similar to: 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 to you that my data were: land use classes, 3 levels

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

Dear R-users, is there a chance to do an nested ANCOVA in R? I've read the archive mails but it doesn't work. We have the following situation: We are interested in differences in carabid biomass at different aged woodlands. Thus, we have a nested design with 2 fixed groups (0=recent, 1=ancient) and 10 woodlands (each with 8 replications/traps) per fixed group. Woodland is therfore

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

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:

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

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

Hello fellow R users, I am having a problem finding the estimates for some overall treatment effects for my mixed models using 'lme' (package nlme). I hope someone can help. Firstly then, the model: The data: Plant biomass (log transformed) Fixed Factors: Treatment(x3 Dry, Wet, Control) Year(x8 2002-2009) Random Factors: 5 plots per treatment, 5 quadrats per plot (N=594 (3*5*5*8)

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,

I have data on the proportion of clutches experiencing different fates (e.g., 4 different sources of mortality) for 5 months . I need to test 1) if the overall proportion of these different fates is different over the entire study and 2) to see if there are monthly differences within (and among) fate types. Thus, I am pretty sure this is an RM analysis -( I measure the same quadrats each month).

Hi. I have a factor and I want to extract just those elements that appear exactly once. How to do this? Toy example follows. > a <- as.factor(c(rep("oak",5) ,rep("ash",1),rep("elm",1),rep ("beech",4))) > a [1] oak oak oak oak oak ash elm beech beech beech beech Levels: ash beech elm oak > table(a) a ash beech elm oak

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'm using R for Mac OSX version 1.14 (2129) and the geoR package version 1.6-5 (the current version in the R repository). I'm running R in OS 10.4.6 on a Mac G4 iBook (933MHz, 640 MB DDR SDRAM). I searched the R archive and did not find a posting on this issue. I want to use the variog and variog4 functions of geoR to characterize the pattern of spatial autocorrelation of tree

Dear R-helpers, Apologies in advance for this (probably) simple question. I've searched the R Archive and can't seem to find a solution to my problem. I have a data frame of vegetation quadrat data with the following format: Q S C 1 A 5 1 B 10 1 C 50 1 D 10 2 A 20 2 E 10 2 C 40 3 D 5 3 F 1 3 G 5 3 B 75 Where Q is the sample (vegetation quadrats), S is the species and C is the

Dear all I have just had the question from a colleague. I know that it is not directly related to R (I will probably use R to do the analysis), but I hope someone can give us some insight: Thanks, AJ Smit I sampled populations of a seaweed in the intertidal in order to estimate the standing biomass of that seaweed at that site. Due to clumped distribution patterns, I chose a stratified

I was surprised to find that just changing the base level of a factor variable changed the number of significant coefficients in the solution. I was surprised at this and want to know how I should choose the order of the factors, if the order affects the result. Here is the small example. It is taken from 'The R Book', Crawley p. 365. The data is at

Hi, I am trying to write a for loop with if else statements to calculate biomass density estimates for different types of sampling gear. My code is: bmd=for (i in 1:length(Gear)){ if (Gear==20) {bioden=Biomass/141} else {if (Gear==23) {bioden=Biomass/68}} else {if (Gear==160) {bioden=Biomass/4120}} else {if (Gear==170) {bioden=Biomass/2210}} else {if (Gear==300)

Hi there, I have a data frame as listed below: > Ca.P.Biomass.A P Biomass 1 334.5567 0.2870000 2 737.5400 0.5713333 3 894.5300 0.6393333 4 782.3800 0.5836667 5 857.5900 0.6003333 6 829.2700 0.5883333 I have fit the data using logistic, Michaelis?Menten, and linear model, they all give significance. > fm1 <- nls(Biomass~SSlogis(P, phi1, phi2, phi3), data=Ca.P.Biomass.A)

Hello, I am trying to plot a quadratcount object over a ppp object in the spatstat package. I ultimately want to get something like this http://bg9.imslab.co.jp/Rhelp/R-2.4.0/src/library/spatstat/man/images/big_quadratcount_001.png http://bg9.imslab.co.jp/Rhelp/R-2.4.0/src/library/spatstat/man/images/big_quadratcount_001.png See