similar to: Running anova on different datasets

Hello, I am running Kruskal-Walis test in R. When I try to save results using write.table it gives me the following error : Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "htest" into a data.frame The overall code is as follows : >data_file = read.table("~/DATA.dir/data_file.txt", header=T)

Hello, R experts, I have a list called dataHP which has 30 elements (m1, m2, ..., m30). Each element is a 7x6 matrix holding yield data from two factors experimental design, with treatment in column, position in row. For instance, the element 20 is: dataHP[[20]] col1 col2 col3 trt1 trt2 trt3 [1,] 22.0 20.3 29.7 63.3 78.5 76.4 [2,]

Hello, I use a type III anova ("car" package) to analyse an unbalanced data design. I have two factors and I would have the effect of the interaction. I read that the result could be strongly influenced by the contrasts. I am really not an expert and I am not sure to understand indeed about what it is... Consequently, I failed to properly used the fit.contrast function (gregmisc

Hi, I wantto use 'mle' function in R on linux. As I see its been integrated into the stats4 package. Am I correct ? If yes, Can anyone suggest how to install the stats4 package to be able to run 'mle' function in R on linux ? Otherwise how to sort out this problem ? Thanks Himanshu [[alternative HTML version deleted]]

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

Hi, I want to generate a heatmap for my data (in a matrix). However, the data has some missing values (represented as blank). I get the following errors (with the blanks and with blanks replaced by NA and including the option rm.na = TURE): > filename = "input_heatmap.txt" > g <- as.matrix(filedata) > fg <- rainbow(nrow(g), start=0, end=.3) > gg <-

Dear R-helpers, In the case of two categorical factors, say a and b, once I have fixed the constrasts, the model matrix is set according to these contrasts with "lm", and the t-tests for the significance of the parameters provided by "summary" indeed concern the comparison of the model with each submodel obtained by removing the corresponding column of the model matrix.

Yesterday I posted the following question to the help list. Thanks to John Fox (copied below) who pointed out the solution. Original question: I have come across a result that I cannot explain, and am hopingthat someone else can provide an answer. A student fitted a mixed model usingthe lme function: out<- lme(fixed=Y~A+B+A:B, random=~1|Site). Y is a continuous variable while A and

I have some doubts about the validity of my procedure to estimeate linear contrasts ina a factorial design. For sake of semplicity, let's imagine a one way ANOVA with three levels. I am interested to test the significance of the difference between the first and third level (called here contrast C1) and between the first and the seconda level (called here contrast C2). I used the following

Dear R help, I've encountered a problem with survreg's anova(). I am currently writing general code to fit a variety of models using different fitting functions. Here's a simple example of what I'm trying to do: ---begin code--- # general function to analyse data analyse.data <- function(formula, FUN, data, ...) { fit <- FUN(formula, data=data, ...) anova(fit)

Dear experts, I just noticed that I get different results conducting type III anova using drop1 or the Anova command from the car package. I suppose I made a mistake and hope you can offer me some help. I have no idea where I got wrong and would be very grateful for explaination as R is new terrain for me. If I run the commands in line, they produce the same results. But if I run them in

Hi, I am very new to R. I am trying to perform an Anova Test and see if it differs or not. Basically, i have 4 tests and 1 control. Tester Test1 Test2 Test3 Test4 Control 20 25 15 10 17 The inference is at alpha=0.05. they are all independent. I am trying to find if they differ or the same. > test1<-c(20) > test2<-c(25) > test3<-c(15) >

First of all thank you for the responses. I appreciate the suggestions i have received thus far. Just to reiterate I am trying to analyze a data set that has been collected from a hierarchical sampling design. The model should be a mixed model nested ANOVA. The purpose of my study is to analyze the variability at each spatial scale in my design (random factors, variance components), and say

Hello dear R help members. I am trying to understand the anova.rq, and I am finding something which I can not explain (is it a bug?!): The example is for when we have 3 nested models. I run the anova once on the two models, and again on the three models. I expect that the p.value for the comparison of model 1 and model 2 would remain the same, whether or not I add a third model to be compared

Dear Dr. Bates, and R-help, I've tried the anova function to test the difference between two coefficients, as shown on page 225 of your book "Mixed Effects Models in S and S-Plus (Statistics and Computing)". When I type: anova( fm2BW.lme, L = c(TimeDiet2 = 1, TimeDiet3 = -1) ) I got the following error message: Error: unexpected '=' in "anova( fm2BW.lme, L =

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all

Hello- I understand that it's convention, when comparing two models using the anova function anova(model1, model2), to put the more "complicated" (for want of a better word) model as the second model. However, I'm using lme in the nlme package and I've found that the order of the models actually gives opposite results. I'm not sure if this is supposed to be the case

the study design of the data I have to analyse is simple. There is 1 control group (CTRL) and 2 different treatment groups (TREAT_1 and TREAT_2). The data also includes 2 covariates COV1 and COV2. I have been asked to check if there is a linear or quadratic treatment effect in the data. I created a dummy data set to explain my situation: df1 <- data.frame( Observation =

Hi, I have been trying to find the appropriate R command to analyse my datasets according to a particular model. Unfortunately, my best attempts at doing so have so far not worked. I am wondering if anybody can help me to figure out what i've been doing wrong, and what i need to do in order to use R correctly? The model is an ANOVA with some crossed factors, interactions, and one nested

Hey! Could you please take a quick look at what I have done? Somehow I get wrong results using the anova(lm()) combination compared to doing a two way ANOVA by hand. Running: Data<-read.table("Data.txt"); g<-lm(ExM~S1*S2,Data); anova(g); Gives: Analysis of Variance Table Response: ExM Df Sum Sq Mean Sq F value Pr(>F) S1 1 4.3679