROW(reserves))+5 res_ext <- pmin((reserves*.5),5) x_test <- c(res_ext,lambda) #print(x_test) for(test_iter in c(1:10...