similar to: GLM with nested design

Dear list members, First of all thank you for your helpful advices. After your answeres to my firt mail I studied a lot (R-News n?5) and I tried to perform my analysis: First, to fit a GLM with a nested design I decided to use the function "lmer" in package "lme4" as suggested by Spencer Graves and Filippo Piro. I remember you that my data were: land use classes, 3 levels

Dear list members, First of all thank you for your helpful advices. After your answeres to my firt mail I studied a lot (R-News n?5) and I tried to perform my analysis: First, to fit a GLM with a nested design I decided to use the function "lmer" in package "lme4" as suggested by Spencer Graves and Filippo Piro. I remember to you that my data were: land use classes, 3 levels

Dear list members, I have a data.frame so shaped: Sector Quadrants Plot Sic Time Species1 Species2 Species3 .. Species-n 1 1 1 1 5 0 0 1 . 0 2 1 1 1 12 1 1 1 . 0 3 1 1 1 34 0 1 0 . 0 4 1 1 1 23 1 1 0 . 0 5 2 1 1 22 1 1 1 . 1 6 2 1 1 10 1 1 1 . 1 7 2 1 1 2 1 0 0 . 0 8 2 1 1 2 0 0 1 . 0 9 3 1 1 12 0 0 0 . 1 . . . . . . . . . .

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 using cocorresp package with predictive co-correspondence analysis model. i would need to visulize inside the graphic (plot function) the sample site numbers (is it possible to consider this a "label"?). is there any function to do this? thanks for your help. Elisa Dr. Elisa Santi Dipartimento di Scienze Ambientali "G. Sarfatti" Unità di Ricerca Biodiversità

Dear list members, I'd like to perform a glm analysis with a hierarchically nested design. In particular, I have one fixed factor ("Land Use Classes") with three levels and a random factor ("quadrat") nested within Land Use Classes with different levels per classes (class artificial = 1 quadrat; class crops = 67 quadrats; and class seminatural = 30 quadrats). I have four

Hi all, I am using quadratcount in spatstat to divide a window containing a point pattern into a grid of quadrats containing the intensity of points in each quadrat. My data is in UTM co-ordinates. My window is defined as follows: >p15<-ppp(x,y,window=owin(c(341710,342100),c(3126465,3126780)),marks=NUL L, checks=TRUE) Giving me a distance of 390m in the 'x' direction and 315m in

Hi, I know there are older threads discussing the quadratcount function in spatstat. Unfortunately, I could not find a solution to my problem there. I'm analyzing a point pattern in an irregular polygonal window. Both the window (an entire country) and the points are projected using WGS84. When I do quadratcount with only one quadrat for the entire country it holds all my 154 points:

On Wed, 24 May 2017 14:02:49 +0200 (CEST) Daniele Bernazzi <daniele at ao-siena.toscana.it> wrote: > > > ----- Messaggio originale ----- > > Da: "Rowland Penny" <rpenny at samba.org> > > A: samba at lists.samba.org > > Cc: "Daniele Bernazzi" <daniele at ao-siena.toscana.it> > > Inviato: Mercoledì, 24 maggio 2017 13:12:07

I'm working with large data frames and running out of memory. I hope some of you may be able to suggest a more efficient approach. I have grid/lattice data representing a time series of 1 m2 quadrats in a grassland: Each 1 cm2 cell or pixel contains one ecological state (ie grass or bare ground). The goal is to calculate, for each cell, the transition probabilities to all available

My name is Michela Marignani and I'm an ecologist trying to solve a problem linked to knight' s tour algorithm. I need a program to create random matrices with presence/absence (i.e. 1,0 values), with defined colums and rows sums, to create null models for statistical comparison of species distribution phenomena. I've seen on the web many solutions of the problem, but none provides

Dear R users, I noticed a problem in the anova command when applied on a single coxph object if there are missing observations in the data: This example code was run on R-2.6.1: > library(survival) > data(colon) > colondeath = colon[colon$etype==2, ] > m = coxph(Surv(time, status) ~ rx + sex + age + perfor, data=colondeath) > m Call: coxph(formula = Surv(time, status) ~ rx +

----- Messaggio originale ----- > Da: "Rowland Penny" <rpenny at samba.org> > A: samba at lists.samba.org > Cc: "Daniele Bernazzi" <daniele at ao-siena.toscana.it> > Inviato: Mercoledì, 24 maggio 2017 13:12:07 > Oggetto: Re: [Samba] samba 4 in AD 2008R2 without winbind > > On Wed, 24 May 2017 12:54:48 +0200 (CEST) > Daniele Bernazzi

I have a function (see below). This function has one object, ID. If I run the loops by themselves using a character value (ie,"VFFF1-7") instead of the function object, then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop

I have a function (see below). This function has one object, ID. If I run the loops by itself using a character value (ie,"VFFF1-7"), then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop criteria. Any obvious problems that

I referred in my reply to a paper by Diaconis and Sturmfels. The exact reference is: Diaconis and Sturmfels, Algebraic algorithms for sampling from conditional distributions, Ann. Stat 26 (1998) 363-397. They cite the following: Besag and Clifford, Generalized Monte Carlo significance tests, Biometrika 76 (1989) 633-42. which actually contains your problem (section 3, Testing the Rasch model)

I have photographs of plots that look like so: http://r.789695.n4.nabble.com/file/n4631960/Untitled.jpg I need to divide it up so each circle has an equal area surrounding it. So into 20 equal segments, each of which contains a circle. Quadratcount is not sufficient because if I divide it up into 36 equal quadrats, some quadrats do not contain one of the circles. I'm not even sure how to

> Da: "Rowland Penny" <rpenny at samba.org> > A: samba at lists.samba.org > Cc: "Daniele Bernazzi" <daniele at ao-siena.toscana.it> > Inviato: Mercoledì, 24 maggio 2017 12:45:56 > Oggetto: Re: [Samba] samba 4 in AD 2008R2 without winbind > > On Wed, 24 May 2017 12:08:09 +0200 (CEST) > Daniele Bernazzi <daniele at ao-siena.toscana.it>

Hi, With the following code i can divides window into quadrats and counts the numbers of points in each quadrat. library(spatstat) X <- runifpoint(50) quadratcount(X) quadratcount(X, 4, 5) quadratcount(X, xbreaks=c(0, 0.3, 1), ybreaks=c(0, 0.4, 0.8, 1)) qX <-? quadratcount(X, 4, 5) plot(X) plot(qX, add=TRUE) But I want to mark each? quadrats? and select/ extract only those points by

I have been looking into both of these approaches to conducting a GLMM, and want to make sure I understand model specification in each. In particular - after looking at Bates' Rnews article and searching through the help archives, I am unclear on the specification of nested factors in lmer. Do the following statements specify the same mode within each approach? m1 = glmmPQL(RICH ~ ZONE,