Displaying 20 results from an estimated 41 matches for "b_i".
Did you mean:
_i
2009 Nov 07
0
solution design for a large scale (> 50G) R computing problem
...and memory issues need to be seriously considered. Below is the problem
description and my tentative approach. I would appreciate if any one can
share thoughts on how to solve this problem more efficiently.
I have 1001 multidimensional arrays -- A, B1, ..., B1000. A takes about
500MB in memory and B_i takes 100MB. I need to run an experiment that
evaluates a function f(A, B_i) for all B_i. f(A, B_i) doesn't change A, B_i
during its evaluation. These evaluations are independent for all i. I also
need to design various evaluation functions. Thus these kind of experiments
need to be performed o...
2012 Oct 18
7
summation coding
I would like to code the following in R: a1(b1+b2+b3) + a2(b1+b3+b4) +
a3(b1+b2+b4) + a4(b1+b2+b3)
or in summation notation: sum_{i=1, j\neq i}^{4} a_i * b_i
I realise this is the same as: sum_{i=1, j=1}^{4} a_i * b_i - sum_{i=j} a_i
* b_i
would appreciate some help.
Thank you.
--
View this message in context: http://r.789695.n4.nabble.com/summation-coding-tp4646678.html
Sent from the R help mailing list archive at Nabble.com.
2009 Aug 06
1
solving system of equations involving non-linearities
Hi,
I would appreciate if someone could help me on track with this problem.
I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this:
sum_i( A+b_i>0 & A+b_i>C+d_i) = x
sum_i( C+d_i>0 & C+d_i>A+b_i) = y
sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z
A, C, E are free variables while the other letters represent values given from sample observations. The equations involve counts of the number of fulfilled conditio...
2003 Oct 23
1
Variance-covariance matrix for beta hat and b hat from lme
Dear all,
Given a LME model (following the notation of Pinheiro and Bates 2000) y_i
= X_i*beta + Z_i*b_i + e_i, is it possible to extract the
variance-covariance matrix for the estimated beta_i hat and b_i hat from the
lme fitted object?
The reason for needing this is because I want to have interval prediction on
the predicted values (at level = 0:1). The "predict.lme" seems to provide
poin...
2008 May 16
1
Making slope coefficients ``relative to 0''.
...rested in whether the slopes in a linear model are different
from 0.
I.e. I would like to obtain the slope estimates, and their standard
errors,
``relative to 0'' for each group, rather than relative to some baseline.
Explicitly I would like to write/represent the model as
y = a_i + b_i*x + E
i = 1, ..., K, where x is a continuous variate and i indexes groups
(levels of a factor with K levels).
The ``usual'' structure (using ``treatment contrasts'') gives
y = a + a_i + b*x + b_i*x + E
i = 2, ..., K. (So that b is the slope for the baseline group, and
b_i me...
2005 Nov 16
6
nlme question
I am using the package nlme to fit a simple random effects (variance
components model)
with 3 parameters: overall mean (fixed effect), between subject
variance (random) and
within subject variance (random).
I have 16 subjects with 1-4 obs per subject.
I need a 3x3 variance-covariance matrix that includes all 3 parameters
in order to
compute the variance of a specific linear
2011 Jan 29
1
Spare matrix multiplication
Dear R,
I have a simple question concerning with a special case of spare matrix
multiplications. Say A is a 200-by-10000 dense matrix. B is a 10000-by-10000
block- diagonal matrix, and each diagonal block B_i is 100-by-100. The usual
way I did A%*%B will take about 30 seconds which is to time consuming
because I have to do this thousands of times. I also tried to partition A
into 100 small blocks and use mapply function to multiply by each B_i, but
that is even slower.
I am wondering if there is an eff...
2013 Jul 02
0
Optimización MINLP
Muy buenas,
Tengo la siguiente duda/problema,
He optimizado con éxito un problema de este tipo:
\sum f(x_i)
donde f es una curva exponencial (función no lineal)
sujeto a:
a_i < x_i < b_i
y
\sum f(x_i) < Presupuesto
Vamos, es repartir un presupuesto forzando a que inviertas como poco a_i y
como mucho b_i para cada i
Esto lo hecho correctamente usando el paquete:
http://cran.r-project.org/web/packages/nloptr/index.html
Uso un algoritmo que no necesita gradiente: NLOPT_LN_COBYL...
2003 Jun 19
2
Fitting particular repeated measures model with lme()
...; applies to only one year. One can think of the first
score on the student as a score from a prior year (for which I have no
teacher links), and the second score is from the current year and is
linked to the teacher. The model for student j in class i is:
Y_{ij0} = a_0 + e_{ij0}
Y_{ij1} = a_1 + b_i + e_{ij1}
with Var(b_i) the teacher variance component and Cov(e_{ij0},e_{ij1})
unstructured. That is, if the data are organized by student, the "Z"
matrix in the usual linear mixed model notation has every other row
equal to a row of zeros.
I am wondering whether there is some way to...
2000 Mar 31
2
linear models
.... fits a random effect intercept
term.
The reason why I want to do this is test for the significance of the
random effect intercept term. anova( , ) does not work for an lm model and
lme model together.
2) Is there some nice way of handling linear models which are of the form
response_ij = a_i + b_i x_ij + \epsilon_ij
where a_i and b_i are fixed effects, x_ij is given (continuous) data,
\epsilon_ij ~ N(0, \sigma^2), and the i's range over some group? This is
basically a group of regression models, but I want them handled as one
unit for the purposes of estimation of \sigma^2 etc. I know...
2008 Jul 31
1
clustering and data-mining...
Hi all,
I am doing some experiment studies...
It seems to me that with different combination of 5 parameters, the end
results ultimately converged to two scalars. That's to say, some
combinations of the 5 parameters lead to one end result and some other
combinations of the 5 parameters lead to the other end result (scalar).
I am thinking of this is sort of something like clustering or
2006 Oct 22
1
Multilevel model ("lme") question
...7150
Intra-subject trajectories of Y very close to linear. I'd like to check
whether slope (and maybe also offset) of this line are (in part) predicted
by X.baseline.
Thus, I think the multilevel model specification should be as follows (i =
subject, j=measurement):
y_ij = \beta_i + b_i * TIME_ij + \epsilon_ij,
with
b_i = \zeta_i0 + \zeta_i1 * X.Baseline
Is this correct?
Now, I am completely unsure how to "translate" this into the syntax needed
by lme.
Is there any standard procedure on how to get from e.g. the Laird&Ware'82
matrix model notation to the lme inpu...
2002 Dec 10
3
clogit and general conditional logistic regression
...efulness is confined to the sort of
data which arise in survival/proportional hazard applications.
My question is: is 'clogit' capable of a general conditional
logistic analysis?
E.g. given a set of data on binomial experiments with Y=1
r_i times out of n_i, associated with levels A_i and B_i
of factors A and B at N_A and N_B levels, would
clogit(Y ~ A+B, method=c(Exact"))
generate something sensible containing the results of a standard
exact conditional logistic regression of Y on A and B?
With thanks,
Ted.
-------------------------------------------------------------------...
2001 May 23
2
help: exponential fit?
Hi there,
I'm quite new to R (and statistics),
and I like it (both)!
But I'm a bit lost in all these packages,
so could someone please give me a hint
whether there exists a package for fitting
exponential curves (of the type
t --> \sum_i a_i \exp( - b_i t))
on a noisy signal?
In fact monoexponential decay + polynomial growth
is what I'd like to try.
Thanks in advance, Mirko.
--
Dr. M. Luedde <Mirko.Luedde at CellControl.De>
CellControl Biomedical Laboratories AG
Am Klopferspitz 19, 82152 Martinsried
+49-89-895275-0 +49-179-5252064
-...
2006 Jun 06
1
Problems using quadprog for solving quadratic programming problem
Hi,
I'm using the package quadprog to solve the following quadratic programming problem.
I want to minimize the function
(b_1-b_2)^2+(b_3-b_4)^2
by the following constraints b_i, i=1,...,4:
b_1+b_3=1
b_2+b_4=1
0.1<=b_1<=0.2
0.2<=b_2<=0.4
0.8<=b_3<=0.9
0.6<=b_4<=0.8
In my opinion the solution should be b_1=b_2=0.2 und b_3=b_4=0.8.
Unfortunately R doesn't find this solution and what's surprising to me, evaluation the solution of solve.QP...
2005 Jan 17
0
a question of mixed effect in R
Dear all,
I have a question about mixed effect model in R. The data set has
5 variables, X(response),subject, times, repeat, indicator
The model is X_hijk=a_h+Z_h*b_i+r(ij)+e_hijk , where
h=0,1(indicator), i=1,...,n(subject), j=1,...,n_i(times within
subject; nested effect),k=1,2,3(repeat).
Z_h=1 if h=1
=0 if h=0
b_i~N(0,c^2) random effect of subject
r(ij)~N(0,d^2) random effect of times within subject
e_hijk~N(0,e^2) error term which is independent with the...
2006 Aug 24
1
lmer(): specifying i.i.d random slopes for multiple covariates
Dear readers,
Is it possible to specify a model
y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k
that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how?
In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify:
lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) ,
that?s how it's done in the lme...
2008 Jul 02
1
Tobit Estimation with Panel Data
Hi all!
Do you know if there is any R function/package that can be used to
estimate "tobit" models with panel data (e.g. with random individual
effects)?
In economics, a "tobit" model is a model with a dependent variable that is
left-censored at zero. Hence, it is a special case of a survival model and
can be estimated using the "survival" package (see e.g.
2010 Feb 03
1
Package plm & heterogenous slopes
Dear r-helpers,
I am working with plm package. I am trying to fit a fixed effects (or
a 'within') model of the form
y_it = a_i + b_i*t + e_it, i.e. a model with an individual-specific
intercept and an individual-
specific slope.
Does plm support this directly?
Thanks in advance!
Otto Kassi
2010 Mar 04
1
logistic regression by group?
Hi,
Looking for a function in R that can help me calculate a parameter that
maximizes the likelihood over groups of observations.
The general formula is:
p = exp(xb) / sum(exp(xb))
So, according to the formulas I've seen published, to do this "by group" is
product(p = exp(x_i * b_i) / sum(exp(x_i b_i)))
Where i represents a "group" and we iterate through each group.
Does anybody have any suggestions?
Thanks!
-N