Displaying 5 results from an estimated 5 matches for "tau_i".
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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...
2007 Jan 20
1
aov y lme
...he 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 data,
purity<-c(1,-2,-2,1,
-1,-3, 0,4,
0,-4, 1, 0,
1,0,-1,0,
-2,4,0,3,
-3,2,-2,2,
2,-2,1,3,
4,0,-1,2,
0,2,2,1)
suppli<-factor(c(rep(1,12),rep(2,12),rep(3,...
2007 Jan 19
0
(no subject)
...he 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 data,
purity<-c(1,-2,-2,1,
-1,-3, 0,4,
0,-4, 1, 0,
1,0,-1,0,
-2,4,0,3,
-3,2,-2,2,
2,-2,1,3,
4,0,-1,2,
0,2,2,1)
suppli<-factor(c(rep(1,12),rep(2,12),rep(3,...
2010 Aug 02
2
Dealing with a lot of parameters in a function
Hi all,
I'm trying to define and log-likelihood function to work with MLE.
There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between
1 to 24. Instead of listing all the parameters, one by one in the
function definition, is there a neat way to do it in R ? The example is
as follows:
ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7)
{ if (tau1>0 && ro<1 && ro>-1)
-s...
2007 Jul 08
0
random effect variance per treatment group in lmer
...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
modelling within group errors). Sample repeated measures code below is
for
a single random effect variance, where the random effect corresponds to
patient.
cheers,
dave
z <- rnorm(24, mean=0, sd=1)
tim...