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Dear list member, My question is related to input file format to an Anova from car package. Here is an example of what I did: My file format is like this (and I dislike the idea that I will need to recode it): Hormone day Block Treatment Plant Diameter High N.Leaves SH 23 1 1 1 3.19 25.3 2 SH 23 1 1 2 3.42 5.5 1 SH 23 1 2 1 2.19 5.2 2 SH 23 1 2 2 2.17 7.6 2 CH 23 1 1 1 3.64 6.5 2 CH 23 1 1 2

I am trying to define groupings from levels of factor variables and this the warning message that R give "& not meaningful for factors". The nature of my task is this. I have a variable stage which has the levels (1B, 2A, 2B) - these are the AJCC TNM stages of cancer, and another variable diameter with factor levels ("=< 4", "4 - 6.5, > 6.5; limit values are

Hi, I have a function that generates a set of data but I am having problems determining the parameters using the nls fitting procedure. #### "MH"<-function(field,diameter,mu=10e-7,sig=0.1,Ms=100,chi=0){ #variables mu, sig, chi, Ms #input: field and diameter #all in CGS rho <- 5 kb <- 1.38e-16 t <- 300 length.d<-length(diameter) length.H<-length(field)

Dear list I'm trying to solve the following problem since 2 days with no success. Could someone help a newbie, please ? I have a dependant variable which is diameter of shell, and two factors which are level on the beach (3 levels 'B', 'E' and 'H') and a hydrodynamics indice (3 levels 1, 2 and 3) To study diameter variations according to these factors, I may use

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

Hello, I want to fit a Gompertz model for tree diameter growth that depends on a 4 levels edaphic factor (?Drain?) and I don?t manage to introduce the factor variable in the formula. Dinc is the annual diameter increment and D is the Diameter. >treestab > Dinc D Drain [1,] 0.03 26.10 2 [2,] 0.04 13.05 1 [3,] 0.00 24.83 1 [4,] 0.00 15.92 4

Hi, Maybe this is more a programming questions than a specific R-project question, but maybe there is someone who can point me in the right direction. I have a picture of cirkels which I took with a digital camera. Now I want to use the diameter of the cirkels on the picture for analysis in R. I can use pixmap to import the picture, but how do I find the outside cirkels and calculate the

I'm experiencing some confusion with the ellipsis argument (...). In my .Rprofile, I have the following functions: stderr <- function(x, ...) { sqrt( var(x, ...) / length(x) ) } se <- stderr I can use tapply to calculate some means: > tapply( Diameter, factor(Region), mean, na.rm=TRUE ) I II III IV V 0.02896429

I have a data set with the following structure (with many more obs.): var1 expt day diameter 1 1 2 0.5 1 1 3 0.9 1 1 4 1.3 1 1 5 1.7 1 2 2 0.3 1 2 3 0.5 1 2 4 0.9 1 2 5 1.6 2 1 2 0.7 2 1 3 1.2 2 1 4 1.6 2 1 5 2.3 2 2 2 0.4 2 2 3 0.8 2 2 4 1.6 2 2 5 3.2 I can get separate regression analysis for each level of var1 and expt with the command: by(data3, data3$var1:data3$expt, function(x)

Hi all, I would like to test the homoegeneity of variances between several linear model for some analysis of covariance. It seems that the Bartlett's test is a good test to use but I am having problem using with linear model and I cannot find any examples on the internet. There are some examples for comparisons of variances but not linear models. If I take the hellung data set, which is

Hello list, I wish to perform a randomization test on the F-statistics of a 2 way ANOVA but have not been able to find out how to do so - is there a package / function that can perform this that I am unaware of? FactorA has 6 levels (0,1,2,3,4,5) whereas FactorB has 3 (1,2,3). A sample: Resp. FactorA FactorB 2 0 2 3 1 2 1 2 2 0 3 2 0 4 2 0 5 2 4 0 1 6 1 1 1 2 1 0 3 1 1 4 1 0 5 1 2 0 2 3 1 2 1

Dear All, I wish to calculate the Gini index (ineq from same package) and some other indices for the diameter distribution of each plot (df dgtot). dgtot: IDPlot Diameter(cm) 1 4 34.0 2 4 23.0 3 4 38.0 ... 51 5 16.0 52 5 8.0 53 5 9.0 ... 5301 140 25.0 5302 140 12.0 5303 140 7.0 I use: >

Hi everyone, I intend to do a discriminant analyse for 2 measures(eye diameter and body length) and for different areas to show differences between those areas if there are any. The raw data (eye diameter, body length) make one cloud of points so it seems there aren't any differences between those areas. If I take the mean values (eye diameter) for length classes, I nearly get linear functions

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a

Hello everbody, I am new to this mailing list and hope to find some help. I'm trying to get into the spatstat package and encountered two problems. First a graphical one: There is an example dataset called "finpines" which has several marks (http://www.oga-lab.net/RGM2/func.php?rd_id=spatstat:finpines) When I pass the given code from the website to R data(finpines)

Dear List, Im new to R - making a transition from SAS. I have a space delimited file with the following structure. Each line in the datafile is identified by the first letter. A = Inventory (Inventory) X = Stratum (Stratum_no Total Ye=year established) P = Plot (Plot_no age slope= species) T = Tree (tree_no frequency) L = Leader (leader diameter height) F = Feature (start_height finish_height

Hello, list, I am making a self-starting nonlinear function to model the relation of tree height (H) and diameter (D) in a forest stand. The function I am trying is H=a*exp[b*(D+5.8)^(-c)]. To calculate the initial estimates of the parameters, I linearized the formula by taking logarithms and fixing the parameter c=1. Then I calculated the initial estimates of a and b using lm() on the

Hi, I am attaching a plot where you can see there are a few "jumps" (plots 1, 4, 5 and 6), due to incidents with the measuring sensors (basically someone touching the sensor). I need to revert those changes to have a plot without unreal measurements, so make those fragments go back to its original pattern before the jump. I have used the function cpt.mean {changepoints} so I can

Dear all, I am making a barplot as following: barplot(c(1,2,3,5,2,3,1),names.arg=c("100","200","300","400","500","600","700"),xlab="diameter",ylab="flow",main="some title",space=0.1) I am also trying to add a probability density curve, however using lines(density(c(1,2,3,5,2,3,1))) does not give

Good morning! I recently implemented a KD tree in JAVA for faster kernel density estimation (part of the code follows). It went well. To hook it with R, however, has proved more difficult. My question is: is it possible to implement the algorithm in R? My impression seems to indicate no as the code requires a complete class-object framework that R does not support. But is there an R package or