similar to: How to get the SS and MS from oneway.test?

Hi I am attempting Anova analysis to compare results from four groups (Samp1-4) which are lists of intensities from the experiment. I am doing this by first creating a structured list of the data and then conducting the ANOVA (Script provided below). Im an R beginner so am not sure if I am using this correctly. Two major questions I have are: 1) Is using the code (zzz.aov <- aov(Intensity ~

Dear R-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions - the Rsquared of

Hi all, I did not get this error in Asterisk 1.2.5 release. I am testing on Asterisk SVN-trunk-r15187 to avail the PARKEDAT variable. - I park the call using ParkAndAnnounce - plays moh. - accept the call using ParkedCall The following errors are coming on the console and there is oneway audio - no audio after Music-On-Hold at caller's side. Please advice. I am testing using cisco 7902

Using a "by() statement, I am preparing ANOVA's for multiple experiments, and using simint() to generate confidence intervals. This works fine. simint.by.fit <- by(analytes.dfr, list(Assay = analytes.dfr$analyte ), function(data) (simint(value ~ tx, data = data,type='Tukey' ) ) ) I can separately prepare plots of the confidence intervals, and I can prepare separate plots

Hi, I need to construct a formula programaticly, and pass it to a function such as the linear mixed model lme. The help says it requires "a two-sided linear formula object describing the fixed-effects part of the model" but I do not know how to create this formula. I have tried various things using formula(x, ...), as.formula(object, env = parent.frame()) and as.Formula(x, ...)

Hi, i'm looking for an R function to fit a one-way ANOVA with one factor containing 10 levels. The factor levels have different numbers of observations (varying between 20 to 40). For most of the dependent variables i'm testing there are unequal variances among the factor levels. I see the function oneway.test: oneway.test(variable ~ factor, data=dataset) which by default does not

Hello everybody, I use the sweetpotato database included in R package: data(sweetpotato) This dataset contains two variables: yield(continous variable) and virus(factor variable). Due to Levene test is significant I cannot assume homogeneity of variances and I apply Welch test in R instead of one-way ANOVA followed by Tukey posthoc. Nevertheless, the problems come from when I apply posthoc

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

Hi, I have the Cisco PIX 506 firewall right in front of the asterisk and I am getting a one-way audio. I need your help/guidance to resolve this problem. I have the "fixups" disabled for SIP in the Cisco PIX 506. Any help rendered by you in this subject is greatly appreciated. I have been breaking my head trying to resolve this problem for more than one month. I have included the

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

Hi, I am encountering a difficulty I don't understand. Be patient, I'm very new to analysis of variance. If I load this data: example12_7=read.table("http://msemac.redwoods.edu/~darnold/math15/data/chapter12/example12_7.dat",header=TRUE) The run the oneway.test: oneway.test(time~drug,data=example12_7,var.equal=TRUE) I get these results: data: time and drug F = 4.1881, num

Hi helpeRs. When I do two-ways tables dimnames in columns are almost left-adjusted: <- factor(rbinom(30,1,.3),labels=c("first","second")) > b <- a > table(a,b) b a first second first 21 0 second 0 9 If I do a oneway table however, dimnames are right-adjusted: table(a) a first second 21 9 Actually I must admit I

Hi all, I want to write a wrapper for an analysis of variance and I face a curious problem. Here are two different wrappers: fun.1 <- function(formula) { summary(aov(formula)) } fun.2 <- function(formula) { oneway.test(formula) } values <- c(15, 8, 17, 7, 26, 12, 8, 11, 16, 9, 16, 24, 20, 19, 9, 17, 11, 8, 15, 6, 14) group <- rep(1:3, each=7) # While the first

Hi. I have data which i would want to assess the degree of agreement between two assays, e.g., to evaluate reproducibility or for inter-rater reliability. I have used the Pearson product-moment correlation coefficient. It looks good ranginging between 0.90 to 0.998. Though this looks good. I am told the Concordance correlation coefficient will give a better picture of how reproducible the assay

Hi all, I apologize for what might be a silly question. I am interested in doing a one way anova. This is not too hard in and of itself, either with anova, aov or oneway.test . However, I need to 1) get pvalues, 2) do a posthoc analysis with Tukey HSD, 3) and have (sometimes) an unbalanced design. I just can't seem to put all the pieces together. Any suggestions? Thanks in advance, Dan.

Hi List I'm having difficulty understanding how plm should work with dynamic formulas. See the commands and output below on a standard data set. Notice that the first summary(plm(...)) call returns the same result as the second (it shouldn't if it actually uses the lagged variable requested). The third call results in error (trying to use diff'ed variable in regression) Other info:

Hi, this should be an easy one, but I can't figure it out. I have a vector of tests, with their units between brackets (if they have units). eg tests <- c("pH", "Assay (%)", "Impurity A(%)", "content (mg/ml)") Now I would like to hava a function where I use a test as input, and which returns the units like: f <- function (x) sub("\\)",

Hello, In my code I found something that looks like an anomaly, I found a reproducible example in which I am just trying to compare each row in a data frame against the first row: a<-data.frame(row=c("B","C","B"),column=c(2,2,10),assay=c("Assay1","Assay1","Assay1"),plate=c(1,1,1),stringsAsFactors=FALSE) apply(a[1:2,],1,function(x) {

I know that there are quite a few packages out that there for cluster analysis. The problem that I am facing is finding a package that will not incorporate all my samples into clusters but just the samples that fit a threshold (that I have not set yet and may need help finding the right level) for genotyping. It should be able to "no call" samples outside the clusters. It also needs to

Is there a good way in R to impute values which exist, but are less than the detection level for an assay? Thanks, Jonathan Williams OPTIMA Radcliffe Infirmary Woodstock Road OXFORD OX2 6HE Tel +1865 (2)24356