search for: treat1

...2437 Random effects: Groups Name Variance Std.Dev. initial.size (Intercept) 71.585 8.4608 Residual 49.856 7.0608 number of obs: 323, groups: initial.size, 292 Fixed effects: Estimate Std. Error t value (Intercept) 12.8846 1.3028 9.890 infl.treat1 -0.4738 1.1819 -0.401 def.treat2 -3.5522 1.6022 -2.217 def.treat3 -2.1757 1.6461 -1.322 def.treat4 -2.1613 1.7003 -1.271 Correlation of Fixed Effects: (Intr) infl.1 df.tr2 df.tr3 infl.treat1 -0.413 def.treat2 -0.616 0.013 def.treat3 -0.641 0.002 0.493 d...

...pare all levels of my interaction with each other. I read the pdf 'Additional multcomp Examples' but even though there is an example with an interaction it doesn't work for me when I want to compare within and between groups. Here is an example: #### d.fr<-data.frame(id=rep(1:16,3),treat1=rep(as.factor(LETTERS[1:3]),each= 16),treat2=rep(as.factor(letters[4:7]),each=4),response=rnorm(48)) require(multcomp) require(lme4) fit1<-lmer(response~treat1*treat2+(1|id),data=d.fr) temp<-expand.grid(treat1=unique(d.fr$treat1),treat2=unique(d.fr$treat2)) X<-model.matrix(~treat1*treat...

...,365,385,400,410,320,340,220) id <- c("1","4","6","7","10","11","14","2","3","5","9","12","13") sequence <- c(rep('for/sal', 7), rep('sal/for', 6)) treat1 <- c(rep('for', 7), rep('sal', 6)) treat2 <- c(rep('sal', 7), rep('for', 6)) study <- data.frame(id, sequence, treat1, pef1, treat2, pef2) studyLong <- as.data.frame(rbind(as.matrix(study[,c('id', 'sequence', 'treat1', 'pef1...

...m trying to do a simple recode which I am stumbling on. I figure there must be any easy way but haven't come across it. Given data of A","B","C","D","E","A" it would be nice to recode this into say three categories ie A and B becomes "Treat1", C becomes "Treat 2" and E becomes "Treat 3". I tried the car library but it didn't seem to like text. Also it seems you can only recode into two categories. library(car) x<-c("A","B","C","D","E","A") x...

I have just realised that I sent this to Per only. For those interested on the list: -----Original Message----- From: Gygax Lorenz FAT Sent: Tuesday, September 14, 2004 4:35 PM To: 'Per Tor??ng' Subject: RE: [R] glmmPQL and random factors Hi Per, > glmmPQL(Fruit.set~Treat1*Treat2+offset(log10(No.flowers)), > random=~1|Plot, family=poisson, data=...) > > Plot is supposed to be nested in (Treat1*Treat2). > Is this analysis suitable? As far as I understand the methods and with my experience using such analyses, I would say that the model is ok the way you...

...n group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into consideration the confounding effect? Any help would be greatly appreciated. Isaac A representation of the experiments follows: Experiment 1 Control1 Treat1 Treat2 Blk1 s1 s2 s3 Blk2 s4 s5 s6 Blk3 s7 s8 s9 Experiment 2 Control2 Treat3 Treat4 Blk1 s1a s2a s3a Blk2 s4a s5a s6a Blk3 s7a...

Hi all! I have 4 treatments on 5 animals Treat1 Treat2 Treat3 Treat4 Animal1 36 37 35 39 Animal2 33 34 36 37 Animal3 37 35 33 38 Animal4 34 36 34 35 Animal5 35 36 33 36 I use an Anova and i try to verify calcul So i re...

Hi, please excuse the most likely very trivial question, but I'm having no idea where to find related information: I try to recapitulate very simple plotting behavior of Excel within R but have no clue how to get where I want. I have tab delimited data like cell treatment value line a treat1 4 line a treat2 3 line b treat1 8 line b treat2 11 I'd like to have a plot (barplot), that specifies 2 scales on the x-axis (cell and treatment condition). In future this might become more complex, so basically I'd like to have a table/matrix as x-axis! Where do I have to look for working...

.... Is there some option I can change on the plot command? Sorry if this is simple but searching for this answer was a little difficult as plot() has many uses. Below is an example. I am using R 2.10.1 and Ubuntu 9.04. Thanks in advance! Sam x <- runif(48, 2, 70) data <- data.frame(x) data$treat1 <- factor(c("ONE", "TWO", "THREE")) data$treat2 <- factor(c("PRUNED", "UNPRUNED")) data$row <- factor(1:12) model <- with(data, aov(x ~ treat1 + treat2 + treat1*treat2 + Error(row))) plot(model) Error in plot.window(...) : need finite...

...ment1 treatment2 scale(covariate) sigma2 [1,] -0.1780735 0.2024111 0.3395233 0.8682119 0.002617449 [2,] -0.1521623 0.1773623 0.2956053 0.8764573 0.003529013 sims.treat0 <- rnorm(no.of.sims, sims.model[,"(Intercept)"], sqrt(sims.model[,"sigma2"])) sims.treat1 <- rnorm(no.of.sims, sims.model[,"(Intercept)"] + sims.model[,"treatment1"], sqrt(sims.model[,"sigma2"])) sims.treat2 <- rnorm(no.of.sims, sims.model[,"(Intercept)"] + sims.model[,"treatment2"], sqrt(sims.model[,"sigma2"])) # Calcul...

...regression of contingency table. Q1¡¢How to understand the "independent poisson process"as many books or paper mentioned? For instance: Table1 ------------------------------------------- treat caner non-cancer sum ------------------------------------------- treat1 52(57.18) 19(13.82) 71 treat2 39(33.82) 3 (8.18) 42 ------------------------------------------- sum 91 22 113 ------------------------------------------- The numbers in parentheses are the expected frequency,which are computed by(57.18 f...

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

...ran a 2-way ANOVA with repeated measures considering time as the within subject factor: aov(response~treat*time+Error(subject/time),dat) The results told me that the treatment is marginally significant (p=0.04). I would like to know where that significance came from, so I did ALL pairwise t tests (treat1 vs. control, treat2 vs. control, treat1 vs. treat2) at each of the time point. There are 2 ways I can do these t tests, using the MSE from the ANOVA (the MSE used for the treatement effect in the ANOVA, i.e. the treatment by time interaction) as the t test error, or simply ran ordinary t tests usin...

...rwise comparisons to see which treatment groups differ.? I tried the glht function in the multcomp package, but I get surprising results, with p=1 for all comparisons.? # My data: surv.data <- data.frame( Site=c(rep("Site1", 9), rep("Site2", 9)), Treatment=rep(c(rep("Treat1", 3), rep("Treat2", 3), rep("Treat3", 3)), 2), survival=c(0.9, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8, 0.4, 0.6, 0.5, 1.0, 0.7, 0.2, 0.2, 0.4) ) # Binomial GLM: glm.out <- glm(survival~Site*Treatment, data=surv.data, family="binomial", weights=rep(10, nro...

..., 'contr.poly')) # create fake data data.df <- data.frame (gruppe= rep (1:4, rep (20, 4))) # create response variable data.df$zv <- rnorm (80, 2) # create potential explanatory variables data.df$explan <- rnorm (80, 2) data.df$treat <- as.factor (sample (1:3, 80, T)) data.df$treat1 <- as.factor (sample (1:4, 80, T)) data.df$treat2 <- as.factor (sample (1:5, 80, T)) data.df$treat3 <- as.factor (sample (1:6, 80, T)) # with each of the explanatory variables withoutInt <- lme (zv ~ explan, data= data.df, random= ~1 | gruppe) withInt <- lme (zv ~ explan, data= data...

I have been trying for hours now to perform an orthogonal contrast through an ANOVA in R. I have done a two-factor factorial experiment, each factor having three levels. I converted this dataset to a dataframe with one factor with nine treatments, as I couldn't work out what else to do. I have set up a matrix with the eight orthogonal contrasts that I wish to perform, but despite

...mary(model) Call: glm(formula = tot.fruit ~ infl.treat + def.treat + initial.size, family = quasipoisson) Deviance Residuals: Min 1Q Median 3Q Max -6.1541 -2.1872 -0.7045 0.9926 7.5629 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.8788185 0.1093306 17.185 < 2e-16 *** infl.treat1 -0.2456909 0.0924677 -2.657 0.00827 ** def.treat2 -0.1478382 0.1277139 -1.158 0.24788 def.treat3 -0.0780282 0.1207796 -0.646 0.51871 def.treat4 -0.2581576 0.1221538 -2.113 0.03532 * initial.size 0.0013834 0.0000993 13.931 < 2e-16 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ?...