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How should I deal with "'someFunction' is not an exported object from 'namespace:somePackage'" error? [closed]
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Closed 2 years ago.
I have some code showing this error, but, I haven't called "overlay", maybe it's a library function that is calling it
Code:
d.mle=likfit(P, ini.cov.pars = c(1,30), cov.model = 'matern', kappa = 0.5)
d.mle
Xb = c(1, size, size, 1)
Yb = c(1, 1, size, size)
bordas = cbind(Xb, Yb)
polygon(bordas)
Ap = matrix(apply(bordas, 2, range))
gr <- expand.grid(x = seq(Ap[1, ], Ap[2, ], by = 1), y = seq(Ap[3, ], Ap[4, ], by = 1))
require(splancs)
gi <- polygrid(gr, borders = bordas) # delimita a area para interpolação
points(gi, pch = "+", col = 2)
KC = krige.control(obj = d.mle, type.krige = "ok", lam = 1)
d.k = krige.conv(P, loc = gr, krige = KC) #Realiza a interpolação por krigagem
valores_preditos = d.k$predict
Ze = matrix(valores_preditos, size, size) # Transforma os valores preditos em matriz
plot(Ze)
plot(image(X, Y, Ze, col = gray((0 : 4) / 4), breaks = c(a., b., c., d., e., f.)))
If you do this:
??overlay
... you should get a list of all the functions in packages that mention the word "overlay". When I do it, I see two functions with that name but I strongly suspect that it is the raster-package's version that is expected by the code you are using. So do this:
install.packages('raster')
library(raster)
#re-run code
Related
I am generating a landscape pattern that evolves over time. The problem with the code is that I have clearly defined a window for the object bringing up the error but the window is not being recognised. I also do not see how any points are falling outside of the window, or how that would make a difference.
library(spatstat)
library(dplyr)
# Define the window
win <- owin(c(0, 100), c(0, 100))
# Define the point cluster
cluster1 <- rMatClust(kappa = 0.0005, scale = 0.1, mu = 20,
win = win, center = c(5,5))
# define the spread of the points
spread_rate <- 1
new_nests_per_year<-5
years<-10
# Plot the initial cluster
plot(win, main = "Initial cluster")
points(cluster1, pch = 20, col = "red")
newpoints<-list()
# Loop for n years
for (i in 1:years) {
# Generate new points that spread from the cluster
newpoints[[1]] <-rnorm(new_nests_per_year, mean = centroid.owin(cluster1)$y, sd = spread_rate)
newpoints[[2]] <-rnorm(new_nests_per_year, mean = centroid.owin(cluster1)$x, sd = spread_rate)
# Convert the list to a data frame
newpoints_df <- data.frame(newpoints)
# Rename the columns of the data frame
colnames(newpoints_df) <- c("x", "y")
# Combine the new points with the existing points
cluster1_df <- data.frame(cluster1)
newtotaldf<-bind_rows(cluster1_df,newpoints_df)
cluster1<-as.ppp(newtotaldf, x = newtotaldf$x, y = newtotaldf$y,
window = win)
# Plot the updated cluster
plot(win, main = paste("Cluster after year", i))
points(cluster1, pch = 20, col = "red")
}
However, when I run line:
cluster1<-as.ppp(newtotaldf, x = newtotaldf$x, y = newtotaldf$y,
window = win)
I recieve the error:
Error: x,y coords given but no window specified
Why would this be the case?
In your code, if you use the command W = win it should solve the issue. I also believe you can simplify the command without specifying x and y:
## ...[previous code]...
cluster1 <- as.ppp(newtotaldf, W = win)
plot(win)
points(cluster1, pch = 20, col = "red")
I want to identify 3d cylinders in an rgl plot to obtain one attribute of the nearest / selected cylinder. I tried using labels to simply spell out the attribute, but I work on data with more than 10.000 cylinders. Therefore, it gets so crowded that the labels are unreadable and it takes ages to render.
I tried to understand the documentation of rgl and I guess the solution to my issue is selecting the cylinder in the plot manually. I believe the function selectpoints3d() is probably the way to go. I believe it returns all vertices within the drawn rectangle, but I don't know how to go back to the cylinder data? I could calculate which cylinder is closest to the mean of the selected vertices, but this seems like a "quick & dirty" way to do the job.
Is there a better way to go? I noticed the argument value=FALSE to get the indices only, but I don't know how to go back to the cylinders.
Here is some dummy data and my code:
# dummy data
cylinder <- data.frame(
start_X = rep(1:3, 2)*2,
start_Y = rep(1:2, each = 3)*2,
start_Z = 0,
end_X = rep(1:3, 2)*2 + round(runif(6, -1, 1), 2),
end_Y = rep(1:2, each = 3)*2 + round(runif(6, -1, 1), 2),
end_Z = 0.5,
radius = 0.25,
attribute = sample(letters[1:6], 6)
)
# calculate centers
cylinder$center_X <- rowMeans(cylinder[,c("start_X", "end_X")])
cylinder$center_Y <- rowMeans(cylinder[,c("start_Y", "end_Y")])
cylinder$center_Z <- rowMeans(cylinder[,c("start_Z", "end_Z")])
# create cylinders
cylinder_list <- list()
for (i in 1:nrow(cylinder)) {
cylinder_list[[i]] <- cylinder3d(
center = cbind(
c(cylinder$start_X[i], cylinder$end_X[i]),
c(cylinder$start_Y[i], cylinder$end_Y[i]),
c(cylinder$start_Z[i], cylinder$end_Z[i])),
radius = cylinder$radius[i],
closed = -2)
}
# plot cylinders
open3d()
par3d()
shade3d(shapelist3d(cylinder_list, plot = FALSE), col = "blue")
text3d(cylinder$center_X+0.5, cylinder$center_Y+0.5, cylinder$center_Z+0.5, cylinder$attribute, color="red")
# get attribute
nearby <- selectpoints3d(value=TRUE, button = "right")
nearby <- colMeans(nearby)
cylinder$dist <- sqrt(
(nearby["x"]-cylinder$center_X)**2 +
(nearby["y"]-cylinder$center_Y)**2 +
(nearby["z"]-cylinder$center_Z)**2)
cylinder$attribute[which.min(cylinder$dist)]
If you call selectpoints3d(value = FALSE), you get two columns. The first column is the id of the object that was found. Your cylinders get two ids each. One way to mark the cylinders is to use "tags". For example, this modification of your code:
# dummy data
cylinder <- data.frame(
start_X = rep(1:3, 2)*2,
start_Y = rep(1:2, each = 3)*2,
start_Z = 0,
end_X = rep(1:3, 2)*2 + round(runif(6, -1, 1), 2),
end_Y = rep(1:2, each = 3)*2 + round(runif(6, -1, 1), 2),
end_Z = 0.5,
radius = 0.25,
attribute = sample(letters[1:6], 6)
)
# calculate centers
cylinder$center_X <- rowMeans(cylinder[,c("start_X", "end_X")])
cylinder$center_Y <- rowMeans(cylinder[,c("start_Y", "end_Y")])
cylinder$center_Z <- rowMeans(cylinder[,c("start_Z", "end_Z")])
# create cylinders
cylinder_list <- list()
for (i in 1:nrow(cylinder)) {
cylinder_list[[i]] <- cylinder3d(
center = cbind(
c(cylinder$start_X[i], cylinder$end_X[i]),
c(cylinder$start_Y[i], cylinder$end_Y[i]),
c(cylinder$start_Z[i], cylinder$end_Z[i])),
radius = cylinder$radius[i],
closed = -2)
# Add tag here:
cylinder_list[[i]]$material$tag <- cylinder$attribute[i]
}
# plot cylinders
open3d()
par3d()
shade3d(shapelist3d(cylinder_list, plot = FALSE), col = "blue")
text3d(cylinder$center_X+0.5, cylinder$center_Y+0.5, cylinder$center_Z+0.5, cylinder$attribute, color="red")
# Don't get values, get the ids
nearby <- selectpoints3d(value=FALSE, button = "right", closest = FALSE)
ids <- nearby[, "id"]
# Convert them to tags. If you select one of the labels, you'll get
# a blank in the list of tags, because we didn't tag the text.
unique(tagged3d(id = ids))
When I was trying this, I found that using closest = TRUE in selectpoints3d seemed to get too many ids; there may be a bug there.
I created a Sankey diagram using the plotly package.
Please look at below example. I tried to make five streams, 1_6_7, 2_6_7, and so on. But two of five links between 6 and 7 disappeared. As far as I see, plotly allows to make only three or less links between two nodes.
Can I remove this restrictions ? Any help would be greatly appreciated.
Here is an example code and the outputs:
d <- expand.grid(1:5, 6, 7)
node_label <- 1:max(d)
node_colour <- scales::alpha(RColorBrewer::brewer.pal(7, "Set2"), 0.8)
link_source_nodeind <- c(d[,1], d[,2]) - 1
link_target_nodeind <- c(d[,2], d[,3]) - 1
link_value <- rep(100, nrow(d) * 2)
link_label <- rep(paste(d[,1], d[,2], d[,3], sep = "_"), 2)
link_colour <- rep(scales::alpha(RColorBrewer::brewer.pal(5, "Set2"), 0.2), 2)
p <- plotly::plot_ly(type = "sankey",
domain = c(x = c(0,1), y = c(0,1)),
orientation = "h",
node = list(label = node_label,
color = node_colour),
link = list(source = link_source_nodeind,
target = link_target_nodeind,
value = link_value,
label = link_label,
color = link_colour))
p
so I am in dire need of help. I have finally managed to construct my R-INLA model and get it to graph as needed. via the code below:
First I create the stacks (note this is the very end of my INLA process, the mesh etc has already been done)
stk.abdu = inla.stack(data = list(y = 1, e = 0), A = list(abdu.mat, 1),tag = 'abdu', effects = list(list(i = 1:sc.mesh.5$n), data.frame(Intercept = 1,dwater=winter.abdu$dwater,elev=winter.abdu$elev,forest=winter.abdu$forest,developed=winter.abdu$developed,openwater=winter.abdu$OpenWater,barren=winter.abdu$barren,shrubland=winter.abdu$shrubland,herb=winter.abdu$herb,planted=winter.abdu$planted,wetland=winter.abdu$wetland,dist=winter.abdu$dwater)))
stk.quad = inla.stack(data = list(y = 0, e = 0.1), A = list(quad.mat, 1),tag = 'quad', effects = list(list(i = 1:sc.mesh.5$n), data.frame(Intercept = 1,dwater=dummy$dwater,elev=dummy$elev,forest=dummy$forest,developed=dummy$developed,openwater=dummy$openwater,barren=dummy$barren,shrubland=dummy$shrubland,herb=dummy$herb,planted=dummy$planted,wetland=dummy$wetland,dist=dummy$dwater)))
stk.prd<-inla.stack(data = list(y = NA), A = list(Aprd, 1),tag = 'prd', effects = list(list(i = 1:sc.mesh.5$n), data.frame(Intercept = 1,dwater=prddf2$dwater,elev=prddf2$elev,forest=prddf2$forest,developed=prddf2$developed,openwater=prddf2$openwater,barren=prddf2$barren,shrubland=prddf2$shrubland,herb=prddf2$herb,planted=prddf2$planted,wetland=prddf2$wetland,dist=prddf2$dwater)))
stk.all.prd = inla.stack(stk.abdu,stk.quad,stk.prd)
Next I fit my model
ft.inla.prd<-inla(y ~ 0 + Intercept + elev + dwater + forest+ developed + f(inla.group(dist,n=50,method="quantile"),model="rw1",scale.model=TRUE)+f(i,model=sc.spde),family="binomial",data=inla.stack.data(stk.all.prd),control.predictor = list(A = inla.stack.A(stk.all.prd),compute=TRUE),E=inla.stack.data(stk.all.prd)$e,control.compute=list(dic = TRUE),control.fixed=list(expand.factor.strategy="INLA"))
Then I change the predicted values from logit to probabilities
ft.inla.prd$newfield <- exp(ft.inla.prd$summary.random$i$mean)/(1 + exp(ft.inla.prd$summary.random$i$mean))
And finally I use inla.mesh.project and levelplot to create my image
xmean <- inla.mesh.project(projgrid,ft.inla.prd$newfield)
levelplot(xmean, col.regions=topo.colors(99), main='Probability of Presence',xlab='', ylab='', scales=list(draw=FALSE))
So my problem is that I now want to export this data (what is projected as the graph) as a raster so that I can work with it in ArcGIS. However, I have not been able to find a way to do so.
Any input is greatly appreciated
I am developing an interactive scatterplot so that when the user rolls over a data point, a label is displayed. However, I would also like to add edges between certain data points.
I am successful at developing the interactive scatterplot using several libraries, including grid, gridSVG, lattice, and adegraphics. Below is a MWE:
library(grid)
library(gridSVG)
library(lattice)
library(adegraphics)
x = rnorm(10)
y = rnorm(10)
dat = data.frame(label = letters[1:10], x, y)
customPanel2 <- function(x, y, ...) {
for (j in 1:nrow(dat)) {
grid.circle(x[j], y[j], r = unit(.5, "mm"),
default.unit = "native",
name = paste("point", j, sep = "."))
}
}
xyplot(y ~ x, panel = customPanel2, xlab = "x variable", ylab=NULL, scales=list(tck = c(1,0), y=list(at=NULL)))
for (i in 1:nrow(dat)) {
grid.text(as.character(dat$label)[i], x = 0.1, y = 0.01, just = c("left", "bottom"), name = paste("label", i, sep = "."), gp = gpar(fontface = "bold.italic"))
}
for (i in 1:nrow(dat)) {
grid.garnish(paste("point", i, sep = "."), onmouseover = paste('highlight("', i, '.1.1")', sep = ""), onmouseout = paste('dim("', i, '.1.1")', sep = ""))
grid.garnish(paste("label", i, sep = "."), visibility = "hidden")
}
grid.script(filename = "aqm.js", inline = TRUE)
grid.export("interactiveScat.svg")
The resulting .svg file accomplishes everything I am aiming for - except that I also wish to add certain non-interactive edges. I tried to do this by incorporating the adeg.panel.edges method from the adegraphics library after defining the edges and the coordinates to be mapped. So, basically my xplot(...) function from before is replaced with:
edges = matrix(c(1, 2, 3, 2, 4, 1, 3, 4), byrow = TRUE, ncol = 2)
coords <- matrix(c(x[1], y[1], x[2], y[2], x[3], y[3], x[4], y[4]), byrow = TRUE, ncol = 2)
xyplot(y ~ x, panel = function(customPanel2){adeg.panel.edges(edges, coords, lty = 1:4, cex = 5)}, xlab = "x variable", ylab=NULL, scales=list(tck = c(1,0), y=list(at=NULL)))
It seems that this simply erases the interactive scatterplot made from the original xyplot, and simply outputs the static edge and coordinate image.
I tried to follow the example as seen in (http://finzi.psych.upenn.edu/library/adegraphics/html/adeg.panel.nb.html). Specifically, this example:
edges <- matrix(c(1, 2, 3, 2, 4, 1, 3, 4), byrow = TRUE, ncol = 2)
coords <- matrix(c(0, 1, 1, 0, 0, -1, -1, 0), byrow = TRUE, ncol = 2)
xyplot(coords[,2] ~ coords[,1],
panel = function(...){adeg.panel.edges(edges, coords, lty = 1:4, cex = 5)})
I am a bit at a loss as to how to troubleshoot this problem, especially as I am mimicking the example code. Any suggestions are greatly appreciated!
If what you are trying to produce is a node-link diagram of a network an alternate solution is to coerce your data into a network object and use the ndtv package to generate svg/htmlwidget interactive plots for your network. The ndtv package is designed for dynamic networks, but will generate interactive plots for static nets as well.
library(ndtv)
data(emon) # load a list of example networks
render.d3movie(emon[[5]]) # render network 5 in the browser
Much more detail is in the tutorial http://statnet.csde.washington.edu/workshops/SUNBELT/current/ndtv/ndtv-d3_vignette.html
However, this does not use grid/lattice graphics at all