similar to: Fwd: logistic regression with combination to distributed lag

Hello all, This is Meghana. Well, I have some analysis output in 3 dimensional array form. for example: , , type1 A B C D 1 2 3 4 1 2 3 4 , , type2 etc. This array is very big. and I want to export this to either text form or excel(csv is preffered) so that different parts of aaray should be easily extractable from that excel/text sheet. How can I go about it? Thank

Hi, This is Meghana Kulkarni. I have a problem regarding the data I am working with. I have a data frame as follows: > x V1 V2 V3............V10 414 A 416 A 417 A 417 B 418 A 421 A 421 B 421 C 422 A I want to conver this data frame in the following format. > x V1 V2 V3............V10 414 A 416

Hi, I am trying to find out a collinearity in explanatory variables with alias(). I creat a dataframe: dat <- ds[,sapply(ds,nlevels)>=2] dat$Y <- Response Explanatory variables are factor and response is continuous random variable. When I run a regression, I have the following error: fit <- aov( Y ~ . , data = dat) Error in "contrasts<-"(`*tmp*`, value =

Hi i need to create a model from 250 + variables with high collinearity, and only 17 data points (p = 250, n = 750). I would prefer to use Cp, AIC, and/or BIC to narrow down the number of variables, and then use VIF to choose a model without collinearity (if possible). I realize that having a huge p and small n is going to give me extreme linear dependency problems, but I *think* these model

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at

Hi all. Sorry for the long email. I have been trying to find someone local to work on this with me, without much luck. I went in to our local stats consulting service here, and the guy there told me that I already know more about model selection than he does. :-< He pointed me towards another professor that can perhaps help, but that prof is busy until mid-June, so I want to get as much

Hello all, I've been having trouble with assessing a breakpoint in a logistic GLM with two explanatory variables. For this analysis I've been using the 'segmented' package version 0.2-9.1. But I keep getting an error and I don't see where I would be going awry. The situation is the following: Two explanatory variables: bedekking - a variable with possible values between 0 and

Hi, I have a dichotomous variable (Q1) whose answers are Yes or No. Also I have 2 categorical explanatory variables (V1 and V2) with two levels each. I used logistic regression to determine whether there is an effect of V1, V2 or an interaction between them. I used the R and SAS, just for the conference. It happens that there is disagreement about the effect of the explanatory variables

>From what I can tell by reading forum posts etc., this is not a trivial issue. An answer in 2008 indicated some directions, but I'm curious whether any developments have been made since then. In my data set I have 182 observations with a binomial response, a 2-level explanatory factor, and x and y coordinates. By visually inspecting the spatial distribution of standardized residual error

Dear Masters, first I'd like to wish u all a great 2011 and happy holydays by now, second (here it come the boring stuff) I have a question to which I hope u would answer: I run a logistic regression by glm(), on the following data type (y1=1,x1=x1); (y2=0,x2=x2);......(yn=0,xn=xn), where the response (y) is abinary outcome on 0,1 amd x is any explanatory variable (continuous or not)

Hi all, is there any package which can do an EM algorithm fitting of logistic regression coefficients given only the explanatory variables? I tried to realize this using the Design package, but I didn't find a way. Thanks a lot & Kind regards Robin Aly

Dear All, I have a short question. Is there any readily available function that could generate either an ARMAX model or, more generally, an AutoRegressive Distributed Lag model? I am looking for a function that is similar to armaSim() function in fArma package. Thank you. MP

Dear R Community, I am pleased to announce the release of a new package called 'dlnm', now available on CRAN (version 0.2.1). The package dlnm provides some facilities to run distributed lag models (DLM's) and their non-linear extension (DLNM's), a modelling framework to describe simultaneously non-linear and delayed effects between predictors and an outcome in time-series

Dear R Community, I am pleased to announce the release of a new package called 'dlnm', now available on CRAN (version 0.2.1). The package dlnm provides some facilities to run distributed lag models (DLM's) and their non-linear extension (DLNM's), a modelling framework to describe simultaneously non-linear and delayed effects between predictors and an outcome in time-series

Dear R-helpers, I tried to create a 3D surface showing the interaction between two continuous explanatory variables; the response variable is binary (0/1). The model is: model<-glm(incidence~sun*trees,binomial) then I used "wireframe" to create a 3D plot: xyz<-expand.grid(sun=seq(30,180,1),trees=seq(0,4000,10)) xyz$incidence<-as.vector(predict(model,xyz))

Dear R users, I'm analysing some data, and I'm using an lme function. I have a problem with choosing the right order for three of my explanatory variables, which shows collinearity. Is there any rules to make the decision?(r.squared?) Or it's better if I choose the order, that I think gives me more information about the data? Say x1 is the variable with the highest r.squared, x3

How do I construct a figure showing predicted value plots for the dependent variable as a function of each explanatory variable (separately) using the results of a logistic regression? It would also be helpful to know how to show uncertainty in the prediction (95% CI or SE). Thanks- This email has been processed by SmoothZap - www.smoothwall.net

Hi, I would like to regress y (dependent variable) on x (independent variable) and y(-1). I have create the y(-1) variable in this way: ly<-lag(y, -1) Now if I do the following regression lm (y ~ x + ly) the results I obtain are not correct. Can someone tell me the code to use in R in order to perform a regression using as explanatory variable a lagged dependent variable? My best regards,

Hello all, Could someone tell me what I am doing wrong here? I am trying to fit a binary logistic model using the lrm function in Design. The dataset I am using has a dichotomous response variable, 'covered' (1-yes, 0-no) with explanatory variables, 'nepall', 'title', 'abstract', 'series', and 'author1.' I am running the following script and

Dear R-help members, I'd like to run a binomial logistic stepwise regression with ten explanatory variables and as many interaction terms as R can handle. I'll come up with the right R command sooner or later, but my real question is whether and how the criterion for the evaluation of the different models can be set to be the probability of the residual deviance in the Chi-Square