.Rhistory

# histograms
hist(distdata$distance,main="",xlab="Distance (m)")
hist(distdata$size,main="",xlab="Cluster size")
# plots of distance vs. cluster size
plot(distdata$distance, distdata$size, main="", xlab="Distance (m)",
ylab="Group size", pch=19, cex=0.5, col=gray(0.7))
# lm fit
l.dat <- data.frame(distance=seq(0,8000,len=1000))
lo <- lm(size~distance, data=distdata)
lines(l.dat$distance, as.vector(predict(lo,l.dat)))
plot(distdata$distance,distdata$beaufort, main="", xlab="Distance (m)",
ylab="Beaufort sea state", pch=19, cex=0.5, col=gray(0.7))
# Chunk 17: loadDistance
library(Distance)
# Chunk 18: hrmodel
detfc.hr.null <- ds(distdata, max(distdata$distance), key="hr", adjustment=NULL)
# Chunk 19: hrmodelsummary
summary(detfc.hr.null)
# Chunk 20: hr-detfct
layout(matrix(c(1, 2), 1, 2), width=c(1.5, 1))
plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2)
gof_ds(detfc.hr.null)
par(mfrow=c(1,1))
# Chunk 21: hrcovdf
detfc.hr.beau<-ds(distdata, max(distdata$distance), formula=~as.factor(beaufort),
key="hr", adjustment=NULL)
# Chunk 22: hrcovdfsummary
summary(detfc.hr.beau)
# Chunk 23: dsm-xy
dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method="REML")
# Chunk 24: dsm-xy-summary
summary(dsm.xy)
# Chunk 25: visgam1
vis.gam(dsm.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", contour.col="black", n.grid=100)
# Chunk 26: depthmodel
dsm.xy.depth <- dsm(count~s(x,y,k=10) + s(depth,k=20), detfc.hr.null, segdata, obsdata, method="REML")
summary(dsm.xy.depth)
# Chunk 27: dsm-xy-depth-depth
plot(dsm.xy.depth, select=2)
# Chunk 28: dsm-est-xy
dsm.est.xy <- dsm(abundance.est~s(x,y), detfc.hr.beau, segdata, obsdata, method="REML")
# Chunk 29: dsm-est-xy-summary
summary(dsm.est.xy)
# Chunk 30: visgam5
vis.gam(dsm.est.xy, plot.type="contour", view=c("x","y"), asp=1, type="response", zlim=c(0, 300), contour.col="black", n.grid=100)
# Chunk 31: tweedie-fit
dsm.xy.tweedie <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, family=tw(), method="REML")
summary(dsm.xy.tweedie)
# Chunk 32: soap-knots
soap.knots <- make.soapgrid(survey.area,c(15,10))
# Chunk 33: soap-setup
x <- segdata$x; y<-segdata$y
onoff <- inSide(x=x,y=y, bnd=as.list(survey.area))
rm(x,y)
segdata.soap <- segdata[onoff,]
# Chunk 34: soap-fit
dsm.xy.tweedie.soap<-dsm(count~s(x, y, bs="so", k=15, xt=list(bnd=list(survey.area))) +
s(depth),
family=tw(), method="REML",
detfc.hr.null, segdata.soap, obsdata, knots=soap.knots)
summary(dsm.xy.tweedie.soap)
# Chunk 35: dsm.xy-check
gam.check(dsm.xy)
# Chunk 36: dsm.xy.tweedie-check
gam.check(dsm.xy.tweedie)
# Chunk 37: dsm.xy.tweedie-rqcheck
rqgam_check(dsm.xy.tweedie)
# Chunk 38: modelcomp
# make a data.frame to print out
mod_results <- data.frame("Model name" = c("`dsm.xy`", "`dsm.xy.depth`", "`dsm.xy.tweedie`", "`dsm.xy.tweedie.soap`",
"`dsm.est.xy`"),
"Description" = c("Bivariate smooth of location, quasipoisson",
"Bivariate smooth of location, smooth of depth, quasipoisson",
"Bivariate smooth of location, smooth of depth, Tweedie",
"Soap film smooth of location, smooth of depth, Tweedie",
"Bivariate smooth of location, smooth of depth, Tweedie, Beaufort covariate in detection function"),
"Deviance explained" = c(unlist(lapply(list(dsm.xy,
dsm.xy.depth,
dsm.xy.tweedie,
dsm.xy.tweedie.soap),
function(x){paste0(round(summary(x)$dev.expl*100,2),"%")})),NA))
# Chunk 39: results-table
kable(mod_results, col.names=c("Model name", "Description", "Deviance explained"))
# Chunk 40: dsm-xy-pred
dsm.xy.pred <- predict(dsm.xy, preddata, preddata$area)
# Chunk 41: dsm.xy-preds
# p <- ggplot() + grid_plot_obj(dsm.xy.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
# p <- p + geom_path(aes(x=x, y=y),data=survey.area)
# p <- p + labs(fill="Abundance")
# print(p)
prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
preddata_sf$Prediction_xy <- dsm.xy.pred
pred <- ggplot() +
geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +
geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
subtitle = "Bivariate smooth of location, quasipoisson") +
scale_fill_viridis_c(option = "viridis", direction = 1)
pred
# p <- ggplot() + grid_plot_obj(dsm.xy.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
# p <- p + geom_path(aes(x=x, y=y),data=survey.area)
# p <- p + labs(fill="Abundance")
# print(p)
prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
preddata_sf$Prediction_xy <- dsm.xy.pred
cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
pred <- ggplot() +
geom_sf(data = cropped_grid, aes(fill = Prediction_xy), color = NA) +
geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
subtitle = "Bivariate smooth of location, quasipoisson") +
scale_fill_viridis_c(option = "viridis", direction = 1)
pred
dsm.xy.depth.pred <- predict(dsm.xy.depth, preddata, preddata$area)
preddata_sf$Prediction_xy_depth <- dsm.xy.depth.pred
cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
pred <- ggplot() +
geom_sf(data = cropped_grid, aes(fill = Prediction_xy_depth), color = NA) +
geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
labs(title="Spotted dolphins, Gulf of Mexico, abundance estimates",
subtitle = "Bivariate smooth of location, smooth of depth, quasipoisson") +
scale_fill_viridis_c(option = "viridis", direction = 1)
pred
# p <- ggplot() + grid_plot_obj(dsm.xy.depth.pred, "Abundance", pred.polys) + coord_equal() +gg.opts
# p <- p + geom_path(aes(x=x, y=y), data=survey.area)
# p <- p + labs(fill="Abundance")
# print(p)
preddata.var <- split(preddata, 1:nrow(preddata))
dsm.xy.var <- dsm_var_gam(dsm.xy, pred.data=preddata.var,
off.set=preddata$area)
summary(dsm.xy.var)
# p <- ggplot() + grid_plot_obj(sqrt(dsm.xy.var$pred.var)/unlist(dsm.xy.var$pred),
#                              "CV", pred.polys) + coord_equal() +gg.opts
# p <- p + geom_path(aes(x=x, y=y), data=survey.area)
# p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
#print(p)
preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction
# p <- ggplot() + grid_plot_obj(sqrt(dsm.xy.var$pred.var)/unlist(dsm.xy.var$pred),
#                              "CV", pred.polys) + coord_equal() +gg.opts
# p <- p + geom_path(aes(x=x, y=y), data=survey.area)
# p <- p + geom_line(aes(x, y, group=Transect.Label), data=segdata)
#print(p)
preddata_sf$CV <- sqrt(dsm.xy.var$pred.var)/preddata_sf$Prediction_xy
cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
CV <- ggplot() +
geom_sf(data = cropped_grid, aes(fill = CV), color = NA) +
geom_sf(data=segdata_sf, fill=NA, color="white", linewidth=.001) +
labs(title="Spotted dolphins, Gulf of Mexico, uncertainty (CV)",
subtitle = "Bivariate smooth of location, quasipoisson") +
scale_fill_viridis_c(option = "viridis", direction = 1)
CV
?fig.dim
data(mexdolphins)
load("mexdolphins-extra.rda")
predsf <- st_as_sf(pred.polys)
# plot as projected
plot(pred.polys, xlab="Northing", ylab="Easting")
axis(1); axis(2); box()
predsf <- st_as_sf(pred.polys)
# plot as projected
plot(st_geometry(pred.polys), axes=TRUE)
predsf <- st_as_sf(pred.polys)
# plot as projected
plot(st_geometry(predsf), axes=TRUE)
axis(1); axis(2); box()
library(sf)
library(plyr)
# tell R that the survey.area object is currently in lat/long
sp::proj4string(survey.area) <- sp::CRS("+proj=longlat +datum=WGS84")
predsf <- st_as_sf(pred.polys)
area.sf <- st_as_sf(survey.area)
st_crs(area.sf) <- "WGS84"
area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))
# Convert preddata to a spatial object
preddata_sf <- st_as_sf(preddata, coords=c("x", "y"))
st_crs(preddata_sf) <- st_crs(area.sf.proj)
# Perform the intersection
preddata_sf <- st_intersection(preddata_sf, area.sf.proj)
coords_preddata <- data.frame(st_coordinates(preddata_sf))
preddata_sf$x <- coords_preddata$X
preddata_sf$y <- coords_preddata$Y
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Gulf of Mexico study area",
subtitle = "Depth in meters") +
scale_fill_viridis_c(option = "viridis", direction = 1)
cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Gulf of Mexico study area",
subtitle = "Depth in meters") +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Gulf of Mexico study area",
subtitle = "Depth in meters") +
geom_point(aes(x, y, size=size), data=distdata) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth
View(distdata)
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Spotted dolphins, Gulf of Mexico",
subtitle = "Depth in meters, size of detected dolphin groups") +
geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.7)) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Spotted dolphins, Gulf of Mexico",
subtitle = "Depth in meters, size of detected dolphin groups") +
geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Spotted dolphins, Gulf of Mexico",
subtitle = "Depth in meters, size of detected dolphin groups",
xlab("Longitude"), ylab=("Latitude")) +
geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Spotted dolphins, Gulf of Mexico",
subtitle = "Depth in meters, size of detected dolphin groups") +
xlab("Longitude") + ylab=("Latitude") +
geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Spotted dolphins, Gulf of Mexico",
subtitle = "Depth in meters, size of detected dolphin groups") +
xlab("Longitude") + ylab=("Latitude") +
geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Spotted dolphins, Gulf of Mexico",
subtitle = "Depth in meters, size of detected dolphin groups") +
xlab("Longitude") + ylab("Latitude") +
geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth
setwd("~/GitHub/dsm/vignettes/points_france")
shape <- st_read("Contour_Rouillacais.shp")
plot(shape)
# provide the correct projection for the data
newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
bob <- st_transform(shape, newproj)
bob <- st_transform(shape, crs=newproj)
CRS(shape) <- 4326
bob <- st_transform(x=shape, src=4326, dst=newproj)
shape <- st_read("Contour_Rouillacais.shp")
sp::CRS(shape) <- 4326
sp:::CRS(shape) <- 4326
?CRS
sp::crs(shape) <- 4326
st_crs(shape)
st_transform(shape, crs=4326)
shape <- st_read("Contour_Rouillacais.shp", crs=st_crs(4326))
shape <- st_read("Contour_Rouillacais.shp", crs=st_crs(27562))
EPP <- readShapeSpatial("Rouillacais_points.shp", crs=st_crs(27562))
# make the object simpler
survey.area <- data.frame(shape@polygons[[1]]@Polygons[[1]]@coords)
library(sfheaders)
install.packages("sfheaders")
library(sfheaders)
survey.area <- sf_to_df(shape)
study <- ggplot() +
geom_sf(data=shape, fill="lightblue") +
geom_sf(data=EPP, color="black") +
theme_minimal()
EPP <- st_read("Rouillacais_points.shp", crs=st_crs(27562))
study <- ggplot() +
geom_sf(data=shape, fill="lightblue") +
geom_sf(data=EPP, color="black") +
theme_minimal()
print(study)
# load raw data
data <- read.table("Hare_data.csv", header = TRUE, sep = ";", stringsAsFactors=FALSE)
str(data)
DSdata <- data.frame(Sample.Label = sub("Rouillacais_2016", "", data$point_ID),
Point = data$point_ID,
Xcoord = as.integer(data$Xcoord),
Ycoord = as.integer(data$Ycoord),
Area = 1)
DSdata$distance <- as.numeric(data$distance)/1000
DSdata$Effort <- 3
shape$AREA/(1000^2)
# construct segment (point) data (x, y, Effort, Sample.Label)
segdata <- as.data.frame(matrix(NA, ncol = 5, nrow=100))
segdata <- DSdata[, c("Sample.Label", "Effort", "Point", "Xcoord", "Ycoord")]
segdata <- segdata[!duplicated(segdata), ]
colnames(segdata) <- c("Sample.Label", "Effort", "Segment.Label", "X", "Y")
obsdata <- DSdata
obsdata$size <- 1
obsdata$object <- 1:nrow(obsdata)
str(obsdata)
prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
hmm <- st_intersection(prediction_grid_sf, shape)
plot(hmm)
plot(prediction_grid_sf)
library("Distance")
# define distance bins
cut <- c(0, 0.180, 0.220, 0.280, 0.300)
df_ht <- ds(DSdata, truncation=0.3, transect="point",
formula=~1, key="hn", adjustment=NULL, cutpoints=cut)
mod_tw <- dsm(count~s(X, Y), ddf.obj=df_ht, segment.data=segdata,
observation.data=obsdata, family=tw(), transect="point")
mod_tw_pred <- predict(mod_tw, preddata, preddata$area)
str(hmm)
preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
colnames(preddata) <- c("X", "Y", "area")
head(hmm$geometry)
head(hmm$geometry)[[1]]
701*24
hmm$geometry[[1]][[1:4]]
hmm$geometry[[1]]
tom <- hmm$geometry[[1]]
str(tom)
tom[[1]][1:4, 1:2]
mean(tom[[1]][1:4, 1])
mean(tom[[1]][1:4, 2])
for (i in 1:length(hmm$geometry)){
preddata[i, c("X")] <- mean(hmm$geometry[[i]][1:4, 1])
preddata[i, c("Y")] <- mean(hmm$geometry[[i]][1:4, 2])
preddata[i, c("area")] <- pred.grid$AREA[[i]]/(1000^2)
}
hmm$geometry[[22]]
hmm$geometry[[22]][1:4, 1]
hmm$geometry[[22]][[1]][1:4, 1]
prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
hmm <- st_intersection(prediction_grid_sf, shape)
preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
colnames(preddata) <- c("X", "Y", "area")
for (i in 1:length(hmm$geometry)){
preddata[i, c("X")] <- mean(hmm$geometry[[i]][[1]][1:4, 1])
preddata[i, c("Y")] <- mean(hmm$geometry[[i]][[1]][1:4, 2])
preddata[i, c("area")] <- pred.grid$AREA[[i]]/(1000^2)
}
prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
hmm <- st_intersection(prediction_grid_sf, shape)
preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
colnames(preddata) <- c("X", "Y", "area")
for (i in 1:length(hmm$geometry)){
preddata[i, c("X")] <- mean(hmm$geometry[[i]][[1]][1:4, 1])
preddata[i, c("Y")] <- mean(hmm$geometry[[i]][[1]][1:4, 2])
preddata[i, c("area")] <- hmm$AREA[[i]]/(1000^2)
}
hmm$geometry[[23]][[1]][1:4,1]
hmm$geometry[[23]][[1]][1:4,2]
hmm$geometry[[700]][[1]][1:4,2]
hmm$geometry[[500]][[1]][1:4,2]
hmm$geometry[[600]][[1]][1:4,2]
hmm$geometry[[700]][[1]][1:4,2]
hmm$geometry[[650]][[1]][1:4,2]
hmm$geometry[[680]][[1]][1:4,2]
hmm$geometry[[690]][[1]][1:4,2]
hmm$geometry[[699]][[1]][1:4,2]
hmm$AREA[600]
hmm$AREA[700]
hmm$AREA[701]
length(hmm$geometry)
# create a prediction grid
# method from http://rfunctions.blogspot.co.uk/2014/12/how-to-create-grid-and-intersect-it.html
library("raster")
library("rgeos")
library("dismo")
# Create an empty raster
grid <- raster(extent(shape))
# Choose its resolution. 500 m in both X and Y (truncation distance)
res(grid) <- 500
# Make the grid have the same coordinate reference system (CRS) as the shapefile.
proj4string(grid) <- proj4string(shape)
area.sf <- st_as_sf(survey.area)
# provide the correct projection for the data
newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
# import shapefile for the survey area
shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
repair=TRUE, force_ring=T, verbose=TRUE)
library(sp)
# provide the correct projection for the data
newproj <- "+proj=lcc +nadgrids=ntf_r93.gsb,null +a=6378249.2000 +rf=293.4660210000000  +pm=2.337229167 +lat_0=46.800000000 +lon_0=0.000000000 +k_0=0.99987742 +lat_1=46.800000000 +x_0=600000.000 +y_0=200000.000 +units=m +no_defs"
# import shapefile for the survey area
shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
repair=TRUE, force_ring=T, verbose=TRUE)
# import shapefile for the points
EPP <- readShapeSpatial("Rouillacais_points.shp", proj4string = CRS(newproj),
repair=TRUE, force_ring=T, verbose=TRUE)
?readShapeSpatial
??readShapeSpatial
prediction_grid <- st_make_grid(shape, cellsize = c(500,500))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
hmm <- st_intersection(prediction_grid_sf, shape)
preddata <- as.data.frame(matrix(NA, ncol=3, nrow=length(hmm$geometry)))
colnames(preddata) <- c("X", "Y", "area")
for (i in 1:length(hmm$geometry)){
preddata[i, c("X")] <- mean(hmm$geometry[[i]][[1]][1:4, 1])
preddata[i, c("Y")] <- mean(hmm$geometry[[i]][[1]][1:4, 2])
preddata[i, c("area")] <- hmm$AREA[[i]]/(1000^2)
}
# create a prediction grid
# method from http://rfunctions.blogspot.co.uk/2014/12/how-to-create-grid-and-intersect-it.html
library("raster")
library("rgeos")
library("dismo")
# Create an empty raster
grid <- raster(extent(shape))
# Choose its resolution. 500 m in both X and Y (truncation distance)
res(grid) <- 500
# Make the grid have the same coordinate reference system (CRS) as the shapefile.
proj4string(grid) <- proj4string(shape)
# import shapefile for the survey area
shape <- readShapeSpatial("Contour_Rouillacais.shp", proj4string = CRS(newproj),
repair=TRUE, force_ring=T, verbose=TRUE)
library(maptools)
pkgdown::build_site()
pkgdown::build_news()
getwd()
setwd("~/GitHub/dsm")
pkgdown::build_news()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
pkgdown::build_site()
library(dsm)
library(ggplot2)
# plotting options
gg.opts <- theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.background=element_blank())
load("mexdolphins-extra.rda")
data(mexdolphins)
data(mexdolphins)
setwd("~/GitHub/dsm/vignettes/lines_gomex")
load("mexdolphins-extra.rda")
library(sf)
library(plyr)
# tell R that the survey.area object is currently in lat/long
sp::proj4string(survey.area) <- sp::CRS("+proj=longlat +datum=WGS84")
predsf <- st_as_sf(pred.polys)
area.sf <- st_as_sf(survey.area)
st_crs(area.sf) <- "WGS84"
area.sf.proj <- st_transform(area.sf, crs = st_crs(predsf))
# Convert preddata to a spatial object
preddata_sf <- st_as_sf(preddata, coords=c("x", "y"))
st_crs(preddata_sf) <- st_crs(area.sf.proj)
# Perform the intersection
preddata_sf <- st_intersection(preddata_sf, area.sf.proj)
coords_preddata <- data.frame(st_coordinates(preddata_sf))
preddata_sf$x <- coords_preddata$X
preddata_sf$y <- coords_preddata$Y
# proj 4 string
# using http://spatialreference.org/ref/esri/north-america-lambert-conformal-conic/
lcc_proj4 <- sp::CRS("+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
# project using LCC
survey.area <- sp::spTransform(survey.area, CRSobj=lcc_proj4)
# simplify the object
survey.area <- data.frame(survey.area@polygons[[1]]@Polygons[[1]]@coords)
names(survey.area) <- c("x", "y")
prediction_grid <- st_make_grid(area.sf.proj, cellsize = c(9000,9000))
prediction_grid_sf <- st_sf(geometry = prediction_grid)
cropped_grid <- st_join(prediction_grid_sf, preddata_sf, join = st_nearest_feature)
cropped_grid <- st_intersection(cropped_grid, area.sf.proj)
depth <- ggplot() +
geom_sf(data=cropped_grid, aes(fill=depth), color=NA) +
labs(title = "Spotted dolphins, Gulf of Mexico",
subtitle = "Depth in meters, size of detected dolphin groups") +
xlab("Longitude") + ylab("Latitude") +
geom_point(aes(x, y, size=size), data=distdata, colour="red",alpha=I(0.5)) +
scale_fill_viridis_c(option = "viridis", direction = 1)
depth
library(Distance)
detfc.hr.null <- ds(distdata, max(distdata$distance), key="hr", adjustment=NULL)
plot(detfc.hr.null, showpoints=FALSE, pl.den=0, lwd=2)
gof_ds(detfc.hr.null)
dsm.xy <- dsm(count~s(x,y), detfc.hr.null, segdata, obsdata, method="REML")
summary(dsm.xy)
unique(detfc.hr.null$ddf$fitted)
library(pkgdown)
build_site()
library(pkgdown)
build_site()
build_site()
build_site()
build_site()
library(pkgdown)
build_site()
build_home_index()
build_site()