[R] Vectorize a series of Matrix Multiplications
Can I have some help in vectorizing a series of matrix multiplications?
Toy Example
mat_size=2; num_matrices=3; num_users=2
ToyArray=array(1,dim=c(mat_size, mat_size, num_matrices, num_users))
/* So I open an 4-dim array to store 3 2 X 2 matrrices for 2 users. For each
user I want to multiple the 3, 2 X 2 matrices so that at the end I have 1
matrix for each of the 2 users */
This works:
output=array(NA,dim=c(mat_size, mat_size, num_users));
for ( i in 1:num_users) {
output[,,i]= as.matrix(ToyArray[,,1,i]) %*% as.matrix(ba[,,2,i]) %*%
as.matrix(ba[,,3,i])
};
Can I do better? Can I vectorize this over the user dimension?
And even better (and a different question): what strategy can I use if the
matrices are not all of the same size?
GR
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[R] Matrix Mulitplication: A*B*C*D...?
Is there a R package or function that will multiple an arbitrary number of matrices supplied to it, preferably optimizing the sequence R_function(matA, matB, matC, matD,) return matA %*% matB %*% matC %matD % and optimizing wether it is better to do A(BC) or (AB)C ? Thanks, Shiv [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] nls and rbinom function: step factor 0.000488281 reduced below 'minFactor' of 0.000976562
...(1000,0,1), I presume.
Yes thank you.
...z - nls(y ~ rbinom(1000,1,a+b*x),data=d,start= list(a =0.1,b=0.2),trace=T);
This makes no sense. Random numbers in a model specification
And maybe that model spec is nonsense, although it seems to me that it
correctly incorporates the idea of a linear density function. But any ideas
on how you might correctly specify such a model?
Thanks,
GR
From: peter dalgaard pda...@gmail.com
Cc: r-help@r-project.org r-help@r-project.org
Sent: Tuesday, January 3, 2012 3:11 AM
Subject: Re: [R] nls and rbinom function: step factor 0.000488281 reduced below
'minFactor' of 0.000976562
On Jan 3, 2012, at 05:25 , G Vishwanath wrote:
I am trying to learn nls using a simple simulation. I assumed that the
binomial prob varies linearly as 0.2 + 0.3*x in x {0,1},
and the objective is to recover the known parameters a=0.2, b=0.3
..data frame d has 1000 rows...
d$x-runif(0,1)
...(1000,0,1), I presume.
d$y-rbinom(1000,1,0.2+0.3*d$x)
table(d$y,cut(d$x,breaks=5));
(-0.000585,0.199] (0.199,0.399] (0.399,0.599] (0.599,0.799] (0.799,0.999]
0 154 149 130 122 114
1 34 48 71 76 102
z - nls(y ~ rbinom(1000,1,a+b*x),data=d,start= list(a =0.1,b=0.2),trace=T);
This makes no sense. Random numbers in a model specification
y~a+b*x might give a result, but you're fitting a model which assumes Gaussian
errors with constant variance to data that are nothing of the sort.
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd@cbs.dk Priv: pda...@gmail.com
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[R] nls and rbinom function: step factor 0.000488281 reduced below 'minFactor' of 0.000976562
I am trying to learn nls using a simple simulation. I assumed that the
binomial prob varies linearly as 0.2 + 0.3*x in x {0,1},
and the objective is to recover the known parameters a=0.2, b=0.3
..data frame d has 1000 rows...
d$x-runif(0,1)
d$y-rbinom(1000,1,0.2+0.3*d$x)
table(d$y,cut(d$x,breaks=5));
(-0.000585,0.199] (0.199,0.399] (0.399,0.599] (0.599,0.799] (0.799,0.999]
0 154 149 130 122 114
1 34 48 71 76 102
z - nls(y ~ rbinom(1000,1,a+b*x),data=d,start= list(a =0.1,b=0.2),trace=T);
374 : 0.1 0.2
361 : 0.1 0.2
350 : 0.1 0.2
Error in nls(y ~ rbinom(1000, 1, a + b * x), data = d, start = list(a = 0.1, :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
I have tried plinear, got the same
Additionaly- why does the parameters not change with the iteration ?
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and provide commented, minimal, self-contained, reproducible code.
