similar to: Integration of vector syntax unknown

I want to estimate the following fixed effect model: y_i,t = alpha_i + beta_1 x1_t + beta_2 x2_i,tx2_i,t = gamma_i + gamma_1 x1_t + gamma_2 Z1_i + gamma_3 Z2_i I can use ivreg from AER to do the iv regression. fm <- ivreg(y_i,t ~ x1_t + x2_i,t | x1_t + Z1_i + Z2_i, data = DataSet) But, I'm not sure how can I add the fixed effects. Thanks! [[alternative HTML

Hello! I have a csv file of intra-day financial data (5-min closing prices) that looks like this: (obs - the dates are formated as day/month/year, as is usual here in Brazil) Date;Time;Close 01/09/2009;10:00;56567 01/09/2009;10:05;56463 01/09/2009;10:10;56370 ##(goes on all day) 01/09/2009;16:45;55771 01/09/2009;16:50;55823 01/09/2009;16:55;55814 ##(jumps to the subsequent day)

I'm wondering how odds ratio is computed. I thought that it is (n11/n12)/(n21/n22), but it is not what fisher.test() computes. Could somebody let me know? > n11=3 > n12=1 > n21=1 > n22=3 > > n1_=n11+n12 > n2_=n21+n22 > > n_1=n11+n21 > n_2=n12+n22 > > x=rbind(c(n11,n12),c(n21,n22)) > > threshold=dhyper(n11,n1_,n2_,n_1) >

Dear R users, how can I test, whether two correlations differ significantly. (I want to prove, that variables are correlated differently, depending on the group a person is in.) Greetings from Freiburg im Breisgau (Germany), Timo Stolz

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with

Dear All, I've been searching relevant topics about replacing values, none seemed to be applicable to me... I have a file with many many varieties, and want to replace some of them into different names. I tried various of ways, still don't know how to do that most efficiently.. Here is part of the example data: Gen Rep A_1 1 A_1 2 A_2 1 A_2 2 B_1 1 B_1

Hi all, I have n independent variables A_1, A_2, A_3,......,A_n, and each with known variances var(A_1), var(A_2),..., but unknown mean. How can I get the approximation of the variance of the product of the variables using numerical computation, i.e. var(A_1*A_2*A_3*.....*A_n)? Thanks. Sincerely, Yanwei Zhang Department of Actuarial Research and Modeling Munich Re America Tel: 609-275-2176

Dear R-list members, I have a problem with translating a mathematica script into R. The whole script is at the end of the email (with initial values for easy reproduction) and can be pasted directly into R. The problematic part (which is included below of course) is <--- Original Mathematica ---> (* p_svbar *) UiA = Ni (Dsi - 2Di A + A^2)/2; UiiA = Nii (Dsii - 2Dii A + A^2)/2; psvbar =

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, Could any one please tell me the exact formula R uses to calculate the sample size for one-sample and two-sample t-tests? Thanks, Caimiao [[alternative HTML version deleted]]

Hello, i wrote this function guete and now i want to plot it: but i get this error message. i hope someone can help me. Error in dim(robj) <- c(dX, dY) : dims [product 16] do not match the length of object [1] p_11=seq(0,0.3,0.1) p_12=seq(0.1,0.4,0.1) guete = function(p_11,p_12) { set.seed(1000) S_vek=matrix(0,nrow=N,ncol=1) for(i in 1:N) { X_0=rmultinom(q-1,size=1,prob=p_0)

Btw, here is another interesting paper about post-dominators and control dependence: https://pdfs.semanticscholar.org/cbb2/9a0e4895025bd9df24f9263217df12f1ed1e.pdf I think a great outcome of your internship would be some precise documentation regarding the guarantees the LLVM dominators give -- possibly also considering classic and weak control dependence and the difference between

Hello, I am looking into description of surf.ls(spatial) and see under value $beta - the coefficients. When I use polynomial of degree 2 to fit surface I expect to get 4 coefficients: z = a_1 x^2 + a_2 xy + a_3 y^2 + a_4 What do beta really stand for and why do I get $beta vector of length 6? Thakns, Mark

Dear list, I found a strange behavior of the graphic display when using bquote to set a title to a plot. The problem arise when you manually resize the graphic window using the mouse. It happens on both quartz and x11 devices. Here's a reproducible example: par(mfrow = c(1,3)) for (i in 1:3){ title <- as.expression(bquote(gamma[.(i)])) plot(1:10, main = title) } Once you ran the

Hello to all, I am having trouble with intregrating a complicated uni-dimensional function of the following form Phi(x-a_1)*Phi(x-a_2)*...*Phi(x-a_{n-1})*phi(x-a_n). Here n is about 5000, Phi is the cumulative distribution function of standard normal, phi is the density function of standard normal, and x ranges over (-infty,infty). My idea is to to use quadrature to handle this integral. But

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 two arbitrarily dimensioned arrays, "a" and "b", with length(dim(a))==length(dim(b)). I want to form a sort of "corner-to-corner" version of abind(), or a multidimensional version of blockdiag(). In the case of matrices, the function is easy to write and if a=matrix(1,3,4) and b=matrix(2,2,2), then adiag(a,b) would return: [,1] [,2] [,3] [,4] [,5]

Dear All, I have six different campaign and 5 different agent have login on that campaign.*Same thing i have done using agi and database,i never use queue management on this scenario. Agent** can also shuffling one campaign to anther campaign. * Now i want to do some work with queue.I want to use single queue to managing this. Eg: campaign Agent Login A a_1,a_3

I''m having this problem with IE and getElementsBySelector, but am a relative js novice, so quite possible -- likely even -- that I''m doing something stupid. Either way, a fresh pair of eyes would help. Basically I''m duplicating a fieldset within a form (so you can upload more than one photo). [BTW, the fieldsets have a CSS id which relates to the object id of the

Hallo everybody, I'm new in r and I"ll appreciate some help! I have a matrix of nrow=30 and ncoll=54,and I would like to generate 50 simulations with tha same size of the matrix!!!That is to say that I want to generate 50 matrices -for my 50 simulations - with the same dimensions! I took my 1st matrix according to the formula that I want to implement: D<-mean_m + U_i*mat_DELTA