similar to: How to extract coefficients from sequential (type 1), ANOVAs using lmer and lme

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

I wantto run sequential ANOVAs (i.e. type I sums of squares), and trying to getresults including ANOVA tables and associated coefficients for predictive variables(I am using the R 3.4.2 version). I think ANOVA tables look right, but believecoefficients are wrong. Specifically, it looks like that the coefficients arefrom ANOVA with ?marginal? (type III sums of squares). I have tried both lme

>>>>> Ben Bolker <bbolker at gmail.com> >>>>> on Fri, 15 Jul 2016 16:45:50 -0400 writes: > I'm sorry I haven't boiled this down to a more-minimal example yet, > but ... > I'm working on an S3 method (tidy.merMod, in the 'broom' package). It > normally handles 'merMod' objects from the lme4 package,

I'm sorry I haven't boiled this down to a more-minimal example yet, but ... I'm working on an S3 method (tidy.merMod, in the 'broom' package). It normally handles 'merMod' objects from the lme4 package, but I'm trying to make it handle 'merModLmerTest' objects from the lmerTest package too. The merModLmerTest class inherits (as an S4) class from the

Hi, The R CMD check is successful in R 3.0.1 but fails to install package lmerTest under R-devel r63910, Here is what I get: ** preparing package for lazy loading Error in reconcilePropertiesAndPrototype(name, slots, prototype, superClasses, : no definition was found for superclass "merMod" in the specification of class "merModLmerTest" In DESCRIPTION file I have:

I have no problem setting up my mixed model, or performing anova or lsmeans on my model?s outputs. However, performing lsd mean separation is giving me fits. So I do not have a problem when using two-way anova model. When using the code: fit.yield.add <- lm(data = ryzup, Yield ~ Rep + Nitrogen + Treatment) LSD.test(fit.yield.add, trt = "Nitrogen", alpha = 0.1, console = TRUE)

Hello all, Everything was working very well. Now when I try to load lmerTest using: library("lmerTest"), I get this error: Error: package or namespace load failed for ?lmerTest? in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ?purrr? Nothing I've done has worked. I have uninstalled both R and R studio. I've

Dear List, I am trying to analyze a dataset where I have 1 continuous between-item variable (C), and 2 factorial within-item variables (3- and 2-level: F3, F2). I'm interested in whether slope of C is different from 0 at different combinations of F3 and F2, and whether it varies between these combinations. And unfortunately I need a decent anova-like table with p-values. The reason is that

I was unable to open this file Bill Venables' excellent "Exegeses on Linear Models" posted at http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.ps.gz I'd be very interested in reading it? Thanks Esther Meenken Biometrician Crop & Food Research Private Bag 4704 Christchurch TEL: (03) 325 9639 FAX: (03) 325 2074 EMAIL:MeenkenE at crop.cri.nz Visit our website at

On 9/29/2017 3:37 PM, Rolf Turner wrote: > On 30/09/17 07:45, JLucke at ria.buffalo.edu wrote: > > <SNIP> > >> >> The conceptual paradigm for R is only marginally commensurate with >> that of >> standard statistical software. >> You must immerse yourself in R to become proficient. > > Fortune nomination. For newer list members wondering what

>>>>> "TL" == Thomas Lumley <tlumley@u.washington.edu> >>>>> on Sun, 6 Oct 2002 09:19:27 -0700 (PDT) writes: TL> On Sat, 5 Oct 2002 roxburg@kih.net wrote: >> This is not a bug report but didn't see another way to >> ask a question. TL> Well, you could try the r-help or r-devel mailing lists >> For

Hello, I have data on the maturity of two morphs of fish. I want to test whether their maturity is evolving differently or not on a temporal scale (month). The maturity variable (independent variable) is continuous and the morph and month variables (dependant variables) are categorical. Because the data show variance heterogeneity, I modeled it with the function gls: kg1 =

Hi, I'm trying to user lmer function from lmerTest package because, if I understood correectly, it allows to make better inference than lmer method from lme4 package. However, whatever I do I keep getting this error: Error in lme4::lFormula(formula = mark ~ ssCount + sTime+ : rank of X = 1660 < ncol(X) = 1895 any ideas what could be a problem? thanks, Srecko [[alternative HTML

Hi Ted, yes this was the problem. Thank you very much. best idaios On Wed, Jan 9, 2013 at 4:51 PM, Ted Harding <Ted.Harding@wlandres.net>wrote: > Ah! You have aqssigned a parameter "equal.var=TRUE", and "equal.var" > is not a listed paramater for t.test() -- see ?t.test : > > t.test(x, y = NULL, > alternative = c("two.sided",

Hello, I have a problem with selecting the right type of sums of squares for an ANCOVA for my specific experimental data and hypotheses. I do have a basic understanding of the differences between Type-I, II, and III SSs, have read about the principle of marginality, and read Venable's "Exegeses on Linear Models" (http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf). I am pretty new to

> On 30 Sep 2017, at 14:22 , Robert Baer <rbaer at atsu.edu> wrote: > > > > On 9/29/2017 3:37 PM, Rolf Turner wrote: >> On 30/09/17 07:45, JLucke at ria.buffalo.edu wrote: >> >> <SNIP> >> >>> >>> The conceptual paradigm for R is only marginally commensurate with >>> that of >>> standard statistical

Dear all, I have a model with simple terms, quadratic effects, and interactions. I am wondering what to do when a variable is involved in a significant interaction and in a non-significant quadratic effect. Here is an example d = data.frame(a=runif(20), b=runif(20)) d$y = d$a + d$b^2 So I create both an simple effect of a and a quadratic effect of b. m = lm(y ~ a + b + I(a^2) + I(b^2) +

I'm fairly new to R and am wondering if anybody knows of R code to calculate confidence intervals for parameters (fixed effects and variance components) from mixed effects models based on Sattherthwaite's method? I'm also interested in Satterthwaite-based confidence intervals for linear combinations (mostly sums) of various variance components. [[alternative HTML version

Dear All: Significance Analysis of Microarrays(SAM) As we know sam do multiple t.test as following ## Default S3 method: t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),mu = 0, paired = FALSE, var.equal = FALSE,conf.level = 0.95, ...) var.equal: a logical variable indicating whether to treat the two variances as being equal. If 'TRUE'

Hi All, I'm taking an Experimental Design course this semester, and have spent many long hours trying to coax the professor's SAS examples into something that will work in R (I'd prefer that the things I learn not be tied to a license). It's been a long semester in that regard. One thing that has really frustrated me is that lme has an extremely counterintuitive way for