similar to: Marginal (type II) SS for powers of continuous variables in a linear model?

Dear all: When I am trying to get the marginal effects: summary(result7,adv_inc_ratio=mean(m9201 $adv_inc_ratio),adv_price_ratio=mean(m9201$adv_price_ratio), ...(SOME MORE CONTINUOUS AND DISCRETE VARIABLES BUT I AM NOT LISTING)... regW=c (0,mean(m9201$regW),1), regWM=c(0,mean(m9201$regWM),1)) It gave out an error message: Error in summary.Design(result7, adv_inc_ratio = mean(m9201

To avoid angry replies, let me first say that I know that the use of Type III sums of squares is controversial, and that some statisticians recommend instead that significance be judged using the non-marginal terms in the ANOVA. However, given that type III SS is also demanded by some… is there a function (equivalent to drop1 for lm) to obtain type III sums of squares for mixed models using 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

Dear all, In many papers regarding time series analysis of acquired data, the authors analyze 'marginal distribution' (i.e. marginal with respect to time) of their data by for example checking 'cdf heavy tail' hypothesis. For i.i.d data this is ok, but what if samples are correlated, nonstationary etc.? Are there limit theorems which for example allow us to claim that

Dear all, I am trying to estimate the marginal effects of a logit regression using the mfx package. It is crucial that the standard errors are clustered at the year level. Hence, the code looks as follows: marginal.t24.2<-logitmfx(stock.market.crash~crash.t24+bubble.t24+RV.t24,data=Data_logitregression_lags, clustervar1 = "year")

Hallo, I would like to add the marginal distributions along the X and the Y axis to a scatter plot. Can anybody help me, please? Thank you, Paolo -- Paolo Bulla Istituto di Metodi Quantitativi Universit?? "L. Bocconi" viale Isonzo 25 20136 Milano paolo.bulla at unibocconi.it

Dear all, I wonder if it is possible to obtain marginal tests for effects in generalized linear models. Indeed, the anova function produces sequential tests and it doesn't have any "type" argument to specify that we would like marginal tests instead, as in the similar anova function for lme objects. Thanks a lot for your help! Eve CORDA Office national de la chasse et de la faune

Hi, I have a problem with the ordered probit model -polr function (library MASS). My independent variables are countinuos. I am not able to understand two main points: a) how to calculate marginal effects b) how to calculate the relative importance of each independent variables If required i will attach my model output. Thanks Franco

I am attempting to plot something called a (marginal) dot diagram which is a regular scatterplot with an extra set of plots in the margins along both the X and Y axes. I have looked everywhere I can think for information. Can anyone give me any suggestions? Is there a single command that generates it or do I have to generate a regular scatterplot then add the additional plots in the margins?

Hello, I have come across a result that I cannot explain, and am hoping that someone else can provide an answer. A student fitted a mixed model using the lme function: out<- lme(fixed=Y~A+B+A:B, random=~1|Site). Y is a continuous variable while A and B are factors. The data set is balanced with the same number of observations in each combination of A and B. There are two hierarchical

Hi, Im just doing some Beta-Regressions with the betareg package. My question is now, is there a possibility to calculate the marginal effects with the betareg package or is there another package which can handle marginal effects on regression output for the "beta" class? I try to calculate the marginal effects also by hand but then i have the problems with the standard errors :-( Thx

Dear all, I am modeling the incidence of recreational anglers along a stretch of coastline, and with a vary large proportion of zeros (>80%) have chosen to use a zero inflated negative binomial (ZINB) distribution. I am using the same variables for both parts of the model, can anyone help me with R code to compute overall marginal effects of each variable? My model is specified as follows:

Dear All, recently, while setting me on the straight and narrow about linear contrasts for a linear mixed effect model, Prof Ripley pointed out that my interpertation of the call anova(mymodel.lme) was not correct, because I was meant to add type = "marginal", as in anova(mymodel.lme, type = "marginal") I tried to look deeper in the issue, asking people, checking on the

Dear All, I carry out negative binomial estimations using the glm.nb command from the MASS package. Is there a command or a simple procedure for computing marginal effects from a glm.nb fitted object? If these are the same as for a Poisson fitted object (glm), my question remains how to compute them. Thanks in advance for your help. Roberto Patuelli ******************** Roberto Patuelli, Ph.D.

Dear list, My question to follow is not a pure R question but contains also a more general statistical/econometrical part, but I was hoping that perhaps someone knowledgable on this list could offer some help. I have estimated a binary logistic regression model and would like to calculate average marginal effects for certain predictors of interest. The average marginal effect for a continuous

Dear R-community, I am currently replicating a study and obtain mostly the same results as the author. At one point, however, I calculate marginal effects that seem to be unrealistically small. I would greatly appreciate if you could have a look at my reasoning and the code below and see if I am mistaken at one point or another. My sample contains 24535 observations, the dependent variable

Is there a way (simple or not) to get the marginal prediction from lme (in nlme) and/or lmer (in lme4)? Rick B.

I have 10000 bivarate samples (x1, x2), and I want to estimate the marginal density of x2. I searched the R manual but couldn't find a function that can do this job. It seems "density" only works for single-variate samples. Can anybody help me with it? Thanks a lot! Best, J. Deng

I tried to use the effect() to get predicted marginals for multinomial logistic as I did for general logistic regression, but failed. Is there anyway to do that? Thx! -- View this message in context: http://www.nabble.com/How-to-get-predicted-marginal-%28aka-predicted-mean%29-after-multinomial-logistic--tp17200114p17200114.html Sent from the R help mailing list archive at Nabble.com.

Hello people, I am in need for testing Marginal Homogeneity for sparse (more then 2X2) matrixes. After searching, what I found by now is that for more then 2 by 2 matrixes, one turns to "stuart maxwell tests" that are available in two packages: irr - see: stuart.maxwell.mh coin - see: mh_test But I couldn't find in the documentation how valid the results are for sparse matrixes,