search for: spp

...quot; 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 = list(c("Spp.80","Spp.111","Spp.129", > "Spp.101","Spp.40","Spp.11","Spp.14","Spp.128","Spp.58","Spp.103","Spp.112", > "Spp.50","Spp.115","Spp.31","Spp.86",&qu...

##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 entered as text in the dataframe "t". ##Full Model > fit<-lm(cmm~inc+factor(mic)+spp/factor(vial),t) > anova(fit) Analysis of Variance Table Response: cmm...

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_, NA_integer_, NA_integer_, N...

..., 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 'Bos' I want to look up the matching 'spp' code in 'tree' and insert the associated 'type' code into a new 'type' column in 'Bos' as in: # add type and keep factors from reference list Bos$type <- tree[as.character(tree$spp)==as.character(Bos$spp), 'type'] Bos$keep <- tree[tree...

...no; * calculate mean dbh in each plot data dbh set datafile; by inventory stratum_no plot_no tree_no leader_no if first.leader_no then output; proc summary data = diameter; by inventory stratum plot tree; var diameter; output out = mean mean=; run; A BENALLA_1 X 1 10 YE=1985 P 1 20.25 slope=14 SPP:P.RAD T 1 25 L 0 28.5 21.3528 F 0 21.3528 SFNSW_DIC:P F 21.3528 100 SFNSW_DIC:P T 2 25 L 0 32 23.1 F 0 6.5 SFNSW_DIC:A F 6.5 23.1 SFNSW_DIC:C F 23.1 100 SFNSW_DIC:C T 3 25 L 0 39.5 22.2407 F 0 4.7 SFNSW_DIC:A F 4.7 6.7 SFNSW_DIC:C P 2 20.25 slope=13 SPP:P.RAD T 1 25 L 0 38 22.1474 F 0 1 SFNSW_DIC:G...

...oduce Ripley's L (the linear version of Ripley's K) for a spatial point dataset (originally a ESRI shapefile). I have coerced the data to a ppp object (because I was originally using 'kest' in the spstat package to produced Ripley's K) but I need to convert my spatial data to a spp object to be able to use 'kfun' in the ads package (this function calculates Ripley's L and confidence intervals). I have searched through help, forums, and package instructions to try and find a way to convert a ppp object to a spp, but I have been unable to find a command. Is there...

....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") rob_xy<-read.dbf("Robbery_98.dbf") auto.spp<-as.points(auto_xy$x/1000, auto_xy$y/1000) rob.spp<-as.points(rob_xy$x/1000, rob_xy$y/1000) image(kernel2d(auto.spp, bbox(auto.spp), h0=4, nx=100, ny=100), col=terrain.colors(10)) pointmap(auto.spp, col="red", add=TRUE) I would need to analyze the relationship betweeb the two Shape...

...m a/x, where a is a factor, are best thought of as 'separate regression models of type 1 + x within the levels of a.'..." This seems very appropriate to my analysis, where I am doing an ANCOVA of tree growth as a function of tree height (ht - continuous) separately for three species (spp) and three light levels (lt). The problem is, I'm not exactly sure how to interpret the results of this model specification, and I can't find any other references to it despite doing searches in the help pages, Google, Jonathon Baron's R site search, and other text books such as Dalgaa...

...s with a name according to the selection. One single selection could be done and named like that: acocar1I=subset(data1,(treat=="I" & data1$block==1 & data1$sp=="acocar" & data1$d0!="NA")) These are some of the functions I have to calculate: acocar1I.spp<-spp(x=acocar1I$gx, y=acocar1I$gy, window=wA) acocar1I.dp<-dval(acocar1I.spp,25,2.5,18,20) And I want to create a 'results' object to access easily all the results: acocar1I.res<-alist() acocar1I.res$data1<-acocar1I acocar1I.res$spp<-acocar1I.spp acocar1I.res$dp<-ac...

[+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 waiting for an okay from you guys. LLILC should mostly Just Work in this new world, with the exception of g...

Dear all: My logistic regression model includes one qualitative and one quantitative predictor variable, aes <- glm(p.a ~ spp * log(light), family=binomial(link=logit)), where spp is abundance of 3 species and light is subcanopy light availability varying from 0 1. I want to test differences among levels of the quantitative variable at a value of x other than the current log(light)=0. I tried using I(log(light+.5...

...omponents for species, population and their interaction. Since both species are present in each population, I figured they are not nested, but are crossed. There is no fixed factor. I conducted a normal aov() to estimate variance components manually via EMS approximation: fm1 <- aov(fruto~pop*spp, data=p1) to test the significance of the variance components I did: fm2 <- aov(fruto~pop+spp+Error(pop:spp), data=p1) Manually, the variance component for the interaction is negative, therefore I turned to lmer() to try to get a better estimate as well as confidence intervals. After invokin...

...nction, and had no problems with indexing: *main=paste("Yield for ", testsub[1,3], " in management region ", testsub[1,2])* The title looks like: "*Yield for Gadus morhua in management region 4X5Y*" I tried bquote from a related help thread, I tried to emulate it: *spp<-testsub[1,3]* *region<-testsub[1,2]* * * *main=bquote(Yield ~ for ~ italic(.(spp)) ~ in ~ management ~ region ~ .(region))* Which doesn't seem to work at all, but when I try not putting anything after the italic: *main=bquote(Yield ~ for ~ italic(.(spp)))* I get: "*Yield for 1*&q...

...= Polygon(10*cbind(c(5,4,2,5),10*c(2,3,2,2))) Sr3 = Polygon(10*cbind(c(4,4,5,10,4),c(5,3,2,5,5))) Sr4 = Polygon(10*cbind(c(5,6,6,5,5),c(4,4,3,3,4)), hole = TRUE) Srs1 = Polygons(list(Sr1), "s1") Srs2 = Polygons(list(Sr2), "s2") Srs3 = Polygons(list(Sr3, Sr4), "s3/4") SpP = SpatialPolygons(list(Srs1,Srs2,Srs3), 1:3) plot(b[[1]]) plot(SpP, add = T) # crop clip1 = crop(b, extent(SpP)) # rasterize returns an error, but documentation says it should return a RasterBrick object clip2 = rasterize(SpP, b, mask = T) Error in v[, r] <- rrv : number of items to replac...

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 Standad deviation 0.08). I explain myself, the model take the mean value of these parameter (ss, emrg, gf, spp, sr) a...

...ugh 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) { > > > data.sp=subset(data.all,CODE_LETTERS==spp) > > responses = colnames(data.all)[c(20,28,29,19)] > #if (spp=="BT") responses = colnames(data.all)[c(19,20,26:29)] > groups=colnames (data.all)[c(9,10,13,16,30)] > > data.sp=subset(data.all,CODE...

I'm stuck on a seemingly simple problem. I'm trying to subset the data by several numbers and it cuts out half of the rows. Here is the sample code: test <- as.matrix(c(1,1,1,1,3,3,7,7,7,7)) Count <- tapply(test[,1], test[,1], length) # count for each value spp <- unique(test[,1]) Count1 <- as.data.frame(cbind(Count,spp)) Max <- max(Count) Count1$sppMax <- ifelse(Count1$Count >= Max, Count1$spp, 0) # only keep values that =Max Count2 <- subset(Count1, sppMax > 0) #get rid of values that are less than Max AllMax <- unique(Count2...

Hello, everybody, I hope somebody could help me with a dist() function. I have a data frame of size 2*4087 (col*row), where col corresponds to the treatment and rows are species, values are Hellinger distances, I should reconstruct a distance matrix with a dist() function. I know that "euclidean" method should be used. When I type: dist(dframe,"euclidean") it gives me a

Hi, consider the following example: I have a matrix like this: spp.mds$points [,1] [,2] CLAP0 1.79703164 -11.66716182 CLAP30 3.87034797 -7.48168377 YBI0000 10.27538316 -3.32226184 YBI0100 0.58463806 -1.25748701 hir10000 5.82907036 -4.09695960 hir10016 -2.22113472 -4.86563557 gyn30000 5.14643671 -3.58706541 gyn30032...

Dear List, I have a data set stored in the following format: > head(dat, n = 10) id sppcode abundance 1 10307 10000000 1 2 10307 16220602 2 3 10307 20000000 5 4 10307 20110000 2 5 10307 24000000 1 6 10307 40210000 83 7 10307 40210102 45 8 10307 45140000 1 9 10307 45630000 1 10 10307 45630600 41 >...