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.... 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the intercept e.g. I have a standard error of 0.4354 from glmm and 0.5338 from glmmML. Any idea about this problem?? Thanks in advance Clemens Tilke library (MASS) data (bacteria) contrasts (bacteria$trt) <- structure (contr.sdif (3), dimnames = list (NULL, c ("drug", "enc"))) bacteria.pql <- glmmPQL (y ~ trt + I(week > 2), random = ~ 1 | ID, family = binomial, data = bacteria) summary (bacteria.pql) library (repeated) y1 <- 1 * (bact...

...eed we are attempting the wrong type of analysis, some guidance about what would be the right thing to do would be greatly appreciated. The details: The data: The data are from the end-point of a survival experiment with fish. The design of the experiment is a 2 x 2 factorial, with each factor (Bacteria and Parasite) at two levels (yes and no). There were 16 fish in each tank, and the treatment was applied to the whole tank. There were in all 10 tanks (160 fish), with 2 tanks for controls (no/no), 2 tanks for (Parasite:yes/Bacteria:no) and 3 tanks for each of the other 2 treatments. A dead fis...

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable. There is pseudoreplication as each patient (ID) is sampled multiple times (week) bacteria...

...;matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects estimates, standard error and p.values). However, Loglikelihood and deviance are different. here is an example reproduced with the bacteria data set available in the MASS package: library(lme4) library(MASS) data(bacteria) bacteria$week2 <- as.factor(ifelse(bacteria$week <=2, 0, 1)) model.PQL <- lmer(y ~ trt + week2 + (1 | ID), family = binomial, data = bacteria, method ="PQL") model.Laplace <- lmer(y ~ trt + w...

I have a data frame that looks like this: > gctablechromonly[1:5,] refseq geometry gccontent X60_origin X60_terminus length kingdom 1 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 2 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 3 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 4 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 5 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria...

...documentation states "Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions". Taking the sample in the documentation: fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, family = binomial, data = bacteria) > head(predict(fit, bacteria, level = 0, type="response")) [1] 0.9680779 0.9680779 0.8587270 0.8587270 0.9344832 0.9344832 > head(predict(fit, bacteria, level = 1, type="response")) X01 X01 X01 X01 X02 X02 0.9828449 0.9828449 0.9198935...

Hi, I was recently misfortunate enough to have to use regular expressions to sort out some data in R. I'm working on a data file which contains taxonomical data of bacteria in hierarchical order. A sample of this file can be generated using: tax.data <- read.table(header=F, con <- textConnection(' G9SS7BA01D15EC Bacteria(100) Cyanobacteria(84) unclassified G9SS7BA01C9UIR Bacteria(100) Proteobacteria(94) Alphaproteobacteria(89) G9SS7BA01CM0...

...6.51 7.8e-11 *** trtdrug 1.367 0.486 2.81 0.00490 ** trtdrug+ 0.786 0.498 1.58 0.11408 week2TRUE 1.623 0.459 3.53 0.00041 *** sd 1.294 0.250 5.17 2.3e-07 *** --- --------------------------------------------------------------- data(bacteria,package="MASS") UseMASS<-T # must restart R after changing because of nlme/lme4 clash if (UseMASS){ library(MASS) # required for bacteria options(digits=3) cat("--- glmmPQL/MASS/Venables&Ripley\n") print(summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,...

I use model comparison with glms without mixed effects with anova(modelA,modelB), with mixed effects glm (glmmPQL), this doesn't work. Is there a way to compare model fits with glmmPQL's? Paula M. den Hartog Behavioural Biology Institute of Biology Leiden Leiden University [[alternative HTML version deleted]]

Hello Everybody, I have an eps figure an awesome bacteria and a plot (generated using R) also in eps format. Now it looks like there is space for only one figure and I have to insert the picture of the bacteria into the plot. Is there a way to insert figures (eps/png/jpg) in to plots (may be control over placement of figures in the plot as well?) ? By plo...

...ous here that I have missed. I am new to creating my own functions in R, and I am uncertain of how they work. I have a data set that I have read into a data frame: > gctable[1:5,] refseq geometry X60_origin X60_terminus length kingdom 1 NC_009484 cir 1790000 773000 3389227 Bacteria 2 NC_009484 cir 1790000 773000 3389227 Bacteria 3 NC_009484 cir 1790000 773000 3389227 Bacteria 4 NC_009484 cir 1790000 773000 3389227 Bacteria 5 NC_009484 cir 1790000 773000 3389227 Bacteria grp feature gene begin dir gc_con...

Dear all, I am interested in correctly testing effects of continuous environmental variables and ordered factors on bacterial abundance. Bacterial abundance is derived from counts and expressed as percentage. My problem is that the abundance data contain many zero values: Bacteria <- c(2.23,0,0.03,0.71,2.34,0,0.2,0.2,0.02,2.07,0.85,0.12,0,0.59,0.02,2.3,0.29,0.39,1.32,0.07,0.52,1.2,0,0.85,1.09,0,0.5,1.4,0.08,0.11,0....

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as...

Dear R community, I would like to compare the degree of aggregation (or dispersion) of bacteria isolated from plant material. My data are discrete counts from leaf washes. While I do have xy coordinates for each plant, it is aggregation in the sense of the concentration of bacteria in high density patches that I am interested in. My attempt to analyze this was to fit negative binomial...

...sCommon=(Human);SpeciesScientific=(Homo sapiens);ReactiveCentres=(N,C,C,C,+ H,O,C,C,C,C,O,H);BondInvolved=(C-H);EzCatDBID=(S00343);BondFormed=(O-H,O-H);Bond+ 255B);Cofactors=(Cu(II),CU,501,A,Cu(II),CU,502,A);CatalyticSwissProt=(P25006);Sp+ eciesScientific=(Achromobacter cycloclastes);SpeciesCommon=(Bacteria);ReactiveCe+ and I want to extract ?SpeciesScientific = (?)? information from this file. Problem is in 3rd line where SpeciesScientific word is divided with +. Could anyone help me please? Thank you -- View this message in context: http://r.789695.n4.nabble.com/Pattern-match-tp3463625p3463625...

...ructive mistake, but I need some advice on how to correct the problem. CentOS 6.3 host system named Earth I was creating some new logical volumes within my exiting volume group for a new virtual machine using the LVM GUI. When I created the LV that I plan to use for root partition of the new VM (Bacteria) I mistakenly clicked on the box to mount the LV, and specified the mount point as /. [root at earth ~]# df -h Filesystem Size Used Avail Use% Mounted on /dev/mapper/vg_mei-lv_earthroot 5.0G 3.9G 880M 82% / tmpfs 5.9G 276K 5.9G 1% /dev/shm /...

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace...

...is within each 'Gram.Staining' categorical level there are some antibiotics does not have value (in my case is log(MIU)). Because some antibiotics are positive gram staining and some are negative. So my question is how to remove these missing value rows in the plot? My code is qplot(Bacteria,log(MIC),data=project2,facets=Gram.Staining~Antibiotics,color=Bacteria,geom='bar',stat='identity',fill=Bacteria,na.rm=TRUE)+coord_flip() I also enclose my plot. Thank you! Wang Zhensheng ________________________________ This e-mail message (including any attachments)...

I am new to R, and I couldn't find the answers to my question in a faq. This could however be because I didn't know what to look for...:) I have three classes of data, data for bacteria, archaea and eukaryotes. I wish to display these in a histogram where all of the values are used to calculate each column. But, I want each column split in three, where the size of each coloured area represents the proportions of the values in that column that comes from each of the three class...

...eping the hierarchical clustering of the horizontal component. The relationships of the vertical component in the generated heatmap are not that of the dendrogram, although the ordering is. In more detail, I am attempting to generate a heatmap from a table that contains the abundance of different bacteria at different locations, with a dendrogram that groups the environments by the pattern of bacterial abundance. This is easy, thanks to a nice code snippet at the R Graph Gallery (http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=66): env <- read.table("env.dat") x <- a...