| title | output | editor_options | ||||||
|---|---|---|---|---|---|---|---|---|
R Notebook |
|
|
library("tidyverse")
library("poppr")
(load("data/sclerotinia_16_loci.rda"))## [1] "dat" "dat11" "datdf" "keeploci"
## [5] "corrected_loci"
The white mold nursery populations are unique because they are not fungicide treated and have the same cultivars planted in them year after year.
The question becomes, are white mold nurseries differentiated from each other or are they more or less homogeneous? We could use AMOVA to test for these with location and binary source (wmn or non-wmn) as the hierarchy.
First, we want to clone-correct our data down to the field level so that we don't accidentally include non-independent samples.
dat11##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 165 original multilocus genotypes
## 366 haploid individuals
## 11 codominant loci
##
## Population information:
##
## 5 strata - MCG, Region, Source, Year, Host
## 14 populations defined - NE, NY, MN, ..., France, Mexico, ND
dat11cc <- clonecorrect(dat11, ~Region/Source/Host/Year)
dat11cc##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 165 original multilocus genotypes
## 318 haploid individuals
## 11 codominant loci
##
## Population information:
##
## 5 strata - MCG, Region, Source, Year, Host
## 14 populations defined - NE, NY, MN, ..., France, Mexico, ND
make_amova_table <- function(am, amt, samples = "Region"){
tot <- nrow(am$results)
res <- data.frame(list(am$results[-tot, c("Df", "Sum Sq")],
Percent = am$componentsofcovariance[-tot, 2],
Pval = rev(amt$pvalue),
# Sigma = rev(amt$)
Phi = rev(am$statphi$Phi[-tot])))
res <- as.matrix(res)
colnames(res) <- c("d.f.", "Sum of Squares", "Percent variation", "P",
"Phi statistic")
names(dimnames(res)) <- c("levels", "statistic")
rownames(res) <- gsub("samples", samples, rownames(res))
return(res)
}
make_amova_printable <- function(amtab, amtabcc){
am_array <- array(dim = c(dim(amtab), 2),
dimnames = c(dimnames(amtab),
list(c("full", "clone-corrected"))))
am_array[, , 1] <- amtab
am_array[, , 2] <- amtabcc
tabfun <- function(x){
x <- paste0(paste0(signif(x, 3), collapse = " ("), ")")
return(x)
}
res <- apply(am_array, c(1, 2), tabfun)
return(res)
}Now that we've done that, we should make a new variable in the strata that separates the white mold nurseries from the others. We'll call this stratum "Source Type".
addStrata(dat11cc) <- strata(dat11cc) %>%
mutate(SourceType = forcats::fct_inorder(ifelse(Source == "wmn", "wmn", "other"))) %>%
select(SourceType)
setPop(dat11cc) <- ~SourceType
dat11cc##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 165 original multilocus genotypes
## 318 haploid individuals
## 11 codominant loci
##
## Population information:
##
## 6 strata - MCG, Region, Source, Year, Host, SourceType
## 2 populations defined - other, wmn
I can perform AMOVA on the newly defined variable using Bruvo's distance.
other(dat11cc)$REPLEN## 5-2(F) 5-3(F) 6-2(F) 7-2(F) 8-3(H) 9-2(F) 12-2(H) 17-3(H)
## 2.00000 4.00000 5.99999 2.00000 2.00000 2.00000 2.00000 3.00000
## 20-3(F) 36-4(F) 50-4(F) 55-4(F) 92-4(F) 106-4(H) 110-4(H) 114-4(H)
## 2.00000 4.00000 4.00000 4.00000 2.00000 4.00000 3.99999 4.00000
bd <- bruvo.dist(dat11cc, replen = other(dat11cc)$REPLEN)
(ssc_amova <- poppr.amova(dat11cc, ~SourceType, dist = bd, quiet = TRUE))## $call
## ade4::amova(samples = xtab, distances = xdist, structures = xstruct)
##
## $results
## Df Sum Sq Mean Sq
## Between samples 1 0.8969901 0.8969901
## Within samples 316 68.7005529 0.2174068
## Total 317 69.5975430 0.2195506
##
## $componentsofcovariance
## Sigma %
## Variations Between samples 0.004391536 1.979968
## Variations Within samples 0.217406813 98.020032
## Total variations 0.221798349 100.000000
##
## $statphi
## Phi
## Phi-samples-total 0.01979968
ssc_amova_test <- randtest(ssc_amova, nrepet = 999)
plot(ssc_amova_test)
ssc_amova_test## Monte-Carlo test
## Call: as.randtest(sim = res, obs = sigma[1])
##
## Observation: 0.004391536
##
## Based on 999 replicates
## Simulated p-value: 0.001
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 7.902623e+00 1.343248e-05 3.069231e-07
This result is telling us that there is some subdivision between white mold nurseries and non-white mold nurseries. Of course, from previous analyses, we know that Mexico is differentiated from other populations, so what happens if we account for Region? Here, we are placing region lower in the heirarchy because we specifically want to test the effect of region on the differentiation between white mold nurseries within different regions.
ssc_amova_region <- poppr.amova(dat11cc, ~SourceType/Region, dist = bd, quiet = TRUE)
ssc_amova_region_test <- randtest(ssc_amova_region, nrepet = 9999)
plot(ssc_amova_region_test)
(ssc_amova_table <- make_amova_table(ssc_amova_region, ssc_amova_region_test))## statistic
## levels d.f. Sum of Squares Percent variation
## Between SourceType 1 0.8969901 -0.1583345
## Between Region Within SourceType 20 12.0338468 13.7964379
## Within Region 296 56.6667061 86.3618966
## statistic
## levels P Phi statistic
## Between SourceType 0.2878 -0.001583345
## Between Region Within SourceType 0.0001 0.137746279
## Within Region 0.0001 0.136381034
Okay! This shows that when we account for Region after accounting for Source Type, we find that the differentiation is coming mainly from the Regions. What happens when we remove Mexico?
datnomex <- setPop(dat11cc, ~Region) %>% popsub(blacklist = "Mexico")
bdnm <- bruvo.dist(datnomex, replen = other(datnomex)$REPLEN)
ssc_amova_nm <- poppr.amova(datnomex, ~SourceType/Region, dist = bdnm, quiet = TRUE)
ssc_amova_nm_test <- randtest(ssc_amova_nm, nrepet = 9999)
plot(ssc_amova_nm_test)
(ssc_amova_nm_table <- make_amova_table(ssc_amova_nm, ssc_amova_nm_test))## statistic
## levels d.f. Sum of Squares Percent variation
## Between SourceType 1 0.7527864 0.115560
## Between Region Within SourceType 19 8.9146924 9.534715
## Within Region 282 55.0854125 90.349725
## statistic
## levels P Phi statistic
## Between SourceType 0.2765 0.00115560
## Between Region Within SourceType 0.0001 0.09545746
## Within Region 0.0001 0.09650275
When we remove the Mexican isolates (which only contained white mold nurseries and shared no genotypes), we see that indeed, the degree of differentiation went down. Of course, if we look at the distribution of isolates between white mold nurseries and regions, we can see things are a bit lopsided:
table(strata(dat11cc, ~SourceType/Region, combine = FALSE))## Region
## SourceType NE NY MN MI OR WA CO WI ID Australia CA France Mexico ND
## other 13 1 0 18 2 23 33 2 1 2 0 4 0 34
## wmn 24 0 9 40 15 35 1 0 0 4 18 17 15 7
The only regions that have at least ten samples in both white mold nurseries and production regions are NE, WA, and MI. We can see what happens when we subsample to these
datnewami <- setPop(dat11cc, ~Region) %>% popsub(sublist = c("NE", "WA", "MI"))
bdnewami <- bruvo.dist(datnewami, replen = other(datnewami)$REPLEN)
ssc_amova_newami <- poppr.amova(datnewami, ~SourceType/Region, dist = bdnewami, quiet = TRUE)
ssc_amova_newami_test <- randtest(ssc_amova_newami, nrepet = 9999)
plot(ssc_amova_newami_test)
(ssc_amova_newami_table <- make_amova_table(ssc_amova_newami, ssc_amova_newami_test))## statistic
## levels d.f. Sum of Squares Percent variation
## Between SourceType 1 0.3672025 -1.831835
## Between Region Within SourceType 4 2.6285603 8.309128
## Within Region 147 30.0470395 93.522707
## statistic
## levels P Phi statistic
## Between SourceType 0.3945 -0.01831835
## Between Region Within SourceType 0.0001 0.08159657
## Within Region 0.0001 0.06477293
make_amova_printable(ssc_amova_table, ssc_amova_newami_table) %>%
as_tibble() %>%
add_column(Hierarchy = c("Between Source", "Between Region within Source", "Within Region"), .before = 1) %>%
readr::write_csv(path = file.path(PROJHOME, "results", "tables", "AMOVA-region.csv"), col_names = TRUE) %>%
rename(ps = `Phi statistic`) %>%
mutate(ps = gsub("0\\.00(\\d{1})(\\d{2})", "\\1.\\2e^-3^", ps)) %>%
mutate(ps = case_when(Hierarchy == "Between Source" ~ ps, TRUE ~ paste0("**", ps, "**"))) %>%
rename(`$\\Phi statistic$` = ps) %>%
rename(`% variation` = `Percent variation`) %>%
rename(S.S. = `Sum of Squares`) %>%
select(-P) %>%
huxtable::as_huxtable(add_colnames = TRUE) %>%
huxtable::set_col_width(c(1.1, 0.6, 0.8, 0.8, 1.1)) %>%
huxtable::set_align(huxtable::everywhere, 2:5, "center") %>%
huxtable::print_md(max_width = 90) -----------------------------------------------------------------------------------
Hierarchy d.f. S.S. % variation $\Phi statistic$
---------------------- --------- ------------- -------------- ---------------------
Between Source 1 (1) 0.897 (0.367) -0.158 (-1.83) -1.58e^-3^ (-0.0183)
Between Region within 20 (4) 12 (2.63) 13.8 (8.31) **0.138 (0.0816)**
Source
Within Region 296 (147) 56.7 (30) 86.4 (93.5) **0.136 (0.0648)**
-----------------------------------------------------------------------------------
We can visualize the partitions if we create distributions showing the genetic distance.
wmn_inds <- (setPop(dat11cc, ~SourceType) %>% pop()) == "wmn"
# Function to set the upper triangle to NA to avoid over-representation.
set_upper_tri_NA <- function(d){d[upper.tri(d)] <- NA; diag(d) <- NA; d}
# dist data converted to matrix fed into here. Removes NAs from previous function
tidy_dist <- . %>%
as.data.frame() %>%
rownames_to_column("from") %>%
gather(to, distance, -from) %>%
filter(!is.na(distance))
wmn_distance <- as.matrix(bd)[wmn_inds, wmn_inds] %>% set_upper_tri_NA() %>% tidy_dist
nwmn_distance <- as.matrix(bd)[!wmn_inds, !wmn_inds] %>% set_upper_tri_NA() %>% tidy_dist
inter_distance <- as.matrix(bd)[wmn_inds, !wmn_inds] %>% tidy_dist
dists <- bind_rows(`White Mold Nurseries` = wmn_distance,
`Between` = inter_distance,
`Other Sources` = nwmn_distance,
.id = "Comparison") %>%
mutate(Comparison = forcats::fct_inorder(Comparison)) %>%
as_tibble()
dists## # A tibble: 50,403 x 4
## Comparison from to distance
## <fctr> <chr> <chr> <dbl>
## 1 White Mold Nurseries 445 444 0.00000000
## 2 White Mold Nurseries 446 444 0.30681818
## 3 White Mold Nurseries 447 444 0.26136364
## 4 White Mold Nurseries 448 444 0.37500000
## 5 White Mold Nurseries 449 444 0.31818182
## 6 White Mold Nurseries 450 444 0.22727273
## 7 White Mold Nurseries 451 444 0.09090909
## 8 White Mold Nurseries 452 444 0.18181818
## 9 White Mold Nurseries 453 444 0.00000000
## 10 White Mold Nurseries 454 444 0.09090909
## # ... with 50,393 more rows
ggplot(dists, aes(y = distance, x = Comparison, fill = Comparison)) +
geom_violin() +
geom_boxplot(width = 0.25) +
scale_fill_manual(values = c(`Other Sources` = "grey35", `White Mold Nurseries` = "grey95", Between = "grey65")) +
scale_color_manual(values = c(`Other Sources` = "grey35", `White Mold Nurseries` = "grey95", Between = "grey65")) +
theme_bw(base_size = 16, base_family = "Helvetica") +
theme(aspect.ratio = 1/2) +
theme(legend.position = "none") +
theme(axis.title.x = element_blank()) +
theme(axis.text = element_text(color = "black")) +
theme(panel.grid.major = element_line(colour = "grey20")) +
theme(panel.grid.minor = element_line(colour = "grey50")) +
theme(panel.grid.major.x = element_blank()) +
theme(panel.border = element_blank()) +
ylab("Bruvo's Distance")
summary_dists <- dists %>%
group_by(Comparison) %>%
summarize(`Average Distance` = mean(distance),
`Standard Deviation` = sd(distance))
knitr::kable(summary_dists, digits = 3)| Comparison | Average Distance | Standard Deviation |
|---|---|---|
| White Mold Nurseries | 0.453 | 0.139 |
| Between | 0.440 | 0.148 |
| Other Sources | 0.410 | 0.165 |
On first look, it appears that there's not much difference. Looking at the table, we can see that the average distance within White Mold Nurseries is 0.453, whereas the average distance within Other Sources is 0.41. This is well within the standard deviation for both distributions, but we also recognize that a single step of Bruvo's distance for 11 loci in a haploid organism is 0.045.
DAPC is a nice way of visualizing these. First, I'm simply going to compare between white mold nurseries and others:
filter <- strata(dat11cc) %>%
group_by(Region) %>%
mutate(filter = length(unique(SourceType)) > 1) %>%
pull(filter)
wmn_compare <- dat11cc[filter]
set.seed(2017-07-12)
wmndapc <- xvalDapc(tab(wmn_compare), pop(wmn_compare), n.rep = 200, n.pca = 1:10)
wmndapc$DAPC## #################################################
## # Discriminant Analysis of Principal Components #
## #################################################
## class: dapc
## $call: dapc.data.frame(x = as.data.frame(x), grp = ..1, n.pca = ..2,
## n.da = ..3)
##
## $n.pca: 3 first PCs of PCA used
## $n.da: 1 discriminant functions saved
## $var (proportion of conserved variance): 0.423
##
## $eig (eigenvalues): 15.52 vector length content
## 1 $eig 1 eigenvalues
## 2 $grp 272 prior group assignment
## 3 $prior 2 prior group probabilities
## 4 $assign 272 posterior group assignment
## 5 $pca.cent 69 centring vector of PCA
## 6 $pca.norm 69 scaling vector of PCA
## 7 $pca.eig 58 eigenvalues of PCA
##
## data.frame nrow ncol
## 1 $tab 272 3
## 2 $means 2 3
## 3 $loadings 3 1
## 4 $ind.coord 272 1
## 5 $grp.coord 2 1
## 6 $posterior 272 2
## 7 $pca.loadings 69 3
## 8 $var.contr 69 1
## content
## 1 retained PCs of PCA
## 2 group means
## 3 loadings of variables
## 4 coordinates of individuals (principal components)
## 5 coordinates of groups
## 6 posterior membership probabilities
## 7 PCA loadings of original variables
## 8 contribution of original variables
scatter(wmndapc$DAPC)
(wmntab <- table(wmndapc$DAPC$assign, wmndapc$DAPC$grp))##
## other wmn
## other 73 40
## wmn 56 103
chisq.test(wmntab, simulate.p.value = TRUE)##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: wmntab
## X-squared = 22.87, df = NA, p-value = 0.0004998
Okay, this shows that there's quite a bit of overlap between these two. What happens if I look at the DAPC when it includes the pouplation groupings. Mainly, I want to know if the white mold nurseries can be compared while considering the pouplations
set.seed(2017-07-12)
wmndapc <- xvalDapc(tab(wmn_compare), strata(wmn_compare, ~SourceType/Region)[[2]],
n.pca = 5:20, n.rep = 100)$DAPC## Warning in xvalDapc.matrix(tab(wmn_compare), strata(wmn_compare,
## ~SourceType/Region)[[2]], : 1 group has only 1 member so it cannot be
## represented in both training and validation sets.
scatter(wmndapc)
LDS <- bind_cols(Population = wmndapc$grp, as.data.frame(wmndapc$ind.coord)) %>%
as_tibble()
LDS_pop <- LDS %>%
group_by(Population) %>%
summarize_all(mean) %>%
rename_all(function(x) gsub("LD", "mean", x))
LDS <- full_join(LDS, LDS_pop) %>%
separate(Population, c("SourceType", "Pop"), remove = FALSE) ## Joining, by = "Population"
LDS_PLOT <- ggplot(LDS, aes(x = LD1, y = LD2, color = SourceType)) +
geom_point(aes(fill = SourceType), alpha = 0.5, pch = 21, color = "black") +
geom_segment(aes(x = mean1, y = mean2, xend = LD1, yend = LD2), alpha = 0.5) +
stat_ellipse(type = "norm", level = 0.66, alpha = 0.75) +
# ggrepel::geom_label_repel(aes(x = mean1, y = mean2, label = Population),
# data = LDS_pop, show.legend = FALSE, color = "black") +
theme_bw() +
theme(aspect.ratio = 1/1.618) +
theme(legend.position = "bottom") +
theme(axis.text = element_blank()) +
theme(axis.title = element_blank()) +
theme(axis.ticks = element_blank()) +
viridis::scale_color_viridis(discrete = TRUE, direction = -1) +
viridis::scale_fill_viridis(discrete = TRUE, direction = -1) +
scale_y_continuous(breaks = 0) +
scale_x_continuous(breaks = 0) +
theme(panel.background = element_rect(fill = "grey95")) +
theme(panel.grid.major = element_line(color = "black"))
LDS_PLOT + facet_wrap(~Pop, ncol = 3)## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Warning: Removed 1 rows containing missing values (geom_path).
There are three main hosts shared within the white mold nurseries:
| Cultivar | Susceptibility |
|---|---|
| Beryl | Susceptible |
| Bunsi | ? |
| G122 | Some Resistance |
These hosts are grown in the nurseries at different times and it is of interest if cultivar can have an effect on population structure. Given the results of Aldrich-Wolfe 2015, I would suspect that cultivar has no effect, but it's important to verify this. We are going to use AMOVA and DAPC for this.
bebug <- dat11cc %>%
setPop(~Source) %>%
popsub("wmn") %>%
setPop(~Host) %>%
popsub(c("Beryl", "Bunsi", "G122"))
bebugd <- bruvo.dist(bebug, replen = other(bebug)$REPLEN)
(ssc_host_amova <- poppr.amova(bebug, ~Host, dist = bebugd, quiet = TRUE))## $call
## ade4::amova(samples = xtab, distances = xdist, structures = xstruct)
##
## $results
## Df Sum Sq Mean Sq
## Between samples 2 0.2965933 0.1482967
## Within samples 154 35.1688676 0.2283693
## Total 156 35.4654609 0.2273427
##
## $componentsofcovariance
## Sigma %
## Variations Between samples -0.00153422 -0.6763594
## Variations Within samples 0.22836927 100.6763594
## Total variations 0.22683505 100.0000000
##
## $statphi
## Phi
## Phi-samples-total -0.006763594
(ssc_host_amova <- poppr.amova(bebug, ~Host/Region, dist = bebugd, quiet = TRUE))## $call
## ade4::amova(samples = xtab, distances = xdist, structures = xstruct)
##
## $results
## Df Sum Sq Mean Sq
## Between Host 2 0.2965933 0.1482967
## Between samples Within Host 27 11.2172536 0.4154538
## Within samples 127 23.9516140 0.1885954
## Total 156 35.4654609 0.2273427
##
## $componentsofcovariance
## Sigma %
## Variations Between Host -0.00648971 -2.861074
## Variations Between samples Within Host 0.04472212 19.716331
## Variations Within samples 0.18859539 83.144743
## Total variations 0.22682779 100.000000
##
## $statphi
## Phi
## Phi-samples-total 0.16855257
## Phi-samples-Host 0.19167923
## Phi-Host-total -0.02861074
The AMOVA makes it clear that there's no differentiation between hosts. Let's see what DAPC shows us.
setPop(bebug) <- ~Host
bebug_dapc <- dapc(bebug, n.pca = 20, n.da = 2)
scatter(bebug_dapc, #clabel = 0,
legend = TRUE,
scree.pca = TRUE,
scree.da = TRUE,
col = viridis::plasma(3, end = 0.85),
bg.inset = "grey90",
bg = "grey90")
ggcompoplot::ggcompoplot(bebug_dapc, setPop(bebug, ~Region), cols = 5, pal = viridis::plasma(3, end = 0.85)) +
theme(legend.position = "top")
apply(bebug_dapc$posterior, 2, mean)## G122 Beryl Bunsi
## 0.3397300 0.3594735 0.3007965
setPop(bebug) <- ~Host/Region
bebug_dapc <- dapc(bebug, n.pca = 20, n.da = 20)
bebug_dapc## #################################################
## # Discriminant Analysis of Principal Components #
## #################################################
## class: dapc
## $call: dapc.genind(x = bebug, n.pca = 20, n.da = 20)
##
## $n.pca: 20 first PCs of PCA used
## $n.da: 20 discriminant functions saved
## $var (proportion of conserved variance): 0.887
##
## $eig (eigenvalues): 31.72 7.385 5.33 4.425 4.026 ...
##
## vector length content
## 1 $eig 20 eigenvalues
## 2 $grp 157 prior group assignment
## 3 $prior 30 prior group probabilities
## 4 $assign 157 posterior group assignment
## 5 $pca.cent 66 centring vector of PCA
## 6 $pca.norm 66 scaling vector of PCA
## 7 $pca.eig 54 eigenvalues of PCA
##
## data.frame nrow ncol
## 1 $tab 157 20
## 2 $means 30 20
## 3 $loadings 20 20
## 4 $ind.coord 157 20
## 5 $grp.coord 30 20
## 6 $posterior 157 30
## 7 $pca.loadings 66 20
## 8 $var.contr 66 20
## content
## 1 retained PCs of PCA
## 2 group means
## 3 loadings of variables
## 4 coordinates of individuals (principal components)
## 5 coordinates of groups
## 6 posterior membership probabilities
## 7 PCA loadings of original variables
## 8 contribution of original variables
LDS <- bind_cols(Population = bebug_dapc$grp, as.data.frame(bebug_dapc$ind.coord)) %>%
as_tibble()
LDS_pop <- LDS %>%
group_by(Population) %>%
summarize_all(mean) %>%
rename_all(function(x) gsub("LD", "mean", x))
LDS <- full_join(LDS, LDS_pop, by = "Population") %>% separate(Population, c("Cultivar", "Population"))
LDS_PLOT <- ggplot(LDS, aes(x = LD1, y = LD2, color = Cultivar)) +
geom_point(aes(fill = Cultivar), alpha = 0.5, pch = 21, color = "black") +
geom_segment(aes(x = mean1, y = mean2, xend = LD1, yend = LD2), alpha = 0.5) +
stat_ellipse(type = "norm", level = 0.66, alpha = 0.75) +
theme_bw(base_size = 16, base_family = "Helvetica") +
theme(aspect.ratio = 1/1.618) +
theme(legend.position = "bottom") +
theme(axis.text = element_blank()) +
theme(axis.title = element_blank()) +
theme(axis.ticks = element_blank()) +
viridis::scale_color_viridis(option = "B", discrete = TRUE, direction = -1, end = 0.85) +
viridis::scale_fill_viridis(option = "B", discrete = TRUE, direction = -1, end = 0.85) +
scale_y_continuous(breaks = 0) +
scale_x_continuous(breaks = 0) +
theme(panel.background = element_rect(fill = "grey95")) +
theme(panel.grid.major = element_line(color = "black")) +
facet_wrap(~Population, nrow = 2)
LDS_PLOT## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Warning: Removed 3 rows containing missing values (geom_path).
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
Session Information
## Session info --------------------------------------------------------------------------------------
## setting value
## version R version 3.4.2 (2017-09-28)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz UTC
## date 2018-04-12
## Packages ------------------------------------------------------------------------------------------
## package * version date source
## ade4 * 1.7-8 2017-08-09 cran (@1.7-8)
## adegenet * 2.1.0 2017-10-12 cran (@2.1.0)
## ape 5.0 2017-10-30 cran (@5.0)
## assertr 2.0.2.2 2017-06-06 cran (@2.0.2.2)
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.2)
## base * 3.4.2 2018-03-01 local
## bindr 0.1 2016-11-13 CRAN (R 3.4.2)
## bindrcpp * 0.2 2017-06-17 CRAN (R 3.4.2)
## boot 1.3-20 2017-07-30 cran (@1.3-20)
## broom 0.4.3 2017-11-20 CRAN (R 3.4.2)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.4.2)
## cli 1.0.0 2017-11-05 CRAN (R 3.4.2)
## cluster 2.0.6 2017-03-16 CRAN (R 3.4.2)
## coda 0.19-1 2016-12-08 cran (@0.19-1)
## codetools 0.2-15 2016-10-05 CRAN (R 3.4.2)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.4.2)
## compiler 3.4.2 2018-03-01 local
## cowplot * 0.9.1 2017-11-16 cran (@0.9.1)
## crayon 1.3.4 2017-09-16 CRAN (R 3.4.2)
## datasets * 3.4.2 2018-03-01 local
## deldir 0.1-14 2017-04-22 cran (@0.1-14)
## devtools 1.13.4 2017-11-09 CRAN (R 3.4.2)
## digest 0.6.12 2017-01-27 CRAN (R 3.4.2)
## dplyr * 0.7.4 2017-09-28 CRAN (R 3.4.2)
## evaluate 0.10.1 2017-06-24 CRAN (R 3.4.2)
## expm 0.999-2 2017-03-29 cran (@0.999-2)
## ezknitr 0.6 2016-09-16 cran (@0.6)
## fastmatch 1.1-0 2017-01-28 cran (@1.1-0)
## forcats * 0.2.0 2017-01-23 CRAN (R 3.4.2)
## foreign 0.8-69 2017-06-21 CRAN (R 3.4.2)
## gdata 2.18.0 2017-06-06 cran (@2.18.0)
## ggcompoplot * 0.1.0 2018-04-09 Github (zkamvar/ggcompoplot@bcf007d)
## ggforce 0.1.1 2016-11-28 cran (@0.1.1)
## ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.4.2)
## ggraph * 1.0.0 2017-02-24 cran (@1.0.0)
## ggrepel 0.7.0 2017-09-29 cran (@0.7.0)
## glue 1.2.0 2017-10-29 CRAN (R 3.4.2)
## gmodels 2.16.2 2015-07-22 cran (@2.16.2)
## graphics * 3.4.2 2018-03-01 local
## grDevices * 3.4.2 2018-03-01 local
## grid 3.4.2 2018-03-01 local
## gridExtra 2.3 2017-09-09 CRAN (R 3.4.2)
## gtable 0.2.0 2016-02-26 CRAN (R 3.4.2)
## gtools 3.5.0 2015-05-29 cran (@3.5.0)
## haven 1.1.0 2017-07-09 CRAN (R 3.4.2)
## highr 0.6 2016-05-09 CRAN (R 3.4.2)
## hms 0.4.0 2017-11-23 CRAN (R 3.4.2)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.4.2)
## htmlwidgets 0.9 2017-07-10 CRAN (R 3.4.2)
## httpuv 1.3.5 2017-07-04 CRAN (R 3.4.2)
## httr 1.3.1 2017-08-20 CRAN (R 3.4.2)
## huxtable 1.1.0 2017-10-20 cran (@1.1.0)
## igraph * 1.1.2 2017-07-21 CRAN (R 3.4.2)
## jsonlite 1.5 2017-06-01 CRAN (R 3.4.2)
## KernSmooth 2.23-15 2015-06-29 cran (@2.23-15)
## knitr * 1.17 2017-08-10 CRAN (R 3.4.2)
## labeling 0.3 2014-08-23 CRAN (R 3.4.2)
## lattice 0.20-35 2017-03-25 CRAN (R 3.4.2)
## lazyeval 0.2.1 2017-10-29 CRAN (R 3.4.2)
## LearnBayes 2.15 2014-05-29 cran (@2.15)
## lubridate 1.7.1 2017-11-03 CRAN (R 3.4.2)
## magrittr 1.5 2014-11-22 CRAN (R 3.4.2)
## MASS 7.3-47 2017-04-21 CRAN (R 3.4.2)
## Matrix 1.2-12 2017-11-16 CRAN (R 3.4.2)
## memoise 1.1.0 2017-04-21 CRAN (R 3.4.2)
## methods * 3.4.2 2018-03-01 local
## mgcv 1.8-22 2017-09-19 CRAN (R 3.4.2)
## mime 0.5 2016-07-07 CRAN (R 3.4.2)
## mnormt 1.5-5 2016-10-15 CRAN (R 3.4.2)
## modelr 0.1.1 2017-07-24 CRAN (R 3.4.2)
## munsell 0.4.3 2016-02-13 CRAN (R 3.4.2)
## nlme 3.1-131 2017-02-06 CRAN (R 3.4.2)
## parallel 3.4.2 2018-03-01 local
## pegas 0.10 2017-05-03 cran (@0.10)
## permute 0.9-4 2016-09-09 cran (@0.9-4)
## phangorn 2.3.1 2017-11-01 cran (@2.3.1)
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.2)
## plyr 1.8.4 2016-06-08 CRAN (R 3.4.2)
## poppr * 2.5.0 2017-09-11 cran (@2.5.0)
## psych 1.7.8 2017-09-09 CRAN (R 3.4.2)
## purrr * 0.2.4 2017-10-18 CRAN (R 3.4.2)
## quadprog 1.5-5 2013-04-17 cran (@1.5-5)
## R.methodsS3 1.7.1 2016-02-16 cran (@1.7.1)
## R.oo 1.21.0 2016-11-01 cran (@1.21.0)
## R.utils 2.6.0 2017-11-05 cran (@2.6.0)
## R6 2.2.2 2017-06-17 CRAN (R 3.4.2)
## Rcpp 0.12.14 2017-11-23 CRAN (R 3.4.2)
## readr * 1.1.1 2017-05-16 CRAN (R 3.4.2)
## readxl 1.0.0 2017-04-18 CRAN (R 3.4.2)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.4.2)
## rlang 0.1.4 2017-11-05 CRAN (R 3.4.2)
## rstudioapi 0.7 2017-09-07 CRAN (R 3.4.2)
## rvest 0.3.2 2016-06-17 CRAN (R 3.4.2)
## scales 0.5.0 2017-08-24 CRAN (R 3.4.2)
## seqinr 3.4-5 2017-08-01 cran (@3.4-5)
## shiny 1.0.5 2017-08-23 CRAN (R 3.4.2)
## sp 1.2-5 2017-06-29 CRAN (R 3.4.2)
## spData 0.2.6.7 2017-11-28 cran (@0.2.6.7)
## spdep 0.7-4 2017-11-22 cran (@0.7-4)
## splines 3.4.2 2018-03-01 local
## stats * 3.4.2 2018-03-01 local
## stringi 1.1.6 2017-11-17 CRAN (R 3.4.2)
## stringr * 1.2.0 2017-02-18 CRAN (R 3.4.2)
## tibble * 1.3.4 2017-08-22 CRAN (R 3.4.2)
## tidyr * 0.7.2 2017-10-16 CRAN (R 3.4.2)
## tidyselect 0.2.3 2017-11-06 CRAN (R 3.4.2)
## tidyverse * 1.2.1 2017-11-14 CRAN (R 3.4.2)
## tools 3.4.2 2018-03-01 local
## tweenr 0.1.5 2016-10-10 cran (@0.1.5)
## udunits2 0.13 2016-11-17 cran (@0.13)
## units 0.4-6 2017-08-27 cran (@0.4-6)
## utils * 3.4.2 2018-03-01 local
## vegan 2.4-4 2017-08-24 cran (@2.4-4)
## viridis * 0.4.0 2017-03-27 CRAN (R 3.4.2)
## viridisLite * 0.2.0 2017-03-24 CRAN (R 3.4.2)
## visNetwork * 2.0.1 2017-07-30 cran (@2.0.1)
## withr 2.1.0 2017-11-01 CRAN (R 3.4.2)
## xml2 1.1.1 2017-01-24 CRAN (R 3.4.2)
## xtable 1.8-2 2016-02-05 CRAN (R 3.4.2)