similar to: epitools question

Here is a working snippet. library(epitools) mat <- matrix(c(10,15,60,25,98, 12,10,70,28,14, 9,11,68,10,12 ,8,13,20,11,58) ,ncol=2) colnames(mat) <- c("treatmentA","treatmentB") row.names(mat) <- paste("Cond",rep(1:10,1)) dimnames(mat) <- list("Condition" = row.names(mat), "instrument" = colnames(mat)) > mat instrument

Here is a working snippet. library(epitools) mat <- matrix(c(10,15,60,25,98, 12,10,70,28,14, 9,11,68,10,12 ,8,13,20,11,58) ,ncol=2) colnames(mat) <- c("treatmentA","treatmentB") row.names(mat) <- paste("Cond",rep(1:10,1)) dimnames(mat) <- list("Condition" = row.names(mat), "instrument" = colnames(mat)) > mat instrument

I am attempting to replicate some of my experience from SAS in R and assume there are best methods for using a combination of summary(), subset, and which() to produce a subset of mean values by categorical or ordinal factors. within sas I would write proc means mean data=dataset; class factor1 factor2 var variable1 variable2; RUN; producing an output with means for each variable by factor

Greetings, I just found out a bug in the function lmList of the package nlme with R 2.8.1 running under windows XP 32-bits. I have a data table with various columns corresponding to continuous variables as well as treatment variables taken on several years and several sites. Here is an example : Id year treatment A treatment B variable1 variable2 variable3 1

Dear R users, I am currently analyzing a dataset using lme(). The model I use has the following structure: model<-lme(response~Covariate+TreatmentA+TreatmentB,random=~1|Block/Plot,method="ML") When I plot the residuals against the fitted values, I see a clear positive trend (meaning that the variance increases with the mean). I tried to solve this issue using weights=varPower(),

I feel bad even asking, but: Rgames> data(OrchardSprays) Rgames> model<-lm(decrease~.,data=OrchardSprays) Rgames> model Call: lm(formula = decrease ~ ., data = OrchardSprays) Coefficients: (Intercept) rowpos colpos treatmentB treatmentC 22.705 -2.784 -1.234 3.000 20.625 treatmentD treatmentE treatmentF treatmentG treatmentH

At work after updating to R 2.5 I get an error using epitab from package epitools, when at home (R 2.4) I get no error. Could someone help me? Thanks Pietro Bulian Servizio di Onco-Ematologia Clinico-Sperimentale I.R.C.C.S. Centro di Riferimento Oncologico Via Franco Gallini 2 33081 Aviano (PN) - Italy phone: +39 0434 659 412 fax: +39 0434 659 409 e-mail: pbulian at cro.it (at work)

Hi, I am new in statistics and i manage to make the linear model analysis but i have some difficulties in explaining the results. Can someone help me explalinig the output of my linear model analysis ? My data are with 2 variables habitat (e,s) and treatment (a,c,p) with multiple trials within. Thank you in advance Call: lm(formula = a$wild ~ a$habitat/a$treatment/a$trial) Residuals: Min

G'day, My problem is I'm not sure how to extract effect sizes from a nonlinear regression model with a significant interaction term. My data sets are multiple measurements of force response to an agonist with two superimposed treatments each having two levels. This is very similar to the Ludbrook example in Venables and Ripley. The experiment is that a muscle is exposed to an agonist

Hi, I have searched the archives and can't quite confirm the answer to this. I appreciate your time... I have 4 treatments (fixed) and I would like to know if there is a significant difference in metal volume (metal) between the treatments. The experiment has 5 blocks (random) in each treatment and no block is repeated across treatments. Within each plot there are varying numbers of

Dear R-help-list, I have a problem in which the explanatory variables are categorical, the response variable is a proportion, and experiment contains technical replicates (pseudoreplicates) as well as biological replicated. I am new to both generalized linear models and mixed- effects models and would greatly appreciate the advice of experienced analysts in this matter. I analyzed the

Hello, I?m using zero-inflated Poisson regression via the zeroinfl() function to analyze data on seed-set of plants, but for some reason, I don?t seem to be getting the output for all three levels of my two categorical predictors. More about my data and model: My response variable is the number of viable seeds (AVInt), and my two categorical predictors are elevation (Elev) and Treatment

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

Dear list, i'm trying to test if a linear combination of coefficients of glm is equal to 0. For example : class 'cl' has 3 levels (1,2,3) and 'y' is a response variable. We want to test H0: mu1 + mu2 - mu3 =0 where mu1,mu2, and mu3 are the means for each level. for me, the question is how to get the covariance matrix of the estimated parameters from glm. but perhaps there

Dear R-helpers, I am trying to construct a confidence interval on a prediction of a gam fit. I have the Wood (2006) book, and section 5.2.7 seems relevant but I am not able to apply that to this, different, problem. Any help is appreciated! Basically I have a function Y = f(X) for two different treatments A and B. I am interested in the treatment ratios : Y(treatment = B) / Y(treatment = A) as

The concrete problem is that I am refereeing a paper where a confidence interval is presented for the risk ratio and I do not find it credible. I show below my attempts to do this in R. The example is slightly changed from the authors'. I can obtain a confidence interval for the odds ratio from fisher.test of course === fisher.test example === > outcome <- matrix(c(500, 0, 500, 8),

# is there a bug in the calculation of the odds ratio in fisher.test? # Nicholas Horton, nhorton at smith.edu Fri Jan 22 08:29:07 EST 2010 x1 = c(rep(0, 244), rep(1, 209)) x2 = c(rep(0, 177), rep(1, 67), rep(0, 169), rep(1, 40)) or1 = sum(x1==1&x2==1)*sum(x1==0&x2==0)/ (sum(x1==1&x2==0)*sum(x1==0&x2==1)) library(epitools) or2 = oddsratio.wald(x1, x2)$measure[2,1] or3 =

Hello, I am looking for a function which allows to calculate the confidence interval for a standard mortality ratio. I do have vectors with the number of observed and expected death. Has anybody a hint where to look? Best, Stefan

Hi, I was wondering if there was some sort of package or function that calculated asymmetric confidence intervals for small proportions. I thought of both the epicalc and epitools package, but I am hoping to find something where you can just plug in a standard error and point estimate and it will output the upper and lower CI bounds. Thanks! Sarah

I would like to compare the incidence rates of three groups. They are supposed to have different risks so I would like to test whether there is a increasing trend in the incidence rates. Does R or any packages provide a trend test for incidence rates? I checked epiR and epitools. It seems they do not have this function. Thank you for the help. -- View this message in context: