scripts for report/Final Visualizations/PA_richness_evenness_misc.R
In asgersvenning/bachelor: Bachelor Data And Functions
tibble(models = finalREModels %>% lapply(function(x) x$mer)) %>%
mutate(residuals = map(models,function(x) tibble(fitted = fitted(x),
residual = residuals(x))),
coords = bind_rows(communityCoords,communityCoords) %>% list,
type = finalModelData %>%
select(!c(x,y,L2_KEY)) %>%
list,
Index = names(models)) %>%
select(!models) %>%
unnest(everything()) %>%
select(residual,fid,type,Index) %>%
pivot_wider(c(fid,Index),names_from=type,values_from=residual) %>%
ggplot(aes(Abundance,`Presence/Absence`,fill=Index)) +
geom_bin_2d(aes(alpha = after_stat(count))) +
geom_abline(slope = 1, size = 1) +
geom_smooth(aes(color = after_scale(fill)),
method = "lm") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 8) +
coord_cartesian(expand = F) +
facet_wrap(~Index, scales = "free") +
baseTheme +
theme(aspect.ratio = 1)
tibble(models = finalREModels %>% lapply(function(x) x$mer)) %>%
mutate(residuals = map(models,function(x) tibble(fitted = fitted(x),
residual = residuals(x))),
coords = bind_rows(communityCoords,communityCoords) %>% list,
type = finalModelData %>%
select(!c(x,y,L2_KEY)) %>%
list,
Index = names(models)) %>%
select(!models) %>%
unnest(everything()) %>%
nest(data = everything()) %>%
summarise(
plots = c(fitted = map(data, ~ ggplot(.x, aes(fitted,residual, fill = type, color = type)) +
geom_bin_2d(aes(alpha = after_stat(count)),
color = NA,
key_glyph = draw_key_point) +
geom_hline(yintercept = 0,
color = "firebrick2",
linetype = "dashed",
size = 1) +
stat_summary_bin(fun.data = mean_sdl,
bins = 20,
show.legend = F) +
scale_color_brewer(palette = "Dark2",
guide = "none") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 6) +
facet_wrap(~Index, scales = "free") +
baseTheme +
guides(alpha = guide_legend(nrow = 2,
override.aes = list(shape = 21,
size = 5)),
fill = guide_legend(ncol = 1,
override.aes = list(shape = 21,
size = 5))) +
theme(legend.box = "horizontal",
legend.box.just = "right",
legend.position = "top",
aspect.ratio = 1) +
labs(x = "Fitted values", y = "Residual",
alpha = "Density", fill = "Analysis\nMode")
),
spatial = map(data, ~ .x %>%
select(x,y,residual,fitted,Index,type) %>%
st_as_sf(coords = c("x","y")) %>%
st_set_crs(st_crs(ecoregions_L2)) %>%
ggplot(aes(fill=residual/abs(residual+fitted))) +
stat_sf_coordinates(aes(after_stat(x),after_stat(y)),
geom = "tile") +
scale_fill_viridis_c(option = "A") +
facet_grid(rows = vars(Index), cols = vars(type)) +
coord_sf() +
baseTheme +
theme(panel.grid.major = element_line(color = "gray65",
size = .25,
linetype = "dashed")) +
labs(x = NULL, y = NULL, fill = latex2exp::TeX("$\\frac{\\hat{y}-y}{y}$"))
),
ecoregion = map(data, ~ ggplot(.x,aes(residual,paste0(str_extract(Ecoregion,".{0,10}"),ifelse(str_detect(Ecoregion,".{11}"),"...","")))) +
geom_violin(scale = "width", size = 1) +
stat_summary(fun.data = mean_sdl,
color = "red",
geom = "errorbar",
size = .75,
width = .5) +
stat_summary(fun = mean,
color = "red",
fill = "gray65",
shape = 21,
geom = "point",
stroke = 1,
size = 4) +
geom_vline(xintercept = 0, color = "black", size = .75) +
baseTheme +
theme(axis.text.y = element_text(hjust = 0)) +
labs(y = NULL, x = "Residual") +
facet_wrap(~type,nrow=1,scales = "free_x")),
samples = map(data, ~ ggplot(.x, aes(exp(nPlots),residual, fill = type, color = type)) +
geom_bin_2d(aes(alpha = after_stat(count)),
color = NA,
key_glyph = draw_key_point) +
geom_hline(yintercept = 0,
color = "firebrick2",
linetype = "dashed",
size = 1) +
stat_summary_bin(fun.data = mean_sdl,
bins = 20,
show.legend = F) +
scale_color_brewer(palette = "Dark2",
guide = "none") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 6) +
scale_x_log10() +
facet_wrap(~Index, scales = "free") +
baseTheme +
guides(alpha = guide_legend(nrow = 2,
override.aes = list(shape = 21,
size = 5)),
fill = guide_legend(ncol = 1,
override.aes = list(shape = 21,
size = 5))) +
theme(legend.box = "horizontal",
legend.box.just = "right",
legend.position = "top",
aspect.ratio = 1) +
labs(x = "# Plots in Grid Cell", y = "Residual",
alpha = "Density", fill = "Analysis\nMode")
),
identity = map(data, ~ ggplot(.x, aes(fitted, fitted + residual, color = type)) +
geom_point(size = .1) +
geom_smooth(method = "lm") +
scale_color_brewer(palette = "Dark2") +
baseTheme +
theme(aspect.ratio = 1) +
facet_wrap(~Index, scales = "free",ncol=1)),
predictors = map(data, ~ .x %>%
pivot_longer(c(Richness,ShannonEvenness),names_to="Predictor") %>%
ggplot(aes(value,residual,fill=type,color=type)) +
geom_bin_2d(aes(alpha = after_stat(count)),
color = NA,
key_glyph = draw_key_point) +
geom_hline(yintercept = 0,
color = "firebrick2",
linetype = "dashed",
size = 1) +
stat_summary_bin(fun.data = mean_sdl,
bins = 20,
show.legend = F) +
scale_color_brewer(palette = "Dark2",
guide = "none") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 6) +
facet_grid(rows = vars(Index), cols = vars(Predictor), scales = "free", switch = "x") +
baseTheme +
guides(alpha = guide_legend(nrow = 2,
override.aes = list(shape = 21,
size = 5)),
fill = guide_legend(ncol = 1,
override.aes = list(shape = 21,
size = 5))) +
theme(legend.box = "horizontal",
legend.box.just = "right",
legend.position = "top",
aspect.ratio = 1) +
labs(x = NULL, y = "Residual",
alpha = "Density", fill = "Analysis\nMode")
)),
names = names(plots),
width = c(4,3.5,4,4,4,4),
height = c(2,3,2.5,2,4,4)
) %>%
mutate(save = pmap(list(names,plots,width,height), function(type,plot,w,h) {
ggsave(paste0(type,"_REResidualPlot.png"),plot,
type = "cairo-png", dpi = 400, scale = 3,
width = w, height = h)
}))
labelPT <- function(p,t,precision=2) map2(p,t, function(x,y) {
xy <- sapply(c(x,y), function(z) {
if (z == 0) {
out <- "0"
}
else if (abs(z) > 0.01) {
out <- str_extract(z,paste0("^.+\\..{0,",precision,"}"))
}
else {
out <- scales::scientific(z,precision) %>%
str_replace("e"," \\\\times \\\\, 10^{")
if (str_detect(out,"[{]")) out <- paste0(out,"}")
}
out
})
paste0("$\\overset{",
"P-value = ", xy[1],"^{",c(paste0(rep("*",3),collapse=""),paste0(rep("*",2),collapse=""),"*",".","")[which(x < c(0.0005, 0.005, 0.05, 0.1, 1))[1]],"}",
"}{",
"t-value = ", xy[2],
"}$",
collapse = "") %>%
latex2exp::TeX(output = "character")
})
# labelPT(0.0001,2.3)
finalREResultPlotData <- finalREResult %>%
filter(!str_detect(term,"Intercept")) %>%
# mutate(term = str_replace(term, "ShannonEvenness","Shannon Evenness") %>%
# str_replace(":"," : "))
mutate(term = factor(str_replace(term, "ShannonEvenness","Shannon Evenness") %>%
str_replace(":"," : "),
levels = c(
"Bias",
"Richness",
"Shannon Evenness",
"Bias : Richness",
"Bias : Shannon Evenness"
) %>% rev)) %>%
rename(estimate = 2, std.error = 3, t.value = 4, p.value = 5) %>%
group_by(Index) %>%
mutate(
offset = max(abs(diff(range(c(estimate+std.error,estimate-std.error))))),
direction = ifelse(estimate+std.error > max(estimate + std.error)*0.65, -1, 1)
) %>%
ungroup %>%
mutate(label = labelPT(p.value,t.value))
finalREResultPlot <- finalREResultPlotData %>%
ggplot(aes(estimate,Index,xmin=estimate-std.error*1.96,xmax=estimate+std.error*1.96,color=term)) +
geom_vline(xintercept = 0, color = "black", size = .75) +
geom_errorbar(width = .5,
size = 1,
position = position_dodge(1),
show.legend = F) +
geom_point(size = 4,
position = position_dodge(1)) +
geom_label(aes(x = estimate + direction * (1.96*std.error + 0.025*offset),
label = label),
data = finalREResultPlotData %>%
mutate(estimate = ifelse(direction == -1,NA,estimate)),
position = position_dodge(1),
hjust = 0,
parse = T,
show.legend = F) +
geom_label(aes(x = estimate + direction * (1.96*std.error + 0.025*offset),
label = label),
data = finalREResultPlotData %>%
mutate(estimate = ifelse(direction == 1,NA,estimate)),
position = position_dodge(1),
hjust = 1,
parse = T,
show.legend = F) +
scale_color_brewer(palette = "Dark2") +
scale_x_continuous(expand = expansion(.15)) +
scale_y_discrete(expand = expansion()) +
# coord_cartesian(ylim = c(.95,1.25)) +
guides(color = guide_legend(override.aes = list(shape = 16,
size = 10,
alpha = 1),
ncol = 1,
reverse = T)) +
labs(y = NULL, x = latex2exp::TeX("$\\hat{\\beta}$"), color = NULL) +
ggpubr::theme_pubr() +
theme(text = element_text(family = "CMU Serif"),
title = element_text(face = "bold",
size = 20),
legend.text = element_text(size = 20),
strip.text = element_text(face = "bold",
size = 20),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
aspect.ratio = .4,
legend.position = "right") +
facet_wrap(~Index, scales = "free", ncol = 1, strip.position = "bottom")
ggsave("finalREResult.png",finalREResultPlot,
type = "cairo-png", dpi = 400, height = 4, width = 4, scale = 3)
finalModelRE_predict <- finalModelData %>%
rename("ShannonEvenness" = `Shannon\nEvenness`) %>%
select(!c(FDis:FRic,fid,L2_KEY)) %>%
summarize(across(where(is.numeric),~runif(20000, min(.x),max(.x))),
across(c(Ecoregion),~sample(unique(.x),20000,T))) %>%
mutate(rid = row_number()) %>%
rowwise() %>%
summarize(
across(everything(),~rep(.x,2)),
type = factor(c("Abundance","Presence/Absence"))
) %>%
mutate(FDis = mgcv::predict.bam(finalREModels$FDis,.),
FDiv = mgcv::predict.bam(finalREModels$FDiv,.),
FEve = mgcv::predict.bam(finalREModels$FEve,.)) %>%
group_by(rid) %>%
summarize(
across(FDis:FEve, ~(.x[1] - .x[2])/.x[1]),
across(c(Richness,ShannonEvenness),first)
)
finalModelRE_predict %>%
pivot_longer(FDis:FEve,names_to="Index") %>%
ggplot(aes(Richness,ShannonEvenness,z=value)) +
stat_summary_2d(aes(color = after_scale(fill))) +
scale_fill_viridis_c() +
coord_cartesian(expand = F) +
facet_wrap(~Index)
asgersvenning/bachelor documentation built on May 2, 2023, 7:06 a.m.
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tibble(models = finalREModels %>% lapply(function(x) x$mer)) %>%
mutate(residuals = map(models,function(x) tibble(fitted = fitted(x),
residual = residuals(x))),
coords = bind_rows(communityCoords,communityCoords) %>% list,
type = finalModelData %>%
select(!c(x,y,L2_KEY)) %>%
list,
Index = names(models)) %>%
select(!models) %>%
unnest(everything()) %>%
select(residual,fid,type,Index) %>%
pivot_wider(c(fid,Index),names_from=type,values_from=residual) %>%
ggplot(aes(Abundance,`Presence/Absence`,fill=Index)) +
geom_bin_2d(aes(alpha = after_stat(count))) +
geom_abline(slope = 1, size = 1) +
geom_smooth(aes(color = after_scale(fill)),
method = "lm") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 8) +
coord_cartesian(expand = F) +
facet_wrap(~Index, scales = "free") +
baseTheme +
theme(aspect.ratio = 1)
tibble(models = finalREModels %>% lapply(function(x) x$mer)) %>%
mutate(residuals = map(models,function(x) tibble(fitted = fitted(x),
residual = residuals(x))),
coords = bind_rows(communityCoords,communityCoords) %>% list,
type = finalModelData %>%
select(!c(x,y,L2_KEY)) %>%
list,
Index = names(models)) %>%
select(!models) %>%
unnest(everything()) %>%
nest(data = everything()) %>%
summarise(
plots = c(fitted = map(data, ~ ggplot(.x, aes(fitted,residual, fill = type, color = type)) +
geom_bin_2d(aes(alpha = after_stat(count)),
color = NA,
key_glyph = draw_key_point) +
geom_hline(yintercept = 0,
color = "firebrick2",
linetype = "dashed",
size = 1) +
stat_summary_bin(fun.data = mean_sdl,
bins = 20,
show.legend = F) +
scale_color_brewer(palette = "Dark2",
guide = "none") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 6) +
facet_wrap(~Index, scales = "free") +
baseTheme +
guides(alpha = guide_legend(nrow = 2,
override.aes = list(shape = 21,
size = 5)),
fill = guide_legend(ncol = 1,
override.aes = list(shape = 21,
size = 5))) +
theme(legend.box = "horizontal",
legend.box.just = "right",
legend.position = "top",
aspect.ratio = 1) +
labs(x = "Fitted values", y = "Residual",
alpha = "Density", fill = "Analysis\nMode")
),
spatial = map(data, ~ .x %>%
select(x,y,residual,fitted,Index,type) %>%
st_as_sf(coords = c("x","y")) %>%
st_set_crs(st_crs(ecoregions_L2)) %>%
ggplot(aes(fill=residual/abs(residual+fitted))) +
stat_sf_coordinates(aes(after_stat(x),after_stat(y)),
geom = "tile") +
scale_fill_viridis_c(option = "A") +
facet_grid(rows = vars(Index), cols = vars(type)) +
coord_sf() +
baseTheme +
theme(panel.grid.major = element_line(color = "gray65",
size = .25,
linetype = "dashed")) +
labs(x = NULL, y = NULL, fill = latex2exp::TeX("$\\frac{\\hat{y}-y}{y}$"))
),
ecoregion = map(data, ~ ggplot(.x,aes(residual,paste0(str_extract(Ecoregion,".{0,10}"),ifelse(str_detect(Ecoregion,".{11}"),"...","")))) +
geom_violin(scale = "width", size = 1) +
stat_summary(fun.data = mean_sdl,
color = "red",
geom = "errorbar",
size = .75,
width = .5) +
stat_summary(fun = mean,
color = "red",
fill = "gray65",
shape = 21,
geom = "point",
stroke = 1,
size = 4) +
geom_vline(xintercept = 0, color = "black", size = .75) +
baseTheme +
theme(axis.text.y = element_text(hjust = 0)) +
labs(y = NULL, x = "Residual") +
facet_wrap(~type,nrow=1,scales = "free_x")),
samples = map(data, ~ ggplot(.x, aes(exp(nPlots),residual, fill = type, color = type)) +
geom_bin_2d(aes(alpha = after_stat(count)),
color = NA,
key_glyph = draw_key_point) +
geom_hline(yintercept = 0,
color = "firebrick2",
linetype = "dashed",
size = 1) +
stat_summary_bin(fun.data = mean_sdl,
bins = 20,
show.legend = F) +
scale_color_brewer(palette = "Dark2",
guide = "none") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 6) +
scale_x_log10() +
facet_wrap(~Index, scales = "free") +
baseTheme +
guides(alpha = guide_legend(nrow = 2,
override.aes = list(shape = 21,
size = 5)),
fill = guide_legend(ncol = 1,
override.aes = list(shape = 21,
size = 5))) +
theme(legend.box = "horizontal",
legend.box.just = "right",
legend.position = "top",
aspect.ratio = 1) +
labs(x = "# Plots in Grid Cell", y = "Residual",
alpha = "Density", fill = "Analysis\nMode")
),
identity = map(data, ~ ggplot(.x, aes(fitted, fitted + residual, color = type)) +
geom_point(size = .1) +
geom_smooth(method = "lm") +
scale_color_brewer(palette = "Dark2") +
baseTheme +
theme(aspect.ratio = 1) +
facet_wrap(~Index, scales = "free",ncol=1)),
predictors = map(data, ~ .x %>%
pivot_longer(c(Richness,ShannonEvenness),names_to="Predictor") %>%
ggplot(aes(value,residual,fill=type,color=type)) +
geom_bin_2d(aes(alpha = after_stat(count)),
color = NA,
key_glyph = draw_key_point) +
geom_hline(yintercept = 0,
color = "firebrick2",
linetype = "dashed",
size = 1) +
stat_summary_bin(fun.data = mean_sdl,
bins = 20,
show.legend = F) +
scale_color_brewer(palette = "Dark2",
guide = "none") +
scale_fill_brewer(palette = "Dark2") +
scale_alpha_continuous(trans = "log10",
range = c(0.05,.75),
n.breaks = 6) +
facet_grid(rows = vars(Index), cols = vars(Predictor), scales = "free", switch = "x") +
baseTheme +
guides(alpha = guide_legend(nrow = 2,
override.aes = list(shape = 21,
size = 5)),
fill = guide_legend(ncol = 1,
override.aes = list(shape = 21,
size = 5))) +
theme(legend.box = "horizontal",
legend.box.just = "right",
legend.position = "top",
aspect.ratio = 1) +
labs(x = NULL, y = "Residual",
alpha = "Density", fill = "Analysis\nMode")
)),
names = names(plots),
width = c(4,3.5,4,4,4,4),
height = c(2,3,2.5,2,4,4)
) %>%
mutate(save = pmap(list(names,plots,width,height), function(type,plot,w,h) {
ggsave(paste0(type,"_REResidualPlot.png"),plot,
type = "cairo-png", dpi = 400, scale = 3,
width = w, height = h)
}))
labelPT <- function(p,t,precision=2) map2(p,t, function(x,y) {
xy <- sapply(c(x,y), function(z) {
if (z == 0) {
out <- "0"
}
else if (abs(z) > 0.01) {
out <- str_extract(z,paste0("^.+\\..{0,",precision,"}"))
}
else {
out <- scales::scientific(z,precision) %>%
str_replace("e"," \\\\times \\\\, 10^{")
if (str_detect(out,"[{]")) out <- paste0(out,"}")
}
out
})
paste0("$\\overset{",
"P-value = ", xy[1],"^{",c(paste0(rep("*",3),collapse=""),paste0(rep("*",2),collapse=""),"*",".","")[which(x < c(0.0005, 0.005, 0.05, 0.1, 1))[1]],"}",
"}{",
"t-value = ", xy[2],
"}$",
collapse = "") %>%
latex2exp::TeX(output = "character")
})
# labelPT(0.0001,2.3)
finalREResultPlotData <- finalREResult %>%
filter(!str_detect(term,"Intercept")) %>%
# mutate(term = str_replace(term, "ShannonEvenness","Shannon Evenness") %>%
# str_replace(":"," : "))
mutate(term = factor(str_replace(term, "ShannonEvenness","Shannon Evenness") %>%
str_replace(":"," : "),
levels = c(
"Bias",
"Richness",
"Shannon Evenness",
"Bias : Richness",
"Bias : Shannon Evenness"
) %>% rev)) %>%
rename(estimate = 2, std.error = 3, t.value = 4, p.value = 5) %>%
group_by(Index) %>%
mutate(
offset = max(abs(diff(range(c(estimate+std.error,estimate-std.error))))),
direction = ifelse(estimate+std.error > max(estimate + std.error)*0.65, -1, 1)
) %>%
ungroup %>%
mutate(label = labelPT(p.value,t.value))
finalREResultPlot <- finalREResultPlotData %>%
ggplot(aes(estimate,Index,xmin=estimate-std.error*1.96,xmax=estimate+std.error*1.96,color=term)) +
geom_vline(xintercept = 0, color = "black", size = .75) +
geom_errorbar(width = .5,
size = 1,
position = position_dodge(1),
show.legend = F) +
geom_point(size = 4,
position = position_dodge(1)) +
geom_label(aes(x = estimate + direction * (1.96*std.error + 0.025*offset),
label = label),
data = finalREResultPlotData %>%
mutate(estimate = ifelse(direction == -1,NA,estimate)),
position = position_dodge(1),
hjust = 0,
parse = T,
show.legend = F) +
geom_label(aes(x = estimate + direction * (1.96*std.error + 0.025*offset),
label = label),
data = finalREResultPlotData %>%
mutate(estimate = ifelse(direction == 1,NA,estimate)),
position = position_dodge(1),
hjust = 1,
parse = T,
show.legend = F) +
scale_color_brewer(palette = "Dark2") +
scale_x_continuous(expand = expansion(.15)) +
scale_y_discrete(expand = expansion()) +
# coord_cartesian(ylim = c(.95,1.25)) +
guides(color = guide_legend(override.aes = list(shape = 16,
size = 10,
alpha = 1),
ncol = 1,
reverse = T)) +
labs(y = NULL, x = latex2exp::TeX("$\\hat{\\beta}$"), color = NULL) +
ggpubr::theme_pubr() +
theme(text = element_text(family = "CMU Serif"),
title = element_text(face = "bold",
size = 20),
legend.text = element_text(size = 20),
strip.text = element_text(face = "bold",
size = 20),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
aspect.ratio = .4,
legend.position = "right") +
facet_wrap(~Index, scales = "free", ncol = 1, strip.position = "bottom")
ggsave("finalREResult.png",finalREResultPlot,
type = "cairo-png", dpi = 400, height = 4, width = 4, scale = 3)
finalModelRE_predict <- finalModelData %>%
rename("ShannonEvenness" = `Shannon\nEvenness`) %>%
select(!c(FDis:FRic,fid,L2_KEY)) %>%
summarize(across(where(is.numeric),~runif(20000, min(.x),max(.x))),
across(c(Ecoregion),~sample(unique(.x),20000,T))) %>%
mutate(rid = row_number()) %>%
rowwise() %>%
summarize(
across(everything(),~rep(.x,2)),
type = factor(c("Abundance","Presence/Absence"))
) %>%
mutate(FDis = mgcv::predict.bam(finalREModels$FDis,.),
FDiv = mgcv::predict.bam(finalREModels$FDiv,.),
FEve = mgcv::predict.bam(finalREModels$FEve,.)) %>%
group_by(rid) %>%
summarize(
across(FDis:FEve, ~(.x[1] - .x[2])/.x[1]),
across(c(Richness,ShannonEvenness),first)
)
finalModelRE_predict %>%
pivot_longer(FDis:FEve,names_to="Index") %>%
ggplot(aes(Richness,ShannonEvenness,z=value)) +
stat_summary_2d(aes(color = after_scale(fill))) +
scale_fill_viridis_c() +
coord_cartesian(expand = F) +
facet_wrap(~Index)
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