similar to: anova F-tests (PR#8875)

Dear useRs, I've just encountered a serious problem involving the F-test being carried out in aov() and anova(). In the provided example, aov() is not making the correct F-test for an hypothesis involving the expected mean square (EMS) of a factor divided by the EMS of another factor (i.e., instead of the error EMS). Here is the example: Expected Mean Square

When I run a summary(anova) I get output for all of the elements (columns) as these are multiple - single anova results. Can I store the F values? I can't find the attribute of the fitted model attributes(fit) that stores these F values, and for that matter, P values. Thanks -- Gareth Campbell PhD Candidate The University of Auckland P +649 815 3670 M +6421 256 3511 E

I am using anova.lm to compare 3 linear models. Model 1 has 1 variable, model 2 has 2 variables and model 3 has 3 variables. All models are fitted to the same data set. anova.lm(model1,model2) gives me: Res.Df RSS Df Sum of Sq F Pr(>F) 1 135 245.38 2 134 184.36 1 61.022 44.354 6.467e-10 *** anova.lm(model1,model2,model3) gives

Hello, Why does anova.lm sometimes return a p-value and at other times not ? Is it because it recognizes nested models from non-nested ones ? > x<-seq(1,100,1) > y<-3*x+rnorm(100) > anova(lm(y~x),lm(y~x+I(x^2)),test="F") Analysis of Variance Table Model 1: y ~ x Model 2: y ~ x + I(x^2) Res.Df RSS Df Sum of Sq F Pr(>F) 1 98 90.449 2 97 90.288 1

Dear R-experts! Running a meta-analysis (using the magnificent metafor-package), I use cohen's d as a main outcome measure in a random-effects model. For most of the samples cohen's d is derived form a comparison of two groups (A & B). However some studies report results from an ANOVA (one-factor with three levels: C,D,E) whereas two groups (C,D) correspond to one group in the other

Hi, I was wondering why anova() and drop1() give different tail probabilities for F tests. I guess overdispersion is calculated differently in the following example, but why? Thanks for any advice, Tom For example: > x<-c(2,3,4,5,6) > y<-c(0,1,0,0,1) > b1<-glm(y~x,binomial) > b2<-glm(y~1,binomial) > drop1(b1,test="F") Single term deletions Model: y ~

Hi, I'm working on fitting a glm model to my data using Gamma error structure and reciprocal link. I've been using dropterm (MASS) in the model simplification process, but the F values from analysis of deviance tables reported by dropterm and anova functions are different - sometimes significantly so. However, the reported residual deviances, degrees of freedom, etc. are not different.

Hi Robert, Unfortunately I am not replying to answer your question but to ask a question on pre-roll. I am working on a similar project but still at the configuration stages. For some reason my intro file is not being played. I am also using Icecast 2.4.4 and have four mount points on one relay server. I am trying to use my intro file on only one mount point. I am also doing mp3 not HLS.

Dear R community, I'm currently using R to analyze functional Magnetic Resonance Imaging data. Each analysis involves running ~120,000 repeated-measures ANOVAs. I would like to know if there is any automatic way to access the F- and P-value data that are associated with each of these 120,000 ANOVAs. For example, if the summary output (for the 1st ANOVA of 120,000) shows the following value

I have a rather basic question. How can I get R to generate a ANOVA table and a f-value for a hypothesis test such as: Data: group1 values: 5.2 4.5 6.0 6.1 6.7 5.8 group2 values: 6.5 8.0 6.1 7.5 5.9 5.6 ... H0: mean1 = mean2 = mean3 = mean4 HA: at least two means different where I want to evaluate using a f test statistic? F = MSTr/MSE I'd like a table similar to one that

Hi! We have run a linear regression model with 3 explanatory variables and get the output below. Does anyone know what type of test the anova model below does and why we get so different result in terms of significant variables by the two tables? Thanks! /Sara > summary(model) Call: lm(formula = log(HOBU) ~ Vole1 + Volelag + Year) Residuals: Min 1Q Median 3Q Max

Hi Martin, my name's Daragh, and I'm a renewal projects architect in the University of Chicago. I saw your name on a listserv - openssh-unix-dev. I was hoping you could lend some insight to a problem we're trying to solve. We are trying to find a way for an Oracle database to connect securely to a mainframe (OS/390 running Model204 DB) through ODBC (Open Database Connection

Version of R: Windows Version 2.0.0 The experimental design contains two plant lines - a control (C) and a mutant (M) - grown out three separate times in plots A, B, C. The design is unbalanced: In plot A, 9 control plants were grown with 29 mutant plants. In plot B, 8 control plants were grown with 20 mutant plants. In plot C, 8 control plants were grown with 22 mutant plants. The

Dear R-list, *When comparing two logistic regression models with the anova CHi test, I obtain the following error: (there are no NA's in the time series). How can this be solved such that I can compare two models on the same dataset were different explanatory variables are used? l.KBDI <- glm(zna.arson2 ~ zna.KBDI,family = binomial) l.NDWI <- glm(zna.arson2 ~ zna.NDWI,family

I'm a bit confused about the anova/aov functions. Both seem to rely on data models, where the relationship between the dependent variables and the independent variables can be expressed as a formula. In what I am trying to do, all of my independent variables are qualitative, not quantitative. For example, for each of two options, "option A" and "option B" I have

Simple example: # Classification Tree with rpart library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start, method="class", data=kyphosis) Now I would like to know how can I measure the "importance" of each of my three explanatory variables (Age, Number, Start) in the model? If this was a regression model, I could have looked at p values from the

hi all - i'm having trouble using lme to specify a mixed effects model. i'm pretty sure this is quite easy for the experienced anova-er, which i unfortunately am not. i have a data frame with the following columns: col 1 : "Score1" (this is a continuous numeric measure between 0 and 1) col 2 : "Score2" (another continuous numeric measure, this time bounded between 0

Hello all, I'm very satisfied to say that my grip on both R and statistics is showing the first hints of firmness, on a very greenhorn level. I'm faced with a problem that I intend to analyze using ANOVA, and to test my understanding of a primitive, one-way ANOVA I've written the self-contained practice script below. It works as expected. But here's my question: How can I not

Hi all This is probably a simple problem but somehow I am having much trouble with finding a solution, so I seek your help! I have a data-set with continuous response variables. The explanatory variably is 4xpH treatments (so 8.08, 7.94, 7.81 and 7.71) so also continuous and not technically factorial. However I have decided to do Anova's (as well as regression) to explore the effect of

Hi all, I have done a backward stepwise selection on a full binomial GLM where the response variable is gender. At the end of the selection I have found one model with only one explanatory variable (cohort, factor variable with 10 levels). I want to test the significance of the variable "cohort" that, I believe, is the same as the significance of this selected model: >