similar to: Restrict AIC comparison to succesful models?

I have several linear models on the same data: m1 <- lm(y ~ poly(x,1)) m2 <- lm(y ~ poly(x,2)) m3 <- lm(y ~ poly(x,3)) What I don't understand is why anova(m1, m2, m3, test="F") - yields the same RSS and SS values, but a different p-value from anova(m1, m2, test="F") - when it also yields the SAME as anova(m2, m3, test="F") What am I missing? Rob

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that

Hi, I am analyzing a repeated-measures Greco-Latin Square with the aov command. I am using aov to calculate the MSs and then picking by hand the appropriate neumerator and denominator terms for the F tests. The data are the following: responseFinger mapping.code Subject.n index middle ring little ---------------------------------------------------------------------------- 1 1

Dear expeRts, there is obviously a general trend to use model comparisons, LRT and AIC instead of Wald-test-based significance, at least in the R community. I personally like this approach. And, when using LME's, it seems to be the preferred way (concluded from postings of Brian Ripley and Douglas Bates' article in R-News 5(2005)1), esp. because of problems with the d.f. approximation.

Hola buenas noches, tengo un problema bastante gordo. ¿A alguno le ha dejado de funcionar las funciones pamer.fnc y mcp.fnc con la nueva versión de R? La semana pasada formatee el ordenador y ahora scripts antiguos no funcionan. La cuestión es que me precupa que no funcione el ejemplo de tutorial del autor. Os dejo un script que debería de funcionar y no lo hace

To whom it may concern I made some analysis with R using the command Anova. However, I found some problmes with the output obtained by selecting type II o type III sum of squares. Briefly, I have to do a 2x3 mixed model anova, wherein the first factor is a between factor and the second factor is a within factor. I use the command Anova in the list below, because I want to obtain also the sum

Hi, I have some doubt about how qualitative factors are coded in R. For instance, I consider a response y, a quantitative factor x and a qualitative factor m at 3 levels, generated as follow : y_c(6,4,2.3,5,3.5,4,1.,8.5,4.3,5.6,2.3,4.1,2.5,8.4,7.4) x_c(3,1,3,1,2,1,4,5,1,3,4,2,5,4,3) m_gl(3,5) lm(y~x+m) Coefficients: (Intercept) x m2 m3 3.96364 0.09818

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

Hi, I have a number of genes (columns) for which I want to examine pairwise associations of genotypes (each row is an individual)...For example (see data below), I would like to compare M1 to M2, M2 to M3, and M1 to M3 (i.e. does ac from M1 tend to be found with bc from M2 more often than expected.) Down stream I will be performing chi square tests for each pair. But I am looking for a way to

hi I'm puzzled as to the relation between the REML score computed by gam and the formula (4) on p.4 here: http://opus.bath.ac.uk/22707/1/Wood_JRSSB_2011_73_1_3.pdf I'm ok with this for poisson, or for quasipoisson when phi=1. However, when phi differs from 1, I'm stuck. #simulate some data library(mgcv) set.seed(1) x1<-runif(500) x2<-rnorm(500)

I would like to merge two parts of words to get a name of the data. First M3$N (invariable) and second is a number from 0001 to 3003 - M3$N0001,M3$N0002,...,M3$N3003. For example if I do it like this: my.data <- paste("M3$N",2456,sep="") I get > my.data [1] "M3$N2456" But I want to get something equivalent to my.data<- M3$N2456 Is there any way to do it?

Kevin Wright wrote: > library(nlme) > m2 <- gnls(conc ~ t1*(1-t2*exp(-k*time)), > data = df.Chloride, > start = list( > t1 = 35, > t2 = 0.91, > k = 0.22)) So my error was to use nls instead that gnls. Thanks a lot, Kevin. > summary(m2) > plot(m2) > lag.plot(resid(m2), do.lines=FALSE) >

I would like to perform partial mantel tests on only within group values, with "between group" values assigned to NA. This is possible in package ncf partial.mantel.test, however this sues a different permutation to that used in ecodist.ecodist will not accept data with NA values, returning a "matrix is not square error. is it possible to perform this test in ecodist? many thanks

Dear R users, I have the following two problems, related to the function sub, grep, regexpr and similia. The header of the file(s) I have to import is like this. c("y (m)", "BD (g/cm3)", "PR (Mpa)", "Ks (m/s)", "SP g./g.", "P (m3/m3)", "theta1 (g/g)", "theta2 (g/g)", "AWC (g/g)") To get rid of spaces and

Dear All, I am new for R and SEM. I try to fit the model with Y (ordinal outcome), X (4 categorical data), M1-M3 (continuous), and 2 covariates (Age&sex) as a diagram. library(polycor) model.ly <-specify.model() 1: x -> m1, gam11, NA 2: x -> m2, gam12, NA 3: x -> m3, gam13, NA 4: age -> m1, gam14, NA 5: age -> m2, gam15, NA 6: age -> m3, gam16, NA 7: sex -> m1,

Hi, I have a data-matrix: > PID sato hrs fim health 214 3 4.376430 6.582958 5 193 6 4.361825 3.138525 6 8441 6 4.205771 3.835886 7 7525 6 4.284489 3.245139 6 6806 7 4.168926 2.821833 7 5682 7 1.788707 1.212653 7 5225 6 1.651463 1.436980 7 4845 6 1.692710 1.267359 4 4552 5 1.686448 1.220539 6

Hi, I am trying to cross compile code for ARM Cortex m0, m3, and m4. For m0, I use: -target armv6--eabi -mcpu=cortex-m0 That seems to work. For m3 and m4, I use the following which does not work (fatal error: error in backend: CPU: 'cortex-m3' does not support ARM mode): -target armv7m--eabi -mcpu=cortex-m3 and -target armv7em--eabi -mcpu=cortex-m4 Who can help me with the

Hi all, I have a matrix simulating migration in a spatial model. I want to be able to define movement (the values of m1, m2 and m3) as only != 0 between adjacent patches and contingent on certain conditions as defined in the function. Here is the code: WET<-function(t) {everglades$precipitation[t]} #simply reads precipitation data from a csv, value is numeric AB<-function(WET,t)

hello all, I have a simple linear model with 4/5 variables that I am trying to fit. I would like to find the lowest AIC value with any combination of all the variables. I would like to implement this with a while/for loop. Possibly I would like to generalize this so then I can use it when I have many more variables. I do not want to use step AIC. At the moment I am doing it manually but I

Dear R users, I have this 2 models that fit to my data: M3varI <- update (M3, weights=varIdent(form= ~ 1|SITE)) M3AR1<-update(M3,correlation=corAR1()) The first one, updates my M3 so that I can account for the variance structure of random erros. The second one, updates my M3 so that I can account for the correlation structure of random errors. How can I put them toghether in one