search for: b_2

...so I'd like that the output is saved for each iteration and not only at completion. Any suggestions on how to get this done would be much appreciated. Kristian Lind *Below an example of what I'm trying to do...* loglik <- function(w){ state <- c( b_1 = 0, b_2 = 0, a = 0) #declaring ODEs Kristian <-function(t, state, w){ with(as.list(c(state, w)), { db_1 = -((w[1]+w[8])*b_1+(w[2]+w[6]*w[8] +w[7]*w[9])*b_2+0.5*(b_1)^2+w[6]*b_1*b_2+0.5* ((w[6])^2+(w[7])^2)*(b_2)^2) db_2 = -w[3]*b_2+1 da = w[1]*...

Hi, I'm using the package quadprog to solve the following quadratic programming problem. I want to minimize the function (b_1-b_2)^2+(b_3-b_4)^2 by the following constraints b_i, i=1,...,4: b_1+b_3=1 b_2+b_4=1 0.1<=b_1<=0.2 0.2<=b_2<=0.4 0.8<=b_3<=0.9 0.6<=b_4<=0.8 In my opinion the solution should be b_1=b_2=0.2 und b_3=b_4=0.8. Unfortunately R doesn't find this solution and what's surpri...

...K_rr = 0.3384, theta_v = 107.4039, theta_r = 5.68, Sigma_rv= 0.0436, Sigma_rr= 0.1145, lambda_v= 0, lambda_r= -0.0764 ) state <- c(b_1 = 0, b_2 = 0, a = 0) Kristian <- function(t, state, parameters){ with(as.list(c(state, parameters)),{ db_1 = -((K_vv+lambda_v)*b_1+(K_rv+Sigma_rv*lambda_v+Sigma_rr*lambda_r)*b_2+0.5*(b_1)^2+Sigma_rv*b_1*b_2+0.5*((Sigma_rv)^2+(Sigma_rr)^2)*(b_2)^2 ) db_2 = -K_rr*b_2+1...

...ameters <- c(K_vv, K_rv, K_rr, theta_v, theta_r, Sigma_rv, Sigma_rr, lambda_v, lambda_r) state <- c( b_1 = 0, b_2 = 0, a = 0) #declaring ODEs Kristian <- function(t, state, parameters){ with(as.list(c(state, parameters)), { db_1 = -((K_vv+lambda_v)*b_1+(K_rv+Sigma_rv*lambda_v +Sigma_rr*lambda_r)*b_2+0.5*(b_1)^2+Sigma_rv*b_1*b_2+0.5* ((Sigma_rv)^2+(Sigma_rr)^2)*(b_...

...representation of additive models, testing among others y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2) (testing for additivity) In mixed model representation, where X represents the unpenalized part of the spline functions and Z the "wiggly" parts, this would be: y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 vs y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 + Z_12 %*% b_12 where b are random effect vectors and the hypothesis to be tested is H_0: Var(b_12)=0 (<=> b_12_i == 0 for all i) the problem: gamm() does not seem to support the use of nested tensor product splines, does anybody know how to work around...

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 i...

...;s. We first fit the data using ADMB-RE's Laplace approximation option. Laplace approximation estimates: # Number of parameters = 4 log-likelihood = -629.817 value std dev P value b_1 -2.3321e+00 7.6973e-01 < 0.0024 b_2 -6.8795e-01 6.6185e-01 0.298 b_3 -4.6134e-01 4.0000e-02 < 0.001 sigma 4.5738e+00 7.0970e-01 The parameter of interest here the treatment effect b_2 which is the parameter reported in Lesaffre et. al. To calculate the exact MLE we fit the model...

Hi, See example. for (i in 1:2) { for (j in 1:3) { b_1[i,j]<-rank(c(a1[i,j],a2[i,j],a3[i,j]))[1] b_2[i,j]<-rank(c(a1[i,j],a2[i,j],a3[i,j]))[2] b_3[i,j]<-rank(c(a1[i,j],a2[i,j],a3[i,j]))[3] } } The inner codes is really repeated, so i want to change the inner codes into loops. Take nn is from 1 to 3, something like, for (nn in 1:3) { b_nn[i,j]<-rank(c(a1[i,j]:a3[i,j]))[nn] }...

...ion model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b_2 * x2_{ij} log(phi_{ij}) = Gamma_{0i} + Gamma_{1i} * z1_{ij} + c_2 * z2_{ij} * *Beta_{0i} = b_0 + u_{0i} Beta_{1i} = b_1 + u_{1i} Gamma_{0i} = c_0 + v_{0i} Gamma_{1i} = c_1 + v_{1i} * *The vector* *(u_{0i}, u_{1i})'* *has normal distribution with mean* *(0, 0)'* *and covariance matrix* *sig...

...e scheme 944 data points, 2828 dummy points Total weight 1098.64 How do I use the Quadrature to model my intensity based off of those two covariates and an intercept term alpha? In mathematical terms, if \lambda is my intensity function, I want to estimate \lambda(s;b) = exp(alpha + b_1 * Z_1 + b_2 * Z_2). Thank you for your help! I really appreciate it. Kind regards, Greg R.

Hello, I have a data set which I am using to find a model with the most significant parameters included and most importantly, the p-values. The full model is of the form: sad[,1]~b_1 sad[,2]+b_2 sad[,3]+b_3 sad[,4]+b_4 sad[,5]+b_5 sad[,6]+b_6 sad[,7]+b_7 sad[,8]+b_8 sad[,9]+b_9 sad[,10], where the 9 variables on the right hand side are all indicator variables. The thing I don't understand is the line ' sad[, 10] NA NA NA NA ' as a result of 'Coef...

...stride for your example. Your code - as far as I can reconstruct it from memory - looks something like preheader: loop: %strided_induction_pointer = phi [preheader, %b], [loop, %str_ind_ptr_inc] = load %strided_induction_pointer %b_1 = gep %strided_induction_pointer, 1 … = load %b_1 %b_2 = gep %strided_induction_pointer, 2 … = load %b_2 %str_ind_ptr_inc = gep %strided_induction_pointer, 3 // Induction variable that strides by 3 %cmp = … br %cmp, loop, exit exit: %strided_induction_pointer here is a pointer induction not a reduction, because a reduction precludes uses like t...

On 23 October 2013 16:05, Arnold Schwaighofer <aschwaighofer at apple.com>wrote: > In the examples you gave there are no reduction variables in the loop > vectorizer’s sense. But, they all have memory accesses that are strided. > This is what I don't get. As far as I understood, a reduction variable is the one that aggregates the computation done by the loop, and is used

...sigma_{11} * *The vector* *(v_{0i}, v_{1i})'* *has normal distribution with mean* *(0, 0)'* *and covariance matrix* *delta_{00} delta_{01} delta_{10} delta_{11} * *The* *w_{ij}s are independents. Each* *w_{ij}* *has mean* *0* *and variance * *W*. *The unknown parameters are:* * b_0, b_1, b_2, c_0, c_1, c_2, sigma_{00}, sigma_{10}, sigma_{11}, delta_{00}, delta_{10}, **and* *delta_{11}*. *The question is: How can I use nlme package in R to fit this model? If you want to know additional information, send me a mail, please. In advance, thanks a lot for your help. Kind regards, Rodrigo....

I'm getting lots of grief from reviewers about figures generated with the envfit function in the Vegan package. Has anyone else struggled to effectively explain this analysis? If so, can you share any helpful tips? The most recent comment I've gotten back: "What this shows is which NMDS axis separates the communities, not the relationship between the edaphic factor and the

...he 3SLS estimator are consistent but > only the 3SLS estimator is (asymptotically) efficient. Under the > alternative hypothesis the 2SLS estimator is consistent but the > 3SLS estimator is inconsistent. > > The Hausman test statistic is > > m = ( b_2 - b_3 )' ( V_2 - V_3 ) ( b_2 - b_3 ) > > where $b_2$ and $V_2$ are the estimated coefficients and their > variance covariance matrix of a _2SLS_ estimation and $b_3$ and > $V_3$ are the estimated coefficients and their variance covariance > matrix of a _3SLS_ esti...

I would like to fit a hierarchical regression model from Witte et al. (1994; see reference below). It's a logistic regression of a health outcome on quntities of food intake; the linear predictor has the form, X*beta + W*gamma, where X is a matrix of consumption of 82 foods (i.e., the rows of X represent people in the study, the columns represent different foods, and X_ij is the amount of

On Thu, 12 Apr 2007, Fernando Magno Quintao Pereira wrote: >> I'm definitely interested in improving coalescing and it sounds like >> this would fall under that work. Do you have references to papers >> that talk about the various algorithms? > > Some suggestions: > > @InProceedings{Budimlic02, > AUTHOR = {Zoran Budimlic and Keith D. Cooper and Timothy