search for: seedling

Dear All, I am analysing a dataset on levels of herbivory in seedlings in an experimental setup in a rainforest. I have seven classes/categories of seedling damage/herbivory that I want to analyse, modelling each separately. There are twenty maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (block...

Dear R gurus, I have XY data giving the locations of tree seedlings that were surveyed during a 210 meter belt transect. This belt transect was taken by stretching a line across the field, then measuring all seedlings within 1 meter on either side of the line. The end result was XY coordinates and height for ~1,300 seedlings. I would like to use that...

Dear all I was trying to put a title for my plot, but i got this result, > x11(width=10,height=5,title="seedling");par(mfrow=c(1,2))Error in x11(width = 10, height = 5, title = "seedling") : unused argument(s) (title = "seedling")> The title is not taking How can i give a title for the plot (where in need to make 2 plots within a window of x11() ) thanking you in anticipation...

Hi everybody, I am trying to fit a model with the lmer function for mixed effects. I have an experimental design consisting of 5 field plots. Each plot is divided in 12 subplots where the influence of three factors on the growing of tree seedlings is tested: (1) seed (1 = presence; 0 = absence); (2) seedling species (oak holm vs. pine); (3) treatment (three different treatments). In each of these subplots we planted 13 seedlings. Therefore I would have a model with three fixed factors and one random factor (a block?). If I´m not wrong the m...

...ok: http://books.google.com/books?id=8D4HVx0apZQC&lpg=PA799&ots=rQgd_8ofeS&dq=r%20coxph%20crawley&pg=PA799#v=onepage&q&f=false --------------------------------- My result: > summary(model1) Call: coxph(formula = Surv(death, status) ~ strata(cohort) * gapsize, data = seedlings) n= 60, number of events= 60 coef exp(coef) se(coef) z Pr(>|z|) gapsize -0.001893 0.998109 0.593372 -0.003 0.997 gapsize:strata(cohort)cohort=September 0.717407 2.049112 0.860807 0.833 0.405...

...ersion: 1.8.0 OS: i686-pc-linux-gnu (Suse 8.2) Submission from: (NULL) (134.84.86.22) Two bugs (perhaps related, perhaps independent) revealed by the same Poisson regression with offset mydata <- read.table(url("http://www.stat.umn.edu/geyer/5931/mle/seeds.txt")) out.fubar <- glm(seedlings ~ burn01 + vegtype * burn02 + offset(log(totalseeds)), data = mydata, family = poisson) summary(out.fubar) out.barfu <- glm(seedlings ~ burn01 + vegtype * burn02, offset = log(totalseeds), data = mydata, family = poisson) summary(out.barfu) out.ok <- glm(seedlings ~ vegtype * burn02...

Hello. I am new to R. And, I want to perform a multiple nested anova on a large datasets (with 9448 observations). Under the helps from R-Sig-ecology mailing list, I have gained many progresses. But I still have some confusions. I want to ask for some helps here. my dataset("SeedL.txt") was not attached. Data are not sorted by factors. In this dataset, "SpecN"

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the soil substrate contribute positively to the survival of the seedlings, over a 3 year time period (for simplicity I...

Hello, I am trying to use the projection.matrix( ) function and am following the example given. I have my data formatted very similar to the test.census example. > str(AsMi05mat) `data.frame': 1854 obs. of 6 variables: $ Tag : num 501 502 503 504 505 506 507 508 509 510 ... $ Year : int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ... $ Length : num 34

...iption and code are below: I have an unbalanced dataset consisting of three different species (hem, yb, and sm), with unequal numbers of wood pieces in each species group. I am trying to generate a plot that will show the size of the wood piece on the X axis, the probability of it having tree seedlings growing on it on the Y (a binomial yes or no variable), and three fitted curves showing how the probability of having tree seedlings changes with increasing wood piece size for each species. I have no problem generating fits using GLM, and no problem creating the plot. However, if I try to...

...le pair of interactions, when I try to evaluate two pairs of interactions( flowers*gopher, flowers*rockiness) my computer runs out of memory, and the larger desktop I use just doesn't go anywhere after about 20 minutes. Is it really that big a calculation? to start: mle2(minuslogl = Lily_sum$seedlings ~ dnbinom(mu = a, size = k), start = list(a = 10, k = 1)) then: i2<-interaction(Lily_sum$flowers, Lily_sum$gopher) i3<-interaction(Lily_sum$flowers, Lily_sum$rockiness) mle2(Lily_sum$seedlings ~ dnbinom(mu = a, size = k), start=list(a=10,k=1) ,parameters=list(a~i3+i2+Lily_sum$flowers)) (...

...1,2,3 not allowed for block > predict.nlme(area3.pen.nlme) does not produce an error. area3.pen.nlme was created with: > area3.pen.nlme <- nlme(area ~ SSlogis(day, Asym, xmid, scal), + data=area.pen.data, + fixed = Asym + xmid + scal ~ dose + block, + random = Asym + xmid + scal ~ 1|seedling, + weights = varPower(), + start = c(2887,0,0,0,10.7,0,0,0,2.29,0,0,0)) > area, day and dose are numeric block and seedling are factors area is the area in mm2 of one growing leaf for each seedling. with fixed = Asym + xmid + scal ~ dose or fixed = Asym + xmid + scal ~ block it works....

..., as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between 2 light treatments (high, low). Within each shadehouse there are 12 seedlings of each of 2 species (hn & sl). 3 damage treatments (0, 0.1, 0.25 leaf area removal) were applied to the seedlings (at random) so that there are 4 seedlings of each species*damage treatment in each shadehouse. There maybe a shadehouse effect, so I need to include it as a random effect. Li...

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the soil substrate contribute positively to the survival of the seedlings, over a 3 year time period (for simplicity I...

Hi All, I am trying to do a survivorship analysis with library(survival)from a data set that looks like this: I followed a bunch of naturally germinated seedlings of an annual plant from germination to death (none made it to reproduce, and died in a period of ~60 days after germination.) I also know the size of the seed of every individual censused. So I am trying to analyze seedling survival as a function of seed size. I performed 5 censuses in unequal in...

...quot;Abundance (%)",ylim=c(0,100),xlim=c(0.7,4.3),pch=22,bg="white",cex=0.75,las=1) par(new=T) plot(c(1:4),mw[5:8],type="o",xaxt="n",xlab="",yaxt="n", ylab="",ylim=c(0,100),xlim=c(0.7,4.3),pch=15,cex=0.75) text(0.7,90,adj=0,"Seedlings",font=2) legend("topright",c("Surr.","Gaps"),bty="n",pch=c(15,0)) ################################################################### par(ps=8,mgp=c(2.25,1,0),mar=c(0,4,0,2)) attach(ad) plot(c(1:4),mw[1:4],type="o",xaxt="n",xlab=&q...

Hi all, I am trying to run a glm with mixed effects. My response variable is number of seedlings emerging; my fixed effects are the tree species and distance from the tree (in two classes - near and far).; my random effect is the individual tree itself (here called Plot). The command I've used is: mod <- glmer(number ~ Species + distance + offset(area) + (1|Plot), family = poisson)...

...ine=='below'],alternative = "two.sided", + mu = 0, sigma.x =(sd(growth[type=='SD'& from_treeline=='above'],na.rm = T)), + sigma.y =(sd(growth[type=='SD'& from_treeline=='below'],na.rm = T)), conf.level = 0.95)> sdgr.ztest # Z-test for Seedling Growth above and below treeline Two-sample z-Test data: growth[type == "SD" & from_treeline == "above"] and growth[type == "SD" & from_treeline == "below"] z = NA, p-value = NAalternative hypothesis: true difference in means is not equal to 0...

dear allI have a data set with three types (Tree, Sapling, Seedling). I have estimated the correlation values. now i need to bring all the correlation values in a table like the one i have shown in attached file with R codes.could you please give me idea on this problem thanking you MSNepal ______________________________________________________________...

...16 1 Group3 57 16 1 ========================== And I need to compare surviving among the particular groups. Fitting works normally: ================================================================ > cph.1 <- coxph(Surv(death, censor) ~ group + cluster(block), data = seedlings) > summary(cph.1) Call: coxph(formula = Surv(death, censor) ~ group+ cluster(block), data = seedlings) n= 27000 coef exp(coef) se(coef) robust se z p groupGroup2 0.436 1.55 0.0539 0.296 1.47 0.14 groupGroup3 3.048 21.06 0.0439 0.283 10.77...