search for: npk

Hi, I am trying to plot an interaction.plot with different color for each level of a factor. It has an erratic behavior. For example, it works for the first interaction.plot below, with the example from the ALDA book, but not with the other plots, from the NPK dataset: # from http://www.ats.ucla.edu/stat/r/examples/alda/ch2.htm tolerance <- read.csv("http://www.ats.ucla.edu/stat/r/examples/alda/data/tolerance1.txt") tolerance.pp <- read.csv("http://www.ats.ucla.edu/stat/r/examples/alda/data/tolerance1_pp.txt") attach(tolerance....

Hi, I've done a repeated ANOVA looking as follows: model<-aov(y~a*b*c+Error(d)) Now I want to get rid of some or all of the interactions. But neither AIC nor anova work. What is the appropriate function to test my model?

Dear Madame, Dear Sir, I am able to obtain the coefficients from a 'summary' of 'lm', but NOT from a 'summary' of 'aov'. The following example shows my steps. ## Initialize rm(list = ls()) # remove (almost) everything in the working environment utils::data(npk, package="MASS") # get data model <- yield ~ block + N*P*K ## Using lm npk.lm <- lm(model, npk) ( s.npk.lm <- summary(npk.lm) ) ... Estimate Std. Error t value Pr(>|t|) (Intercept) 54.8750 0.8021 68.415 < 2e-16 *** block1 1.7125 1.3893...

...,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5, 55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), K=factor(K), yield=yield) npk.aov <- aov(yield ~ block + N*P*K, npk) proj(npk.aov) ## as a test, not particularly sensible options(contrasts=c("contr.helmert", "contr.treatment")) npk.aovE <- aov(...

A non-text attachment was scrubbed... Name: not available Type: text Size: 3126 bytes Desc: not available Url : https://stat.ethz.ch/pipermail/r-help/attachments/19991130/5cb00c0f/attachment.pl

...,0,0) P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5, 55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,NA) # My change npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), K=factor(K), yield=yield) npk.aov <- aov(yield ~ block + N*P*K, npk) model.tables(npk.aov, "means", se=TRUE) you get the error > model.tables(npk.aov, "means", se=TRUE) Error...

...#but it won't work with aov or null setClassUnion("aovOrNULL", c("aov", "NULL")) setClass("c1", representation(value = "aovOrNULL")) y1 = new("c1", value = NULL) #trying to assign an aov object to the slot doesn't work utils::data(npk, package="MASS") npk.aov <- aov(yield ~ block + N*P*K, npk) y2 = new("c1", value = npk.aov ) Any ideas? Thank you Thomas Kaliwe

Hello dear R members. I have been learning the Anova syntax in order to perform an SS type III Anova with repeated measures designs (thank you Prof. John Fox!) And another question came up: where/what are the (between/within) residuals for my model? ############ Play code: phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),

...1,1,0,0,1,0,1,0,1,1,0,0) + P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) + K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) + yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,55.0, + 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) + npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), + K=factor(K), yield=yield) + } > > op <- options(contrasts=c("contr.helmert", "contr.poly")) > npk.aov <- aov(yield ~ block + N*P*K, npk) Error in terms.formula(formula, "Error&quot...

...9,0.9,0.9,0.9,0.9,0.9,0.9) > > > cycl<-c(5000,5000,5000,5000,10000,10000,10000,10000,5000,5000,5000,5000,10000,10000,10000,10000) > > yield<-c(0.26, 1.77, 0.29, 1.75, 0.26, 1.77, 0.31, > > 1.75, 0.29, 1.78, 0.35, 1.83, 0.29, 1.79, 0.35, > 1.83) > > > > npk <- data.frame(H=factor(hidden), > > T=factor(theres), > > L=factor(lrate), > > C=factor(cycl), yield=yield) > > > > ( npk.aov <- aov(yield ~ H*T*L*C, npk) ) > > summary(npk.aov) > > > > when I run it, I got the...

The following R commands were written: >help.search("factorial") >data(npk) >npk >coef(npk.aov) In the output of coef command, please explain me the interpretation of coefficients of block1 to block 6 in this 2^3 confounded factorial experiment. Thanks. [[alternative HTML version deleted]]

Hello!!! I'm trying to do multiple ANOVA tests with R (testing the affect off different factors on the same response). As a result I get many ANOVA tables, and I want to extract a list of the "Pr(>F)" from all the tables. Maybe someone have an idea how to do this? Thanks Imri -- View this message in context:

...ORKS aovfit <- aov(as.formula(frmlb), dat); print(model.tables(aovfit, "means")); frmlc <- "yield ~ block + Error(N/block)"; # DOES NOT WORK aovfit <- aov(as.formula(frmlc), dat); print(model.tables(aovfit, "means")); } utils::data(npk, package="MASS"); frmla <- "yield ~ block + Error(N/block)"; # WORKS aovfit <- aov(as.formula(frmla), npk); print(model.tables(aovfit, "means")); test(npk); ## End of example Output of sessionInfo(): R version 2.11.1 (2010-05-31) i386-pc-mingw32 locale:...

Doea anyone know how to compute components of variance analysis? For example, I have the score of pupils on a test. Each pupil belongs to a school and within each school I may have several classes? How can I estimate the variance of the pupils, schools, classes and the errror variance? Any examples? Any help will be appreciated. Jason Dr. Iasonas Lamprianou Department of Education The

...1,1,0,0) > P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) > K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) > yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,55.0, + 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) > npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), + K=factor(K), yield=yield) > > ( npk.aov <- aov(yield ~ block + N*P*K, npk) ) Call: aov(formula = yield ~ block + N * P * K, data = npk) Terms: block N P...

Is there a way to prevent the re-ordering of factors by aov? I do have a three-way interaction that I do want to fit before a two-way interaction (different factors, so they are not nested), but R moves the two-way interaction to the front. I know it generally makes sense to fit the two-way interactions first, but in this case I think I know what I'm doing. + hv

Hi all, I have been trying to reproduce an analysis from Douglas Montgomery?s book on design and analysis of experiments. Table 6.10 of example 6.2 on page 246, gives a table as follows: > NPK <- expand.grid(A=mp,B=mp,C=mp,D=mp) > Rate <- c(45,71,48,65,68,60,80,65,43,100,45,104,75,86,70,96) > filtration <- cbind(NPK,Rate) > filtration A B C D Rate 1 - - - - 45 2 + - - - 71 3 - + - - 48 4 + + - - 65 5 - - + - 68 6 + - + - 60 7 - + + - 80 8 + +...

...model selection with mixed models is a tricky issue, the two packages currently produce different AIC scores for the same model, but they systematically differ by 2. In looking at the logLik values for each method, I find that they indeed differ by 1. So, the following code: utils::data(npk, package="MASS") library(lme4) a<-lmer(yield ~ 1+(1|block), data=npk) logLik(a) library(nlme) b<-lme(yield ~ 1, random=~1|block, data=npk) logLik(b) produces a df of 2 for a, and a df of 3 for b. I'm guessing that lmer is not accounting for the level-1 variance. Is this t...

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

Is there an easy way of converting an aov.summary into a matrix in which the rows are the factor names and the columns are Df, Sum Sq, Mean Sq, F value and Pr. For example, convert Df Sum Sq Mean Sq F value Pr(>F) block 5 343.29 68.66 4.4467 0.015939 * N 1 189.28 189.28 12.2587 0.004372 ** P 1 8.40 8.40 0.5441 0.474904 K