similar to: linear model with coefficient constraints

i am trying to duplicate R's computation of standard errors but having some trouble. i loaded some data into R and ran summary(lm(y~x1+x2+x3+0, data=data)), but i am not sure how the "Std. Error" values are computed. let y be the nx1 vector of dependent variables and X be the nx3 matrix of independent variables. let T(.) denote the transpose of a matrix/vector, and let I(.) denote

I have a file like this: a1,a2,a3,b1,b2,b3 1,2,3,4,5,6 0,1,2,3,4,5 ... In interactive command-line R, I type data<-read.table('file.txt', sep=',', header=TRUE) I then want to get all the columns which start with the letter a. For this particular file, I can the type: data[1:3] What if i don't know that the columns that start with 'a' are columns 1-3? Is there

i have a series of regressions i need to run where everything is the same except for the dependent variable, e.g.: lm(y1 ~ x1+x2+x3+x4+x5, data=data) lm(y2 ~ x1+x2+x3+x4+x5, data=data) lm(y3 ~ x1+x2+x3+x4+x5, data=data) is it possible to run all these regs with a single command? given that the bulk of the work for linear regressions is inverting a matrix that depends only on the independent

Hello, I'd like to simulate data according to an SVAR model in order to demonstrate how other techniques (such as arima) yield biased estimates. I am interested in a 2 variable SVAR with 2 lags (in the notation of the vars vignette, K = 2, P = 2, where B = I_K). I'm using the {vars} package outlined here: http://cran.r-project.org/web/packages/vars/vignettes/vars.pdf I thought that the

I'm using nls to fit a variety of different models. Here I use SSgompertz as an example. I want the ability to fix one (or more) of the coefficients that would normally be optimised (e.g. fix b3=0.8). Examples; based on and using data from example(SSgompertz) #--------------------- # vanilla call to nls, no coefficients fixed, works fine nls(density ~ SSgompertz(log(conc), Asym, b2, b3),

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k

i have a data frame and a numeric vector indexed as a subset of the rows in the data.frame. what command can i use to assign the values in the vector to the appropriate rows of the data.frame? here's my failed attempt. what i would want is data[1,'z'] == 2, data[5,'z'] == -4, data[8,'z'] == -5, data[9,'z'] == 5, and for the other values of 'z' to

The nls2 package can be used to get starting values. On Thu, Apr 28, 2016 at 8:42 AM, Aleksandrovic, Aljosa (Pfaeffikon) <Aljosa.Aleksandrovic at man.com> wrote: > Hi Gabor, > > Thanks a lot for your help! > > I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to

This is a quadratic programming problem that you can solve using either a quadratic programming solver with constraints or a general nonlinear solver with constraints. See https://cran.r-project.org/web/views/Optimization.html for more info on what is available. Here is an example using a nonlinear least squares solver and non-negative bound constraints. The constraint that the coefficients sum

Hi Gabor, Thanks a lot for your help! I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to be inside an interval, let?s say, between 0 and 1, what kind of starting values are normally recommended for the start argument (e.g. Using a 4 factor model with b1, b2, b3 and b4, I tried

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

Hi, I'm trying to verify the calculation of coefficient of determination (r squared) for linear regression. I've done the calculation manually with a simple test case and using the definition of r squared outlined in summary(lm) help. There seems to be a discrepancy between the what R produced and the manual calculation. Does anyone know why this is so? What does the multiple r squared

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Hi all, I was investigating the behaviour of the -p switch of flac. This switch should enable exhaustive search for the optimal qlp coefficient precision, but the resulting files are usually 0.5% larger then when not using the switch. I stumbled upon this code in stream_encoder > if(encoder->protected_->do_qlp_coeff_prec_search) { > min_qlp_coeff_precision =

Hi all, I am running 4 series of quantile regressions with tau=10:90/100, each series corresponding to a different year. I would like to restrict ylim for each coefficient to be the same across years in order to help comparing coeff across years. Therefore, I need to specify ylim for each coef. I have tried:

dear R experts---quick question. I need to estimate a model that looks like y = (b*T+d*T^3) + (1-b-3*d*T^2)*x + (3*d*T)*x^2 + (-d)*x^3 I only have three parameters. Is nls() the right tool for the job, or is there something faster/better? /iaw ---- Ivo Welch (ivo.welch@brown.edu, ivo.welch@gmail.com) [[alternative HTML version deleted]]

Hi R-users, I am trying to estimate function parameters using optim(). My count observations follows a Poisson like distribution. The problem is that I wanna express the lambda coefficient, in the passion likelihood function, as a linear function of other covariates (and thus of other coefficients). The codes that I am using (except data frame) are the following (FYI the parameters need to be

Greetings: We are evaluating the performance of R matrix algebra es as we port a MATLAB R14 script into R. The MATLAB code basically evaluates the AX=B system on sparse matrices that result in output matrices of 100 to 1,000,000 rows/columns. Our R prototype script uses spase base matrices and the methods qr() and qr.coeff(). The following statements are called inside a doubly-nested loop: G

I'm having difficulty figuring out how to implement the following set of constraints in Quadprog: 1). x1+x2+x3+x4=a1 2). x1+x2+x5+x6=a2 3). x1+x3+x5+x7=a3 4). x1+x2=b1 5). x1+x3=b2 6). x1+x5=b3 for the problem: MIN (x1-c1)2+(x2-c2)2+...+(x8-c8)2. As far a I understand, "solve.QP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)" reads contraints using an element-by-element