similar to: Extrapolated regression lines

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) =

When I perform a two-tailed sign test with the following simple syntax, binom.test(59,100) R returns a P-value (0.088) but nothing else. As I want the result for a one-tailed test I take P/2 = 0.044). However, the journal to which I've submitted my results requests the test statistics, not just the P-values. How can I make R return the test statistics? Best regards, Johan Stenberg, Umea

Dear R-help, I have fitted a glm logistic function to dichotomous forced choices responses varying according to time interval between two stimulus. x values are time separation in miliseconds, and the y values are proportion responses for one of the stimulus. Now I am trying to extrapolate x values for the y value (proportion) at .25, .5, and .75. I have tried several predict parameters, and they

Dear all, How do I make R show the R-squared (deviance explained by the model) in a logistic regression? Below is how I write my syntax. Basically I want to investigate density-dependence in parasitism of larvae. Note that in the end I perform a F-test because the dispersion factor (residual deviance / residual df) is significantly higher than 1. But how do I make R show the

Dear all I generated predicted risk of death for each subject in the study by means of Cox proportional hazards model at 8 year of follow-up, a time point at which follow-up was more than 90% complete. It is possible to extrapolate to 10-year the predicted risk of each subjet by assuming an exponential distribution? Any help would be greatly appreciated. Thanks for your consideration.

I'm having the most curious error while using the sem package. For the model I'm working with, I keep getting the following error: Error in J[cbind(1:n, observed)] <- 1 : subscript out of bounds I''ve used debug=TRUE with sem, and there don't appear to be any problems with model - there are no latent variables in this model. The variables in the covariance matrix

Readers, Data was imported using the read csv command: dataimport<-read.csv("/path/to/dataimport.csv") 10,2000 12,2001 13,2002 15,2003 17,2004 Using the help contents for 'predict.lm' (i.e. ?predict.lm) a new data frame was created dataimportextra<-data.frame(x=seq(1990,2010,1)) predict(lm(dataimport),dataimportextra[,2],se.fit=TRUE)

I have created a list of tables with the same columns but different number of row. Example (actual list has ~200 elements): > temp1<- data.frame(ID=c("Herb","Shrub"),stat=c(4,5),pvalue=c(.03,.04)) > temp2<- data.frame(ID=c("Herb","Shrub", > "Tree"),stat=c(12,15,13),pvalue=c(.2,0.4,.3)) > L<-list(a=temp1,b=temp2) > L $a

typically, the apply family wants you to use vectors to run functions on. However, I have a function, kruskal.test, that requires 2 arguments. kruskal.test(Herb.df$Score,Herb.df$Year) This easily computes the KW ANOVA statistic for any difference across years.... However, my data has multiple sites on which KW needs to be run... here's the data: Herb.df<-

Hi all, I was just wondering if this is the intended behavior. Here is my setup: controller def index respond_to do |f| f.xml { render :xml => true } f.html { render :layout => :none } end end In my views I have a file for each type index.herb index.xerb The first request I send is cached and interferes with the other one. For example, if I send an xml request

Recently, I ran a series of Kruskal-Wallace tests [kruskal.test()] using by() to group by site Output is a list: >Herb.KW Herb.df$ID: 10-1 Kruskal-Wallis rank sum test data: Indicator_Rating by Year Kruskal-Wallis chi-squared = 15.24, df = 7, p-value = 0.03302 ----------------------------------------------------------------------------------------------------- Herb.df$ID: 18-1

I want to make a log-log plot with a regression line, but I can't figure out what I'm doing wrong. What I'm trying is: plot(mass,area, log="xy", pch=as.numeric(food)) abline(lm(mass~area)) or plot(mass,area, log="xy", pch=as.numeric(food)) islands$logmass <- log(mass) islands$logarea <- log(area) attach(islands) abline(lm(logmass~logarea)) But that does

Dear R-Experts, Here below my R code working but I don't know how to complete/finish my R code to get the final plot with the extrapolation for the10 more years. Indeed, I try to extrapolate my data with a linear fit over the next 10 years. So I create a date sequence for the next 10 years and store as a dataframe to make the prediction possible. Now, I am trying to get the plot with the

Dear members of the R help list, I want to do a hierarchical glm with binomial family but am unsure about how to write the syntax which involves nesting. I want to test whether the risk of being attacked by Herbivores for Meadowsweet plants is significantly dependent on the Distance to heterospecific source plants. Dependent variable = Herbivory (yes/no) Explanatory continuous variable =

If I have a data frame where a species occupies several rows with different phases such as (both col's ar factors): species,phase Populus tremula,1 Populus tremula,2 Populus tremula,3 Calluna vulgaris,1 Calluna vulgaris,2 Betula alba,1 Betula alba,2 Betula alba,3 Primula veris,1 Primula veris,2 and another df where each species only have one row: species,growth_form Populus tremula,tree Acer

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I

Hi In R, using plot(x,y) followed by abline(lm(y~x)) produces a graph with a regression line spanning the whole plot . This means that the line extends beyond the swarm of data points to the defined of default plot region. With par(xpd=T) it will span the entire figure region. But how can I limit a regression line to the data range, i.e between (xmin,ymin) and (xmax,ymax)? Sorry for

Hi, We have an experiment where individuals responses were measured over 5 days. Some responses were not obtained because we only allowed individuals to respond within a limited time-frame. These individuals are given the maximum response time as they did not respond, yet we feel they may have done if given time (and by looking at the rest of their responses over time, the non-response days stand

I have run a kruskal.test() using the by() function, which returns a list of results like the following (subset of results): Herb.df$ID: 4-2 Kruskal-Wallis chi-squared = 18.93, df = 7, p-value = 0.00841 -------------------------------------------------------------------- Herb.df$ID: 44-1 Kruskal-Wallis chi-squared = 4.43, df = 6, p-value = 0.6187 So then, how do extract a vector

> Hello, Hi :) First off, could you try to set your email client to break long lines before transmitting? In my (somewhat outdated) pine, the lines appear as VERY long lines when I try to reply, making it hard to read :) Minor detail though, I should probably fix pine. Some day. > The way you describe how the jitter buffer should be implemented makes me > wonder: How does the