similar to: model II major axis regression

Hello, I am using function lmodel2 to calculate RMA regression between life-history traits in ladybirds beetles. It works well but I am not able to plot an RMA regression line on the scatterplot of my data. I am of course unable to plot the confidence intervals. For ordinary least square regression abline(lm(x~y))works well but for RMA regression, abline(lmodel2(x~y))does not do it. If somebody

Hi, Does anybody know whether it's possible (and sensible) to calculate R^2 (or something approximating R^2) on the results of a major axis Model II regression done by lmodel2? The function lmodel2 provides an R^2 for the OLS regression only (example below), but not for major axis regression. Also, is it possible to extract major axis regression residuals? (Resid did not work on the lmodel2

Hi, Does anybody know whether it's possible (and sensible) to calculate R^2 (or something approximating R^2) on the results of a major axis Model II regression done by lmodel2? The function lmodel2 provides an R^2 for the OLS regression only (example below) Also, is it possible to extract major axis regression residuals? Many thanks, Karen Example: > Result <- lmodel2(

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,

Hello, I am currently trying to run a series of models (all combinations of 4 independent factors, non-duplicate) and selecting the best one to describe my data. I was wondering if anyone knew of a way to get AIC and Likelihood values from R when using reduced major axis regression (RMA). I can get the values using the "lm" function, but that uses OLS to calculate the parameters. I

Hi *, is there a way to obtain some kind of p-value for a model fitted with RMA using the lmodel2 package? I know that p-values are discussed and criticized a lot and as you can image from my question I'm not very much of a statistican (only writing my bachelor thesis). As fare as I understood the confidence interval statistic correctly, a coefficient is regarded as statistically significant

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

Greetings R-help! I'm fairly new to R and am trying to expand my knowledge beyond using R for simple summary statistics and basic tests. To that end I am attempting to write an interactive R-script that will perform a general rational function fit to a given dataset based on the example given at http://www.itl.nist.gov/div898/handbook/pmd/section6/pmd64.htm. The problem that I seem to have

> 3) a more general question: > i have not been able to find any reason why > 10 lpcs are used. i suppose 10 lpcs are > enough for prediction and using more coeffs > would not have made too much difference. > and 10 is even, good for lpc->lsf. had read > somewhere "historically", during the analog > era, this number is used. or maybe 10 is analagous > to

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Hi there, I've been using the SMATR package to do standardized major axis (SMA) regression on allometric data, and I've been able to to pairwise comparisons of slope & elevation between multiple study sites. Now I'm trying to do the same thing using major axis (MA) regression, but I'm getting errors (SMATR should be able to do MA as well as SMA regression for everything,

Hi everyone, I wonder if anyone knows the reason why the outputs of the same reprojection in r and arcgis are different?. The magnitude of the change can be up to 40 km in the poles. Basically, I have a database of points equally separated by one degree over the globe. In ARCGIS, I am projecting the data in GCS-WGS-1984 and then reprojected it to Berhmann to ensure equal area

Hello, The quick version of my question is how can I extract a matrix instead of a vector using tapply()? I would like to be able to access both the results of tapply() and also the index variables. In case further explanation would help: I am analyzing a large (3million rows x 9 columns) spatial/temporal dataset and am attempting to calculate the number of unique years containing any data

The following message is provided by Erik Please provide the reproducible code to do this. Generate a sample data set using the random data generating functions and show us what you'd like, we can then more easily help. ctu at bigred.unl.edu wrote: > Hi, > How about using "subset"? > x1<-tapply(subset(years, length(area)>20), function(x) length(unique(x))) >

Hi, I'm regressing various body dimensions upon body mass using the 'lmodel2' function, as I'm keen to obtain both OLS and RMA slope values. I also wish to create a plot of the regressions, with the 95% confidence interval of both the slope and intercept. I know how to plot 95% ci bands of the OLS slope using lm with the 'predict' function and 'matlines'. Does

Hi All, I am confused with SMATR's test for common slope. My null hypothesis here is that all slopes are parallel (common slopes?), right? So if I get a p value < 0.05 means that we can have confidence to reject it? That slopes are different? Or the other way around? it means that we have statistical confidence that the slopes are parallel? thanks -- Eugenio Larios PhD Student University

Hello R list, I need to compute empirical variograms using data from a large geographic area (~10^6 km2). Although I could not find a specific reference, I assume that both geoR and gstat calculate distances among data points assuming points are on a flat surface (using the Pythagorean Theorem). Because the location of my data is large and located near the pole, assuming that latitude and

Hi, hres <- hclust(smatr,method="single") hresd<-as.dendrogram(hres) as.dendrogram(hres) `dendrogram' with 2 branches and 380 members total, at height 2514.513 plot(hresd,leaflab="none") #<-error here. #the plotted dendrogram is incomplete. The x axis is not drawn. #The interested reader can download the save(hresd,file="hres.rda") #from the

Hi, for testing if the slope of experimental data differs from a given slope I'm using the function "test_regression_against_slope" (see below). I am now confronted with the problem that I have data which requires a modelII regression (also called reduced major axes regression (RMA) or geometric mean regression). For this I use the function "modelII" (see below). What

I'm presently working on an office move and evaluation of telecommunications services needed at the new location. I'm presently wrastling with an issue related to portability and geography between landline carriers. Presently certain people within the organization are hopelessly in love with our 909-822-xxxx number(provided by pacbell/att). As that number is presently provisioned it