suppressPackageStartupMessages({
library(here)
source(here::here("Code/00_Configuration.R"))
lapply(package_list, require, character = TRUE)
library(corrr)
library(fastDummies)
library(inspectdf)
library(caret)
library(summarytools)
})dta <- readRDS(here::here("Data/output/1_all_predictors_merged.rds")) %>%
filter(samplingPeriodID == 1)sp_id <-
c("verbatimIdentification", "scientificName")
H1 <-
c("Mass", "GlobRangeSize_km2", "Migration", "Habitat_5", "Generalism", "Threatened", "pd")
H2 <-
c("D_AOO_a", "mean_lnLac", "AOO", "joincount_delta", "circNorm", "minDist_toBorder_centr")
H3 <-
c("datasetID")
predictors <-
c(H1, H2, H3)
responses <-
c("Jaccard_dissim", "log_R2_1", "log_R2_1_per_year")dta_new <-
dta %>%
select(all_of(c(sp_id, responses, H3, H1, H2))) %>%
ungroup()We will dummy code the ordinal variables to include them in the correlation matrix
dummy_reduced <-
dta_new %>%
select(
-datasetID,
-verbatimIdentification,
-scientificName,
-Jaccard_dissim,
-log_R2_1,
-log_R2_1_per_year
) %>%
## turn the level "NA" into a real NA
mutate(
Migration = na_if(as.character(Migration), "NA"),
Threatened = na_if(as.character(Threatened), "NA"),
Habitat_5 = na_if(as.character(Habitat_5), "NA"),
Generalism = na_if(as.character(Generalism), "NA")
) %>%
mutate(
Migration = factor(
Migration,
levels = c("1", "2", "3"),
),
Threatened = factor(
Threatened,
levels = c("1", "0")
),
Habitat_5 = factor(
Habitat_5,
levels = c("closed", "freshwater", "open", "human", "marine")
),
Generalism = factor(
Generalism,
levels = c("1", "0")
)
) %>%
na.omit()
dta_dummies <-
fastDummies::dummy_cols(dummy_reduced,
remove_first_dummy = FALSE, # avoids multicollinearity
remove_selected_columns = TRUE
)
cor_matrix <-
corrr::correlate(dta_dummies,
use = "pairwise.complete.obs",
quiet = TRUE
) %>%
tidyr::replace_na(list(r = 1)) %>%
rearrange()Registered S3 methods overwritten by 'registry':
method from
print.registry_field proxy
print.registry_entry proxyRegistered S3 method overwritten by 'seriation':
method from
reorder.hclust veganrplot(cor_matrix)
GGally::ggpairs(
ggplot2::aes(colour = datasetID),
data = dta_new %>%
select(
-verbatimIdentification,
-scientificName
) %>% as.data.frame(),
progress = FALSE
)
inspect_types(dta_new) %>%
show_plot()
inspect_num(dta_new) %>%
show_plot()
inspect_imb(dta_new %>%
select(
-verbatimIdentification,
-scientificName
)) %>%
show_plot()
inspect_cat(dta_new %>%
select(-verbatimIdentification, -scientificName)) %>%
show_plot()
ggplot(dta_new, aes(x = Threatened, fill = datasetID)) +
geom_bar()
ggplot(dta_new, aes(x = Habitat_5, fill = datasetID)) +
geom_bar()
inspect_cor(dta_new) %>%
show_plot()Warning: Columns with 0 variance found: Jaccard_dissim, log_R2_1,
log_R2_1_per_year, D_AOO_a
Categorical Variables
# summarytools::freq(dta_new)
summarytools::ctable(
x = dta_new$Habitat_5,
y = dta_new$Threatened
)Cross-Tabulation, Row Proportions
Habitat_5 * Threatened
Data Frame: dta_new
------------ ------------ ------------- ------------- ----------- ---------------
Threatened 0 1 <NA> Total
Habitat_5
closed 364 (92.6%) 29 ( 7.4%) 0 ( 0.0%) 393 (100.0%)
freshwater 215 (81.1%) 50 (18.9%) 0 ( 0.0%) 265 (100.0%)
human 40 (81.6%) 9 (18.4%) 0 ( 0.0%) 49 (100.0%)
marine 74 (77.9%) 21 (22.1%) 0 ( 0.0%) 95 (100.0%)
open 202 (83.1%) 41 (16.9%) 0 ( 0.0%) 243 (100.0%)
<NA> 7 (77.8%) 0 ( 0.0%) 2 (22.2%) 9 (100.0%)
Total 902 (85.6%) 150 (14.2%) 2 ( 0.2%) 1054 (100.0%)
------------ ------------ ------------- ------------- ----------- ---------------summarytools::ctable(
x = dta_new$datasetID,
y = dta_new$Threatened
)Cross-Tabulation, Row Proportions
datasetID * Threatened
Data Frame: dta_new
----------- ------------ ------------- ------------- ---------- ---------------
Threatened 0 1 <NA> Total
datasetID
5 178 (88.6%) 23 (11.4%) 0 (0.0%) 201 (100.0%)
6 207 (88.8%) 24 (10.3%) 2 (0.9%) 233 (100.0%)
13 179 (86.1%) 29 (13.9%) 0 (0.0%) 208 (100.0%)
26 338 (82.0%) 74 (18.0%) 0 (0.0%) 412 (100.0%)
Total 902 (85.6%) 150 (14.2%) 2 (0.2%) 1054 (100.0%)
----------- ------------ ------------- ------------- ---------- ---------------summarytools::descr(dta_new)Non-numerical variable(s) ignored: verbatimIdentification, scientificName, datasetID, Migration, Habitat_5, Generalism, ThreatenedDescriptive Statistics
dta_new
N: 1054
AOO circNorm D_AOO_a GlobRangeSize_km2 Jaccard_dissim
----------------- ------------ ---------- --------- ------------------- ----------------
Mean 700709.67 76.29 1.30 14098593.16 0.50
Std.Dev 1353154.87 102.62 0.51 14053605.33 0.29
Min 0.69 1.27 0.00 587.60 0.00
Q1 13566.10 17.40 0.95 4856303.72 0.26
Median 72260.14 44.09 1.40 10151559.24 0.50
Q3 474578.44 90.35 1.73 18860060.90 0.74
Max 5626163.31 733.99 2.00 118014530.88 1.00
MAD 102734.28 46.11 0.54 9904227.49 0.36
IQR 456992.66 72.91 0.77 13970352.29 0.49
CV 1.93 1.35 0.39 1.00 0.57
Skewness 2.19 3.09 -0.66 2.41 0.02
SE.Skewness 0.08 0.08 0.08 0.08 0.08
Kurtosis 3.66 11.25 -0.43 9.47 -1.15
N.Valid 1054.00 1054.00 1054.00 1051.00 1054.00
N 1054.00 1054.00 1054.00 1054.00 1054.00
Pct.Valid 100.00 100.00 100.00 99.72 100.00
Table: Table continues below
joincount_delta log_R2_1 log_R2_1_per_year Mass mean_lnLac
----------------- ----------------- ---------- ------------------- ---------- ------------
Mean 0.94 0.17 0.01 453.65 1.40
Std.Dev 0.72 0.67 0.02 1059.38 1.10
Min -0.03 -3.17 -0.07 3.09 0.06
Q1 0.35 -0.04 0.00 19.90 0.55
Median 0.80 0.05 0.00 77.50 1.13
Q3 1.39 0.26 0.01 452.10 2.10
Max 2.98 7.96 0.28 10682.04 5.73
MAD 0.73 0.22 0.01 98.30 1.16
IQR 1.04 0.31 0.01 432.20 1.55
CV 0.77 4.00 4.18 2.34 0.78
Skewness 0.71 3.13 3.21 5.34 0.83
SE.Skewness 0.08 0.08 0.08 0.08 0.08
Kurtosis -0.33 27.92 26.24 36.16 0.03
N.Valid 1050.00 1054.00 1054.00 1045.00 1054.00
N 1054.00 1054.00 1054.00 1054.00 1054.00
Pct.Valid 99.62 100.00 100.00 99.15 100.00
Table: Table continues below
minDist_toBorder_centr pd
----------------- ------------------------ ---------
Mean 72934.72 8.20
Std.Dev 67093.11 6.31
Min 537.50 1.11
Q1 27412.12 4.36
Median 53372.12 6.19
Q3 84898.34 9.71
Max 439876.69 56.96
MAD 45151.34 3.40
IQR 57471.17 5.36
CV 0.92 0.77
Skewness 1.88 2.72
SE.Skewness 0.08 0.08
Kurtosis 4.30 11.38
N.Valid 1054.00 1045.00
N 1054.00 1054.00
Pct.Valid 100.00 99.15Numerical variables:
summarytools::dfSummary(dta_new, plain.ascii = FALSE)### Data Frame Summary
#### dta_new
**Dimensions:** 1054 x 19
**Duplicates:** 0
--------------------------------------------------------------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
---- ------------------------- ---------------------------------- ---------------------- ------------------------------------ ---------- ---------
1 verbatimIdentification\ 1\. Accipiter gentilis\ 4 ( 0.4%)\ \ 1054\ 0\
[character] 2\. Anas crecca\ 4 ( 0.4%)\ \ (100.0%) (0.0%)
3\. Anas platyrhynchos\ 4 ( 0.4%)\ \
4\. Asio otus\ 4 ( 0.4%)\ \
5\. Falco peregrinus\ 4 ( 0.4%)\ \
6\. Gallinula chloropus\ 4 ( 0.4%)\ \
7\. Hirundo rustica\ 4 ( 0.4%)\ \
8\. Loxia curvirostra\ 4 ( 0.4%)\ \
9\. Mergus merganser\ 4 ( 0.4%)\ \
10\. Nycticorax nycticorax\ 4 ( 0.4%)\ \
[ 752 others ] 1014 (96.2%) IIIIIIIIIIIIIIIIIII
2 scientificName\ 1\. Accipiter gentilis\ 4 ( 0.4%)\ \ 1054\ 0\
[character] 2\. Anas crecca\ 4 ( 0.4%)\ \ (100.0%) (0.0%)
3\. Anas platyrhynchos\ 4 ( 0.4%)\ \
4\. Asio otus\ 4 ( 0.4%)\ \
5\. Corvus corone\ 4 ( 0.4%)\ \
6\. Falco peregrinus\ 4 ( 0.4%)\ \
7\. Gallinula chloropus\ 4 ( 0.4%)\ \
8\. Hirundo rustica\ 4 ( 0.4%)\ \
9\. Loxia curvirostra\ 4 ( 0.4%)\ \
10\. Mareca strepera\ 4 ( 0.4%)\ \
[ 716 others ] 1014 (96.2%) IIIIIIIIIIIIIIIIIII
3 Jaccard_dissim\ Mean (sd) : 0.5 (0.3)\ 631 distinct values \ \ : : \ \ : . : \ \ \ \ .\ 1054\ 0\
[numeric] min < med < max:\ . : : : : : : : : :\ (100.0%) (0.0%)
0 < 0.5 < 1\ : : : : : : : : : :\
IQR (CV) : 0.5 (0.6) : : : : : : : : : :\
: : : : : : : : : :
4 log_R2_1\ Mean (sd) : 0.2 (0.7)\ 689 distinct values \ \ \ \ :\ 1054\ 0\
[numeric] min < med < max:\ \ \ \ \ :\ (100.0%) (0.0%)
-3.2 < 0 < 8\ \ \ \ \ :\
IQR (CV) : 0.3 (4) \ \ \ \ : :\
\ \ \ \ : : .
5 log_R2_1_per_year\ Mean (sd) : 0 (0)\ 130 distinct values \ \ \ \ :\ 1054\ 0\
[numeric] min < med < max:\ \ \ . :\ (100.0%) (0.0%)
-0.1 < 0 < 0.3\ \ \ : :\
IQR (CV) : 0 (4.2) \ \ : :\
\ \ : : .
6 datasetID\ 1\. 5\ 201 (19.1%)\ III \ 1054\ 0\
[factor] 2\. 6\ 233 (22.1%)\ IIII \ (100.0%) (0.0%)
3\. 13\ 208 (19.7%)\ III \
4\. 26 412 (39.1%) IIIIIII
7 Mass\ Mean (sd) : 453.6 (1059.4)\ 657 distinct values :\ 1045\ 9\
[numeric] min < med < max:\ :\ (99.1%) (0.9%)
3.1 < 77.5 < 10682\ :\
IQR (CV) : 432.2 (2.3) :\
: .
8 GlobRangeSize_km2\ Mean (sd) : 14098593 (14053605)\ 723 distinct values :\ 1051\ 3\
[numeric] min < med < max:\ :\ (99.7%) (0.3%)
587.6 < 10151559 < 118014531\ : .\
IQR (CV) : 13970352 (1) : :\
: : : .
9 Migration\ 1\. 1\ 235 (22.5%)\ IIII \ 1045\ 9\
[factor] 2\. 2\ 247 (23.6%)\ IIII \ (99.1%) (0.9%)
3\. 3\ 563 (53.9%)\ IIIIIIIIII \
4\. NA 0 ( 0.0%)
10 Habitat_5\ 1\. closed\ 393 (37.6%)\ IIIIIII \ 1045\ 9\
[factor] 2\. freshwater\ 265 (25.4%)\ IIIII \ (99.1%) (0.9%)
3\. human\ 49 ( 4.7%)\ \
4\. marine\ 95 ( 9.1%)\ I \
5\. open 243 (23.3%) IIII
11 Generalism\ 1\. 0\ 918 (87.1%)\ IIIIIIIIIIIIIIIII \ 1054\ 0\
[factor] 2\. 1 136 (12.9%) II (100.0%) (0.0%)
12 Threatened\ 1\. 0\ 902 (85.7%)\ IIIIIIIIIIIIIIIII \ 1052\ 2\
[factor] 2\. 1 150 (14.3%) II (99.8%) (0.2%)
13 pd\ Mean (sd) : 8.2 (6.3)\ 645 distinct values :\ 1045\ 9\
[numeric] min < med < max:\ :\ (99.1%) (0.9%)
1.1 < 6.2 < 57\ : .\
IQR (CV) : 5.4 (0.8) : :\
: : : .
14 D_AOO_a\ Mean (sd) : 1.3 (0.5)\ 736 distinct values \ \ \ \ \ \ \ \ \ \ \ \ \ \ . : .\ 1054\ 0\
[numeric] min < med < max:\ \ \ \ \ \ \ \ \ \ \ \ \ . : : :\ (100.0%) (0.0%)
0 < 1.4 < 2\ \ \ \ \ \ \ \ \ . : : : : :\
IQR (CV) : 0.8 (0.4) \ \ \ \ \ \ . : : : : : :\
: : : : : : : : : :
15 mean_lnLac\ Mean (sd) : 1.4 (1.1)\ 1051 distinct values : .\ 1054\ 0\
[numeric] min < med < max:\ : :\ (100.0%) (0.0%)
0.1 < 1.1 < 5.7\ : : .\
IQR (CV) : 1.6 (0.8) : : : : .\
: : : : : : . .
16 AOO\ Mean (sd) : 700709.7 (1353155)\ 1051 distinct values :\ 1054\ 0\
[numeric] min < med < max:\ :\ (100.0%) (0.0%)
0.7 < 72260.1 < 5626163\ :\
IQR (CV) : 456992.7 (1.9) :\
: .
17 joincount_delta\ Mean (sd) : 0.9 (0.7)\ 1010 distinct values . :\ 1050\ 4\
[numeric] min < med < max:\ : : : .\ (99.6%) (0.4%)
0 < 0.8 < 3\ : : : : .\
IQR (CV) : 1 (0.8) : : : : : : . .\
: : : : : : : : . .
18 circNorm\ Mean (sd) : 76.3 (102.6)\ 1051 distinct values :\ 1054\ 0\
[numeric] min < med < max:\ :\ (100.0%) (0.0%)
1.3 < 44.1 < 734\ :\
IQR (CV) : 72.9 (1.3) : .\
: :
19 minDist_toBorder_centr\ Mean (sd) : 72934.7 (67093.1)\ 1051 distinct values :\ 1054\ 0\
[numeric] min < med < max:\ : .\ (100.0%) (0.0%)
537.5 < 53372.1 < 439876.7\ : :\
IQR (CV) : 57471.2 (0.9) : :\
: : : . .
--------------------------------------------------------------------------------------------------------------------------------------------------summarytools::stby(
data = dta_new,
INDICES = dta_new$datasetID,
FUN = summarytools::descr,
stats = "common",
transpose = T
)Error in match.call(f, call): ... in einer Situation benutzt, in der es nicht existiertWarning in parse_call(mc = match.call(), var_name = (ncol(xx) == 1), var_label
= (ncol(xx) == : metadata extraction terminated unexpectedly; inspect results
carefullyError in match.call(f, call): ... in einer Situation benutzt, in der es nicht existiertWarning in parse_call(mc = match.call(), var_name = (ncol(xx) == 1), var_label
= (ncol(xx) == : metadata extraction terminated unexpectedly; inspect results
carefullyError in match.call(f, call): ... in einer Situation benutzt, in der es nicht existiertWarning in parse_call(mc = match.call(), var_name = (ncol(xx) == 1), var_label
= (ncol(xx) == : metadata extraction terminated unexpectedly; inspect results
carefullyError in match.call(f, call): ... in einer Situation benutzt, in der es nicht existiertWarning in parse_call(mc = match.call(), var_name = (ncol(xx) == 1), var_label
= (ncol(xx) == : metadata extraction terminated unexpectedly; inspect results
carefullyNon-numerical variable(s) ignored: verbatimIdentification, scientificName, datasetID, Migration, Habitat_5, Generalism, ThreatenedDescriptive Statistics
Mean Std.Dev Min Median Max
---------------------------- ------------- ------------- ----------- ------------- -------------
AOO 40551.97 29464.72 136.03 42194.75 78308.81
circNorm 29.09 25.54 1.28 19.89 99.62
D_AOO_a 1.43 0.56 0.00 1.61 2.00
GlobRangeSize_km2 17897011.73 11653330.19 250505.58 15439514.89 63574544.76
Jaccard_dissim 0.42 0.31 0.00 0.40 1.00
joincount_delta 0.39 0.37 -0.03 0.32 1.69
log_R2_1 0.14 0.43 -1.17 0.03 2.37
log_R2_1_per_year 0.01 0.02 -0.06 0.00 0.12
Mass 431.05 1049.59 5.54 77.14 10682.04
mean_lnLac 0.50 0.59 0.09 0.20 2.59
minDist_toBorder_centr 74163.15 18565.12 773.21 81937.12 102402.45
pd 7.81 5.40 1.20 5.92 29.59
Table: Table continues below
N.Valid N Pct.Valid
---------------------------- --------- -------- -----------
AOO 201.00 201.00 100.00
circNorm 201.00 201.00 100.00
D_AOO_a 201.00 201.00 100.00
GlobRangeSize_km2 201.00 201.00 100.00
Jaccard_dissim 201.00 201.00 100.00
joincount_delta 197.00 201.00 98.01
log_R2_1 201.00 201.00 100.00
log_R2_1_per_year 201.00 201.00 100.00
Mass 199.00 201.00 99.00
mean_lnLac 201.00 201.00 100.00
minDist_toBorder_centr 201.00 201.00 100.00
pd 199.00 201.00 99.00Non-numerical variable(s) ignored: verbatimIdentification, scientificName, datasetID, Migration, Habitat_5, Generalism, Threatened
N: 233
Mean Std.Dev Min Median Max
---------------------------- ------------- ------------- ---------- ------------ -------------
AOO 36317.49 41373.36 4.98 14775.02 124025.31
circNorm 174.79 173.29 1.27 116.16 733.99
D_AOO_a 1.31 0.55 0.00 1.41 1.98
GlobRangeSize_km2 10454413.81 12006642.63 38728.84 6322724.06 75222636.54
Jaccard_dissim 0.61 0.28 0.00 0.68 1.00
joincount_delta 0.70 0.51 0.00 0.59 2.16
log_R2_1 0.06 0.66 -1.79 0.01 3.75
log_R2_1_per_year 0.00 0.03 -0.07 0.00 0.14
Mass 324.87 747.25 3.09 47.18 5791.37
mean_lnLac 1.32 1.23 0.06 0.81 4.51
minDist_toBorder_centr 35110.41 17495.36 1179.47 35571.94 93755.94
pd 7.55 6.26 1.47 5.65 51.14
Table: Table continues below
N.Valid N Pct.Valid
---------------------------- --------- -------- -----------
AOO 233.00 233.00 100.00
circNorm 233.00 233.00 100.00
D_AOO_a 233.00 233.00 100.00
GlobRangeSize_km2 231.00 233.00 99.14
Jaccard_dissim 233.00 233.00 100.00
joincount_delta 233.00 233.00 100.00
log_R2_1 233.00 233.00 100.00
log_R2_1_per_year 233.00 233.00 100.00
Mass 230.00 233.00 98.71
mean_lnLac 233.00 233.00 100.00
minDist_toBorder_centr 233.00 233.00 100.00
pd 230.00 233.00 98.71Non-numerical variable(s) ignored: verbatimIdentification, scientificName, datasetID, Migration, Habitat_5, Generalism, Threatened
N: 208
Mean Std.Dev Min Median Max
---------------------------- ------------- ------------- --------- ------------ --------------
AOO 66495.56 86418.79 0.69 29119.11 345243.91
circNorm 58.98 44.85 1.27 52.49 189.98
D_AOO_a 1.05 0.55 0.00 1.08 1.96
GlobRangeSize_km2 14255068.43 16200414.70 587.60 9621437.80 110453714.25
Jaccard_dissim 0.69 0.23 0.00 0.74 1.00
joincount_delta 0.58 0.40 -0.03 0.58 1.87
log_R2_1 0.38 1.05 -1.86 0.16 7.96
log_R2_1_per_year 0.01 0.04 -0.06 0.00 0.28
Mass 366.69 824.55 5.54 89.94 8785.99
mean_lnLac 1.42 1.00 0.24 1.07 4.30
minDist_toBorder_centr 29552.26 25679.79 1718.86 21550.57 235785.26
pd 7.96 5.84 1.11 6.29 51.14
Table: Table continues below
N.Valid N Pct.Valid
---------------------------- --------- -------- -----------
AOO 208.00 208.00 100.00
circNorm 208.00 208.00 100.00
D_AOO_a 208.00 208.00 100.00
GlobRangeSize_km2 208.00 208.00 100.00
Jaccard_dissim 208.00 208.00 100.00
joincount_delta 208.00 208.00 100.00
log_R2_1 208.00 208.00 100.00
log_R2_1_per_year 208.00 208.00 100.00
Mass 206.00 208.00 99.04
mean_lnLac 208.00 208.00 100.00
minDist_toBorder_centr 208.00 208.00 100.00
pd 206.00 208.00 99.04Non-numerical variable(s) ignored: verbatimIdentification, scientificName, datasetID, Migration, Habitat_5, Generalism, Threatened
N: 412
Mean Std.Dev Min Median Max
---------------------------- ------------- ------------- --------- ------------- --------------
AOO 1718699.01 1726344.06 784.13 1011167.89 5626163.31
circNorm 52.35 37.20 1.43 43.35 206.16
D_AOO_a 1.35 0.37 0.11 1.43 1.89
GlobRangeSize_km2 14209971.35 14546523.20 8313.39 10569402.42 118014530.88
Jaccard_dissim 0.38 0.22 0.00 0.34 1.00
joincount_delta 1.50 0.68 0.00 1.49 2.98
log_R2_1 0.13 0.47 -3.17 0.06 3.11
log_R2_1_per_year 0.00 0.01 -0.07 0.00 0.07
Mass 580.55 1283.92 5.54 104.37 9512.09
mean_lnLac 1.87 0.97 0.64 1.66 5.73
minDist_toBorder_centr 115628.17 85258.05 537.50 103462.17 439876.69
pd 8.88 6.90 1.20 6.67 56.96
Table: Table continues below
N.Valid N Pct.Valid
---------------------------- --------- -------- -----------
AOO 412.00 412.00 100.00
circNorm 412.00 412.00 100.00
D_AOO_a 412.00 412.00 100.00
GlobRangeSize_km2 411.00 412.00 99.76
Jaccard_dissim 412.00 412.00 100.00
joincount_delta 412.00 412.00 100.00
log_R2_1 412.00 412.00 100.00
log_R2_1_per_year 412.00 412.00 100.00
Mass 410.00 412.00 99.51
mean_lnLac 412.00 412.00 100.00
minDist_toBorder_centr 412.00 412.00 100.00
pd 410.00 412.00 99.51trellis.par.set(theme = col.whitebg(), warn = FALSE)H1 ~ Jaccard
featurePlot(
x = dta_new %>% select(datasetID, all_of(H1)),
y = dta_new$Jaccard_dissim,
group = dta_new$datasetID,
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Scatterplot Matrix of traits (H1) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
H2 ~ Jaccard
featurePlot(
x = dta_new %>% select(datasetID, all_of(H2)),
y = dta_new$Jaccard_dissim,
group = dta_new$datasetID,
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Scatterplot Matrix of range geometry (H2) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
H1 ~ log ratio
featurePlot(
x = dta_new %>% select(datasetID, all_of(H1)),
y = dta_new$log_R2_1,
group = dta_new$datasetID,
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Scatterplot Matrix of traits (H1) - Log Ratio",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
H2 ~ log ratio
featurePlot(
x = dta_new %>% select(datasetID, all_of(H2)),
y = dta_new$log_R2_1,
group = dta_new$datasetID,
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Scatterplot Matrix of range geometry (H2) - log Ratio",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
H2 ~ Jaccard
featurePlot(
x = dta_new %>% filter(datasetID == 5) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 5) %>%
select(Jaccard_dissim),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Czechia: Scatterplot Matrix of traits (H1) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
featurePlot(
x = dta_new %>% filter(datasetID == 6) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 6) %>%
select(Jaccard_dissim),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "New York: Scatterplot Matrix of range geometry (H2) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
featurePlot(
x = dta_new %>% filter(datasetID == 13) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 13) %>%
select(Jaccard_dissim),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Japan: Scatterplot Matrix of range geometry (H2) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
featurePlot(
x = dta_new %>% filter(datasetID == 26) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 26) %>%
select(Jaccard_dissim),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Europe: Scatterplot Matrix of range geometry (H2) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
H2 ~ log ratio
featurePlot(
x = dta_new %>% filter(datasetID == 5) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 5) %>%
select(log_R2_1),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Czechia: Scatterplot Matrix of traits (H1) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
featurePlot(
x = dta_new %>% filter(datasetID == 6) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 6) %>%
select(log_R2_1),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "New York: Scatterplot Matrix of range geometry (H2) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
featurePlot(
x = dta_new %>% filter(datasetID == 13) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 13) %>%
select(log_R2_1),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Japan: Scatterplot Matrix of range geometry (H2) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)
featurePlot(
x = dta_new %>% filter(datasetID == 26) %>%
select(all_of(c(H2))),
y = dta_new %>% filter(datasetID == 26) %>%
select(log_R2_1),
plot = "pairs",
pch = 16,
alpha = 0.3,
cex = 0.5,
xlab = "Europe: Scatterplot Matrix of range geometry (H2) - Jaccard 1",
auto.key = list(columns = 4),
par.settings =
list(fontsize = list(text = 6))
)