The rimu package handles multiple-response data, a generalisation of factor data. With factor data, there is a defined set of categories and each observation comes from one category. With multiple-reponse data, there is a defined set of categories, but each observation could come from multiple categories. We provide two classes: mr for multiple-response presence/absence data, and ms for scored or ranked multiple-responses where each category gets a non-zero score or rank.
The birds dataset is a small subset of data from the Great Backyard Bird Count, in the US and Canada. We have counts of 12 birds by US county and Canadian province. The twelve birds are
data(birds)
names(birds)[1:12]## [1] "Phalaenoptilus nuttallii" "Fregata magnificens"
## [3] "Melanerpes lewis" "Melospiza georgiana"
## [5] "Rallus limicola" "Myioborus pictus"
## [7] "Poecile gambeli" "Aythya collaris"
## [9] "Xanthocephalus xanthocephalus" "Gracula religiosa"
## [11] "Icterus parisorum" "Coccyzus erythropthalmus"There’s a thirteenth column giving the location name.
These birds are perhaps more familiar as
First, let’s put them into the data structures
bird_count <- as.ms(birds[,-13],na.rm=TRUE)
bird_presence <- as.mr(bird_count)The bird counts will print like a sparse matrix
head(bird_count)## Phalaenoptilus nuttallii Fregata magnificens Melanerpes lewis
## [1,] . . .
## [2,] . . .
## [3,] . . .
## [4,] . . .
## [5,] . . .
## [6,] . . .
## Melospiza georgiana Rallus limicola Myioborus pictus Poecile gambeli
## [1,] . . . .
## [2,] . . . .
## [3,] 5 . . .
## [4,] . . . 30
## [5,] . . . .
## [6,] 1 . . .
## Aythya collaris Xanthocephalus xanthocephalus Gracula religiosa
## [1,] . . .
## [2,] 1 . .
## [3,] 4 . .
## [4,] 10 . .
## [5,] . . .
## [6,] 1 . .
## Icterus parisorum Coccyzus erythropthalmus
## [1,] . .
## [2,] . .
## [3,] . .
## [4,] . .
## [5,] . .
## [6,] . .but the bird presence/absence data has a more compact character form
head(bird_presence)## [1] "" "Aythya collaris"
## [3] "Melospiza georgiana+Aythya collaris" "Poecile gambeli+Aythya collaris"
## [5] "" "Melospiza georgiana+Aythya collaris"What birds are most often present?
mtable(bird_presence)## Phalaenoptilus nuttallii Fregata magnificens Melanerpes lewis
## 9 16 87
## Melospiza georgiana Rallus limicola Myioborus pictus Poecile gambeli
## 876 121 4 317
## Aythya collaris Xanthocephalus xanthocephalus Gracula religiosa
## 1090 80 1
## Icterus parisorum Coccyzus erythropthalmus
## 8 1And what birds tend to go together? We can draw an upset chart
plot(bird_presence,nsets=12)
That’s all a bit clumsy because of the long names,but you can see, for example, that the swamp sparrow and ring-necked duck tend to co-occur. Let’s recode to shorter names.
bird_presence<-mr_recode(bird_presence,
poorwill="Phalaenoptilus nuttallii",
frigatebird="Fregata magnificens",
woodpecker ="Melanerpes lewis",
sparrow="Melospiza georgiana",
rail="Rallus limicola",
redstart="Myioborus pictus",
chickadee="Poecile gambeli",
duck="Aythya collaris",
yellowhead="Xanthocephalus xanthocephalus",
myna="Dracula religiosa",
oriole="Icterus parisorum",
cuckoo="Coccyzus erythropthalmus")## Error in mr_recode.default(bird_presence, poorwill = "Phalaenoptilus nuttallii", : non-existent levels Dracula religiosaOops.
bird_presence<-mr_recode(bird_presence,
poorwill="Phalaenoptilus nuttallii",
frigatebird="Fregata magnificens",
woodpecker ="Melanerpes lewis",
sparrow="Melospiza georgiana",
rail="Rallus limicola",
redstart="Myioborus pictus",
chickadee="Poecile gambeli",
duck="Aythya collaris",
yellowhead="Xanthocephalus xanthocephalus",
myna="Gracula religiosa",
oriole="Icterus parisorum",
cuckoo="Coccyzus erythropthalmus")Now try again:
mtable(bird_presence)## poorwill frigatebird woodpecker sparrow rail redstart chickadee duck
## 9 16 87 876 121 4 317 1090
## yellowhead myna oriole cuckoo
## 80 1 8 1mtable(bird_presence,bird_presence)## poorwill frigatebird woodpecker sparrow rail redstart chickadee
## poorwill 9 0 2 0 3 0 5
## frigatebird 0 16 0 12 8 0 0
## woodpecker 2 0 87 13 29 3 72
## sparrow 0 12 13 876 72 4 34
## rail 3 8 29 72 121 4 52
## redstart 0 0 3 4 4 4 3
## chickadee 5 0 72 34 52 3 317
## duck 8 13 70 593 114 4 188
## yellowhead 2 3 27 22 22 3 43
## myna 0 1 0 1 1 0 0
## oriole 0 0 4 5 4 3 4
## cuckoo 0 0 0 1 0 0 0
## duck yellowhead myna oriole cuckoo
## poorwill 8 2 0 0 0
## frigatebird 13 3 1 0 0
## woodpecker 70 27 0 4 0
## sparrow 593 22 1 5 1
## rail 114 22 1 4 0
## redstart 4 3 0 3 0
## chickadee 188 43 0 4 0
## duck 1090 73 1 7 1
## yellowhead 73 80 0 5 0
## myna 1 0 1 0 0
## oriole 7 5 0 8 0
## cuckoo 1 0 0 0 1plot(bird_presence, nsets=12,nint=30)
The default image plot is of the table of the variable by itself and shows the number of co-occurences. With type="conditional", the plot shows the proportion of each bird (on the y-axis) given that a particular bird (on the x-axis) is present.
image(bird_presence)
image(bird_presence, type="conditional")
We might want to focus on just the more commonly observed birds
common_birds<-mr_lump(bird_presence,n=4)
mtable(common_birds)## sparrow rail chickadee duck Other
## 876 121 317 1090 163mtable(common_birds,common_birds)## sparrow rail chickadee duck Other
## sparrow 876 72 34 593 44
## rail 72 121 52 114 48
## chickadee 34 52 317 188 97
## duck 593 114 188 1090 135
## Other 44 48 97 135 163plot(common_birds)
Or consider just the rare and interesting ones
rare_birds<-mr_lump(bird_presence,n=-5,other_level="Common")
mtable(rare_birds)## poorwill redstart myna oriole cuckoo Common
## 9 4 1 8 1 1513mtable(rare_birds,rare_birds)## poorwill redstart myna oriole cuckoo Common
## poorwill 9 0 0 0 0 9
## redstart 0 4 0 3 0 4
## myna 0 0 1 0 0 1
## oriole 0 3 0 8 0 7
## cuckoo 0 0 0 0 1 1
## Common 9 4 1 7 1 1513plot(rare_birds,nsets=6)
plot(mr_drop(rare_birds,"Common"),nsets=5)