search for: elevated

Hello, I am new and am having a hard time getting the proper syntax for output from loops. I am working on a simulation to generate a null expectation of bee behavior. Pieces of it work. The part that I am having specific difficulty is in output of a vector from within the while loop that I am using. Basically the simulation works as such: I have a starting point and a neighbor matrix and a

Dear R users, I have created a Loess surface in R, in which x is relative longitude by miles, y is relative latitude by miles, and z is population density at the neighborhood level. The purpose is to identify some population centers in the region. I'm wondering if there is a way to determine the coordinates (x,y) of each center, so I can know exactly where they are. Let me use the

Dear R community, I'm having hard time to understand the kde function in "ks" package. Let me use a 3-dimensional kernel smooth example to explain my question using the elevation data in geoR. ### here is what I did ### library(ks) require(geoR) data(elevation) elev.df <- data.frame(x = elevation$coords[,"x"], y = elevation$coords[,"y"], z = elevation$data)

Dear R users, I'm trying to determine local population centers in a region through kernel smoothing. What I have is population density in each neighborhood, which can be presented as point density. So in my data set “pop”, I have three columns for about 1000 neighborhoods: x (x coordinate), y (y coordinate), and z (population density at xy). I searched R documentations and found that the

I would like to plot a map of, say, California, showing the elevation areas. I am sure there must be a source where I can find the dataset for the elevation and maybe there's an implementation in R that allows me to map it. Any ideas would be highly appreciated. -- View this message in context: http://r.789695.n4.nabble.com/elevation-map-with-R-tp4641116.html Sent from the R help mailing

# This is more about trying to find a more effecient way to code some simple vectorized computations using ifelse(). # Say you have some vector representing a factor with a number of levels (6 in this case), representing the location that samples were collected. Population <- gl( n=6, k=5,length=120, labels =c("CO", "CN","Ga","KO", "Mw",

Dear Rusers, I am now using R and SAS to fit the piecewise linear functions, and what surprised me is that they have a great differrent result. See below. #R code--Knots for distance are 16.13 and 24, respectively, and Knots for y are -0.4357 and -0.3202 m.glm<-glm(mark~x+poly(elevation,2)+bs(distance,degree=1,knots=c(16.13,24)) +bs(y,degree=1,knots=c(-0.4357,-0.3202

Hello R Users, I am trying to do a quadrat count defined by covariate properties in spatstat. I have read my elevation raster into R (from ascii) and converted to class "im" for use in spatstat. Now I have point data of class "ppp" which window is the same extent as the elevation image. I can conveniently plot the image and the points on the image as follows:

Hello R Users, I am trying to do a quadrat count defined by covariate properties in spatstat. I have read my elevation raster into R (from ascii) and converted to class "im" for use in spatstat. Now I have point data of class "ppp" which window is the same extent as the elevation image. I can conveniently plot the image and the points on the image as follows:

Dear list, i'm a new R user, so I apologize if the topic is already being addressed by some other user. I'm trying to determine if the reproductive success of a species of bird is related to a list of covariates. These are the covariates: ? elev: elevation of nest (meters) ? seadist: distance from the sea (meters) ? meanterranova: records of temperature ? minpengS1: records

I'm completely new at R... I have sinkhole survey data (lat, long, and elevation) and have been trying to plot a rotatable 3d plot for several hours... cannot get it right. Examples I see tend to be grid data (one elevation value per grid cell); however, my data are more random (known elevations at known (but random) x-y positions). Data format is tab separated, i.e.: lat long

# Thanks again to everyone who provided suggestions. # I was curious about which approaches would be the fastest... so a little benchmarking # My approach was by far the worst :) # The approach suggested by Duncan Murdoch and Peter Langfelder, based on indexing , was by far the fastest (~ 66times faster than using nested ifelse() ). All the details can be found below for those who are

Hi, First, thanks for all people that continuously giving great advice on this list! I am setting up a standalone server (Debian 10, Samba 4.9.5+dfsg-5 from Debian). The following directory is shared and contains two directories: /home/eleve/partage/ ??? Documents ??? Travail I want user "eleve" to be able to modify everything, and guest users to have read access on

Dear exports,I have created a hypsometric curve (area-elevation curve) for my watershed by using simple command hypsometric(X,main="Hypsometric Curve", xlab="Relative Area above Elevation, (a/A)", ylab="Relative Elevation, (h/H)", col="blue")It plots the hypsometric curve in "RGraphics window", My question is how can I export values which is used

Hello, I have a data set that needs to be combined so that rows are summed by a group based on a certain variable. I'm pretty sure rowsum() or rowsums() can do this but it's difficult for me to figure out how it will work for my data based on the examples I've read. My data are structured like this: Plot SizeClass Stems 12 Class3 1 12 Class4

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Hello, I?m using zero-inflated Poisson regression via the zeroinfl() function to analyze data on seed-set of plants, but for some reason, I don?t seem to be getting the output for all three levels of my two categorical predictors. More about my data and model: My response variable is the number of viable seeds (AVInt), and my two categorical predictors are elevation (Elev) and Treatment

Dear all; I have a dataframe with several columns. The columns are the elevation, volume and the area of the cells (which were placed inside a polygon). I have extracted them from DEM raster to calculate the volume under polygon and the elevation for a specific volume of the reservoir. > head(x6,2) Elevation Vol Area V_sum A_sum 1 2145 13990.38 85.83053 13990.38

Hi all, I'm working to apply a function that will generate a matrix of results only when a specific criteria is met. I want my final results to be a matrix with both the values that meet the criteria (the results of the function), and those that to do in the same positions in the matrix (the original numbers). Here's a sample of what I would like to do: t.mean.1.c <- c(-15, -20,

I am sorry if this question is vague or uninformed. I am just learning R and struggling. I am using the book Hierarchical Modeling and Inference in Ecology and they provide examples of R code. I have the following code from the book but when I run it I don't get any output. I cannot get the values of 'out' to show up. Basically, I just want to see my estimates for b0,