similar to: glm: calculating average marginal effects for dummies

Hello all, This question concerns the function Dotplot from the Hmisc package. My aim is to compare values between groups in each panel of the Dotplot, with the values of different groups clearly distinguishable by different symbols. All lines and symbols should be coloured in black. Before adding the panel function to the Dotplot, the groups behaved as desired and were marked by different

Hello, I would like, if it is possible, to compare the effect of a variable across regression models. I have looked around but I haven't found anything. Maybe someone could help? Here is the problem: I am studying the effect of a variable (age) on an outcome (local recurrence: lr). I have built 3 models: - model 1: lr ~ age y = \beta_(a1).age - model 2: lr ~ age + presentation

Hello R users, I have a puzzle with the VGAM package, on my first excursion into generalized additive models, in that this very nice package seems to want to do either more or less than what I want. Precisely, I have a 4-component outcome, y, and am fitting multinomial logistic regression with one predictor x. What I would like to find out is, is there a single nonlinear function f(x) which acts

Hi! I've been looking for a way to calculate std errors of marginal effects when I use interaction terms, but with no success. I pretty much have two cases: continuous variable * continuous variable, and continuous variable * binary variable. In both cases, I know how to calculate the marginal effects, even with simulation. But I still can't figure out how to calculate the std errors of

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

Hello there, I have a linear regression model for which I estimated heteroskedasticity-robust (Huber-White) standard errors using the coeftest function in the lmtest-package. Now I would like to inspect the predicted values of the dependent variable for particular groups and include a confidence interval for this prediction. My question: is it possible to estimate confidence intervals for the

hi there, I have a list of 5 identical dataframes: mydf <- data.frame(x=c(1:5), y=c(21:25)) mylist <- rep(list(mydf),5) and a factor variable with 5 levels: foo <- c(letters[1:5]) foo <- as.factor(foo) Question: I'd like to add a new variable to each dataframe in the list, each containing only one level of the factor variable. So mylist[[1]] should have a new variable z

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

hello, the following questions will without doubt reveal some fundamental ignorance, but hopefully you can still help me out. I'd like to bootstrap a coefficient gained on the basis of the coefficients in a logistic regression model (the mean differences in the predicted probabilities between two groups, where each predict() operation uses as the newdata-argument a dataframe of equal size as

Thanks a lot you both, I'll try to update to the latest version, By the way, Pete, I am having this the problem with different email clientes: Thunderbird, Outlook and Kmail I'll let you know how it goes. Thanks again Esther On 28/04/11 15:33, Francisco Wagner C. Freire wrote: > I think a good try is update the new server to latest dovecot version. > I migrate about 1.5M

Hi all... again. I have uploaded the new server to dovecot 2.0.12, and still having the same issue. I have even tried changing the hostname, and still the same. I have checked the site http://wiki2.dovecot.org/Migration at the "POP3 migration" section, and I can't see the problem. The UIDLs between hosts are still the same, and I am lost at what is making me download the mails

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")

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the

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 list subscribers, Recently I upgraded my samba version from 2.0.5 to 3.0.8-2. The setup I use is basically very simple: a W 98 box should be able to access some disk-shares and a printed, connected to a Linux box, running Debian's sarge distribution. Accessing the disks is no problem, but the printer can't be accessed any more. My log.smbd logfile shows lines suggesting

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

Dear all, I'd like to plot the marginal effect of a variable in a multiplicative interaction regression, that is, the effect of a variable conditional on the values of another variable. As an illustration, given model lm1 lm1 <- lm(y ~ x*z) I'd like to get the effects of x on y conditional on the values of z, with the corresponding confidence intervals if possible. Does anyone know

Hi all, first of all, I must say that I am pretty new to mailing lists, so please, if I do something wrong, just tell me. Now, my problem. I'm migrating a mail service from a temporal to a definitive server. Both servers have the same versions (Postfix, Dovecot -1.0.7, POP3 & IMAP services-, MySQL) and it all works perfectly. The only problem is with the users that store their mail on

I have a question regarding calculating marginal effects (change in the probability due to a one unit change in x) for a probit or for that matter any discrete choice model. Does R have a function that will essentially do this: dE[y|x] ------- = f(x'beta)*beta dx Stata has a command called "mfx" that calculates the marginal effects at the mean (of the x