similar to: help on spatial data

Dear list, When I use VennDiagram package, I got a error as follow: venn.plot <- draw.pairwise.venn( area1 = 3186, area2 = 325, cross.area = 5880); Error in draw.pairwise.venn(area1 = 3186, area2 = 325, cross.area = 588) : Impossible: cross section area too large. Does anyone have suggestion? Thank you.

I'm reading Torgo (2010) *Data Mining with R*<http://www.liaad.up.pt/~ltorgo/DataMiningWithR/code.html>in preparation for a class I'll be teaching next quarter. Here's an example that is very non-functional. > pH <- c(4.5,7,7.3,8.2,6.3) > names(pH) <- c('area1','area2','mud','dam','middle') > pH area1 area2 mud dam

Hi there, I am not getting anywhere near a solution, so here is my question. I have searched the archives and didn't find a solution, but maybe my search didn't use the right words. So here it is: I have a dataframe with the following structure factor1 factor2 value In the (rather large) dataset I have to work on, there are some factor combinations missing, e.g. factor1 <-

My data frame looks like: SightingID PA1 PA2 PlotID InOverlap Area1 2001 1 -99 392 Y 0.22 2002 1 -99 388 Y 0.253 2008 1 NA 104 N 0.344 2010 1 NA 71 N 0.185 2012 1 NA 61 N 0.166 2013 1 NA 61 N 0.227 2014 1 NA 62

Dear laurent, > But there are lots of commands I found in R docs which are "not found", > such as getTable. Deprecated (I successfully used dbListTables) or do I > simply miss a package ? >From RMySQL\NEWS: * added (almost) trivial convenience RS-DBI functions getTable(), assignTable(), existsTable(), and removeTable(). They all mimic the corresponding R/S get(),

Hi there, Could someone help me with the following programming problem..? I have written a function that works for my intended purpose, but it is quite closely tied to a particular dataframe and the names of the variables in this dataframe. However, I'd like to use the same function for different dataframes and variables. My problem is that I'm not quite sure how to tell my function in

Dear all, I am trying to merge two data frames based on a common column but for this common column both data frame do not have the same length and associated information. I checked previous exemples in the list but was not able to apply them in my case... Is someone know how to do that? Below is my code with the expected result: # data frame 1 Id1 <- c(1,1,1,2,2,2,3,3,3) Habit1 <-

I'm using area() to evaluate the area under 2 functions (defined using approxfun()) and the result is in contradiction with visual inspection (please see: http://sites.google.com/site/eospansite/dummy/problem_area.jpg ) The area under the red curve should be larger than the one under the black curve, but I'm geting the opposite result using area(). This is what I'm doing:

hello, I want to do a test for spatial correlation. I tried it with geary.test() but I don't understand the required input. x= a numeric vector the same length as the neighbours list in listw (my sampled data, I assume) listw= a listw object created for example by nb2listw (well when I check nb2listw() I get to "neighbours - an object of class nb" - but I couldn't figure

Note: I don't know if this is a problem withe I20 drivers or Rsync so I'm submitting to both the Kernel Bugzilla and the Rsync mailing list. I couldn't find a bugzilla for Rsync. I hope this was the correct way to submit this issue. Distribution: Debian Hardware Environment: Intel 850MV Mother board, Pentium 4 processor, 1Gig of RAM, Adaptec 2400A RAID Controler. Both the

Dear All I would really appreciate some help with a script which a colleague wrote for me (attached), but I am having problems running (and have not been able to contact my colleague). The script is designed to compare the area of suitable habitat in binary projections of a large number of species current and future distributions, and create an excel file detailing the total area suitable in the

Hello my name is Piercarlo and I've discovered a bug in smbd_audit when I trace a Samba 2.2.7 or 2.2.7a on red hat 7.3 -the error exist on the redhat 2.4.18-17kernel ,on the original rh73 2.4.18-3 kernel and on the standard linus kernel (I tried on 2.4.20)-->these kernels are all the kernels that I tried. This error appear when samba is configured as pdc with the roaming of the users

Hello, I'm trying to analyse the spatial organization of different fields planted with different varieties (each field has only one variety), but I have problems trying to understand the results of the test I did. To do this, I created different neighbourhood matrix. For example, for the first matrix, fields are considered as neighbours if they are distant from 0 to 1000 meters from each

I used the supersmoother function in the modreg package as follows: super <- supsmu(ilogemp,award) Then I decided that I might want additional explanatory variables (other than ilogemp) in my model. The ppr function in modreg seemed a logical extension of supsmu from univariate to multidimensional explanatory variables. As a "check" I ran the following: pprest <-

Hello, I'm new to R and to Spatial Analysis and got a problem trying to create a Spatial Weights Matrix. *I us the following code to create the Neighbourslist:* >library(maptools) >library(spdep) >library(rgdal) >location_County<- readShapePoly("....") >proj4string(location_County)<- CRS("+proj=longlat ellps=WGS84") >location_nbq<-

Hi all: Here's a question about result of loglinear analysis. There're 2 factors:area and nation.The raw data is in the attachment. I fit the saturated model of loglinear with the command: glm_sat<-glm(fre~area*nation, family=poisson, data=data_Analysis) After that,I extract the coefficients: result_sat<-summary(glm_sat) result_coe<-result_sat$coefficients I find that all the

Hello, I am interested in using Morans I for different time intervals to detect disease clusters. Ultimately I would like to use CUSUM - or similar monitoring statistic to monitor the results of Morans I - similar to the work by Rogerson (2005) Spatial Surveillance and Cummulative Sum Methods in Spatial and Syndromic Surveillance for Public Health. Thus far - thanks to the list I have

Dear all, I would like to implement a spatial quantile regression using instrumental variable estimation (according to Su and Yang (2007), Instrumental variable quantile estimation of spatial autoregressive models, SMU economics & statistis working paper series, 2007, 05-2007, p.35 ). I am applying the hedonic pricing method on land transactions in Luxembourg. My original data set contains

Given the data structure below, how can I create a bar plot for the values of disc for each area grouped by year? bar <-structure(list(year = c(2003, 2003, 2003, 2003, 2003, 2003, 2003, 2007, 2007, 2007, 2007, 2007, 2007, 2007), area = structure(c(6L, 4L, 1L, 2L, 3L, 5L, 7L, 6L, 4L, 1L, 2L, 3L, 5L, 7L), .Label = c("AK", "ID", "MT", "NW",

Estimad en s, quería realizar una consulta Dado un data frame llamado aves, como puedo sustiuir las variables area, dist y distm por sus logaritmos en base10 y volver a crear un data frame? Muchas gracias Saludos Susana [[alternative HTML version deleted]]