similar to: stupid lm() question

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

Ok, so I'm slowly figuring out what a factor is, and was able to follow the related thread about finding a mode by using constructs like my_mode = as.numeric(names(table(x))[which.max(table(x))]) Now, suppose I want to keep looking for other modes? For example, Rgames> sample(seq(1,10),50,replace=TRUE)->bag Rgames> bag [1] 2 8 8 10 7 3 2 9 8 3 8 9 6 6 10 10 7 1

"Not that it really matters, but" Can someone explain how the row numbers get assigned in the following sequence? It looks like something funky happens when rbind() coerces 'bar' into a dataframe. In either sequence of rbind below, once you get past the first two rows, the row numbers count normally. Rgames> (foo<-data.frame(x=5,y=4,r=3)) x y r 1 5 4 3 Rgames>

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

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(),

Hi, Here's the problem I ran into: the gmp package has a method for apply() so it masks the base::apply function. With gmp installed, I tried to run the function turnpoints() from the pastecs package. It fails because it calls apply() internally, like this: apply(mymatrix,1,max,na.rm=TRUE) , but the code in the gmp package which sets up the operator overload for apply() strictly

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

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

Hi all, I'm working with a bunch of large graphs, and stumbled across something useful. Probably many of you know this, but I didn't and so others might benefit. Using pch="." speeds up plotting considerably over using symbols. > x <- runif(1000000) > y <- runif(1000000) > system.time(plot(x, y, pch=".")) user system elapsed 1.042 0.030 1.077

Dear list, I have been struggling for some time now with this code... I have this vector of unique ID "EID" of length 821 extracted from one of my dataframe (skate). It looks like this: > head(skate$EID) [1] "896-19" "895-8" "899-1" "899-5" "899-8" "895-7" I would like to remove the complete rows in another dataframe

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

Hello, everybody! I have a matrix "input" (see example below) - with all unique entries that are actually unique ranks (i.e., start with 1, no ties). I want to assign a value of 100 to the first row of the column that contains the minimum (i.e., value of 1). Then, I want to assign a value of 100 to the second row of the column that contains the value of 2, etc. The results I am looking

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

Dear R users, I'd like to count the number of integers in a vector y. Here is an example. y <- c(0,1,1,3,3,3,5,5,6) In fact, I know how to count the number of specific number in y. sum(y==0) -> 1 sum(y==1) -> 2 sum(y==2) -> 0 sum(y==3) -> 3 sum(y==4) -> 0 sum(y==5) -> 2 sum(y==6) -> 1 However, in one computation I want to get this vector [1,2,0,3,0,2,1]. Thank

Dear R-group, I am trying desperately to get this Tukey test working. Its my first time here so I hope my question is not too stupid, but I couldn't find anything helpful in the help or in the forum. I am analysing a dataset of treated grasses. I want to find out, if the grasses from different Provenances react differently. In the aov test I found a significance for the combination Treatment

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

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

Dear R list: I modelled changes in a variable (mconc) over time (d) for individuals (replicate) given one of three treatments (treatment) using: mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate, data=my.data) summary(mconc.lme) shows that the linear coefficient of one of the treatments is significantly different to zero, viz. Value Std.Error