[R] Loop overwrite and data output problems
Ivan Calandra
ivan.calandra at uni-hamburg.de
Fri Feb 26 15:40:12 CET 2010
Hi,
Since I'm not an expert, I still have problems understanding when it's
not my own work, but I have something that might help you.
if (I==1) Sample.dat<-tmp[sample(1:max,1),] else {
Sample.dat<-rbind(Sample.dat,tmp[sample(1:max,1),])
This part might not be the best.
I would do something like:
Sample.dat[[I]] <- tmp[sample(1:max, 1),]
That way, you will store your line in the Ith element of the list
"Sample.dat". 5you might have to define it first like: Sample.dat <-
list() )
You can then convert it to a matrix using: do.call(rbind, Sample.dat)
It might get you started
HTH,
Ivan
Le 2/26/2010 14:31, RCulloch a écrit :
> Hello R users,
>
> I have been using R for a while now for basic stats but I'm now trying to
> get my head around looping scripts and in some places I am failing!
>
> I have a data set with c. 1200 data points on 98 individual animals with
> data on each row representing a daily measure and I am asking the question
> "what variables affect the animal's behaviour?"
>
> the dataset includes these variables for analyses:
>
> presence of behaviour, absence of behaviour, site, year, rain, air temp, ID,
> Day
>
> Listed below as they appear in the data set:
>
> BEH_T, BEH_F, SITE, YEAR, PRECIP_MM_DAY, PUP_AGE_EST, MO_AIR_TEMP, ID2,
> DAY
>
> with BEH_T& BEH_F = the response variable for a binomial GLM
>
> here is the head of the dataset
> (NB there are only two years and two sites)
>
> BEH_T BEH_F SITE YEAR PRECIP_MM_DAY PUP_AGE_EST MO_AIR_TEMP ID2 DAY
> [1,] 14 10 1 2007 0 12 10.98750 1 1
> [2,] 37 23 1 2007 0 13 11.47333 1 2
> [3,] 56 22 1 2007 0 14 12.16667 1 3
> [4,] 43 23 1 2007 0 16 10.91515 1 5
> [5,] 62 16 1 2007 0 17 12.81026 1 6
> [6,] 30 20 1 2007 0 19 8.67037 1 8
>
> (Sorry the headings are skewed)
>
> Because I don't want to do too complex a model to start with (just wanting
> to learn first with a 'simple' model) I have issues with independence of the
> data as there are repeats of individuals - i.e. data taken on the same IDs
> on different days. So in order to account for that I have decided to random
> sample one data point for each ID then run the GLM on that data for x number
> of simulations to see if the explanatory variables are the same/similar
> across all models. (This will reduce my data set to 98 data points, but it
> is the best way I can see of doing this without doing mixed-effects models,
> since not all IDs are seen at both sites in both years).
>
> I am also using the MuMIn package for running all subsets of your model
>
>
> the code I'm using is:
>
>
> for (S in 1:2){
> Sample.dat<-ALL.R[1,]
> for (I in 1:98) {
> tmp<-ALL.R[ALL.R$ID2==I,]
> max<-dim(tmp)[1]
> if (I==1) Sample.dat<-tmp[sample(1:max,1),] else {
> Sample.dat<-rbind(Sample.dat,tmp[sample(1:max,1),])
> m1.R<-glm(cbind(Sample.dat$BEH_T, Sample.dat$BEH_F) ~ Sample.dat$SITE +
> Sample.dat$YEAR + Sample.dat$PRECIP_MM_DAY + Sample.dat$PUP_AGE_EST +
> Sample.dat$MO_AIR_TEMP, family="binomial")
> mod<-dredge(m1.R)}}}
>
> At this point I have two issues if I do it manually then it seems to work
> i.e. gives me one output (e.g shown at bottom of post) where I then want to
> take the first line, the model with the best AIC using mod[1,] - no problem!
>
> However, letting the code run and for example using print ((mod[1,])) at the
> end it prints out the first line of 98 outputs - so I'm not too sure what
> I've done wrong here, but it appears to be running a model for each ID -
> something basic no doubt!
>
> Ideally, what I want to do is take a random sample of the data then run the
> model get one output for that take the top line (i.e. the best AIC) and save
> this, then run this routine say 100 times, saving that top line every time,
> then having a look at the results and take a model average. Anytime I've got
> close to this I have issues with overwriting the previous first line of the
> model selection and I can't seem to identify how to set this loop up
> properly.
>
> Any advice or guidance would be most appreciated, I have tried to explain my
> issues clearly but if more info is required please just ask,
>
> Many thanks in advance to those of you that took the time to read this!
>
> Ross
>
> Ross Culloch
> Ph.D. Student
> Durham University
> UK
>
>
>
>
>
>
>
> Here is an example of the model selection table from usingMuMIn:
>
>
> Model selection table
> (Intr) S.$MO_ S.$PRE S.$PUP S.$SIT S.$YEA k Dev. AIC AICc
> delta weight
> 30 645.8000 0.03841 -0.02148 0.2882 -0.3212 5 304.0 687.1 687.7
> 0.000 0.707
> 32 648.8000 0.03811 0.0009399 -0.02172 0.2857 -0.3227 6 304.0 689.0 690.0
> 2.249 0.230
> 26 785.1000 -0.02543 0.4678 -0.3905 4 312.8 693.9 694.3
> 6.630 0.026
> 31 794.2000 0.0037260 -0.02627 0.4519 -0.3950 5 312.5 695.5 696.2
> 8.493 0.010
> 22 582.7000 0.04703 0.2641 -0.2899 4 314.7 695.8 696.2
> 8.529 0.010
> 21 582.8000 0.06893 -0.01967 -0.2899 4 314.9 696.0 696.4
> 8.717 0.009
> 29 573.1000 0.04787 -0.0039980 0.2762 -0.2851 5 314.3 697.4 698.0
> 10.330 0.004
> 28 600.1000 0.06612 0.0046710 -0.02092 -0.2985 5 314.4 697.4 698.1
> 10.370 0.004
> 20 0.7526 0.05509 -0.01808 0.2450 4 321.0 702.0 702.5
> 14.770 0.000
> 10 530.4000 0.07447 -0.2639 3 324.0 703.1 703.3
> 15.640 0.000
> 27 0.7493 0.05556 -0.0022820 -0.01753 0.2519 5 320.8 703.9 704.6
> 16.850 0.000
> 19 530.0000 0.07455 -0.0001489 -0.2637 4 324.0 705.1 705.5
> 17.820 0.000
> 16 743.4000 0.4875 -0.3698 3 328.7 707.8 708.0
> 20.310 0.000
> 9 0.5512 0.06094 0.2286 3 328.8 707.9 708.1
> 20.430 0.000
> 8 0.6828 0.08019 -0.01688 3 328.9 708.0 708.2
> 20.540 0.000
> 18 0.5584 0.06173 -0.0059840 0.2481 4 327.8 708.9 709.3
> 21.620 0.000
> 25 739.9000 -0.0016930 0.4944 -0.3681 4 328.6 709.7 710.1
> 22.410 0.000
> 17 0.6856 0.07953 0.0012680 -0.01720 4 328.9 709.9 710.4
> 22.670 0.000
> 2 0.4985 0.08406 2 335.8 712.8 713.0
> 25.270 0.000
> 7 0.4996 0.08516 -0.0023780 3 335.6 714.7 714.9
> 27.240 0.000
> 14 1.0760 -0.02288 0.5151 3 340.8 719.9 720.1
> 32.420 0.000
> 23 1.0760 0.0003492 -0.02296 0.5136 4 340.8 721.9 722.3
> 34.590 0.000
> 5 0.8587 0.5304 2 354.0 731.0 731.1
> 43.440 0.000
> 12 0.8663 -0.0042170 0.5473 3 353.5 732.5 732.8
> 45.070 0.000
> 24 967.8000 0.0198500 -0.03274 -0.4813 4 358.3 739.4 739.8
> 52.140 0.000
> 15 942.8000 -0.02909 -0.4689 3 370.5 749.5 749.8
> 62.090 0.000
> 13 915.8000 0.0151200 -0.4556 3 384.7 763.7 764.0
> 76.290 0.000
> 6 900.3000 -0.4478 2 391.8 768.9 769.0
> 81.320 0.000
> 11 1.3530 0.0176300 -0.02957 3 402.3 781.3 781.6
> 93.890 0.000
> 4 1.3940 -0.02630 2 412.3 789.4 789.5
> 101.800 0.000
> 3 1.1010 0.0134300 2 424.4 801.4 801.6
> 113.800 0.000
> 1 1.1550 1 430.3 805.4 805.4
> 117.700 0.000
>
>>
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