similar to: glm with linear restrictions

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, I would like to set a constraint on the fixed effect estimates in a GLM model, such as b1=b2. Is this possible in the glm package? Similarly I would like to set some to equal zero too. I have tried searching the information with this package, but I can't find anything for this. Thanks in advance for any help! David -- View this message in context:

I have a model Q=K*A*(R^r)*(S^s) A, R, and S are data I have and K is a curve fitting parameter. I have linearized as log(Q)=log(K)+log(A)+r*log(R)+s*log(S) I have taken the log of the data that I have and this is the model formula without the K part lm(Q~offset(A)+R+S, data=x) What is the formula that I should use? Thanks for all of your help. I can provide a subset of data if necessary.

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Hello I'm trying to calculate 2d confindence bounds into a scatterplot using the function "kde2d" (package MASS) and a contour plot. I found a similar post providing a solution - unfortunatly I do not realy understand which data I have to use to calculated the named "quantile": Post URL: http://tolstoy.newcastle.edu.au/R/help/03b/5384.html > (...) > >> Is

I would like to perform a regression like the one below: lm(x ~ 0 + a1 + a2 + a3 + b1 + b2 + b3 + c1 + c2 + c3, data=data) However, the data has the property that a1+a2+a3 = A, b1+b2+b3 = B, and c1+c2+c3 = C, where A, B, and C are positive constants. So there are two extra degrees of freedom, and R handles this by producing NA for two of the coefficients. Instead, I would prefer to remove the

Dear List, I''m thinking about using xen productive in our datacenter, i''m still testing around with it. Now I got some questions, just for basic understanding, we got for example this environment: 2 Nodes 1 SCSI Pool server (Connected via scsi to both nodes) Now I want to build a "cluster" so i would like to make this: Node 1 -> Primary -| | --> domU

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

am I right in the assumption, that for calculation of the condition numbers I have to use the correlation matrix of X, and not t(x) %*% x? > e <- eigen(t(x) %*% x) better (x must not have a first column of ones): > e <- eigen(cor(x)) > e$val [1] 6.6653e+07 2.0907e+05 1.0536e+05 1.8040e+04 2.4557e+01 2.0151e+00 > sqrt(e$val[1]/e$val) [1] 1.000 17.855 25.153 60.785 1647.478

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

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[ CCed VHOST contacts ] On Thu, Oct 28, 2010 at 01:22:02PM -0700, Jesse Gross wrote: > On Thu, Oct 28, 2010 at 4:54 AM, Simon Horman <horms at verge.net.au> wrote: > > My reasoning is that in the non-mirroring case the guest is > > limited by the external interface through wich the packets > > eventually flow - that is 1Gbit/s. But in the mirrored either > > there

[ CCed VHOST contacts ] On Thu, Oct 28, 2010 at 01:22:02PM -0700, Jesse Gross wrote: > On Thu, Oct 28, 2010 at 4:54 AM, Simon Horman <horms at verge.net.au> wrote: > > My reasoning is that in the non-mirroring case the guest is > > limited by the external interface through wich the packets > > eventually flow - that is 1Gbit/s. But in the mirrored either > > there

Hi all, I hope you are doing well? I?m currently using the lm() function from the package stats to fit linear multifactor regressions. Unfortunately, I didn?t yet find a way to fit linear multifactor regressions with constraint coefficients? I would like the slope coefficients to be all inside an interval, let?s say, between 0 and 1. Further, if possible, the slope coefficients should add up to

Hi, My requirement is something like as given below, a.c => a.obj contains a1() and a2() function b.c => b.obj contains b1() and b2() function main.c => main.obj call to a1, a2, b1, b2 Now, I want to move a1(), a2() from a.obj to b2.obj and on top of function b1() When I call b1() from main, it should call first a1, a2 and then function definition of b1 Can you please give me some

Dear R people I came across a strange thing: sd(rep(0.01, 15)) #0 sd(rep(0.001, 15)) #4.489023e-19 sd(rep(0.00001, 15)) #0 sd(rep(0.00000001,15)) #1.712427e-24 sd(rep(0.01, 13)) #1.805557e-18 sd(rep(0.001, 13)) #4.513894e-19 sd(rep(0.00001, 13)) #0 sd(rep(0.00000001,13)) #0 sd(rep(5.01, 15)) #0 sd(rep(5.001, 15)) #4.489023e-19 sd(rep(5.00001, 15))

Hi all, I?m fitting GLM?s and I can?t interprete the coefficients when I run a model with interaction terms. When I run the simpliest model there is no problem: Model1<-glm (Fishes ~ Year + I(Year^2) + Kind.Geographic + Kind.Fishers + Zone.2 + Hours + Fishers + Month, family = poisson(log)) # Fishes, Year, Hours, and Fishers are numeric, Kind.Geographic, Kind.Fishers, Zone.2 and

Hello everybody, I have a question. I would like to get a correlation between constitutive and induced plant defence which I messured on 30 plant species. So I have table with Species, Induced defence (ID), and constitutive defence (CD). Since Induced and constitutive defence are not independant (so called spurious correlation) I should do a randomisation test. I have a syntax of my

I am just about working through Faraways excellent tutorial "practical regression and ANOVA using R" on page 24 he makes the x matrix: x <- cbind(1,gala[,-c(1,2)]) how can I understand this gala[,-c(1,2)])... I couldn't find an explanation of such "c-like" abbreviations anywhere. thanks for a hint. another problem: I couldn't load the faraway library, using the