[R] survival object
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 intervals of time starting 15 days after germination until everyone died. Does that make my data right censored? So I have the following variables: seed size (as a continuous variable and as a categorized variable in big and small with 0=small and 1=big), the 5 census events (with 0=dead 1=survivors) First, I want to make a survival object with Surv() but apparently this function only takes two intervals of time (time, and time2). Is there a way to include my five census events in it? With the survival object I can go on and fit a model with survfit(survivalobject~seedsize,data=mydata), right? thanks -- Eugenio Larios PhD Student University of Arizona. Ecology Evolutionary Biology. elari...@email.arizona.edu [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] SMATR common slopes test
Hi All, I am confused with SMATR's test for common slope. My null hypothesis here is that all slopes are parallel (common slopes?), right? So if I get a p value 0.05 means that we can have confidence to reject it? That slopes are different? Or the other way around? it means that we have statistical confidence that the slopes are parallel? thanks -- Eugenio Larios PhD Student University of Arizona. Ecology Evolutionary Biology. (520) 481-2263 elari...@email.arizona.edu [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] SMATR common slopes test
great thanks a lot! On Sat, Nov 6, 2010 at 3:54 PM, Kevin Middleton k...@csusb.edu wrote: Eugenio - I am confused with SMATR's test for common slope. My null hypothesis here is that all slopes are parallel (common slopes?), right? So if I get a p value 0.05 means that we can have confidence to reject it? That slopes are different? Or the other way around? it means that we have statistical confidence that the slopes are parallel? Try this: set.seed(5) n - 20 x - rnorm(n) y1 - 2 * x + rnorm(n) y2 - 2 * x + rnorm(n) y3 - 4 * x + rnorm(n) # Slopes approximately equal slope.com(x = c(x, x), y = c(y1, y2), groups = rep(c(1,2), each = n)) #$p #[1] 0.4498037 # Slopes of 2 and 4 slope.com(x = c(x, x), y = c(y1, y3), groups = rep(c(1,2), each = n)) #$p #[1] 0.0003850332 Cheers, Kevin - Kevin M. Middleton Department of Biology California State University San Bernardino -- Eugenio Larios PhD Student University of Arizona. Ecology Evolutionary Biology. (520) 481-2263 elari...@email.arizona.edu [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] help with an unbalanced split plot
Hi Dennis, The first thing I did with my data was to explore it with 6 graphs (wet-high, med, and solo-; dry-high, med, and solo-) and gave me very interesting patterns: seed size in wet treatments is either negatively correlated (high and medium densities) or flat (solo). But dry treatments are all positively correlated! There is a very interesting switch there. I also figured out why I can't do three way interactions. I explored the structure of my data with str(mydata) and it shows that water treatment has three levels when it should have just two. Then I went back to the excel sheet, tried to sort the data by water treatment and I discover a single data point from the wet treatment sticking out by itself. That is why R reads three levels and since it is only one point, there cannot be any stats of course. thanks E On Thu, Oct 14, 2010 at 9:27 PM, Dennis Murphy djmu...@gmail.com wrote: Hi: On Thu, Oct 14, 2010 at 7:50 PM, Eugenio Larios elari...@email.arizona.edu wrote: Hi Dennis, thank you very much for your help, I really appreciate it. I forgot to say about the imbalance, yes. I only explained the original set up, sorry. Let me explain. It is because in the process of the experiment which lasted 3 months I lost individuals within the plots and I actually ended up losing 2 whole plots (one dry and one wet) and some other individuals in other plots. That still leaves you balanced at the plot level :) Fortunately, you have enough replication. If you have missing subplots within the remaining plots, that would be another source of imbalance at the subplot level, but you should have enough subplots to be able to estimate all of the interactions unless an entire treatment in one set of plots was missing. It's worth graphing your data to anticipate which effects/interactions should be significant; graphs involving the spatial configuration of the plots and subplots would also be worthwhile. My study system has this special feature that allows me to track parental seed sizes in plants germinated in the field, a persistent ring that stays attached to the root even when the plant has germinated, so some of the plants I lost did not have this ring anymore. It happens sometimes but most of the time they have it. Also, some plants disappeared probably due to predation, etc That made my experiment imbalanced. That's common. No big deal. Do you think that will change the analysis? Also, do you think I should use least squares ANOVA (perhaps type III due to the imbalance?) instead of LMM? What about the random effects that my blocking has created? Actually, with unbalanced data it's to your advantage to use lme() over ANOVA. Just don't place too much importance on the p-values of tests; even the degrees of freedom are debatable. With unbalanced data, it's hard to predict what the sampling distribution of a given statistic will actually be, so the p-values aren't as trustworthy. You mentioned that you couldn't fit a three-way interaction; given your data configuration, that shouldn't happen. (1) Get two-way tables of water * density, one for the counts and one for the averages, something like with(mydata, table(water, density)) aggregate(log(fitness) ~ water + density, data = mydata, FUN = mean, na.rm = TRUE) In the first table, unless you have very low frequencies in some category, your data 'density' should be enough to estimate all the main effects and interactions of interest. The second table is to check that you don't have NaNs or missing cells, etc. I am new to R-help website so I wrote you this message to your email but I would like to post it on the R website, do you know how? Wag answer: I hope so, since I managed to view and respond to your message :) More seriously, in gmail, the window that opens to produce replies has an option 'Reply to all'. I don't know if your e-mail client at UofA has that feature, but if not, you could always cc R-help and put the e-mail address in by hand if necessary. Most mailers are smart enough to auto-complete an address as you type in the name, so you could see if that applies on your system. I keep a separate account for R-help because of the traffic volume - if you intend to subscribe to the list, you might want to do the same. It's not unusual for 75-100 e-mails a weekday to enter your inbox... Thanks again! Eugenio On Thu, Oct 14, 2010 at 5:34 PM, Dennis Murphy djmu...@gmail.com wrote: Hi: On Thu, Oct 14, 2010 at 3:58 PM, Eugenio Larios elari...@email.arizona.edu wrote: Hi Everyone, I am trying to analyze a split plot experiment in the field that was arranged like this: I am trying to measure the fitness consequences of seed size. Factors (X): *Seed size*: a continuous variable, normally distributed. *Water*: Categorical Levels- wet and dry. *Density*: Categorical Levels- high, medium and solo *Plot*: Counts from 1 to 20 The *response variable *(Y
[R] help with an unbalanced split plot
Hi Everyone, I am trying to analyze a split plot experiment in the field that was arranged like this: I am trying to measure the fitness consequences of seed size. Factors (X): *Seed size*: a continuous variable, normally distributed. *Water*: Categorical Levels- wet and dry. *Density*: Categorical Levels- high, medium and solo *Plot*: Counts from 1 to 20 The *response variable *(Y) was the number of seeds produced at the end of the season. The experiment started 15 days after plants germinated in the field. 20 plots were chosen where there was high enough density so I could manipulate it. In an area where artificial irrigation was possible for the wet treatment, dry treatment was natural precip. Water was blocked so 10 plots were wet and the other 10 were dry. Randomly assigned. Within those 20 plots 6 focal plants were chosen and randomly assigned the three densities. (split plot design) I did not control for seed size since it is continuous and normally distributed, hoping that with 120 plants total (6 in each 20 blocks) I could get all kind of sizes for every treatment. It worked ok. I have been trying to analyze this with lme (library NLME). I am not quiet sure which are my random variables. models I have used are: m-lme(log(fitness)~seedsize*density,random=~1|plot,data=dataset) m-lme(log(fitness)~seedsize+density+water,random=~1|plot,data=dataset) I have also tried to include plot and water as random effects: m-lme(log(fitness)~seedsize+density+water,random=~1|plot/water,data=dataset) I am actually not sure if I am using the right random variables here. Also for some reason, it won't let me include seedsize*density*water triple interaction help! thanks -- Eugenio Larios PhD Student University of Arizona. Ecology Evolutionary Biology. (520) 481-2263 elari...@email.arizona.edu [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
