Displaying 20 results from an estimated 1000 matches similar to: "maximum likelihood accuracy - comparison with Stata"
2009 Aug 18
3
R formula
Hi
I was trying to estimate simultaneous equation system in R using systemfit.
I used the following commands
>library(systemfit)
> data(Kmenta)
> attach(Kmenta)
>eqDemand<-consump~price+income
> eqSupply<-consump~price+farmprice+trend
> fitsur<-systemfit("SUR",list(demand=eqDemand, supply=eqSupply))
and got the following error messege
Error in
2012 Oct 28
6
Hausman test in R
Hi there,
I am really new to statistics in R and statistics itself as well.
My situation: I ran a lot of OLS regressions with different independent
variables. (using the lm() function).
After having done that, I know there is endogeneity due to omitted
variables. (or perhaps due to any other reasons).
And here comes the Hausman test. I know this test is used to identify
endogeneity.
But what I
2008 Aug 12
2
Maximum likelihood estimation
Hello,
I am struggling for some time now to estimate AR(1) process for commodity price time series. I did it in STATA but cannot get a result in R.
The equation I want to estimate is: p(t)=a+b*p(t-1)+error
Using STATA I get 0.92 for a, and 0.73 for b.
Code that I use in R is:
p<-matrix(data$p) # price at time t
lp<-cbind(1,data$lp) # price at time t-1
2005 Dec 02
3
masked from package:base?
I am confused by the following description in
http://www.maths.lth.se/help/R/.R/library/systemfit/html/hausman.systemfit.html
what does the "Not run" mean? if we do not load systemfit, how can we run
the following code?
## Not run: library( systemfit )
data( kmenta )
attach( kmenta )
...
I install the package of systemfit, and run the code.
I got the warning:
> library( systemfit
2005 May 25
3
Problem with systemfit 0.7-3 and transformed variables
The 'systemfit' function in systemfit 0.7-3 CRAN package seems to have a
problem with formulas that contain transformed (eg. log) variables. If I
have my data in a data frame, apparently systemfit doesn't "pass" the
information of where the variables should be taken to the transforming function.
I'm not entirely sure if this is a bug or just a limitation, I was just
2011 Dec 01
1
Estimation of AR(1) Model with Markov Switching
Dear R users,
I have been trying to obtain the MLE of the following model
state 0: y_t = 2 + 0.5 * y_{t-1} + e_t
state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t
where e_t ~ iidN(0,1)
transition probability between states is 0.2
I've generated some fake data and tried to estimate the parameters using the
constrOptim() function but I can't get sensible answers using it. I've tried
using
2003 Oct 17
2
nlm, hessian, and derivatives in obj function?
I've been working on a new package and I have a few questions regarding the
behaviour of the nlm function. I've been (for better or worse) using the nlm
function to fit a linear model without suppling the hessian or gradient
attributes in the objective function. I'm curious as to why the nlm requires
31 iterations (for the linear model), and then it doesn't work when I try to
add
2004 Mar 02
2
Problem with Integrate
The background: I'm trying to fit a Poisson-lognormal distrbutuion to
some data. This is a way of modelling species abundances:
N ~ Pois(lam)
log(lam) ~ N(mu, sigma2)
The number of individuals are Poisson distributed with an abundance
drawn from a log-normal distrbution.
To fit this to data, I need to integrate out lam. In principle, I can
do it this way:
PLN1 <- function(lam, Count,
2017 Sep 02
2
Strange lazy evaluation of default arguments
Another way to avoid the problem is to not redefine variables that are
arguments. E.g.,
> Su3 <- function(u=100, l=u, mu=0.53, sigma2=4.3^2, verbose)
{
if (verbose) {
print(c(u, l, mu))
}
uNormalized <- u/sqrt(sigma2)
lNormalized <- l/sqrt(sigma2)
muNormalized <- mu/sqrt(sigma2)
c(uNormalized, lNormalized, muNormalized)
}
> Su3(verbose=TRUE)
2011 Jul 20
1
Fwd: Help please
Hi All,
This is not really an R question but a statistical one. If someone could
either give me the brief explanation or point me to a reference that might
help, I'd appreciate it.
I want to estimate the mean of a log-normal distribution, given the (log
scale normal) parameters mu and sigma squared (sigma2). I understood this
should simply be:
exp(mu + sigma2)
... but I the following code
2017 Sep 02
0
Strange lazy evaluation of default arguments
Dear Bill,
All makes perfect sense (including the late evaluation). I actually discovered the problem by looking at old code which used your proposed solution. Still I find it strange (and, hnestly, I don?t like R?s behavior in this respect), and I am wondering why u is not being copied to L just before u is assigned a new value. Of course, this would require the R interpreter to track all these
2017 Sep 02
0
Strange lazy evaluation of default arguments
Hello,
One way of preventing that is to use ?force.
Just put
force(l)
right after the commented out print and before you change 'u'.
Hope this helps,
Rui Barradas
Citando Matthias Gondan <matthias-gondan at gmx.de>:
> Dear R developers,
>
> sessionInfo() below
>
> Please have a look at the following two versions of the same function:
>
> 1. Intended
2017 Sep 02
6
Strange lazy evaluation of default arguments
Dear R developers,
sessionInfo() below
Please have a look at the following two versions of the same function:
1. Intended behavior:
> Su1 = function(u=100, l=u, mu=0.53, sigma2=4.3^2)
+ {
+ print(c(u, l, mu)) # here, l is set to u?s value
+ u = u/sqrt(sigma2)
+ l = l/sqrt(sigma2)
+ mu = mu/sqrt(sigma2)
+ print(c(u, l, mu))
+ }
>
> Su1()
[1] 100.00 100.00 0.53
[1]
2006 Mar 01
1
a strange problem with integrate()
Dear all,
I am stuck on the following problem with integrate(). I have been out of
luck using RSiteSearch()..
My function is
g2<-function(b,theta,xi,yi,sigma2){
xi<-cbind(1,xi)
eta<-drop(xi%*%theta)
num<-exp((eta + rep(b,length(eta)))*yi)
den<- 1 + exp(eta + rep(b,length(eta)))
result=(num/den)*exp((-b^2)/sigma2)/sqrt(2*pi*sigma2)
2017 Sep 05
0
Strange lazy evaluation of default arguments
Mathias,
If it's any comfort, I appreciated the example; 'expected' behaviour maybe, but a very nice example for staff/student training!
S Ellison
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Matthias
> Gondan
> Sent: 02 September 2017 18:22
> To: r-help at r-project.org
> Subject: [R] Strange lazy evaluation of
2008 Jun 16
1
Error in maximum likelihood estimation.
Dear UseRs,
I wrote the following function to use MLE.
---------------------------------------------
mlog <- function(theta, nx = 1, nz = 1, dt){
beta <- matrix(theta[1:(nx+1)], ncol = 1)
delta <- matrix(theta[(nx+2):(nx+nz+1)], ncol = 1)
sigma2 <- theta[nx+nz+2]
gamma <- theta[nx+nz+3]
y <- as.matrix(dt[, 1], ncol = 1)
x <- as.matrix(data.frame(1,
2005 Nov 17
3
loess: choose span to minimize AIC?
Is there an R implementation of a scheme for automatic smoothing
parameter selection with loess, e.g., by minimizing one of the AIC/GCV
statistics discussed by Hurvich, Simonoff & Tsai (1998)?
Below is a function that calculates the relevant values of AICC,
AICC1 and GCV--- I think, because I to guess from the names of the
components returned in a loess object.
I guess I could use
2003 Apr 18
1
MCMCpack gelman.plot and gelman.diag
Hi,
A question. When I run gelman.diag and gelman.plot
with mcmc lists obtained from MCMCregress, the results are following.
> post.R <- MCMCregress(Size~Age+Status, data = data, burnin = 5000, mcmc = 100000,
+ thin = 10, verbose = FALSE, beta.start = NA, sigma2.start = NA,
+ b0 = 0, B0 = 0, nu = 0.001, delta = 0.001)
> post1.R <- MCMCregress(Size~Age+Status, data
2003 Apr 02
2
lme parameterization question
Hi,
I am trying to parameterize the following mixed model (following Piepho
and Ogutu 2002), to test for a trend over time, using multiple sites:
y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij]
where:
y[ij]= a response variable at site i and year j
mu = fixed intercept
Beta=fixed slope
w[j]=constant representing the jth year (covariate)
b[j]=random effect of jth year, iid N(0,sigma2[b])
a[i]=random
2006 Sep 28
1
Nonlinear fitting - reparametrization help
Hi,
I am trying to fit a function of the form:
y = A0 + A1 * exp( -0.5* ( (X - Mu1) / Sigma1 )^2 ) - A2 * exp ( -0.5*
( (X-Mu2)/Sigma2 )^2 )
i.e. a mean term (A0) + a difference between two gaussians.
The constraints are A1,A2 >0, Sigma1,Sigma2>0, and usually Sigma2>Sigma1.
The plot looks like a "Mexican Hat".
I had trouble (poor fits) fitting this function to toy data