similar to: [off-topic] The better analysis

I have a dataset that is based on crop output for a single crop *sugarcane*...but each observation is further subdivided into 3 kinds of sugarcane i have read the file and have also used melt it to sort it on the basis of *crop group* using melt(sugarcane, m=c("Crop_group")) -> canefile this is how it has cast the data variable value 1 Crop_group Sugarcane

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

Dear R friends, I have a matrix with 2060 rows and 41 columns. One column is Date, another is Transect, and another is Segment. I want to ensure that there are 9 Transects (1 to 9) for each Date, and 8 Segments (1 to 8) for each Transect in the matrix, by inserting rows where these are missing. I am new to coding, but am trying to write a loop which checks if each of the transects already

Hi all - R2.0.1 on OSX;MASS library;nlme library I am trying to emulate the solution to a problem set that has normally been run in Genstat, using R. The problem that I am having at the moment is with the following glmm question (using glmmPQL from the MASS library): "We have two different forest habitats (first rotation thicket, and high forest) which we want to survey for the

# I need to create an xyplot() where I control the specific order of # both my conditioning variables. The default code below plots the # data correctly (dispersed across all 14 columns), but fails in two # ways. Both the primary conditioning variable (Transect), and the # secondary conditioning variable (Offset) are in alphanumeric order, # rather than the specific order I need. # Here

Dear R Community! We analyse the impact of climbing activity on cliff vegetation. During our fieldwork, we recorded 90 Transects in 3 climbing sites. The aim is to see, if the plant cover (response: Cover) is influenced only by crevice availability (predictor: Cracs), or, additional, by the distance to the climbing route (predictor: Distance). Six plots are nested within one Transect

Due to an error on my part, I have renamed the previously attached file from T_5-04b_LTC-SE-SO-Compared.csv to T_5-04b_LTC-SE-SO-Compared.txt. It remains a comma-delimited file so the extension can be changed and used per the script, or loaded separately. My sincere apologies, Guy -----Original Message----- From: Guy Jett Sent: Friday, March 18, 2011 1:13 PM To: 'r-help at

Hello all, This may be a simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas with unbalanced design in R. In my case, I have 11 vegetation transects (with 1000 10cmx10cm squares), where we estimated shrub cover. We have two different treatments: wildfire (4 transects) and prescribed burning (7 transects) and we want to

Dear R community, I'm trying to analyze a model with an ordinal response variable. I wonder if clm()s (Cumulative Link Models) are appropriate in my case. The study compares parasite infestation of porpoises in 1995 and 2009. The degree of infestation is a rank (mild to severe, as ordered factor). In some parasite species clm gives meaningful results. But in one case I started to wonder. In

Hi list members. I’ll try to plot the abundance means of nine transects as lines, with five points on each transect (A to I). I will also need to plot for each point, it’s standard deviation (once each point will have tree replicates) as whiskers. Another problem will be that all the points should exist for each transect, but we know that six of the nine transects will have blanks (A1, A2, A3, -,

Hi Sherri, There are examples of topographic maps which you have been pointed to, however, I suspect that you want to know where you can obtain topographic data from rather than a canned example. There are quite a few intricacies to the process so I will go through them for you. (1) Topography files can be found in the geomapdata library. You will probably want to use the maps package too (if

Hi R people: I am new to R. I am writing a function to (1) produce a sparse stochastic Gaussian 2D field and (2) perform a systematic transect sampling on this field, this carried out many times in a simulation framework. My function does a good job at producing the random field (a matrix of zeros and some manifestations of the stochastic process, depending on a parameter of the function

Dear R-users, I have the following problem. I have performed an experiment for which I gathered a lot of data which I now want to test. The problem is that I cannot find an appropriate test in R (I am a starter) and someone might give me a hand. This is what I have done: Across three sites (Site), I have laid out five transects (Trans)...meaning five transects in each sites. In each transect I

Hi, We are analizing the relationship between the abundance of groupers in line transects and some variables. We are using the quasipoisson distribution. Do we need to include the length of the transects as an offset if they all have the same length?? Also, can we include in the gam models variables that are measured at different spatial scales? We have done an analysis to see what variables

Dear listers, I am trying to assess variance components for a nested, mixed-effects model. I think I got an answer that make sense from R, but I have a warning message and I wanted to check that what I am looking at is actually what I need: my data are organized as transects within stations, stations within habitats, habitats within lagoons. lagoons: random, habitats: fixed the question is:

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Dear R gurus, I have XY data giving the locations of tree seedlings that were surveyed during a 210 meter belt transect. This belt transect was taken by stretching a line across the field, then measuring all seedlings within 1 meter on either side of the line. The end result was XY coordinates and height for ~1,300 seedlings. I would like to use that data to calculate density of

Fairly new at this. Trying to create a conditional density plot. >cdplot(status~harvd.l,data=phy) Error in cdplot.formula(status~harvd.l,data=phy): dependent variable should be a factor What does this error mean? Status is a binary response of infestation (0/1) and harvd.l is the log of timber harvest density per catchment. Thanks. -- View this message in context:

El jue, 22-02-2018 a las 09:52 +0100, Carlos Ortega escribió: > Aquí tienes un par de formas... ... y aún una más, con el siempre fiel lenguaje SQL: library(sqldf)# corriendo antes lo que hizo don Carlos pero dejándolo como un data.frame tradicional:DF<-as.data.frame(DF)sqldf("select * from DF where resultado=' NA '") ¡Saludos! -- «...homines autem hominum causa esse

Hello, is there a package in R that would allow map drawing: coastlines, country/state boundaries, maybe topography, rivers etc? Thanks for any guidance, Mark