search for: startvalu

...o: sum D= 1 (BTW, n is less than 300) I´ve tried to use maxBFGS, as follows: ##################################### objectiveFunction<-function(x) { return(t(D)%*%W%*%D) } Amat<-diag(nrow(D)) Amat<-rbind((rep(-1, nrow(D))), Amat) bvec<-matrix( c(0), nrow(D)+1, 1) bvec[1,1]<-c(1) startValues=rep(1/nrow(D),nrow(D)) #Istart value is homogeneous distribution res <<- maxBFGS(objectiveFunction, start=startValues, constraints=list(ineqA=Amat, ineqB=bvec)) ######################################## The outcome is equal to the startValues. I´ve tried several initial values and nothing ch...

Hello useRs, I`m running structural equation models with R, but for one of my models the below error message apears. I`m trying to change startvalues but without success. The manual for sem package did not help me. Does anyone knows how to change startvalues for iteration in sem package? Or it can be another problem with the model? Error in startvalues(S, ram, debug = debug, tol = start.tol) : subscript out of bounds The model (with thre...

...ing functions as you can see here: rTaylorVal=function(exp,x0,dx,n) { ls=list(x=x0) newexp=eval(exp,ls) exp0=exp for (i in 1:n){ exp0=D(exp0,"x") newexp=newexp+eval(exp0,ls)/factorial(i)*dx^i } return(newexp) } Where exp is an expression like exp=expression(x^2*sin(x)), x0 is the startvalue, dx the difference between startvalue and searched value and n is the length of the series. So I tried to remove dx as a value, to get a Taylor series expression, but it doesn't work as simple multiplication (*) and accumulation (+) is not good for expressions. That's my point so far, no...

...function as follows. Thank you all in advance. # Main program rm(list=ls()) x <- c(0,1,3,4,5,2,10,4,6,8,7) y <- seq(0,10,1) ftestnls(x,y) # Call the function # function 'ftestnls(v1,v2)' # v1 <- x # v2 <- y ftestnls <- function(v1,v2){ datalist <- list(v1=v1,v2=v2) startvalues <- list(a0=v1[1],a1=0,a2=0) # Perform nonlinear regression require(nls2) nlsmodel <- nls(v1~a0 + a1*v2 + a2 *sin(2*pi*v2/365.25),data=datalist,start=startvalues, trace=TRUE) # Fitted data and prediction interval fitted <- predict(as.lm(nlsmodel),se.fit=TRUE,interval=&quot...

...lt;- as.matrix(x) YP <- as.vector(y) xbeta <- X %*% par[1:K] p <- par[K1:K1] sum( -lgamma(p) +lgamma(p+(p/xbeta-p)) -lgamma(p/xbeta-p) +(p-1)*log(YP) +log(1-YP)*(p/xbeta-p-1) ) } llik.beta # Now use the above function to estimate the model. First, create a set of reasonable start values. startvalues <- c(coefficients(ols.model),74) startvalues # Now call optim mod.beta <- optim(startvalues,llik.beta, method = "BFGS", control = list(trace, maxit=1000,fnscale = -1), hessian = TRUE) mod.beta B. Dan Wood, Professor and University Faculty Fellow Texas A&M University Depart...

I have just started using the SEM package in R, so I'm not sure I'm doing everything right, but I keep getting an error concerning startvalues and I can't figure out how to fix it. Is anyone willing to read over my code and help me out??? Please email me if you are willing to look at my code and I will send it to you. Thank you so much, and Happy New Year! cathy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...

...gamma(r+1)*(alpha/alpha+t[i]))**r)*(t[i]/alpha+t[i]))**trans[i] } return(sum(l)) } i'm confused how i have to set r and alpha and i found no related help in archives? ...in Excel it works with solver but only for ~65.000 rows :-) #This notation is 1 for trans and 1 for time instead the Startvalues for r and alpha? optim(c(1,1),-LL) many thanks for an easy example or hint regards,christian

...gt; exp0=exp >> >> for (i in 1:n){ >> exp0=D(exp0,"x") >> newexp=newexp+eval(exp0,ls)/factorial(i)*dx^i >> } >> >> return(newexp) >> >> } >> >> Where exp is an expression like exp=expression(x^2*sin(x)), x0 is the >> startvalue, dx the difference between startvalue and searched value >> and n is the length of the series. >> >> So I tried to remove dx as a value, to get a Taylor series >> expression, but it doesn't work as simple multiplication (*) and >> accumulation (+) is not good f...

...NA [3,] 1 3 6 3 NA [4,] 1 4 6 4 NA [5,] 1 1 7 5 NA [6,] 1 2 7 6 NA [7,] 1 5 7 7 NA [8,] 1 6 8 8 NA [9,] 1 7 8 9 NA [10,] 2 6 7 10 NA Error in startvalues(S, ram, debug = debug, tol = start.tol) : subscript out of bounds > Celpe.cov T1 T2 T3 T4 PI T1 1.2459212 0.6072653 0.6410553 0.4030267 0.7001731 T2 0.6072653 0.9369251 0.5762526 0.3293337 0.6282211 T3 0.6410553 0.5762526 1.5346904 0.3888291 0.606...

...;-> ANXIETY, phi_AA, NA DEPRESS <-> DEPRESS, phi_DD, NA FEAR <-> FEAR, phi_FF, NA ANXIETY <-> FEAR, phi_AF, NA ANXIETY <-> DEPRESS, phi_AD, NA DEPRESS <-> FEAR, phi_DF, NA # Running the estimation using sem: sem.anxiety1<-sem(model1, COVAR, N=150, par.size="startvalues") # Looking at the results: summary(sem.anxiety1) # Calling modification indices: mod.indices(sem.anxiety1) summary(mod.indices(sem.anxiety1)) # Creating a path diagram path.diagram(model1,minrank='a1,a2,a3,d1,d2,d3,f1,f2,f3', maxrank='ANXIETY,DEPRESS,FEAR') Thank you ver...

In the file \lib\Support\Process.cpp on line 60, it seems as though an unnecessary heap allocation and memory leak occurs. This is the offending code: static TimeValue getElapsedWallTime() { static TimeValue &StartTime = *new TimeValue(TimeValue::now()); return TimeValue::now() - StartTime; } The issue is that the StartTime variable's value is allocated on the heap, after which a

...r pointer inductions we store the start value. When we come to actually vectorize the loop we create a canonical induction variable that starts at zero and strides by the vector factor (VF). Any use of the original pointer induction variable in the vectorized loop is simply: Builder.CreateGEP(II.StartValue, cannonical_induction_variable); For a non-unit pointer induction this would be Builder.CreateGEP(II.StartValue, stride * cannonical_induction_variable); (I have simplified the problem a little the code in the vectorizer is a little bit more complicated but essentially boils down to the abov...

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

Hello- If I input a variance-covariance matrix and specify NA for start values, how does sem determine the start value? Is there a default? Anthony -- Anthony Steven Dick, Ph.D. Post-Doctoral Fellow Human Neuroscience Laboratory Department of Neurology The University of Chicago 5841 S. Maryland Ave. MC-2030 Chicago, IL 60637 Phone: (773)-834-7770 Email: adick at uchicago.edu Web:

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

...te is probably solution. However, at the end of the process I get this message: Warning message: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. I tried setting par.size='startvalues' and it didn't make any difference. Is it just the size of my model? F1->reFDE, lam1, NA F1->ReFUIDGreg, lam2, NA F1->reFDRwithDDRV, lam3, NA F1->reparD, lam4, NA F1->reparDR, lam5, NA F1->reparRisk, lam6, NA F1->reWDD, lam7, NA F1->reWDH, lam8, NA F1->reWSP,...

Using R-studio, I am trying to run a structural equation model and I am running into problems with testing my primary model. Once I specify everything and try to run it I get this error: Error in eigen(S, symmetric = TRUE, only.values = TRUE) : 0 x 0 matrix And when I look at the object for my primary model in my workspace, which is created after I specify it, it lists all my model components,

Hi R-colleagues. In the sem-package i have a problem to introduce hidden variables. As a simple example I take an ordinary factor analysis. The program: cmat=c(0.14855886, 0.05774635, 0.08003300, 0.04900990, 0.05774635, 0.18042029, 0.11213013, 0.03752475, 0.08003300, 0.11213013, 0.24646337, 0.03609901, 0.04900990, 0.03752475, 0.03609901, 0.31702970)