similar to: fitting allometric equation using a for a power model

Hello all, * * I'm having some difficulty, and I think the problem is with how I'm using append() nested inside a for loop. The data are: y,x 237537.61,873 5007.148438,227 17705.77306,400 12396.64369,427 228703.4021,1173 350181.9752,1538 59967.79376,630 140322.7774,710 42650.07251,630 5382.858702,264 34405.82429,637 92261.34614,980 144927.1713,1094 362998.7355,1420 203313.6442,1070

Hi everyone, I am desperately looking for a method to interpolate strings within an SQL statement as follows: I get a lot of rows out of a database (in my example POSITION_to_ZIPCODE Database with holds records for German ZIP Code <--> Gauss-Krueger Coordinate System ) and want this to be selected and computed individually row by row as follows: library(RODBC) channel <-

Hi everyone, I experience some problems with adressing of data.frames when I retrieve some information for geographical position (ypos, xpos) ot of a MySQL Database and want to perform some simple statistics. The problem is adressing the dataframes with a construct like rawdata[c(type)] vs. rawdata$TEMPMIN to retrieve the numerical information and not a string (I want to store the numerical

Hello, Is there any way we convert SAS dataset into R dataset? Kindest Regards, Peppy Adi-Purnomo ------ Peppy Adi-Purnomo Energy Market Analyst Energy Link Ltd Dunedin - New Zealand Ph.: +64 3 479 2475 Fax: +64 3 477 8424 Email: s.adi.purnomo at energylink.co.nz www.EnergyLink.co.nz -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

please add the following diffs to the vorbis/vq dir. - include files changed so things actually compile in new scheme - _ogg_...alloc cleanups caught a half-dozen typos or so - minor Makefile touchup. (stuff is still not tested, but this will compile at least) Would someone with cvs write access commit them for me please? Erik diffs: ------------------------ diff -bBu2r vorbis/vq/Makefile

Hi R experts, I'm attempting to run Linear Discriminant Analysis using the lda function in the MASS package. I've got around 50 predictor variables and one response variable. My response variable has 5 numeric categories that represent different clusters of fish abundance data (clusters were developed using Bray-Curtis and NMDS), and my predictor variables are environmental variables that

After a lot of effort I developed the following function to compute the bounding ellipse (also known a as minimum volume enclosing ellipsoid) for any set of points. This script is limited to two dimensions, but I believe with minor modification the algorithm should work for 3 or more dimensions. It seems to work well (although I don't know if can be optimized to run faster) and

Hello R experts, I have having a difficult time figuring out how to perform and interpret an ANCOVA of my nested experimental data and would love any suggestions that you might have. Here is the deal: 1) I have twelve tanks of fish (1-12), each with a bunch of fish in them 2) I have three treatments (1-3); 4 tanks per treatment. (each tank only has one treatment applied to it) 3) I sampled

The mvee() function is intended to be released under the BSD license. Copyright (c) 2009, Nima Moshtagh Copyright (c) 2011, Andy Lyons All rights reserved. http://www.opensource.org/licenses/bsd-license.php Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must

Dear R helpers, For an allometric analysis (allometric equation y = a*x^b) I would like to apply a non-linear regression instead of using log-log transformations of the measured parameters x and y and a Model II linear regression. Since both of the variables x and y are random, I would like to apply a Model II non-linear analog of either Reduced Major Axis or Major Axis Regression. The

Dear list members, I would like to know the difference in outputs and calculation processes between residuals.glm(object, type="response") and residuals.lm(object). For above-ground biomass estimation of trees, I estimated parameters of an allometric equation (ln y = b0 + b1*ln x) using glm as follows: fm <- glm(Ws~log(Wb), family=quasi(link="log",

Dear all, I've been reading for many days trying to predict with lmer but I haven't managed to do it. I've fitted an allometric model for trees where I have included climatic variables and diameter in the fixed part and in the random part I've included the experimental sites where trees are and also their provenance region. The model is like this :

Hello folks, I'm seeking opinions about the validity of the following use of the nls function... A colleague and myself are working with tree allometric data consisting of measurements of individual trees in semi-arid Australian woodland species. We need to make predictions of trunk diameter (DBH: diameter at breast height) given tree height and vice versa. I _think_ this falls into the

I am having trouble extracting residuals from Major Axis and Standardized Major Axis fits, using the smatr package. I wish to understand the relationship between density of wood in twigs and trunks, and how the slope of their relationship varies among sites, among families, and with tree size. These variance-partitioning desires push the analysis toward a mixed model framework. However,

Also, this works (taking out multcomp=TRUE, multcompmethod="adjusted"): com.test=ma(Head.W1~Leg.3.1+Site, type="elevation", data=queens) print(com.test) ....so for some reason it will do an MA regression on all my data point together, but shows an error when I try to do pairwise comparisons between groups. Thank you, Ioulia -------- On Fri, May 11, 2012 at 2:09 AM, Ioulia

Sorry, for disturbing the list again. > Also I got one suggestion of using ORDPACK at http://www.netlib.org/. It's ODRPACK at http://www.netlib.org/, not ORDPACK. Best, -- Etsushi Kato ekato at ees.hokudai.ac.jp -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

hi, i'd like to calculate a temperatursum, adding the value of each element. let's say the data looks like this: x<-c(1,2,3,4,5) what i want to do, is ploting not the sum in the end but all the subresults, too, so my vector holds: x[i] [1] 1,3,6,10,15 here is what i tried, which seems to be right to me, bu doesn't work out: x<-c(1,2,3,4,5) i<-1 j<-1

Dear R community, I am trying to write my own user defined split function for rpart. I read the example in the tests directory and I understand the general idea of the how to implement user defined splitting functions. However, I am having troubles with addressing the data frame used in calling rpart in my split functions. For example, in the evaluation function that is called once per node,

Hi there are some good tips in "The R Inferno" http://www.burns-stat.com/documents/books/the-r-inferno/ or connect C++ to R with Rcpp http://dirk.eddelbuettel.com/code/rcpp.html or byte code compiler (library(compiler)) or library(data.table) but do you have an idea to fasten standard R source code, with the following Rprof output self.time self.pct total.time

Dear all, To modelize the abundance of fish (4 classes) with a set of environmental variables, I used the polr and predict.polr functions. I would like to know how to bring the cumulated probabilities back to a discrete ordinal scale. For the moment I used the predict.polr function with the argument "class". Is there an other way? polrf <- polrf <- polr_mod(formula =