similar to: Marginality rule between powers and interaction terms in lm()

Hi Everybody, please can you help me how to call BayesX in R in order to see the graph already exist in BayesX Thanks ---------- Forwarded message ---------- From: <r-help-request@r-project.org> Date: Sat, Jan 22, 2011 at 5:00 AM Subject: R-help Digest, Vol 95, Issue 22 To: r-help@r-project.org Send R-help mailing list submissions to r-help@r-project.org To subscribe or

Dear devellopeRs, I've altered factor.scope() from the stats package so it can take marginality into account. Therefore I've added the argument marginal. When marginal is NULL, factor.scope() works like the original version. The marginality is to be specified as a named list. The name of the elements is the name of a variable. The content of the element is a character vector with the

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

Hi Arie, Thank you for your further research into the issue. Regarding Stata: On the other hand, JMP gives model matrices that use the main effects contrasts in computing the higher order interactions, without the dummy variable encoding. I verified this both by analyzing the linear model given in my first example and noting that JMP has one more degree of freedom than R for the same model, as

Hi Arie, The book out of which this behavior is based does not use factor (in this section) to refer to categorical factor. I will again point to this sentence, from page 40, in the same section and referring to the behavior under question, that shows F_j is not limited to categorical factors: "Numeric variables appear in the computations as themselves, uncoded. Therefore, the rule does not

Hi Arie, I understand what you're saying. The following excerpt out of the book shows that F_j does not refer exclusively to categorical factors: "...the rule does not do anything special for them, and it remains valid, in a trivial sense, whenever any of the F_j is numeric rather than categorical." Since F_j refers to both categorical and numeric variables, the behavior of

Hi Arie, Given the heuristic, in all of my examples with a missing two-factor interaction the three-factor interaction should be coded with dummy variables. In reality, it is encoded by dummy variables only when the numeric:numeric interaction is missing, and by contrasts for the other two. The heuristic does not specify separate behavior for numeric vs categorical factors (When the author of

Hello Tyler, Thank you for searching for, and finding, the basic description of the behavior of R in this matter. I think your example is in agreement with the book. But let me first note the following. You write: "F_j refers to a factor (variable) in a model and not a categorical factor". However: "a factor is a vector object used to specify a discrete classification"

Hello Tyler, I want to bring to your attention the following document: "What happens if you omit the main effect in a regression model with an interaction?" (https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction). This gives a useful review of the problem. Your example is Case 2: a continuous and a categorical regressor.

Hello Tyler, I rephrase my previous mail, as follows: In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2, which in the example is dropped from the model. Hence the X3 in T_i must be encoded by dummy variables, as indeed it is. Arie On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com> wrote: > Hi

Hi Michael, > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Michael > Friendly > Sent: Thursday, June 29, 2017 9:04 AM > To: Beno?t PELE <benoit.pele at acoss.fr>; r-help at r-project.org > Subject: Re: [R] Help : glm p-values for a factor predictor > > On 6/29/17 11:13 AM, Beno?t PELE wrote: > > My question is

I've uploaded to CRAN a new package called effects. The package contains functions for tabular or graphical display of terms in a linear or generalized linear model, and is particularly suitable for models that contain terms -- such as main effects and interactions, or polynomial regressors -- related by marginality (hierarchy). A draft paper describing the package is located at

I've uploaded to CRAN a new package called effects. The package contains functions for tabular or graphical display of terms in a linear or generalized linear model, and is particularly suitable for models that contain terms -- such as main effects and interactions, or polynomial regressors -- related by marginality (hierarchy). A draft paper describing the package is located at

Dear all, I have a question about the glmulti package. I want to include some variables in all models. To that end I applied the wrapper function as shown in the examples (http://www.inside-r.org/packages/cran/glmulti/docs/glmulti). To include the variable "Geslacht" in all models: > glm.redefined = function(formula, data, always="", ...)

Hello Tyler, You write that you understand what I am saying. However, I am now at loss about what exactly is the problem with the behavior of R. Here is a script which reproduces your experiments with three variables (excluding the full model): m=expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C")) model.matrix(~(X1+X2+X3)^3-X1:X3,data=m)

(This time with the r-help in the recipients...) Be careful when mixing lme4 and lmerTest together -- lmerTest extends and changes the behavior of various lme4 functions. >From the help page for lme4-anova (?lme4::anova.merMod) > ?anova?: returns the sequential decomposition of the contributions > of fixed-effects terms or, for multiple arguments, model >

Hi, I know that the type of model described in the subject line violates the principle of marginality and it is rare in practice, but there may be some circumstances where it has sense. Let's take this imaginary example (not homework, just a silly made-up case for illustrating the rare situation): I'm measuring the energy absorption of sports footwear in jumping. I have three models (S1,

Author: osaeed Repository: /hg/zfs-crypto/gate Revision: 3881b24561f17fe3d8926e85bbbefa1de3c86c43 Log message: 6375225 PM_IDLE_DOWN is not enforced after console framebuffer powers off Files: update: usr/src/uts/common/os/sunpm.c

I'm trying to produce a series of powers of a number as follows: |> 0.05^0:5 [1] 1 2 3 4 5 This is not the result I expected. I guess some kind of coercion happened, since, |> class(0.05^0:5) [1] "integer" Could anyone explain me what is happening here? Thanks, -Sergio.

At the risk of being beaten about the face and body, can somebody explain why the middle example: log2(2^3); floor(log2(2^3)) is different than examples 1 and 3? > log2(2^2); floor(log2(2^2)) [1] 2 [1] 2 > log2(2^3); floor(log2(2^3)) [1] 3 [1] 2 > log2(2^4); floor(log2(2^4)) [1] 4 [1] 4 > DrC [[alternative HTML version deleted]]