similar to: Matrix eigenvectors in R and MatLab

Hi, I have a set of locations defined by longitude and latitude (in degrees), and want to calculate the spatial (or geographic) distance among all locations. I did not find such a function in the spatial-related packages. (I *cannot* use 'dist', as I have geographic, not cartesian coordinates). thanks! Robert Robert Ptacnik Norwegian Institute for Water Research (NIVA) Gaustadall?en 21

Hei, I have two spatial datasets Sa and Sb, both with lat-lon coordinates and from same geographic area, but from different localities within the area (independent samples). Sa is biotoc data, Sb is some environmental parameter (fertility). I 'know' that Sb affects Sa, but wonder on which scale. I tried different interpolations by creating different grids of Sb (e.g. 20x20 and 100x100

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

Dear Listers, I have a regression problem (x->y) with biological data, where x influences y in two ways, (1) y increases with x and (2) the variation around the mean (residuals) decreases with increasing x, i.e. y becomes more 'predictable' as x increases. The relationship is saturating, y~a + bx + cx^2, gives a very good fit. I know basically how to test for heteroscedasticity. My

Dear R-listers I wonder if someone can help me with the syntax for the random effect in glmmPQL()? I have a data set with a response variable "y" (counts), two dependent variables: "treat" (4 levels) and "site" (2 levels). The latter, I want to use as a random variable. How do I specify this in the function? Is it like this:

Hei Group, I want to compare the relative importance of predictors in a multiple linear regression y~a+bx1+cx2... However, bptest indicates heteroskedasticity of my model. I therefore perform a robust regression (rlm), in combination with bootstrapping (as outlined in J. Fox, Bootstrapping Regression Models). Now I want to compare the relative importance of my predictors. Can I rely on the

Hi all! I have a problem setting ACLs on a remote file on a Windows XP Pro SP2 box. I issue the following command: smbcacls -a 'ACL:BBI-DEV\beakid:ALLOWED/0/0x00100116' -U 'BBI-DEV \Admin' //BBI-DEV/Data /Niva.txt And I get this response from debug level 3. Password: Connecting to host=BBI-DEV Connecting to 192.168.1.124 at port 445 Doing spnego session setup (blob

Hi all! I have a problem setting ACLs on a remote file on a Windows XP Pro SP2 box. I issue the following command: smbcacls -a 'ACL:BBI-DEV\beakid:ALLOWED/0/0x00100116' -U 'BBI-DEV \Admin' //BBI-DEV/Data /Niva.txt And I get this response from debug level 3. Password: Connecting to host=BBI-DEV Connecting to 192.168.1.124 at port 445 Doing spnego session setup (blob

Dear R-listers I'm a new R-user who needs some help with a test that I want to do. I have done a field experiment: four treatments (cont, x, y and xy) at three sites (A, B and C), the response is count data (0 - 15). I've done a Poisson regression: >glm(response~as.factor(treatment)*as.factor(site), family=quasipoisson, offset(max.response), data=dat) The "offset" is the

I am having a problem with the package SSPIR. The code below illustrates it. I keep getting the message: "Error in y - f : non-conformable arrays." I tried to tweak the code below in many different ways, for example, substituting rbind for cbind, and sometimes I get a different error message, but I could not find a variation of this code that would work. Any help will be greatly

I'm currently running into a little trouble with the kfilter method, and would love some clarification if you are able to offer it. When trying to run kfilter, I've been running into errors that seem to result from having mismatched dimensions. Specifically, the dimension of my observations is 2, while the dimension of the state space is 4. In the filterstep function (file sspir_kfs.R),

Hello, I apologize for such a basic question, but I have been trying to do this in multiple packages without much success. I am trying to set up a state space model for Kalman filtering. I am using package dlm. The DLM is specified by: observation: y(t) = F(t)*theta(t) + v(t) state: theta(t) = G(t)*theta(t-1) + w(t) I have no problem setting up a simple example where F is constant. I am

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 For those interested in some more detail of the EventLog features in the upcoming Samba 3.0.21 release, see: http://www.samba.org/~jerry/Samba-EventLog-HOWTO.txt Thanks to Brian Mroan and Marcin Porwit at Centeris for all their hard work. cheers, jerry -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.0 (GNU/Linux) Comment: Using GnuPG with

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and

Generally speaking, this sort of detailed statistical question about a speccial package in R does not get a reply on this general R programming help list. Instead, I suggest you either email the maintainer (found by ?maintainer) or ask a question on a relevant R task view, such as https://cran.r-project.org/web/views/Econometrics.html . (or any other that you judge to be more appropriate).

Dear R-Users, I'm using SSPIR package for a spatio-temporal application. Is it possible to modify the structure of the involved matrixes (Fmat, Gmat, Vmat,Wmat)? I want to create a model like this #y(t)=k*theta(t)+epsilon(t) #theta(t)=h*theta(t-1)+eta(t) #epsilon(t) N(0,V) V=sigma2*I #eta(t) N(0,W) W=sigma2_eta where the state variable theta has dimension 1(p=1) and at

dear listers, I use gam (from mgcv) for evaluation of shape and strength of relationships between a response variable and several predictors. How can I interpret the 'F' values viven in the GAM summary? Is it appropriate to treat them in a similar manner as the T-statistics in a linear model, i.e. larger values mean that this variable has a stronger impact than a variable with smaller F?

Dear R-users, I need some  assistance. I am running some interactive variables for categorical variables. I have dgen(2 levels converted to dummy variables)  and dtoe(4-levels also converted to  dummy variables). So I have worked with them in two ways: i created a variable X1 = dgen*dtoe  and I get an error "Error in dgen * dtoe : non-conformable arrays"then i run a glm, binomial

Dear R Community, Below is a problem with a simple ggplot2 graph. The code returns the error message below. Error: stat_count() must not be used with a y aesthetic. My code is below and the data is attached as a ?text? file. # Graph of the probabilities library(digest) library(DT) datatable(probability) str(probability) probability$Fertilizer <- as.factor(probability$Fertilizer)

Greetings everyone, I'm trying to add a specific table or a specific number of rows (e.g.44) to a table with no success. This is my basic table > head(dataA) year plot spp prop.B DCA1 DCA2 DCA3 DCA4 1 2000 1 a1 0.031079 -0.0776 -0.0009 0.0259 -0.0457 2 2000 1 a2 0.968921 -0.0448 0.1479 -0.1343 0.1670 3 2000 2 a1 0.029218