similar to: How to get F values for a Generalized Linear Model?

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

I've set the path for each user in pdbedit and created a login script with drive mapping etc etc The network drives aren't being mapped when I login each user: smb.conf [global] printcap name = cups cups options = raw map to guest = Bad User # include = /etc/samba/dhcp.conf logon path = \\%L\profiles\.msprofile logon home = \\%L\%U\.9xprofile

Hello, I have interval censored data, censored between (0, 100). I used the tobit function in the AER package which in turn backs on survreg. Actually I'm struggling with the distribution. Data is asymmetrically distributed, so first choice would be a Weibull distribution. Unfortunately the Weibull doesn't allow for zero values in time data, as it requires x > 0. So I tried the

Note the edited subject line! I don't know why I typed it as it was before. This says that as.numeric(as.character(f)) will work regardless of the implementation, and I agree. It's the recommendation to use as.numeric(levels(f))[f] that has me wondering about section 2.3.1 of the language definition. I expect that this idiom is in widespread use, and perhaps the language definition

Dear all, I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1). The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as

Hi All, I wonder if you can help me with an aparently simple task.  I have been searching examples for this without any luck: #Assume x<-1:10  #x ranges from 1 to 10. y<-x*runif(10)+ 1.5*x  #y is a linear function of x with some error. Add uniform error that is scaled to be larger as x values also become larger #error is proportional to x size, this should cause heterocedasticity. #I

Dear all, I need to run a simple linear regression such that: y = b0 + b1*x1 + (1-b1)*x2 + e which I know I can use: lm(y ~ I(x1 - x2) + offset(x2)). However, I also need to restrict the coefficient b1 to be between 0 and 1. Is there any way to include such restriction in the linear regression estimation? I saw suggestion related with the function Solve.QP, but I really did not understand such

Hello everybody, This is my firth question in this mailing list. The question is about the how to deal with a model with both random and fixed factors. I know to do the analysis using SPSS. The command line wit be as follows: UNIANOVA alpha.ug.mg BY Line Year Rep /RANDOM=Year Rep /METHOD=SSTYPE(4) /INTERCEPT=EXCLUDE /PRINT=HOMOGENEITY /CRITERIA=ALPHA(0.05) /DESIGN= Year Rep*Year Line

Hello there, I am new to using regression in R. I wanted to solve a simple regression problem where I have 2 equations and 2 unknowns. So lets say - y1 = alpha1*A + beta1*B y2 = alpha2*A + beta2*B y1 <- runif(100000, 0,1) y2 <- runif(100000,0,1) alpha1 <- 0.6 alpha2 <- 0.75 beta1 <- 1-alpha1 beta2 <- 1-apha2 I now want this equation to estimate the values of A and B. Both A

Hi, I saw you discussed about the meaning of the R squared in a nls regression. Do you have a source or a quotation please? kind regards, Pierre Grison Tel: 06 01 79 74 22 Mail: pgrison@hotmail.fr [[alternative HTML version deleted]]

Hello, I try to find a function for M-estimation in multivariate linear regression model (function that can estimate betas in my model: y=x * beta + e, where y is a matrix). I´ve searched R-site for a long time, but I am hopeless. I would like to ask, if there is any function for M-estimation in multivariate linear regression model in R. I know I can estimate betas in my model by rlm() function

Hi all, I've fit a mixed linear model to some longitudinal data. I'm interested in the differences in patterns of decrease in the dependent variable according to group status, and my hypothesis particularly predicts a difference between the groups in trajectory of change at between specific ages. The data shows a significant interaction between group and the linear and quadratic effects

Dear all, I am trying to use R to fit mixed models. Take the following example, where ind is a random effect and sample is fixed. I wanted to fit Model 1: values = ind + sample Model 2: values =ind * sample Model 3: values=ind(sample) + sample Tried to use the below for mod1, but it did not work. Can anyone give some help on this. Thanks so much. mod1 <- lme(values ~ sample +

Hi there, I am trying to write a tool which involves implementing logistic regression. With the batch gradient descent method, the convergence is guaranteed as it is a convex problem. However, I find that with the stochastic gradient decent method, it typically converges to some random points (i.e., not very close to the minimum point resulted from the batch method). I have tried different ways

Dear list, I'm quite confused when interpreting results from a mixed linear model. For example, working on Iris data frame, I want to know the effect of species on slope of the model "Petal.Length~Sepal.Length" I write this : data(iris) reg01 <- lm(Petal.Length~Sepal.Length + Sepal.Length:Species, data=iris) summary(reg01) It gives me a summary table with lm for the first

Hi, I want to know if it's possible to pass a vector v=c(x,y,...) to a function f(x,y,...) so that each vector element corresponds to a formal argument of the function. For python programmers: f(*v). Specifically, what I'm trying to achieve is: given a list of coordinates l=list(c(x1,y1,z1), c(x2,y2,z2),...) I would like to obtain the corresponding elements in some array A (3-dim in this

Hi, I am wondering if anyone knows how to perform an F-test on the change in R square between hierarchical models in R? SPSS provides this information and a researcher that I am working with is interested in getting this information. Alternatively, if someone knows how I can calculate the test statistic (SPSS calls it F-change?) and dfs that would be helpful as well. The output and the test I am

Hi guys, I'm very new to R and have been teaching myself over the past few months - it's a great tool and I'm hoping to use it to analyse my PhD data.As I'm a bit of a newb, I'd really appreciate any feedback and/or guidance with regards to the following questions that relate to generalized linearmixed modelling (or, at least, I think they do!)(if there is a 'better',

Dear All: I want to do association study based on mixed linear model, My model not only includes serval fixed effects and random effects but also incorporates some covariates such as "birth weight". Otherwise, the size of the data are about 180 individuals and 12 variables and 60000 Fixed effect estimates As asreml-R is not free ,is there any packages for my study? I heard nlme or

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep