search for: treatmenta

...y 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 groupings as below: *factor1 factor2 obs variable mean* Level A treatmentA 3 variable1 10 variable2 22 treatmentB 3 variable1 12 variable2 30 Level B treatmentA 3 variable1 10...

...itate:a$treatmentc 7.3581 2.8753 2.559 0.01054 * a$habitats:a$treatmentc -4.9803 2.8753 -1.732 0.08335 . a$habitate:a$treatmentp -13.9906 2.8753 -4.866 1.19e-06 *** a$habitats:a$treatmentp -16.1311 2.8753 -5.610 2.17e-08 *** a$habitate:a$treatmenta:a$trial -0.3204 0.3808 -0.841 0.40030 a$habitats:a$treatmenta:a$trial -0.1319 0.3808 -0.346 0.72905 a$habitate:a$treatmentc:a$trial -1.1250 0.3808 -2.954 0.00316 ** a$habitats:a$treatmentc:a$trial -0.4236 0.3808 -1.112 0.26608 a$habitate:a$treatmentp:a$trial...

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(), but it doesn?t change the residual plot at all. Ho...

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 Condition treatmentA treatmentB Cond 1 10 9 Cond 2...

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 Condition treatmentA treatmentB Cond 1 10 9 Cond 2...

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 Condition treatmentA treatmentB Cond 1 10 9 Cond 2...

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

...dbrook example in Venables and Ripley. The experiment is that a muscle is exposed to an agonist and the force response is measured. The resulting data is fit to a logistic fit (a three parameter rather than the four parameter used by Ludbrook) . This is done for each combination of two factors (treatmentA and Treatment B) each having two levels (- and +). Each set of measurements is obtained on a muscle from a different animal (i.e. each dose response curve represents an independent experiment). The data are stored as follows: expt treatA treatB dose force I use a groupedData object mydata=gro...

...108 3 1 1 6.34 9.34 5.98 As you can see, data for variable3 are missing for the first year, but otherwise there is little data missing. I call upon lmList : lmgroup.data <- lmList (variable1 ~ treatmentB | year/treatmentA, data = data, na.action = na.omit) When I call the object, I see : Call: Model: variable1 ~ treatmentB | year/treatmentA Data: data Coefficients: (Intercept) treatment B year2/treatmentA0 44.08387 81.11284 year2/treatme...

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