similar to: Replicated goodness-of-fit test

[This was filtered (looking like spam) and manually approved by your list maintainer, MM ] EvolDir wrote: > Colleagues, > > A few days ago I posted a message inquiring what folks were using for > teaching biostats. I received a stunning 38 replies offering various > opinions, some quite surprising, along with links to web pages, pointers for > coverage, commentaries on

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

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 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.

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 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 /

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

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 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

I would suggest the method of Sokal and Rholf (1995) S. 498, using the F test. Below I repeat the analysis by Spencer Graves: Spencer: > df1 <- data.frame(x=1:3, y=1:3+rnorm(3)) > df2 <- data.frame(x=1:3, y=1:3+rnorm(3)) > fit1 <- lm(y~x, df1) > s1 <- summary(fit1)$coefficients > fit2 <- lm(y~x, df2) > s2 <- summary(fit2)$coefficients > db <-

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

I am doing hierarchical clustering with cluster package.  I couldnot find similarity measures like matching coefficient, Jaccard coefficient and sokal and sneath. Could anyone please tell package with similarity measures for binary data? kind regards, Ms.Karunambigai M PhD Scholar Dept. of Biostatistics NIMHANS Bangalore India From cricket scores to your friends. Try the Yahoo! India

Hello, We are trying to migrate from an old application based on VOS to some linux based telephony server. We are investigating bayonne and asterisk, and we still don't know what is the best option for us. One of the limitations is our old hardware, we have in stock some old Dialogic boards. Does asterisk work with such boards? The other important limitation is that the application needs to