I am working on a project looking at how private docks are distributed along the coast with respect to a few demographic and landscape variables at the census block group level. Sample data and a .txt with descriptions of the the variable names can be found at (https://1drv.ms/u/s!AvVJV9wE-yCEiqQ-qZkfm2DVfXyb2w?e=cXNCTy) and code is below. I am trying to fit a CAR model using the spaMM package and have a working model that I can run a LRT on/get confidence intervals for my covariates, but when I try to plot marginal effects using spaMM’s built in plot_effects() function I get this error:
Error in .make_new_corr_lists(object = object, locdata = locdata, which_mats = which_mats, :
Found new levels for a 'adjacency' random effect.
I’m at a loss for what this is telling me and am unsure how to resolve this error as I’ve found nothing about this in spaMM vignettes or on any other help forums. Additionally, I recognize the plot_effects() function makes plots in base R and I was hoping to plot them using ggplot2. I’ve tried the ggeffects package and it doesn’t seem to recognize the spaMM model object – any suggestions on packages that can handle spaMM objects for plotting in ggplot2? Or will I need to do it by hand?
# Load packages/data and make neighbor list/weight matrix #
library(tidyverse)
library(spaMM)
library(rgdal)
library(rgeos)
library(sp)
library(sf)
library(spdep)
library(ggeffects)
load("Salt1KM.Rda") # This is a .Rda of my data
Salt1KM <- subset(Salt1KM, Block.Group != 252) # This removes one weird outlier
AllFiltersShape <- readOGR(dsn = getwd(), layer = "All_Filter_BGs") # Load the shapefile of block groups
Neighbors.QN <- poly2nb(AllFiltersShape, queen = TRUE, row.names = AllFiltersShape$BlockID, snap = 1) # Identify neighbors
Neighbors.QN # Check ouput
QN.adjMat.B <- nb2mat(Neighbors.QN, zero.policy = TRUE, style = "B") #Make binary weights matrix
# Model call and attempt to plot #
QN.B.Dock <- fitme(Dock ~ MHIE + PercentWE + PercentHomeE + Population.Density + Cutoff.Extend.Shore.Estuary.km + (1|Unique.Tract.ID) + adjacency(1|Block.Group), adjMatrix = QN.adjMat.B, data = Salt1KM, family = negbin())
summary(QN.B.Dock)
plot_effects(QN.B.Dock, focal_var = "PercentWE", add = TRUE) # This gives the error in the post
ggpredict(QN.B.Dock, terms = c("PercentWE", "MHIE")) # Attempt at using ggpredict, does not recognize the object at all
Related
I am using the statspat package because I am working on spatial patterns.
I would like to do in ggplot and with colors instead of numbers (because it is not too readable),
the following graph, produced with the plot.quadratest function: Polygone
The numbers that interest me for the intensity of the colors are those at the bottom of each box.
The test object contains the following data:
Test object
I have looked at the help of the function, as well as the code of the function but I still cannot manage it.
Ideally I would like my final figure to look like this (maybe not with the same colors haha):
Final object
Thanks in advance for your help.
Please provide a reproducible example in the future.
The package reprex may be very helpful.
To use ggplot2 for this my best bet would be to convert
spatstat objects to sf and do the plotting that way,
but it may take some time. If you are willing to use base
graphics and spatstat you could do something like:
library(spatstat)
# Data (using a built-in dataset):
X <- unmark(chorley)
plot(X, main = "")
# Test:
test <- quadrat.test(X, nx = 4)
# Default plot:
plot(test, main = "")
# Extract the the `quadratcount` object (regions with observed counts):
counts <- attr(test, "quadratcount")
# Convert to `tess` (raw regions with no numbers)
regions <- as.tess(counts)
# Add residuals as marks to the tessellation:
marks(regions) <- test$residuals
# Plot regions with marks as colors:
plot(regions, do.col = TRUE, main = "")
I have applied DBSCAN algorithm on built-in dataset iris in R. But I am getting error when tried to visualise the output using the plot( ).
Following is my code.
library(fpc)
library(dbscan)
data("iris")
head(iris,2)
data1 <- iris[,1:4]
head(data1,2)
set.seed(220)
db <- dbscan(data1,eps = 0.45,minPts = 5)
table(db$cluster,iris$Species)
plot(db,data1,main = 'DBSCAN')
Error: Error in axis(side = side, at = at, labels = labels, ...) :
invalid value specified for graphical parameter "pch"
How to rectify this error?
I have a suggestion below, but first I see two issues:
You're loading two packages, fpc and dbscan, both of which have different functions named dbscan(). This could create tricky bugs later (e.g. if you change the order in which you load the packages, different functions will be run).
It's not clear what you're trying to plot, either what the x- or y-axes should be or the type of plot. The function plot() generally takes a vector of values for the x-axis and another for the y-axis (although not always, consult ?plot), but here you're passing it a data.frame and a dbscan object, and it doesn't know how to handle it.
Here's one way of approaching it, using ggplot() to make a scatterplot, and dplyr for some convenience functions:
# load our packages
# note: only loading dbscacn, not loading fpc since we're not using it
library(dbscan)
library(ggplot2)
library(dplyr)
# run dbscan::dbscan() on the first four columns of iris
db <- dbscan::dbscan(iris[,1:4],eps = 0.45,minPts = 5)
# create a new data frame by binding the derived clusters to the original data
# this keeps our input and output in the same dataframe for ease of reference
data2 <- bind_cols(iris, cluster = factor(db$cluster))
# make a table to confirm it gives the same results as the original code
table(data2$cluster, data2$Species)
# using ggplot, make a point plot with "jitter" so each point is visible
# x-axis is species, y-axis is cluster, also coloured according to cluster
ggplot(data2) +
geom_point(mapping = aes(x=Species, y = cluster, colour = cluster),
position = "jitter") +
labs(title = "DBSCAN")
Here's the image it generates:
If you're looking for something else, please be more specific about what the final plot should look like.
I'm trying to perform Kernel density estimation in R using some GPS data that I have. My aim is to create a contoured output with each line representing 10% of the KDE. From here i want to import the output (as a shapefile or raster) into either QGIS or arcmap so I can overlay the output on top of existing environmental layers.
So far i have used AdehabitatHR to create the following output using the below code:
kud<-kernelUD(locs1[,1], h="href")
vud<-getvolumeUD(kud)
vud <- estUDm2spixdf(vud)
xyzv <- as.image.SpatialGridDataFrame(vud)
contoured<-contour(xyzv, add=TRUE)
Aside from being able to remove the colour, this is how i wish the output to appear (or near to). However i am struggling to figure out how i can export this as either a shapefile or raster? Any suggestions would be gratefully received.
With the amt package this should be relatively straightforward:
library(adehabitatHR)
library(sf)
library(amt)
data("puechabonsp")
relocs <- puechabonsp$relocs
hr <- as.data.frame(relocs) %>% make_track(X, Y, name = Name) %>%
hr_kde(trast = raster(amt::bbox(., buffer = 2000), res = 50)) %>%
hr_isopleths(level = seq(0.05, 0.95, 0.1))
# Use the sf package to write a shape file, or any other supported format
st_write(hr, "~/tmp/home_ranges.shp")
Note, it is also relatively easy to plot
library(ggplot2)
ggplot(hr) + geom_sf(fill = NA, aes(col = level))
I want to get the results of PC1 and PC2 to plot courbe of both in the same graph with tableau desktop.
How to do?
data = read.csv(file="data.csv",header=TRUE, sep=";")
data.active <- data[, 1:30]
library(factoextra)
res.pca <- prcomp(data.active,center = TRUE, scale. = TRUE)
fviz_eig(res.pca)
I think you need to write a csv with the results in between R and Tableau. The code for that is written bellow :
# Principal Components Analysis
res.pca <- stats::prcomp(iris[,-5],center = TRUE, scale. = TRUE)
# Choose number of dimension kept
factoextra::fviz_eig(res.pca)
# Some visualisation
factoextra::fviz_pca_var(res.pca)
factoextra::fviz_pca_ind(res.pca)
factoextra::fviz_pca_biplot(res.pca)
# access transformed points
str(res.pca)
res.pca$x
# save points in csv to use outside of R
utils::write.csv(x = res.pca$x, file = "path/data_pca.csv")
# Load your data and do graphs the usual way with tableau
I used ?prcomp to find the data in the result, you may also push further your analysis and use some nice graphics (biplots of individual / variable, clustering, ...) with R (and import only images in Tableau) using : link
I'm an R programming beginner and I'm trying to implement the clustering.plot method available in R package EMA. My clustering works fine and I can see the results populated as well. However, when I try to generate a heat map using clustering.plot, it gives me an error "Error in plot.new (): graphic edges too large". My code below,
#Loading library
library(EMA)
library(colonCA)
#Some information about the data
data(colonCA)
summary(colonCA)
class(colonCA) #Expression set
#Extract expression matrix from colonCA
expr_mat <- exprs(colonCA)
#Applying average linkage clustering on colonCA data using Pearson correlation
expr_genes <- genes.selection(expr_mat, thres.num=100)
expr_sample <- clustering(expr_mat[expr_genes,],metric = "pearson",method = "average")
expr_gene <- clustering(data = t(expr_mat[expr_genes,]),metric = "pearson",method = "average")
expr_clust <- clustering.plot(tree = expr_sample,tree.sup=expr_gene,data=expr_mat[expr_genes,],title = "Heat map of clustering",trim.heatmap =1)
I do not get any error when it comes to actually executing the clustering process. Could someone help?
In your example, some of the rownames of expr_mat are very long (max(nchar(rownames(expr_mat)) = 271 characters). The clustering_plot function tries to make a margin large enough for all the names but because the names are so long, there isn't room for anything else.
The really long names seem to have long stretches of periods in them. One way to condense the names of these genes is to replace runs of 2 or more periods with just one, so I would add in this line
#Extract expression matrix from colonCA
expr_mat <- exprs(colonCA)
rownames(expr_mat)<-gsub("\\.{2,}","\\.", rownames(expr_mat))
Then you can run all the other commands and plot like normal.