similar to: [Fwd: FW: Biostats text/software]

I've written a g.test() aka log-likelihood ratio test function for my opwn use. It's something I've seen requested (and looked to find myself) on this list a few times. It has the same basic syntax as chisq.test(). It does both goodness of fit tests and tests of independence. Yates' and Williams' corrections are implemented. I've put some examples from Sokal & Rohlf

Dear professors and collegues I need to perform a analysis of dates from a nested experimental design. From "Bioestatical Analysis" of Zar "Bimetry of Sokal" & Rohlf "Design and Analysis of Experiments" of Montgomery I have: Sum (mean(x)_i - mean(x)_T)2 / (a-1) -> var(epsilon) + n sigma2_B + n b (sum alfa_i)2 / (a-1) Sum (mean(x)_ij - mean(x)_i)2 /

This question is mainly aimed at Kurt Hornik as author of the ctest package, but I'm cc'ing it to r-help as I suspect there will be other valuable opinions out there. I have been attempting 2 sample Kolmogorov-Smirnov tests using the ks.test function from the ctest package (ctest v.0.9-15, R v.0.63.3 win32). I am comparing fish length-frequency distributions. My main reference for the

Dear R-users, I am trying to analyse the data of the box 10.5 in the Biometry from Sokal and Rohlf (2001) using R. This is a three-level nested anova with equal sample size : 3 different treatments are compared ; 2 rats (coded 1 or 2) / treatment are studied ; 3 preparations (coded 1, 2 or 3) / rats are available ; 2 readings of the glycogen content / preparations are realised. Treatment is

Hi, I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct",

Hello R friends, I'm wondering why I get funny qqnorm() results. It seems that they should all be reflected in the normal qqline(). For instance: if I qqnorm() bimodal or uniform data I get a sigmoidal in which the qqnorm() points lie above the qqline() at -ve theoretical quantiles, and the qqnorm() points lie below the qqline() at +ve theoretical quantiles. Yet I expect such platykurtic

Dear r-helpers, Prior to analysis of variance, I ran the Boxcox function (MASS library) to find the best power transformation of my data. However, reading the Boxcox help file, I cannot figure out if this function (through its associated log-likelihood function) corrects for * normality only * or if it also induces * homogeneity of variances *. I found in Biometry (Sokal and Rohlf, p. 419)

I frequently want to test for differences between animal size frequency distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov two sample test (provided in R as the function ks.test in package ctest). The KS test is for continuous variables and this obviously includes length, weight etc. However, limitations in measuring (e.g length to the nearest cm/mm, weight to the nearest

Hi, You didn't specify the satistical model you are intereted in, I will suppose it is something like: #Organims ~ Landscape + Soil + Depth + Species I suppose you have a table of with something like... Spec Lands Soil Depth #Organisms A 1 1 1 10 A 1 2 1 2 B 1 1 1 0 B 2 2 2 2 ... etc, Normally, the count of organisms in soil samples follows a posisson distribution, not a normal one.

Dear all, I am facing a problem that seems to me more tricky now that it did at first sight. I have a collection of data which consist in frequency distributions: 6 patches had been proposed to female insects (for oviposition), 3 of them corresponding to one treatment (A), the other 3 to another treatment (B). The results are the number of patches that have been chosen for oviposition by

Hi, I''m trying to understand the lme output and procedure. I''m using the Crawley''s book. I''m try to analyse the rats example take from Sokal and Rohlf (1995). I make a nested analysis using aov following the book. > summary(rats) Glycogen Treatment Rat Liver Min. :125.0 Min. :1 Min. :1.0 Min. :1 1st Qu.:135.8

Hello all. This may be a trivially simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas in R. If I understand correctly, the F statistic for the among-subgroups but within groups hypothesis is calculated as MS_subgroups/MS_error, while the F statistic for the factor is calculated as MS_factor/MS_subgroups (I'm

Dear all, During my pre-R era I tried (yes, tried) to understand mixed models by working through the 'rat example' in Sokal and Rohlfs Biometry (2000) 3ed p 288-292. The same example was later used by Crawley (2002) in his Statistical Computing p 363-373 and I have seen the same data being used elsewhere in the litterature. Because this example is so thoroughly described, I thought

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

Hi, I am trying to run the command "forward.sel.par," however I receive the error message: "Error: could not find function 'simpleRDA2'." I have the vegan library loaded. The documentation on "varpart" has not helped me to understand why I cannot call this function. Maybe I am missing something obvious because I am still an 'R' novice. Below is a

Hey, So, I'm having a minor issue. Today, I configured one of our CentOS systems to use our local mirror (mirror.clarkson.edu) for its repositories. After doing this, I ran a yum update and received the following warning in the output along with the updates available: Not using downloaded repomd.xml because it is older than what we have: Current : Thu May 7 14:41:57 2009 Downloaded:

Hola, tengo una serie de datos datExpr, al usar cor() : cor(datExpr ,method = "pearson", use ="pairwise.complete.obs") me da el siguiente error allocMatrix: too many elements specified Trate con "complete.obs", "na.or.complete", y el resto de las opciones para "use", pero siempre me da algun error.  ¿Alguna idea de como puedo hacer que cor() lea

Dear all, I am trying to repeat an example from Sokal and Rohlfs "Biometry" -- Box 11.4, example of a randomized-complete-blocks experiment. The data is fairly simple: series genotype weight 1 pp 0.958 1 pb 0.985 1 bb 0.925 2 pp 0.971 2 pb 1.051 2 bb 0.952 3 pp 0.927 3 pb 0.891 3 bb 0.892 4

Hi, I'm having difficulty to replicate in R a nested (hierarchical) anova example found in p. 308 of Zar, J.H. 1996. Bostatistical Analysis. Prentice Hall. 3rd ed. The example (15.1) is as follows: The variable is blood cholesterol concentration in women (in mg/100 ml of plasma). This variable was measured after the administration of one of three different drugs, each drug having been

Hi everyone, I'm working with the Epical library, specicatly using the power test in proportions. I think this test is not working like in the book: Biostatistical Analysis (4th Edition): Jerrold H. Zar In the example 23.25. (I attach this Pic) It's not the same answer. Using the follow command don't give the same answer. library(epicalc) power.for.2p(0.75, 0.50, 50, 45, alpha =