similar to: Dealing with a lot of parameters in a function

Displaying 15 results from an estimated 15 matches similar to: "Dealing with a lot of parameters in a function"

2017 Aug 06
1
Help with optim function in R, please?
Hi all, Many thank in advance for helping me.? I tried to fit Expectation Maximization algorithm for mixture data. I must used one of numerical method to maximize my function. I built my code but I do not know how to make the optim function run over a different value of the parameters.? That is, For E-step I need to get the value of mixture weights based on the current (initial) values of
2011 Sep 22
1
Error in as.vector(data) optim() / fkf()
Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on
2011 Nov 12
1
State space model
Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal
2004 Jun 07
2
MCLUST Covariance Parameterization.
Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where
2011 Oct 19
1
Estimating bivariate normal density with constrains
Dear R-Users I would like to estimate a constrained bivariate normal density, the constraint being that the means are of equal magnitude but of opposite signs. So I need to estimate four parameters: mu (meanvector (mu,-mu)) sigma_1 and sigma_2 (two sd deviations) rho (correlation coefficient) I have looked at several packages, including Gaussian mixture models in Mclust, but I am not sure
2008 Aug 04
2
Multivariate Regression with Weights
Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??
2007 Apr 16
1
Greek symbols in xtable rows
Dear R-helpers, I am using xtable package to prepare a Latex code of some R tables. Is this possible to have a greek symbols in xtable cells? How can I get for example a string of : $\Delta$ > "$\Delta$" [1] "$Delta$" And string: > "$\\Delta$" [1] "$\\Delta$" Gives a latex aoutput like: \$$\backslash$Delta\$ Thank You in advance Andris
2006 Sep 01
0
defining error structure in bivariate mixed models
Hi, Using indicator variables I have been able to fit and run the code for fitting a bivariate mixed model using unstructured covariance matrix The code is lme.fit1<- lme(one.var~-1+indic1+indic2+I(indic1*d.time)+I(indic2*d.time), random =~ -1+indic1+indic2|m.unit, weights = varIdent(~1|indic1) ,data = new.data) My variables are one.var :- the two response variables stacked one after
2001 Oct 04
0
Summary on random data with zero skew and some kurtosis
Thanks to all who response my problem. Here are my summary : 1. from Dirk Eddelbuettel <edd at debian.org> We could try a mixture of normals -- ie flip a coin (use a uniform with some cutoff c where 0 < c < 1 ) to choose between N(0, sigma_1) and N(0, sigma_2). 2. from Michaell Taylor <michaell.taylor at reis.com> We could use the gld library to specify the lambdas of
2001 Oct 03
0
Summary : Generate random data from dist. with 0 skewness and some kurtosis
Thanks to all who response my problem. Here are my summary : 1. from Dirk Eddelbuettel <edd at debian.org> We could try a mixture of normals -- ie flip a coin (use a uniform with some cutoff c where 0 < c < 1 ) to choose between N(0, sigma_1) and N(0, sigma_2). 2. from Michaell Taylor <michaell.taylor at reis.com> We could use the gld library to specify the lambdas of
2010 Mar 25
1
how to deal with vector[0]?
Hi, I have a vector with 4 elements, e.g., tau_i=c(100,200,300,400), but potentially tau_i[0]=0. In a "for" loop, tau_i=c(100,200,300,400) m=4 tau_i[0]=0 # <------- ? P_i=1 for(i in 2:m) { P_i = P_i*(tau_i[i-1]-tau_i[i-2]) } Error in P_i = P_i * (tau_i[k - 1] - tau_i[k - 2]): replacement has length zero Unfortunately, I can add this potential element into
2013 Apr 07
0
Fitting distributions to financial data using volatility model to estimate VaR
Ok, I try it again with plain text, with a simple R code example and just sending it to the r list and you move it to sig finance if it is necessary. I try to be as detailed as possible. I want to fit a distribution to my financial data using a volatility model to estimate the VaR. So in case of a normal distribution, this would be very easy, I assume the returns to follow a normal distribution
2007 Jan 20
1
aov y lme
Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the
2007 Jan 19
0
(no subject)
Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the
2007 Jul 08
0
random effect variance per treatment group in lmer
All, How does one specify a model in lmer such that say the random effect for the intercept has a different variance per treatment group? Thus, in the model equation, we'd have say b_ij represent the random effect for patient j in treatment group i, with variance depending on i, i.e, var(b_ij) = tau_i. Didn't see this in the docs or Pinherio & Bates (section 5.2 is specific for