similar to: Creating vegetation distance groups from one column

Hi R-listers, I am trying to do an ANOVA for the following scatterplot and received the following error: library(car) scatterplot(HSuccess ~ Veg, data = data.to.analyze, xlab = "Vegetation border (m)", ylab = "Hatching success (%)") anova(HSuccess ~ Veg, data=data.to.analyze) Error in UseMethod("anova") : no applicable

Hi R-listers, I have developed 47 GLM models with different combinations of interactions from 1 variable to 5 variables. I have manually made each model separately and put them into individual tables (organized by the number of variables) showing the AIC score. I want to compare all of these models. 1) What is the best way to compare various models with unique combinations and different number

Hi R-listers, Q1) What is the difference between the scatterplot and plot function? Q2) I am able to make a graph with the scatterplot function: scatterplot(DevelopIndex ~ Veg, + data = Turtle, + xlab = "Vegetation border (m)", + ylab = "Embryonic development index") And have been successful. But I do not know if the lines are for:

Hello all, turns out i'm having a bad R week. I am at my wits end with a function that I am trying to write. When I run the lines of code outside of a function, I get the desired output. When I wrap the lines of code into a function it doesn't work as expected. Not sure what is going on here. I suspected that the syntax of the if statement with the for loop was the culprit, but when I

Hi R-listers, I am wondering if the function aov() in plyr is appropriate for two different types of tests: 1) > summary(aov(EDI ~ VegIndex, data=data.to.analyze)) AND 2) > summary(aov(HSuccess ~ VegIndex + Aeventexhumed + VegIndex:Aeventexhumed, data=data.to.analyze)) the later inclusive of an interaction of the two explanatory variables. This is for obtaining P value and F

Hello all, I am going slightly mad trying to create a table for running co-correspondence analysis. What I have is seed bank and vegetation data, and my aim is to see if the vegetation found in a site (containing several seed bank samples) can predict the composition of a seed bank sample within that site. So for this I need two tables with matching rows. I have created an empty matrix,

Hi All, I am wondering if anyone can tell me what the warning message below the model means? J add1(DTA.glm,~ Aeventexhumed + Veg + Berm + HTL + Estuary + Rayos) Single term additions Model: cbind(MaxHatch, TotalEggs - MaxHatch) ~ Aeventexhumed + Veg + Berm + HTL Df Deviance AIC <none> 488.86 4232.9 Estuary 1 454.96 4201.0 Rayos 3 258.80 4008.9 Warning

After looking at 48 glm binomial models I decided to try the quasibinomial with the top model 25 (lowest AIC). To try to account for overdispersion (residual deviance 2679.7/68 d.f.) After doing so the dispersion factor is the same for the quasibinomial and less sectors of the beach were significant by p-value. While the p-values in the binomial were more significant for each section of the

Hi everybody, I know this might be very off topic and it took me quite a while to up my courage to post this?. But I remember a thread some time ago about how we can find the packages we need to do specific tasks in R if we don?t know before hand which ones actually do it. Now all the packages are listed alphabetically on the web site. Since I am not very advanced in writing my own functions I

Hi R-users: [R 2.2 on OSX 10.4.3] I have a (sparse) vegetation data frame with 500 rows (sampling units) and 177 columns (plant species) where the data represent % cover. I need to summarize the cover data by returning the names of the most dominant and the second most dominant species per plot. I reduced the data frame to omit cover below 5%; this is what it looks like stacked. I have

Hi, I would like to construct a transition matrix from a data frame with annual transitions of marked plants. plant<-c(1:6) class<-c("seed","seed", "seed", "veg", "rep", "rep") fate<-c("dead", "veg","veg","rep", "rep", "veg") trans<-data.frame(plant, class, fate)

I am trying to make some plots for a poster. Each one needs to be 6 inches by inches. Right now the fonts are too small and the plots don't read well on my poster. I am pretty much an R newbie and I can't figure out how to change the fonts. It would also be nice to know how to specify the size of the plot and maybe how to save the plots as jpeg files. Any help would be much appreciated! I

i want to tag categories to its menuname. i have a csv containing menu item name and in other csv i have a column containing some strings, i want to pick that strings from categories and look into menu items if any menu item containing that string i want to create a new column next to menu item name flagged as 1 otherwise 0 and the only condition is once a menu item flagged as 1 i don't need

Hello. First post, though I have read scores. Mostly i can solve my problems reading the archives, but I lack the vocabulary to ask this one right. Any social faux pais, I apologize. I am sure this is a very basic question and I am embarrassed to ask, but I spent several hours scouring the archives and trying various suggestions, but I am too new to get any of them to work. I am trying to

Hi all, How can I overlay these two histograms? ggplot(gg, aes(gg$Alz, fill = gg$veg)) + geom_histogram(alpha = 0.2) ggplot(tt, aes(tt$Cont, fill = tt$veg)) + geom_histogram(alpha = 0.2) thanks for any help! Elahe

Hello I've been asked to help evaluate a vegetation data set, specifically to examine it for community similarity. The initial problem I see is that the data is ordinal. At best this only captures a relative ranking of abundance and ordinal ranks are assigned after data collection. I've been trying to find a procedure in R that can handle ordinal based classification and so far have

Hello I've created a 3d scatterplot, and had no problems labeling the points. However, I've been really struggling to change the color of the points based on a factor (see 'group' below). Is such a thing possible? My data look like this: food group x y z apple fruit 0.216 -0.112 -0.893 orange fruit 0.814 0.097 0.460 broccoli veg -0.239 0.240 -0.425 banana

Hello I'm trying to create a barplot with a couple of stacked positive values and with one negative value for each group. example: trees<-c(20,30,10) shrubs<-c(12,23,9) veg<-c(2,3,4) soil<-c(-100,-123,-89) example1<-t(cbind(trees,shrubs,veg)) barplot(example1) #this works so far #but now: example2<-t(cbind(trees,shrubs,veg,soil)) barplot(example2) This shows no more

Hi, everyone, I haven't found anything similar in the forum, so here's my problem (I'm no expert in R nor statistics): I have a data set of 59.000 cases with 9 variables each (fractional coverage of 9 different plant types, such as deciduous broad-leaved temperate trees or evergreen tropical trees etc.), which was generated by a vegetation model. In order to evaluate the quality of

Hi R-listers, I am trying to compare sections of the beach separated from the HTL to the Veg (east to west), separated into indices (-5 to 30m), HTLIndex. Cross parallel (north to south) are major beach sections (Rayos 1, 2, 3, 4 and MNB). I am thinking to do an ANOVA for each independent rectangle of beach (not exactly but will be treated as). The HTL from (0-5m) in Rayo 1 is one rectangle of