similar to: glm or transformation of the response?

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Hola, Tengo una base de datos con estructura jerárquica en la que quiero clasificar observaciones en distintas categorías. En el caso más simple, tengo una variable con dos categorías (variable Y1) y dentro de cada una de ellas hay otras dos categorías (variable Y2). Además tengo una variable explicativa cuantitativa discreta X. El banco de datos sería de este tipo: X Y1 Y2 5 0 1 9 0 0 2

Hi Listers, seems I have forgotten some basics re offset in glm: data: counts (y) from locations off different size (area), explanatory variable: x Model: y ~ x+offset(area) Predictions (pred) using Poisson errors plot(x,y) and points(x,pred) gives neat "line" of estimated values. However, for ease of understanding graphs are better using plot(x,y/area). Question: How to display

In the logistic regression model, there is no residual log (pi/(1-pi)) = beta_0 + beta_1*X_1 + ..... But glm model will return residuals What is that? How to understand this? Can we put some residual in the logistic regression model by replacing pi with pi' (the estimated pi)? log (pi'/(1-pi')) = beta_0 + beta_1*X_1 + .....+ ei Thanks! [[alternative HTML version deleted]]

Hi all Assume I have a data set xx; Group: 1=group1 ?, 2=group2 IQ: ?1= High, 0 =low fit <- glm(IQ ~group, data = xx, family = binomial()) summary(fit) Results ?????? ????????????Estimate Std. Error z value Pr(>|z|) (Intercept) -2.55456??? 0.210 -12.273? < 5e-16 *** group????????? 0.36180 ?????0.076?? 3.952 ????5.24e-05 *** the odd ratio = exp(0.36180 )= 1.435912 My question

Hi all I know about producing a minimal example to show my problem. But I'm having trouble producing a minimal example that displays this behaviour, so please bear with me to begin with. Observe: I create an array called model.mat. Some details on this: > str(model.mat) num [1:18, 1:4] -0.170 -0.304 -2.617 2.025 -1.610 ... - attr(*, "dimnames")=List of 2 ..$ : chr

Hi R users! I am trying to learn some multilevel analysis, but unfortunately i am now very confused. The reason: http://www.ats.ucla.edu/stat/hlm/seminars/hlm_mlm/mlm_hlm_seminar.htm http://www.ats.ucla.edu/stat/sas/seminars/sas_mlm/mlm_sas_seminar.htm and MlmSoftRev. pdf from mlmRev package. >From what i see, the first two links seem to declare the level one variable as a random part (i

Hello, I've some questions concerning log-transformations and plotting of the regression lines. So far as I know is it a problem to log-transform values smaller than 1 (0-1). In my statistics lecture I was told to do a log(x+1) transformation in such cases. So I provide here a small example to explain my questions: # Some example data for testing a1

Dear all,?I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...).?I have realised a sqrt(variable) transformation...?The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in

Hi to all This is the first time I am quoting a question and I hope, my question is not too basic... For the following data, I wish to draw a fitted curve. x <- c(123,129,141,144,144,145,149,150,158,159,163,174,183,187,242,248) y <- c(14.42,26.96,31.3,19.95,36.36,15.4,24.76,35.39,28.07,40.97,26.23,42.83,46.53,14.79,49.18,48.08) If I plot the data, it looks somehow that a logistic

Dear R-Help, I am using the 'mfp' package. It produces three plots (as I am using the Cox model) simultaneously which can be viewed together using the following code: fit <- mfp(Surv(rem.Remtime,rem.Rcens)~fp(age)+strata(rpa),family=cox,data=nearma,select=0.05,verbose=TRUE) par(mfrow=c(2,2)) plot(fit) They can be viewed separately but the return key must be pressed after each graph

Hello, R-Help answers questions on R code, your question is about statistics. You should try posting the question to https://stats.stackexchange.com/ Hope this helps, Rui Barradas Em 23-10-2017 18:54, kende jan via R-help escreveu: > Dear all, I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...). I have

The data are not normally distributed when untransformed and I'm trying various transformations to see if any would be appropriate to use. The lattice book (fig. 3.10) shows a 2-sample Q-Q plot with an abline but the code for the figure does not include the line. I'd appreciate a pointer to a reference on how to add an abline to a one-sample qqmath() plot in lattice. Rich

On Wednesday 17 December 2003 15:53, Macsym wrote: > Hi everybody, > > If I understand well, the purpose of XSL files is re-formatting XML files. > When I checked the admin folder, I saw status.xsl but I did not see the > source XML file (status.xml?). > > If status.XML (NOT status.XSL) exists, where is it located? Is there any > way I can store status.xml or status.xsl on

Hi there I'm trying to find the mle fo a multinomial model ->*L(N,h,S?x)*. There is only *N* I want to estimate, which is used in the number of successes for the last cell probability. These successes are given by: p^(N-x1-x2-...xi) All the other parameters (i.e. h and S) I know from somewhere else. Here is what I've tried to do so far for a imaginary data set:

Dear List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple

I have written a function to plot data which will be used for various different chemistries. A simplified version is: plot_data <- function(risk,levels,chem,sd2,measure){ plot(risk, levels,main=paste ("per", sd2, measure, "\n in usual", chem)) } The problem is with the title. This works fine if the variable "chem" is just text, but if it is an expression then

I have recently compiled and installed openssh-2.1.1p1 on a linux box. The login reporting does not seem to work properly. When logging into the box via ssh (protocol 1) utmp shows the user logged in and the tty properly, but the field for the login date/time and the field for originating host contain all NULLs. Is anyone else seeing this same behavior, or have I just done something really

Hello, my enviroment: All Servers are Ubuntun 16.04-18.04 SAMBA AD DC Server and several SAMABA DOMAIN MEMBER (connected via WINBIND). In ADDC I've created a group "restrictaccess" and added some users. Now when im typing "id <username>" on a Domain Member, for some users the group "restrictaccess" are listed for some not! For example: ON DC: #

Hello all, I have count data with about 36% of observations being zeros. I found in some of the examples of the r-help mail archives that a tweedie family of distributions could be used to fit a model with random effects. Upon installing the tweedie package and attempting to fit the following model: lmer(SUS ~ 1 + (1|