Nikos:
> > I finally ran mrpp tests. I think all is fine but one very important
> > issue: I
> > have no idea how to export/save an "mrpp" object. Tried anything I
> > know and
> > searched the archives but found nothing.
David W:
> And what happened when you tried what seems like the obvious:
> save(mrpp_obj, file=)
> # rm(list=ls() ) # Only uncomment if you are ready for your workspace
> to clear
> load("mrpp_store.Rdata")
Right, "clearing" did the trick.
> > Any ideas? Is really copy-pasting the mrpp results the only way?
>
> Many of us have no idea what such an object is, since you have not
> described the packages and functions used to create it. If you want an
> ASCII version then dput or dump are also available.
Multiresponse Permuation Procedures (MRPP) is implemented in the "vegan"
package. The function mrpp() returns (an object of class "mrpp") something
like:
--%<---
# check class
class ( samples_bitemporal_modis.0001.mrpp )
[1] "mrpp"
# check structure
str ( samples_bitemporal_modis.0001.mrpp )
List of 12
$ call : language mrpp(dat = samples_bitemporal_modis.0001[, 1:5],
grouping = samples_bitemporal_modis.0001[["Class"]])
$ delta : num 0.126
$ E.delta : num 0.202
$ CS : logi NA
$ n : Named int [1:5] 335 307 183 188 27
..- attr(*, "names")= chr [1:5] "Urban" "Vegetation" "Bare ground" "Burned"
...
$ classdelta : Named num [1:5] 0.1255 0.1045 0.1837 0.0981 0.1743
..- attr(*, "names")= chr [1:5] "Urban" "Vegetation" "Bare ground" "Burned"
...
$ Pvalue : num 0.001
$ A : num 0.378
$ distance : chr "euclidean"
$ weight.type : num 1
$ boot.deltas : num [1:999] 0.202 0.202 0.202 0.203 0.202 ...
$ permutations: num 999
- attr(*, "class")= chr "mrpp"
-->%---
Now I've tried the following:
--%<---
# 1. save(d) it
save ( samples_bitemporal_modis.0001.mrpp , file="exported.mrpp.R" )
# 2. loade(d) it in a new object...
loadedmrpp <- load ( "exported.mrpp.R")
# 3. (tried) to check it...
str ( "exported.mrpp.R")
chr "samples_bitemporal_modis.0001.mrpp"
# it did not cross my mind immediately to...
get(loadedmrpp)
Call:
mrpp(dat = samples_bitemporal_modis.0001[, 1:5], grouping =
samples_bitemporal_modis.0001[["Class"]])
Dissimilarity index: euclidean
Weights for groups: n
Class means and counts:
Urban Vegetation Bare ground Burned Water
delta 0.1255 0.1045 0.1837 0.0981 0.1743
n 335 307 183 188 27
Chance corrected within-group agreement A: 0.3778
Based on observed delta 0.1258 and expected delta 0.2022
Significance of delta: 0.001
Based on 999 permutations
# ...or to work on a clean workspace!
-->%---
Thank you David. Cheers, Nikos
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