similar to: "/" operator in model formula

Dear statistics and R experts, I am a new R-user and my statistics is probably more than a bit rusty. So forgive me if the following question is relatively simple. I would like to plot the predicted values from a quantile regression analysis (quantreg v.3.31; R v.1.7.1) so that I can evaluate the fit. My response variable is tree growth (continuous), and my predictor variables are height

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

Dear All, I am a researcher doing research in plant growth and I have a statistical problem that seems to not be able to handle. Recently, I conducted an experiment about plant growing in three different nutrient-level sediments. I harvested these every three week (three harvests in all). Some growth traits of these plants were recorded (e.g. total biomass, leaf biomass and stem biomass). In

Good day to the R community, I am interested to run the plot.count() function in the "untb" package. My script is as follows:- > library(untb) > Community1 <- > structure(c(371,167,119,78,74,53,50,31,28,25,20,19,19,17,13,12,12,10, > 9,9,8,8,7,7,7,7,6,6,6,6,5,5,5,5,4,4,4,3,3,3,2,2,2,2,2,2,2,1,1,1,1,1, > 1,1,1,1,1,1,1,1), .Dim = 60, .Dimnames =

##I am trying to test for fixed factor main effects in an unbalanced mixed effects model but when I fit the reduced model for "mic" factor effects, the extra degrees of freedom are being allocated to a nested term rather than the residuals. The model has inc, mic and spp are independent variables and vial nested within spp. inc and spp are already coded as factors since they were

I'm a programmer in a biology lab who is starting to use R to automate some of our statistical analysis of growth rate determination. But I'm running into some problems as I re-code. 1) Hypotheses concerning Slope similarity/difference: I'm using R's anova(lm()) methods to analyse a model which looks like this: growth.metric ~ time * test.tube I understand that

Dear all, I'm quite sure that this is a stupid question, but I'll ask anyway. I want to perform an ANCOVA with two continuous factors and three categorical factors. Plant population growth rate (GR) = dependent variable Seed reduction due to herbivory (SR) = continuous explanatory variable Herbivore species (HS, 2 levels) = categorical explanatory variable Population (Pop, 24 levels) =

Hello, I'm trying to convert a character column in several dataframes to lower case. ### # # Sample data and 'spp' column summaries: # dput(ban.ovs.1993[sample(row.names(ban.ovs.1993), 20), 1:4]) ban.ovs.93 <- structure(list(oplt = c(43L, 43L, 38L, 26L, 35L, 8L, 39L, 1L, 34L, 50L, 10L, 29L, 31L, 24L, 18L, 12L, 27L, 49L, 28L, 51L), rplt = c(NA_integer_, NA_integer_,

Hello, I am unable to figure out how to code a new column in a data frame based on an existing column that matches a column in a reference data frame, in a relational-db fashion. I would like this to maintain a minimum set of reference tables that may be reused over several similar datasets. Specifically, I have two data frames as listed below, 'Bos' and 'tree.' For each case in

Hi! as my subject says I am struggling with the different of a two-way ANOVA and a (two-way) ANCOVA. I found the following examples from this webpage: http://www.statmethods.net/stats/anova.html # One Way Anova (Completely Randomized Design) fit <- aov(y ~ A, data=mydataframe) # Randomized Block Design (B is the blocking factor) fit <- aov(y ~ A + B, data=mydataframe) # Two Way

Hi, I would need some help with the splans package in R. I am using a Shapefile (downloadable at) http://rapidshare.com/files/215206891/Redlands_Crime.zip and the following execution code setwd("C:\\Documents and Settings\\Dejan\\Desktop\\GIS\\assignment6\\DataSet_Redlands_Crime\\Redlands_Crime") library(foreign) library(splancs) auto_xy<-read.dbf("Auto_theft_98.dbf")

Dear all, I have a big data frame: str(data1) 'data.frame': 18272 obs. of 11 variables: $ tag : int 100001 100002 100003 100005 100007 100008 100009 100011 100012 100014 ... $ sp : Factor w/ 18 levels "acassp","acocar",..: 13 5 7 14 14 18 3 11 13 10 ... $ gx : num 20 10 35 68 88 63 123 115 137 136 ... $ gy : num 30 25 24 1 10 40 45 25 23 45 ...

Hello: I am using R mainly on windows XP, version 2.5. I?m a biologist, with a medium level statistics background. I have a problem stating a two-way factorial design where both factors are random. I?m using the lmer() function implemented in the Matrix package version 0.99. My design is as follows: Two species were randomly selected from a total of 4 species. This species are present

I've run an ancova, edadysexo is a factor with 3 levels,and log(lcc) is the covariate (continous variable) I get this results > ancova<-aov(log(peso)~edadysexo*log(lcc)) > summary(ancova) Df Sum Sq Mean Sq F value Pr(>F) edadysexo 2 31.859 15.9294 803.9843 <2e-16 *** log(lcc) 1 11.389 11.3887 574.8081 <2e-16 ***

Hi all! I've written a handy script that uses a for loop to allow me to generate a large number of figures and statistical outputs for a large dataset. I am using indexing to retrieve a species name for the title of my graphs- which worked fine. However, I need to italicize these species names. I originally used the paste function, and had no problems with indexing: *main=paste("Yield

I am trying to rasterize a SpatialPolygon object by a RasterBrick object. The documentation of the raster::rasterize function explicitly says this is allowed. Here's what I am doing # load the raster package library("raster") # create a raster brick object using the example from the brick function documentation b <- brick(system.file("external/rlogo.grd",

R-users - I must preface this question by saying that I'm a relative newbie to both R and mixed-modeling. I'm using lme fit an ANCOVA-like model. My data consist of bone length measurements for a developmental series of two capuchin monkey species. I'm interested in whether the rate of bone length scaling to body mass (i.e., growth) differs between species. My call to lme

I am trying to create a heap of boxplots, by looping though a series of factors and variables in a large data.frame suing paste to constrcut the facto and response names from the colnames I thought I could do this using get() however it is not working what am I doing wrong? thanks Nevil Amos > sp.codes=levels(data.all$CODE_LETTERS) > > for(spp in sp.codes) { > > >

[+CC llvm-dev this time] Hi, As discussed in the review thread in http://reviews.llvm.org/D16439, the future plan around statepoints, deopt bundles, PlaceSafepoints etc. is to "constant fold" -spp-no-statepoints and -rs4gc-use-deopt-bundles to true. We (Azul) have moved to a representation of safepoint polls, deopt state etc. that enables us to do the above; and at this point I'm

Dear all of you, I would like to get some help from you. Here I attach you a model, that I would like to be stochastic so I would need each time the value of x changed, the values of parameters (ss, emrg, gf, spp, sr) did too according to a normal distribution, with mean its value and standard deviation given by the parameter name preceded by SDV (In the case of parameter ss, mean is 0.3 and