similar to: help on warning message when using anova

Hi can someone help me with the following warning message I got when I used two way anova: Warning messages: 1: Models with response "NULL" removed because response differs from model 1 in: anova.lmlist(object, ...) 2: Models with response "NULL" removed because response differs from model 1 in: anova.lmlist(object, ...) (I have two rows of separate data sets) Thanks in

Hi there, I have a data frame as listed below: > Ca.P.Biomass.A P Biomass 1 334.5567 0.2870000 2 737.5400 0.5713333 3 894.5300 0.6393333 4 782.3800 0.5836667 5 857.5900 0.6003333 6 829.2700 0.5883333 I have fit the data using logistic, Michaelis?Menten, and linear model, they all give significance. > fm1 <- nls(Biomass~SSlogis(P, phi1, phi2, phi3), data=Ca.P.Biomass.A)

In R, we currently have the functions anovalist.lm and anova.glmlist S / S-plus has anova.lmlist anova.glmlist On the other hand, the [n]lme package (library) of Doug Bates and Jose Pinheiro has an "lmList" class and an anova.lmList(.) method for that. We are considering to use anovalist.lm and anovalist.glm instead of the S/S-plus names mentioned above. These functions

Hello I am trying to compare two models using anova(), however I get a message error (see below). In the net I only found some information on certain library(car) for which one should use anova with A capital letter (Anova instead of anova), but I could not find car library as it says it does not exist. > Model <- lm(interceptG ~ SW + TSC + FSC + PF + SlopeG + K, data=AllTrait) >

R-help: I am trying to decide between using a multiple linear regression or a linear mixed effects model for my data: model1 <- lm (responsevariable ~ predictor1 + predictor2 + predictor3 + predictor4, data= data) model2 <- lme (responsevariable ~ predictor1 + predictor2 + predictor3 + predictor4, random = ~1 | site, data= data) anova (model1, model2) but I keep getting the

anova() on three or more objects behaves inconsistently in R. In R anovalist.lm does a sequential ANOVA using pairwise F tests, ignoring all the other objects, so the larger of the two models provides the denominator. In S anova.lmlist uses the denominator from the largest model (smallest residual df) in the set, as does anova.glmlist in both. I suggest that R's anovalist.lm is wrong (that

Hello: Let me rephrase my question to attract interest in the problem I'm having. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or "nls"

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

Hello: I am really stuck on this problem. Why do I get an error message with anova() when I compare these two equations? Hope someone can help. ANDREW ____________________________ > fm1 <- glmmPQL(choice ~ day + stereotypy, + random = ~ 1 | bear, data = learning, family = binomial) > fm2 <- glmmPQL(choice ~ day + envir + stereotypy, + random = ~ 1 |

Hello: I am getting an error message when appplying anova() to two equations estimated using glmmPQL. I did look through the archives but didn't finding anything relevant to my problem. The R-code and results follow. Hope someone can help. ANDREW ____________________________ > fm1 <- glmmPQL(choice ~ day + stereotypy, + random = ~ 1 | bear, data = learning, family =

Hello: Will someone PLEASE help me with this problem. This is the third time I've posted it. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or

I stumbled across a mild glitch when trying to compare the result of gam() fitting with the result of lm() fitting. The following code demonstrates the problem: library(gam) x <- rep(1:10,10) set.seed(42) y <- rnorm(100) fit1 <- lm(y~x) fit2 <- gam(y~lo(x)) fit3 <- lm(y~factor(x)) print(anova(fit1,fit2)) # No worries. print(anova(fit1,fit3)) # Likewise. print(anova(fit2,fit3)) #

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

Hi, Did anyone sucessfully install SJava package to R and was able to call R function from java in redhat linux8.0? I tried several days it still give me error either in libR.so or libjvm.so. for example, I can compile JavaRCall.java(a example with the SJava package) without problem. When I run it, it can connect to R and accomplish part of results, but fail for other calls. The outputs are:

Dear useRs, I am working on a series of field experiments (159 in total) carried out in different years in several locations. The cultivars in each experiment are not always the same, in fact they change over time. I would like to get the fitted values and MSE of the individual fits from the following lmList object, so I can use them to fit a mixed model using the fitted values and weight each

Hi all, I'm trying to run a repeated measures ANOVA on some univariate ecological data that was collected over two growing seasons. I ran the test using the methodology found on this website: http://rtutorialseries.blogspot.com/2011/02/r-tutorial-series-one-way-repeated.html Upon running the actual ANOVA I got this error message: "> rmanova=anova(yearmodel, idata=yearframe,

Is it possible to vectorize anova over the output of a vectorized lm? I have a gene expression matrix with each row being a gene and columns for samples. There are several factors with interactions. I can get p values by looping over the matrix with lm and anova, but I would like to make this as computationally efficient as possible. I am able to vectorize the lm command, but when I try to use

Dear r-helpers, Can anyone suggest why lm() doesn't complain here: summary(osss.lm1 <- lm(logOdds ~ c.setSize %in% task, data = osss)) whereas in package:nlme (and in package:lme4) osss.lmL <- lmList(logOdds ~ c.setSize %in% task | subj, data = osss) # Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : # contrasts can be applied only to factors with 2 or more

Hello R-Help, ----------------------------------------------------------------------------------------------------------------------------------------- Issues (there are 2): 1) Possible bug when using lmtest::encomptest() with a linear model created using nlme::lmList() 2) Possible modification to lmtest::encomptest() to fix confusing fail when models provided are, in fact, nested. I have

Dear all, I'm using the latest version of the package lme4 and R version 2.7.2 (2008-08-25). After I run the model, I get the results of the model (cf. below). Then, I run an ANOVA using the "anova" function and I get the following message "Error in anova(lmer1) : Calculated PWRSS for a LMM is negative". I went trough the R-mailing list and a similar error message was