similar to: Detecting Disconnected Numbers - PRI

Greetings, all. I've got a datafile I've been working with that has an ideosyncratic, heterogeneous format. It's grossly like: [...] DISKREAD,metadata about disks MEM,metadata about memory ZZZZ,observation-identifier,time,date DISKREAD,observation-identifier,data about disks MEM,observation-identifier,data about memory [ and repeat for each observation ] What I've done in

Hello I'm very much a beginner on meta-analysis, so apologies if this is a trivial posting. I've been sent a set data from separate experimental studies, Treatment and Control, but no measure of the variance of effect sizes, numbers of replicates etc. Instead, for each study, all I have is the mean value for the treatment and control (but not the SD). As far as I can tell, this forces

Sorry for disturbing you, but we have some problems with an installation with multiple (84) T1s from Quest. Now, our Problem is disconnected numbers are reported by sending in- band channel alert message and the B-Channel will have the tri tone and respective message but the line is never "picked up" and stays in ringing when dialling. So disconnected numbers are never detected as

Hello developers folks! First, congratulations for the wonderful work with R. For science, barplots with error bars are very important. We were wondering that is so easy to use the boxplot function: boxplot(Spores~treatment, col=treatment_colors) But there is no such function for barplots with standard deviation or standard error. It becomes a "journey" to plot a simple graph (e.g.

Hi, I''m trying to understand the lme output and procedure. I''m using the Crawley''s book. I''m try to analyse the rats example take from Sokal and Rohlf (1995). I make a nested analysis using aov following the book. > summary(rats) Glycogen Treatment Rat Liver Min. :125.0 Min. :1 Min. :1.0 Min. :1 1st Qu.:135.8

Hi, I'm analysing a dataset in which the same 5 subjects (male.pair) were subjected to two treatments (treatment) and were measured for 12 successive days within each treatment (layingday). Overall 5*2*12=120 observations. I want to test the effect of treatment, time (layingday) and their interaction. I have done so through the ANOVA below: >

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

I have a data frame that looks something like... Column 1 is an experiment_id, Column 2 is the type of treatment ("control", "full treatment", or "partial treatment"), and Column 3 is a value. Experiment_id Treament_type Value 12345 "control" 3 12345 "full treatment" 4 12345 "full treatment" 5 12345 "partial

Hi, Assuming that your dataset is similar to the one below: set.seed(25) dat1<- data.frame(Algae.Mass=sample(40:50,10,replace=TRUE),Seagrass.Mass=sample(30:70,10,replace=TRUE),Terrestrial.Mass=sample(80:100,10,replace=TRUE),Other.Mass=sample(40:60,10,replace=TRUE),Site.X.Treatment=rep(c("ALA1A","ALA1U"),each=5),stringsAsFactors=FALSE) library(reshape2)

Each register is a 4-component (namely, r, g, b, a) vector register. They are actually defined as llvm packed [4xfloat]. The instruction: add_sat r0.a, r1_bias.xxyy, r3_x2.zzzz Explaination: '.a' is a writemask. only the specified component will be update '.xxyy' and '.zzzz' are swizzle masks, specify the component permutation, simliar to the Intel SSE permutation

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

Hi, I performed an experiment where I raised different families coming from two different source populations, where each family was split up into a different treatments. After the experiment I measured several traits on each individual. To test for an effect of either treatment or source as well as their interaction, I used a linear mixed effect model with family as random factor, i.e.

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, using Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below is

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Gday, This is a repost since I only had one direct reply and I remain mystified- This may be stupidity on my part but it may not be so simple. In brief, my problem is I'm not sure how to extract parameter values/effect sizes from a nonlinear regression model with a significant interaction term. My data sets are dose response curves (force and dose) for muscle that also have two

It is frustrating to see the labels I want in the dimensions of a list but not be able to extract those labels into titles for plots generated from component objects. If someone could set me straight, I would appreciate it. For your amusement, I have provided an example of the Byzantine code I am currently using to avoid loops: # Simulate ANOVA type test data sex<-c(rep(1,8),rep(0,8))

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

Dear all, I'm trying to fit a curve to some 1 year failure-time data, so that I can extrapolate and predict failure rates up to 3 years. The data is in the general form: Treatment Time Status Treatment A 28 0 Treatment B 28 0 Treatment B 28 0 Treatment A 28

Hi I would really appreciate some help with my code for coxme... My data set I'm interested in survival of animals after an experiment with 4 treatments, which was performed on males and females. I also have two random factors: Response variable: survival (death) Factor 1: treatment (4 levels) Factor 2: sex (male / female) Random effects 1: person nested within day (2 people did

Hi all, I am performing hundreds of kruskal wallis tests and trying to figure out how to create a file of the p.values I obtain. This is the code I use for the tests: A2<-kruskal.test(X2~treatment) A3<-kruskal.test(X3~treatment) A4<-kruskal.test(X4~treatment) A5<-kruskal.test(X5~treatment) A6<-kruskal.test(X6~treatment) A7<-kruskal.test(X7~treatment)