R help - Jul 2007 - (no subject)

Hi All,

I want to automatically generate a number of data frames, each with an
automatically generated name and an automatically generated number of
rows. The number of rows has been calculated before and is different for
all data frames (e.g. c(4,5,2)). The number of columns is known a priori
and the same for all data frames (e.g. c(3,3,3)). The resulting data
frames could look something like this:
> auto.data.1
  X1 X2 X3
1  0  0  0
2  0  0  0
3  0  0  0
4  0  0  0
> auto.data.2
  X1 X2 X3
1  0  0  0
2  0  0  0
3  0  0  0
4  0  0  0
5  0  0  0
> auto.data.3
  X1 X2 X3
1  0  0  0
2  0  0  0

Later, I want to fill the elements of the data frames with values read
from somewhere else, automatically looping through the previously
generated data frames.

I know that I can automatically generate variables with the right number
of elements with something like this:
> auto.length <- c(12,15,6)
> for(i in 1:3) {
+ nam <- paste("auto.data",i, sep=".")
+ assign(nam, 1:auto.length[i])
+ }
> auto.data.1
 [1]  1  2  3  4  5  6  7  8  9 10 11 12
> auto.data.2
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
> auto.data.3
[1]  1 2 3 4 5 6

But how do I turn these variables into data frames or give them any
dimensions? Any commands such as 'as.matrix', 'data.frame', or
'dim' do
not seem to work. I also seem not to be able to access the variables
with something like "auto.data.i" since:
> auto.data.i
Error: object "auto.data.i" not found

Thus, how would I be able to automatically write to the elements of the
data frames later in a loop such as ...
> for(i in 1:3) {
+ for(j in 1:nrow(auto.data.i)) {	### this obviously does not work
since 'Error in nrow(auto.data.i) : object "auto.data.i" not
found'
+ for(k in 1:ncol(auto.data.i)) {
+ auto.data.i[j,k] <- 'some value'
+ }}}

Thanks a bunch for all your help.

Best, Michael


Michael Drescher
Ontario Forest Research Institute
Ontario Ministry of Natural Resources
1235 Queen St East
Sault Ste Marie, ON, P6A 2E3
Tel: (705) 946-7406
Fax: (705) 946-2030

Hi All,

I want to automatically generate a number of data frames, each with an
automatically generated name and an automatically generated number of
rows. The number of rows has been calculated before and is different for
all data frames (e.g. c(4,5,2)). The number of columns is known a priori
and the same for all data frames (e.g. c(3,3,3)). The resulting data
frames could look something like this:
> auto.data.1
  X1 X2 X3
1  0  0  0
2  0  0  0
3  0  0  0
4  0  0  0
> auto.data.2
  X1 X2 X3
1  0  0  0
2  0  0  0
3  0  0  0
4  0  0  0
5  0  0  0
> auto.data.3
  X1 X2 X3
1  0  0  0
2  0  0  0

Later, I want to fill the elements of the data frames with values read
from somewhere else, automatically looping through the previously
generated data frames.

I know that I can automatically generate variables with the right number
of elements with something like this:
> auto.length <- c(12,15,6)
> for(i in 1:3) {
+ nam <- paste("auto.data",i, sep=".")
+ assign(nam, 1:auto.length[i])
+ }
> auto.data.1
 [1]  1  2  3  4  5  6  7  8  9 10 11 12
> auto.data.2
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
> auto.data.3
[1]  1 2 3 4 5 6

But how do I turn these variables into data frames or give them any
dimensions? Any commands such as 'as.matrix', 'data.frame', or
'dim' do
not seem to work. I also seem not to be able to access the variables
with something like "auto.data.i" since:
> auto.data.i
Error: object "auto.data.i" not found

Thus, how would I be able to automatically write to the elements of the
data frames later in a loop such as ...
> for(i in 1:3) {
+ for(j in 1:nrow(auto.data.i)) {	### this obviously does not work
since 'Error in nrow(auto.data.i) : object "auto.data.i" not
found'
+ for(k in 1:ncol(auto.data.i)) {
+ auto.data.i[j,k] <- 'some value'
+ }}}

Thanks a bunch for all your help.

Best, Michael


Michael Drescher
Ontario Forest Research Institute
Ontario Ministry of Natural Resources
1235 Queen St East
Sault Ste Marie, ON, P6A 2E3
Tel: (705) 946-7406
Fax: (705) 946-2030

Is this what you want to do:
> auto.length <- c(12,15,6)
> for(i in 1:3) {
+ nam <- paste("auto.data",i, sep=".")
+ assign(nam, as.data.frame(matrix(1:auto.length[i], ncol=3)))
+ }
> auto.data.1
  V1 V2 V3
1  1  5  9
2  2  6 10
3  3  7 11
4  4  8 12
> auto.data.2
  V1 V2 V3
1  1  6 11
2  2  7 12
3  3  8 13
4  4  9 14
5  5 10 15
> # output the data
> for(i in 1:3){
+     cat(x <- paste('auto.data.', i, sep=''), '\n')
+     print(get(x))
+ }
auto.data.1
  V1 V2 V3
1  1  5  9
2  2  6 10
3  3  7 11
4  4  8 12
auto.data.2
  V1 V2 V3
1  1  6 11
2  2  7 12
3  3  8 13
4  4  9 14
5  5 10 15
auto.data.3
  V1 V2 V3
1  1  3  5
2  2  4  6
>

On 7/12/07, Drescher, Michael (MNR) <michael.drescher at ontario.ca>
wrote:
> Hi All,
>
> I want to automatically generate a number of data frames, each with an
> automatically generated name and an automatically generated number of
> rows. The number of rows has been calculated before and is different for
> all data frames (e.g. c(4,5,2)). The number of columns is known a priori
> and the same for all data frames (e.g. c(3,3,3)). The resulting data
> frames could look something like this:
>
> > auto.data.1
>  X1 X2 X3
> 1  0  0  0
> 2  0  0  0
> 3  0  0  0
> 4  0  0  0
>
> > auto.data.2
>  X1 X2 X3
> 1  0  0  0
> 2  0  0  0
> 3  0  0  0
> 4  0  0  0
> 5  0  0  0
>
> > auto.data.3
>  X1 X2 X3
> 1  0  0  0
> 2  0  0  0
>
> Later, I want to fill the elements of the data frames with values read
> from somewhere else, automatically looping through the previously
> generated data frames.
>
> I know that I can automatically generate variables with the right number
> of elements with something like this:
>
> > auto.length <- c(12,15,6)
> > for(i in 1:3) {
> + nam <- paste("auto.data",i, sep=".")
> + assign(nam, 1:auto.length[i])
> + }
> > auto.data.1
>  [1]  1  2  3  4  5  6  7  8  9 10 11 12
> > auto.data.2
>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
> > auto.data.3
> [1]  1 2 3 4 5 6
>
> But how do I turn these variables into data frames or give them any
> dimensions? Any commands such as 'as.matrix', 'data.frame',
or 'dim' do
> not seem to work. I also seem not to be able to access the variables
> with something like "auto.data.i" since:
>
> > auto.data.i
> Error: object "auto.data.i" not found
>
> Thus, how would I be able to automatically write to the elements of the
> data frames later in a loop such as ...
>
> > for(i in 1:3) {
> + for(j in 1:nrow(auto.data.i)) {       ### this obviously does not work
> since 'Error in nrow(auto.data.i) : object "auto.data.i" not
found'
> + for(k in 1:ncol(auto.data.i)) {
> + auto.data.i[j,k] <- 'some value'
> + }}}
>
> Thanks a bunch for all your help.
>
> Best, Michael
>
>
> Michael Drescher
> Ontario Forest Research Institute
> Ontario Ministry of Natural Resources
> 1235 Queen St East
> Sault Ste Marie, ON, P6A 2E3
> Tel: (705) 946-7406
> Fax: (705) 946-2030
>
> ______________________________________________
> R-help at stat.math.ethz.ch 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.
>

-- 
Jim Holtman
Cincinnati, OH
+1 513 646 9390

What is the problem you are trying to solve?

Hi All,

I want to test an H0 hypothesis about the proportions of observed counts
in k classes. I know that I can do this with the chisq.test.

However, besides of the overall acceptance or rejection of the H0, I
would like to know which of the k classes cause(s) rejection and I would
like to know the observation-based confidence envelopes for the
proportions for the k classes.

My quick-and-dirty approach thus far is to do an initial chisq.test on
the original k classes and then to lump data into two classes (=one of
the original classes and all other original classes lumped into one new
class) and do a binom.test. I interpret the result of the binom.test as
indicating whether the current class might be the reason for the
rejection of the overall H0. Additionally, it gives me a confidence
envelope for this class.

This approach seems fairly straightforward, but I just do not feel
totally comfortable with it. I would feel so much better if there was
something like a multinom.test, but to my knowledge there is none.

Do you have any suggestions what I could rather do? For instance, I
might follow a Monte Carlo-like approach:
I simulate proportions for the k classes based on the proportions of
observed counts with rmultinom. After exclusion of the most extreme
values I construct my confidence envelope based on the remaining
simulated proportions. Based on whether the hypothesized proportions
fall into the observation-based confidence envelopes, I accept or
reject.

Do you think that either of these approaches is better or would you
suggest doing something totally different?

All comments and suggestions are highly appreciated.

Kind regards, Michael

PS: I guess my request parallels that of Matthias Schmidt from Apr 5,
2004, that was answered by Brian Ripley ...


Michael Drescher
Ontario Forest Research Institute
Ontario Ministry of Natural Resources
1235 Queen St East
Sault Ste Marie, ON, P6A 2E3
Tel: (705) 946-7406
Fax: (705) 946-2030