Hi Jim. If I avoid the dataframe, how can use the function model.matrix() to
build the incident matrices X, And Z? I tried saving the design as matrix
but ghen I got the wrong design matrix.
Thanks.
Laz
Sent from my LG Optimus G™, an AT&T 4G LTE smartphone
------ Original message ------
From: jim holtman
Date: 6/18/2014 3:49 PM
To: Laz;
Cc: R mailing list;
Subject:Re: [R] How can I avoid the for and If loops in my function?
First order of business, without looking in detail at the code, is to avoid
the use of dataframes. If all your values are numerics, then use a matrix.
It will be faster execution.
I did see the following statements:
newmatdf<-Des[[i]]
Des[[i]]<-newmatdf
why are you just putting back what you pulled out of the list?
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Wed, Jun 18, 2014 at 12:41 PM, Laz <[1]lmra...@ufl.edu> wrote:
Dear R-users,
I have a 3200 by 3200 matrix that was build from a data frame that had
180 observations, with variables: x, y, blocks (6 blocks) and
treatments (values range from 1 to 180) I am working on. I build other
functions that seem to work well. However, I have one function that has
many If loops and a long For loop that delays my results for over 10
hours ! I need your help to avoid these loops.
########################################################
## I need to avoid these for loops and if loops here :
########################################################
### swapsimple() is a function that takes in a dataframe, randomly swaps
two elements from the same block in a data frame and generates a new
dataframe called newmatdf
### swapmainF() is a function that calculates the trace of the final N
by N matrix considering the incident matrices and blocks and treatments
and residual errors in a linear mixed model framework using Henderson
approach.
funF<- function(newmatdf, n, traceI)
{
# n = number of iterations (swaps to be made on pairs of elements of the
dataframe, called newmatdf)
# newmatdf : is the original dataframe with N rows, and 4 variables
(x,y,blocks,genotypes)
matrix0<-newmatdf
trace<-traceI ## sum of the diagonal elements of the N by N matrix
(generated outside this loop) from the original newmatdf dataframe
res <- list(mat = NULL, Design_best = newmatdf, Original_design =
matrix0) # store our output of interest
res$mat <- rbind(res$mat, c(value = trace, iterations = 0)) #
initialized values
Des<-list()
for(i in seq_len(n)){
ifelse(i==1,
newmatdf<-swapsimple(matrix0),newmatdf<-swapsimple(newmatdf))
Des[[i]]<-newmatdf
if(swapmainF(newmatdf) < trace){
newmatdf<-Des[[i]]
Des[[i]]<-newmatdf
trace<- swapmainF(newmatdf)
res$mat <- rbind(res$mat, c(trace = trace, iterations = i))
res$Design_best <- newmatdf
}
if(swapmainF(newmatdf) > trace & nrow(res$mat)<=1){
newmatdf<-matrix0
Des[[i]]<-matrix0
res$Design_best<-matrix0
}
if(swapmainF(newmatdf)> trace & nrow(res$mat)>1){
newmatdf<-Des[[length(Des)-1]]
Des[[i]]<-newmatdf
res$Design_best<-newmatdf
}
}
res
}
The above function was created to:
Take an original matrix, called matrix0, calculate its trace.
Generate a new matrix, called newmatdf after swapping two elements of the
old one and calculate the trace. If the trace of the newmatrix is
smaller than
that of the previous matrix, store both the current trace together
with the older trace and their iteration values. If the newer matrix has
a trace larger than the previous trace, drop this trace and drop this
matrix too (but count its iteration).
Re-swap the old matrix that you stored previously and recalculate the
trace. Repeat the
process many times, say 10,000. The final results should be a list
with the original initial matrix and its trace, the final best
matrix that had the smallest trace after the 10000 simulations and a
dataframe showing the values of the accepted traces that
were smaller than the previous and their respective iterations.
$Original_design
x y block genotypes
1 1 1 1 29
7 1 2 1 2
13 1 3 1 8
19 1 4 1 10
25 1 5 1 9
31 1 6 2 29
37 1 7 2 4
43 1 8 2 22
49 1 9 2 3
55 1 10 2 26
61 1 11 3 18
67 1 12 3 19
73 1 13 3 28
79 1 14 3 10
------truncated ----
the final results after running funF<-
function(newmatdf,n,traceI) given below looks like this:
ans1
$mat
value iterations
[1,] 1.474952 0
[2,] 1.474748 1
[3,] 1.474590 2
[4,] 1.474473 3
[5,] 1.474411 5
[6,] 1.474294 10
[7,] 1.474182 16
[8,] 1.474058 17
[9,] 1.473998 19
[10,] 1.473993 22
---truncated
$Design_best
x y block genotypes
1 1 1 1 29
7 1 2 1 2
13 1 3 1 18
19 1 4 1 10
25 1 5 1 9
31 1 6 2 29
37 1 7 2 21
43 1 8 2 6
49 1 9 2 3
55 1 10 2 26
---- truncated
$Original_design
x y block genotypes
1 1 1 1 29
7 1 2 1 2
13 1 3 1 8
19 1 4 1 10
25 1 5 1 9
31 1 6 2 29
37 1 7 2 4
43 1 8 2 22
49 1 9 2 3
55 1 10 2 26
61 1 11 3 18
67 1 12 3 19
73 1 13 3 28
79 1 14 3 10
------truncated
Regards,
Laz
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