[R] error in mixed logit model using mlogit function
Dear all, I am trying to estimate an mlm with 8 alternatives, but I received the following error message: Error in `colnames<-`(`*tmp*`, value = "disto.disto") : attempt to set colnames on object with less than two dimensions My data look like this: > head(dtb) votechoice disto.CVP disto.FDP disto.GPS disto.LPS disto.PBD disto.SP disto.SVP disto.VL risk 1GPS 1.3797308 2.1734424 0.7447605 2.7584803 2.1036997 1.2393141 4.320573 0.70274439 2SVP 1.6202692 0.8265576 3.7447605 0.2415197 0.8963003 4.2393141 1.320573 2.29725572 3CVP 0.6202692 0.1734424 2.7447605 0.7584803 0.1036997 3.2393141 2.320573 1.29725573 4SVP 4.6202692 3.8265576 6.7447605 3.2415197 3.8963003 7.2393141 1.679427 5.29725579 5SVP 2.6202692 1.8265576 4.7447605 1.2415197 1.8963003 5.2393141 0.320573 3.29725576 6GPS 2.3797308 3.1734424 0.2552395 3.7584803 3.1036997 0.2393141 5.320573 1.70274434 When I estimate a simple logit model it is working: rpl2 <- mlogit(votechoice ~ disto | risk,data=newdtb, shape = 'wide',varying=2:9, correlation=T, halton = NA,R = 10, tol = 10, print.level = 0);summary(rpl2) but when I am trying to estimate an mlm, I get the following: > rpl2 <- mlogit(votechoice ~ disto | risk,data=newdtb, shape = > 'wide',varying=2:9,correlation=T, + rpar=c(disto='l'),halton = NA,R = 10, tol = 10, print.level = 2) Error in `colnames<-`(`*tmp*`, value = "disto.disto") : attempt to set colnames on object with less than two dimensions I would be grateful for any help, thanks for reading this post. danielle -- View this message in context: http://r.789695.n4.nabble.com/error-in-mixed-logit-model-using-mlogit-function-tp4690489.html Sent from the R help mailing list archive at Nabble.com. __ 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] error message - unexpected input
I have been using R the past couple of years to run models on data we are collecting. I recently got a new computer and updated to a new version of R (2.60 -> 2.90). Since the update, I cannot get my syntax to run. I have tried copying the file it is looking for into many different directories to try and run it. In the last version, I found that it was easiest if the file was copied into the R program file folder. I have also tried changing the font the code is written in and turning off "smart quotes" in MS word. Additionally, I have right clicked on R and set myself as the only user on this computer to run this program. The syntax line that appears to be causing problems is: dataname="Igt2model.txt" and the error message is: Error: unexpected input in "dataname="" Does anyone have any suggestions? Thanks for all of your help, Danielle Danielle Dandreaux, Ph.D. Faculty Research Associate Arizona State University Department of Psychology Tempe, AZ 85287 480-965-3053 [[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] creating multiple plots using a splitting factor
Hello, I am new to R. I often collect data at multiple sites and need to create separate graphs (such as scatterplots or histograms) of specific variables for each site. I have tried to do this by splitting the data frame and then using lapply, but it seems that the graphing commands cannot be called as functions. Here is a sample of my data, called "seeddist2": siteDaysSinceRelease distance_cm 10 GVM 1 17.8 11 GVM 1 17.8 12 GVM 1 14.0 13 GVM 1 14.0 14 GVM 1 14.9 15 GVM 1 25.4 16 WRR 1 25.4 17 WRR 1 35.0 18 WRR 1 45.0 19 WRR 1 55.0 20 WRR 1 60.0 Here is what I tried to get separate histograms of distance_cm by site: splitlist<- split(seeddist2, site) lapply(splitlist, hist(distance_cm, breaks=10)) I then get an error message saying that "match.fun" didn't find the function. Is there another way to produce multiple graphs at once? Thank you, Danielle B. Johnston, Habitat Researcher Colorado Division of Wildlife [[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] creating multiple plots using a splitting factor
Thank you for the responses. The Lattice library is indeed useful for producing the graphs in which I am interested, and I appreciate the clarification between a function and the result of a function. Ideally, I would like to be able to page through the graphs rather than (or in addition to) having them displayed on the same page. Is there a way to do this? Danielle B. Johnston, Habitat Researcher Colorado Division of Wildlife -Original Message- From: Phil Spector [mailto:spec...@stat.berkeley.edu] Sent: Tuesday, November 10, 2009 12:46 AM To: Johnston, Danielle Subject: Re: [R] creating multiple plots using a splitting factor Danielle - What you meant to say was lapply(splitlist, function(x)hist(x$distance_cm, breaks=10)) but you might also be interested in trying library(lattice) histogram(~distance_cm|site,data=seeddist2) - Phil Spector Statistical Computing Facility Department of Statistics UC Berkeley spec...@stat.berkeley.edu On Mon, 9 Nov 2009, Johnston, Danielle wrote: > Hello, > > > > I am new to R. I often collect data at multiple sites and need to > create separate graphs (such as scatterplots or histograms) of specific > variables for each site. I have tried to do this by splitting the data > frame and then using lapply, but it seems that the graphing commands > cannot be called as functions. Here is a sample of my data, called > "seeddist2": > > > > siteDaysSinceRelease distance_cm > > 10 GVM 1 17.8 > > 11 GVM 1 17.8 > > 12 GVM 1 14.0 > > 13 GVM 1 14.0 > > 14 GVM 1 14.9 > > 15 GVM 1 25.4 > > 16 WRR 1 25.4 > > 17 WRR 1 35.0 > > 18 WRR 1 45.0 > > 19 WRR 1 55.0 > > 20 WRR 1 60.0 > > > > Here is what I tried to get separate histograms of distance_cm by site: > > splitlist<- split(seeddist2, site) > > lapply(splitlist, hist(distance_cm, breaks=10)) > > > > I then get an error message saying that "match.fun" didn't find the > function. Is there another way to produce multiple graphs at once? > > > > Thank you, > > > > Danielle B. Johnston, Habitat Researcher > > Colorado Division of Wildlife > > > > > [[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-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] Error in anova.cca by="axis" after Vegan update
Hi, I am trying to replicate some code I previously used to undertake a dbRDA analysis, however since updating my R software and all associated packages to the latest version it seems the analysis does not work. I have two dist objects: gendist and geodist. I first reduce geodist using pcnm(). geo_pcnm<-pcnm(geodist) I then use these scores in capscale(). geo_rda<-capscale(gendist~scores(geo_pcnm)) To forward select the variables to use in further analyses, so I want to know which ones are significant - and herein lies my problem. After running: anova(geo_rda,by="axis",perm=) I get the following error: Error in eval(expr, envir, enclos) : could not find function "Condition" I have run traceback() and this is what I get: traceback() 14: eval(expr, envir, enclos) 13: eval(predvars, data, env) 12: model.frame.default(formula, data, na.action = na.pass, xlev = xlev) 11: model.frame(formula, data, na.action = na.pass, xlev = xlev) 10: ordiParseFormula(fla, if (is.data.frame(data) && !is.null(comm)) cbind(data, comm) else data, envdepth = 1, na.action = na.action, subset = substitute(subset)) 9: capscale(formula = gendist ~ CAP1 + Condition(CAP2 + CAP3 + CAP4 + CAP5 + CAP6 + CAP7 + CAP8 + CAP9 + CAP10 + CAP11 + CAP12 + CAP13 + CAP14 + CAP15 + CAP16 + CAP17 + CAP18 + CAP19 + CAP20 + CAP21 + CAP22 + CAP23 + CAP24 + CAP25 + CAP26 + CAP27 + CAP28 + CAP29 + CAP30 + CAP31 + CAP32 + CAP33 + CAP34 + CAP35 + CAP36 + CAP37 + CAP38 + CAP39 + CAP40 + CAP41 + CAP42 + CAP43 + CAP44 + CAP45 + CAP46 + CAP47 + CAP48 + CAP49 + CAP50 + CAP51 + CAP52 + CAP53 + CAP54 + CAP55 + CAP56 + CAP57 + CAP58 + CAP59 + CAP60 + CAP61 + CAP62 + CAP63 + CAP64 + CAP65), data = lc) 8: eval(expr, envir, enclos) 7: eval(call, parent.frame()) 6: update.default(object, fla, data = lc) 5: update(object, fla, data = lc) 4: anova(update(object, fla, data = lc), ...) 3: anova.ccabyaxis(object, alpha = alpha, beta = beta, step = step, perm.max = perm.max, by = NULL, ...) 2: anova.cca(geo_rda, by = "axis", perm = ) 1: anova(geo_rda, by = "axis", perm = ) Is this a problem with the update? This code has worked multiple times for me in the past using older versions. Cheers Dan Dan Edwards Postdoctoral Research Associate Department of Ecology & Evolutionary Biology Yale University 21 Sachem St, New Haven CT 06520 E: danielle.edwa...@yale.edu W: https://sites.google.com/site/drdanielleedwards/ [[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] Best way to specify a mixed ANCOVA in R?
I initially posted this question to one of the StackExchange sites, and they suggested that I repost my problem here. After using ezANOVA as my primary way of specifying mixed ANOVAs, I've hit a stumbling block when it come to adding a covariate to the model. I am using an ANCOVA in order to determine if there is a developmental trajectory in my data; namely, I need to be able to see the F-statistic and p-values for interactions with the covariate (see p.466 onwards here [http://www.psyc.bbk.ac.uk/research/DNL/personalpages/annaz_etal_2009.pdf] if you want an example). Using ezANOVA, I can include covariates but the output does not show the F-statistic and p-values for interactions with the covariate - the main effect of the covariate is also not tested using this method. My ezANOVA model is as follows: aov.model<-ezANOVA( data=textureView.child.outliersRemoved , dv=.(x) , wid=.(ID) , within=.(Texture,View) , between=.(TNOGroup) , between_covariates=.(Age) , type=3 , return_aov=TRUE ) Another option is to use lm or Anova, but I don't know how to specify the error terms properly for either and I'm limited because I want to use Type-III sums of squares (drop1 doesn't work in the cases where I've tried to use the aov wrapper for lm; it fails while reporting 'Error in formula.default(object, env = baseenv()) : invalid formula'). Finally, I've heard about using the nlme package to specify my ANCOVA as a mixed model instead, but I don't know where to begin here (despite spending a while reading about it). To give a summary, I'm trying to do a 2 (between; TNOGroup) x 2 (within, Texture) x2 (within, View) mixed ANCOVA, with age as a covariate. I want to use Type-III sums of squares, and see the F-statistic and p-values for interactions with the covariate, as well as for the main effect of the covariate. Any advice on the best way to do this would be much appreciated. Thanks, Danielle __ 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] Looping over graphs in igraph
Hi,
I'm trying to do some basic social network analysis with igraph in R, but
I'm new to R and haven't been able to find documentation on a couple basic
things:
I want to run igraph's community detection algorithms on a couple thousand
small graphs but don't know how to automate igraph looking at multiple
graphs described in a single csv file. My data look like something in ncol
format, but with an additional column that has an ID for which graph the
edge belongs in:
Graph ID | Vertex1 | Vertex2 | weight
1 | Alice | Bob | 2
1 | Alice | Chris | 1
1 | Alice | Jane | 2
1 | Bob | Jane | 2
1 | Chris | Jane | 3
2 | Alice | Tom | 2
2 | Alice | Kate | 1
2 | Kate | Tom | 3
2 | Tom | Mike | 2
so on and so forth for about 2000 graph IDs, each with about 20-40
vertices. I've tried using the "split" command but it doesn't recognize my
graph id: ("object 'graphid' not found)--this may just be because I don't
know how to classify a column of a csv as an object.
Ultimately, I want to run community detection on each graph separately--to
look only at the edges when the graph identifier is 1, make calculations on
that graph, then do it again for 2 and so forth. I suspect that this isn't
related to igraph specifically--I just don't know the equivalent command in
R for what in pseudo Stata code would read as:
forvalues i of 1/N {
temp_graph=subrows of the main csv file for which graphid==`i'
cs`i' = leading.eigenvector.community.step(temp_graph)
convert cs`i'$membership into a column in the original csv
}
I want the output to look something like:
Graph ID | Vertex1 | Vertex2 | weight | Vertex 1 membership | Vertex 2
membership | # of communities in the graph
1 | Alice | Bob | 2 | A | B | 2
1 | Alice | Chris | 1 | A | B | 2
1 | Alice | Jane | 2 | A | B | 2
1 | Bob | Jane | 2 | B | B | 2
1 | Chris | Jane | 3 | B | B | 2
2 | Alice | Tom | 2 | A | B | 3
2 | Alice | Kate | 1 | A | C | 3
2 | Kate | Tom | 3 | C | B | 3
2 | Tom | Mike | 2 | B | C | 3
Here, the graphs are treated completely separately so that community A in
graph 1 need not have anything to do with community A in graph 2.
I would really appreciate any ideas you guys have.
Thank you!
Danielle
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[R] multinomRob - error message
Hi, I would like to use the multinomRob function to test election results. However, depending on which independent variables I include and how many categories I have in the dependent variable, the model cannot be estimated. My data look like this (there are 68 observations): head(database) RESTE09 GAUCHE09 PDC09 PLR09 UDC09 MCG09RESTE05 GAUCHE05 PDC05 D11455 5931 4726 7283 3611 5179 0.04487642 0.1707559 0.27561790 D2173610548 6905 27660 5430 5360 0.04487109 0.1797425 0.13782229 D3420812452 1721 8210 3179 8960 0.18580836 0.3218897 0.05659316 D42182 9208 7288 10654 2951 5205 0.08227831 0.2591124 0.19605699 D5612913909 12691 15560 3129 12551 0.10438878 0.2362780 0.23772964 D6302713517 4702 17899 5299 5758 0.10507522 0.2034102 0.07564233 PLR05 UDC05 MCG05 dim1dim2dim3 D1 0.3006495 0.08555836 0.12254194 0.4327918 -0.37563170 0.23139759 D2 0.4879385 0.09903729 0.05058832 1.2723817 0.03128996 -0.10629471 D3 0.2296300 0.07822276 0.12785602 -0.8002237 -0.12194377 0.18147181 D4 0.3051325 0.06789533 0.08952449 0.4321835 0.11445829 0.24636903 D5 0.2811336 0.06624300 0.07422700 0.2560408 0.07019505 0.05611872 D6 0.4186317 0.10281440 0.09442613 0.4487914 -0.15539599 -0.04779844 I defined the model as follows: mnr<-multinomRob(list( + GAUCHE09~GAUCHE05+dim1+dim2+dim3, + PDC09~PDC05+dim1+dim2+dim3, + PLR09~PLR05+dim1+dim2+dim3, + UDC09~UDC05+dim1+dim2+dim3, + MCG09~MCG05+dim1+dim2+dim3, + RESTE09~0),database,print.level=0) And the error message is: Error in multinomT.foo$se$beta : $ operator is invalid for atomic vectors In addition: There were 11 warnings (use warnings() to see them) warnings() Warning messages: 1: In optim(param, fn = mt.dev, method = method, control = control, ... : unknown names in control: tol 2: In fn(par, ...) : value out of range in 'lgamma' 3: In fn(par, ...) : value out of range in 'lgamma' 4: In fn(par, ...) : value out of range in 'lgamma' 5: In fn(par, ...) : value out of range in 'lgamma' 6: In fn(par, ...) : value out of range in 'lgamma' 7: In fn(par, ...) : value out of range in 'lgamma' 8: In fn(par, ...) : value out of range in 'lgamma' 9: In fn(par, ...) : value out of range in 'lgamma' 10: In fn(par, ...) : value out of range in 'lgamma' 11: In fn(par, ...) : value out of range in 'lgamma' Is my model uncorrectly defined? many thanks, danielle martin graduate student department of political science university of michigan __ 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] Survdiff for multiple comparisons
Hello all- I am doing a survival analysis for two species of invasive plants I outplanted to edges and interiors of island and mainland sites in a local reservoir. I am using the KM estimate and had no problem doing survdiff for my data using the following code: S4<-Surv(outplant$SurvTime, outplant$StatusD6) diff4=survdiff(S4 ~ outplant$Species+outplant$SiteType+outplant$EdgInt) diff4 Species = ALBIJU or LONIJA SiteType = Island or Mainland EdgInt = Edge or Interior The overall test for difference among the 8 curves is highly significant (p<0.001), but what I would like to know is how I would go about testing for pairwise differences among the eight curves, as what is most informative would be pinpointing exactly which curves are significantly different from each other for each species. I have not been able to find any code or function out there that will give me this test with adjusted p-values. I was thinking about using subset, but this will not adjust the p-values for multiple tests. Thank you for your time - Danielle -- Danielle C. Zoellner-Kelly PhD candidate - DeWalt Lab Department of Biological Sciences Clemson University 132 Long Hall Clemson, SC 29634 Lab: (864) 656-2639 Cell: (864) 376-9583 [[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] Logistic Regression with genetic component
Greetings, I have a question that I'd like to get input on. I have a classic toxicology study where I artificially fertilized and exposed embryos to a chemical and counted defects. In addition, I kept track of male-female pairs that I used to artificially fertilize and generate embryos with. I need to use logistic regression to model the response, but also check that the genetics of the pairings did or did not have an effect on the response. My data looks a bit like this: response matrix chemical concentration Genetic Matrix Present AbsentMale Female 2 152 0.13 a 1 6 121 1 a 2 21 92 2 b 3 24 89 5 b 4 0141 10 c 5 5 95 15 c 6 R code: DA<-cbind(Present, Absent) glm<-(DA ~ chemical concentration) If I do glm<-(DA ~ chemical concentration + Male + Female, I get every possible combination, but I only want specific pairs. So, I am thinking about doing: MF<-cbind(Male, Female) glm<-(DA ~ chemical concentration + MF) Is this correct? Any help on how to model this would be greatly appreciated! Thanks in advance! [[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] Calculating NOEL using R and logistic regression - Toxicology
Hello, I used the glm function in R to fit a dose-response relationship and then have been using dose.p to calculate the LC50, however I would like to calculate the NOEL (no observed effect level), ie the lowest dose above which responses start occurring. Does anyone know how to do this? [[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] Calculating NOEL using R and logistic regression - Toxicology
Thanks, that is interesting, but what I'm really after is an easy "no observed effect level", using a binomial logistic model ie glm. Have a great day! On Mon, Apr 2, 2012 at 3:38 PM, vito.muggeo wrote: > dear Danielle, > > The NOEL is a threshold value or breakpoint in the range of dose. Have a > look to the > package segmented to estimate a GLM with unknown breakpoints. The code > (untested) should > be something like > > library(segmented) > o<-glm(y~1, family=binomial) > os<-segmented(o, ~dose, psi=starting_psi) > > Also the package segmented includes the dataset down that can be useful as > an example.. > > data(down) > with(down, plot(age, cases/births)) > > There is a paper of mine on R news 2008 discussing the package.. > > hope this helps you, > vito > > > > On Mon, 2 Apr 2012 14:45:06 -0800, Danielle Duncan wrote > > Hello, I used the glm function in R to fit a dose-response relationship > and > > then have been using dose.p to calculate the LC50, however I would like > to > > calculate the NOEL (no observed effect level), ie the lowest dose above > > which responses start occurring. Does anyone know how to do this? > > > > [[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. > > > -- > Open WebMail Project (http://openwebmail.org) > > [[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] Calculating NOEL using R and logistic regression - Toxicology
Thanks for the response, I should have clarified that the NOEL is the smallest dose above which there is a statistically significant effect. On Tue, Apr 3, 2012 at 12:44 PM, Drew Tyre wrote: > Is this the smallest observed dose that has an effect? If so, then you > don't need the glm to find it. Here is a simulated example: > > set.seed(101) > X = rep(1:10,each=10) > lp = -5 + 0.5*X > Y = rbinom(length(X),size=1,p=1/(1+exp(-lp))) > # is this the NOEL? > min(X[Y==1]) # picks out observations with adverse effects, chooses the > smallest value > glmfit = glm(Y~X,family=binomial) > plot(1:10, predict(glmfit,newdata=data.frame(X=1:10),type="response"), > type="l",ylim=c(0,1),xlab="X",ylab="Y") > rug(jitter(X[Y==0]),side=1) > rug(jitter(X[Y==1]),side=3) > > On Tue, Apr 3, 2012 at 3:19 PM, Danielle Duncan wrote: > >> Thanks, that is interesting, but what I'm really after is an easy "no >> observed effect level", using a binomial logistic model ie glm. Have a >> great day! >> >> On Mon, Apr 2, 2012 at 3:38 PM, vito.muggeo wrote: >> >> > dear Danielle, >> > >> > The NOEL is a threshold value or breakpoint in the range of dose. Have a >> > look to the >> > package segmented to estimate a GLM with unknown breakpoints. The code >> > (untested) should >> > be something like >> > >> > library(segmented) >> > o<-glm(y~1, family=binomial) >> > os<-segmented(o, ~dose, psi=starting_psi) >> > >> > Also the package segmented includes the dataset down that can be useful >> as >> > an example.. >> > >> > data(down) >> > with(down, plot(age, cases/births)) >> > >> > There is a paper of mine on R news 2008 discussing the package.. >> > >> > hope this helps you, >> > vito >> > >> > >> > >> > On Mon, 2 Apr 2012 14:45:06 -0800, Danielle Duncan wrote >> > > Hello, I used the glm function in R to fit a dose-response >> relationship >> > and >> > > then have been using dose.p to calculate the LC50, however I would >> like >> > to >> > > calculate the NOEL (no observed effect level), ie the lowest dose >> above >> > > which responses start occurring. Does anyone know how to do this? >> > > >> > > [[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. >> > >> > >> > -- >> > Open WebMail Project (http://openwebmail.org) >> > >> > >> >>[[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. >> > > > > -- > Drew Tyre > > School of Natural Resources > University of Nebraska-Lincoln > 416 Hardin Hall, East Campus > 3310 Holdrege Street > Lincoln, NE 68583-0974 > > phone: +1 402 472 4054 > fax: +1 402 472 2946 > email: aty...@unl.edu > http://snr.unl.edu/tyre > http://aminpractice.blogspot.com > http://www.flickr.com/photos/atiretoo > [[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] Calculating NOEL using R and logistic regression - Toxicology
Thanks everyone for the advice, you raise interesting points. Maybe the best thing for me to do is do an ANOVA in R with binomial data (if possible) and find the lowest dose that gives a significant difference from the controls. On Mon, Apr 2, 2012 at 2:45 PM, Danielle Duncan wrote: > Hello, I used the glm function in R to fit a dose-response relationship > and then have been using dose.p to calculate the LC50, however I would like > to calculate the NOEL (no observed effect level), ie the lowest dose above > which responses start occurring. Does anyone know how to do this? -- "Wilderness isnt the wide open spaces. Its the wild things in it [[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] Calculating NOEL using R and logistic regression - Toxicology
I suppose I'll just report a LC10 using the dose.p function in the package MASS using my glm fitted logistic regression on binomial data. Thanks everyone for ideas & input! The LOEC seems to be a flawed calculation...I'll research it. Again, thanks all! On Mon, Apr 2, 2012 at 2:45 PM, Danielle Duncan wrote: > Hello, I used the glm function in R to fit a dose-response relationship > and then have been using dose.p to calculate the LC50, however I would like > to calculate the NOEL (no observed effect level), ie the lowest dose above > which responses start occurring. Does anyone know how to do this? -- "Wilderness isnt the wide open spaces. Its the wild things in it [[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.
