similar to: Collinearity in Moderated Multiple Regression

Dear all, I would like to estimate a system of regression equations of the following form: y1 = a1 + b1 x1 + b2x2 + e1 y2 = a2 + c1 y1 + c2 x2 + c3 x3 + e2 Specifically the dependent variable in Equation 1 appears as an independent variable in Equation 2. Additionally some independent variables that appear in Equation 1 are also included in Equation 2. I assume that I cannot estimate these two

Dear all, I have a large dataset (N=100,000 with 89 variables per subject). This dataset is stored in a 100.000 x 89 matrix where each row describes one individual and each column one variable. What is the easiest way of selecting a subset of let's say 1.000 individuals out of that whole matrix? Thanks, Michael Michael Haenlein Associate Professor of Marketing ESCP-EAP European School of

Dear all, I'm running a coxph model of the form: coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3) Within this model, I would like to compare the influence of x1 and x2 on the hazard rate. Specifically I am interested in testing whether the estimated coefficient for x1 is equal (or not) to the estimated coefficient for x2. I was thinking of using a Chow-test for this but the Chow

Dear all, I'm struggling with predicting "expected time until death" for a coxph and survreg model. I have two datasets. Dataset 1 includes a certain number of people for which I know a vector of covariates (age, gender, etc.) and their event times (i.e., I know whether they have died and when if death occurred prior to the end of the observation period). Dataset 2 includes another

Dear all, I'm currently running R on my laptop -- a Lenovo Thinkpad X201 (Intel Core i7 CPU, M620, 2.67 Ghz, 8 GB RAM). The problem is that some of my calculations run for several days sometimes even weeks (mainly simulations over a large parameter space). Depending on the external conditions, my laptop sometimes shuts down due to overheating. I'm now thinking about buying a more

Dear all, I am trying to simulate a series of networks that have characteristics similar to real life social networks. Specifically I am interested in networks that have (a) a reasonable degree of clustering (as measured by the transitivity function in igraph) and (b) a reasonable degree of degree polarization (as measured by the average degree of the top 10% nodes with highest degree divided by

Dear all, I would like to calculate a Moran's I statistic using the moran function in the spdep package. The problem I'm having deals with how to create the listw object. My data stems from the area of social network analysis. I have list of poeple and for each pair of them I have a measure of their relationship strength. So my dataset looks like: Jim; Bob; 0.5 This measure of

Dear all, is there a convenient way to determine the effect size for a regression coefficient in a multiple regression model? I have a model of the form lm(y ~ A*B*C*D) and would like to determine Cohen's f2 (http://en.wikipedia.org/wiki/Effect_size) for each predictor without having to do it manually. Thanks, Michael Michael Haenlein Associate Professor of Marketing ESCP Europe Paris,

Dear all, I have a discrete distribution showing how age is distributed across a population using a certain set of bands: Age <- matrix(c(74045062, 71978405, 122718362, 40489415), ncol=1, dimnames=list(c("<18", "18-34", "35-64", "65+"),c())) Age_dist <- Age/sum(Age) For example I know that 23.94% of all people are between 0-18 years, 23.28%

Dear all, I have several character strings with a high number of different levels. unique(x) gives me values in the range of 100-200. This creates problems as I would like to use them as predictors in a coxph model. I therefore would like to convert each of these strings to a new string (x_new). x_new should be equal to x for the top n categories (i.e. the top n levels with the highest

Dear all, I have written a relatively brief R-Code to run a series of simulations. Currently the code runs for a very long time (up to several days, depending on the conditions) and I expect this to be the case because it might not be very efficiently written. I am, for example, relying on several for(...) loops which could probably be done much faster using a different way of programming. I am

Dear R users, I have a problem with multicollinearity in mixed models and I am using lmer in package lme4. From previous mailing list, I learn of a reply "http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg38537.html" which states that if not for interpretation but just for prediction, multicollinearity does not matter much. However, I am using mixed model to interpret something,

Dear all, I'm asking this question again as I didn't get a reply last time: I'm doing a survival analysis with time-dependent covariates. Until now, I have used a simple Cox model for this, specifically the coxph function from the survival library. Now, I would like to try out an accelerated failure time model with a parametric specification as implemented for example in the survreg

Dear all, this is probably more a statistics question than an R question but probably there is somebody who can help me nevertheless. I'm running a regression with four predictors (a, b, c, d) and all their interaction effects using lm. Based on theory I assume that a influences y positively. In my output (see below) I see, however, a negative regression coefficient for a. But several of the

I am running a logistic regression model with a random effect using lmer. I am uncertain how to check for collinearity between my parameters. I have already run cor() and linear regression for each combination of parameters, and all Rsqr values were <0.8….but I am analyzing ecological data so a 0.8 cutoff may be unrealistic. -is there a way to check variance inflation factors or tolerance

I have a linear model y~x1+x2 of some data where the coefficient for x1 is higher than I would have expected from theory (0.7 vs 0.88) I wondered whether this would be an artifact due to x1 and x2 being correlated despite that the variance inflation factor is not too high (1.065): I used perturbation analysis to evaluate collinearity library(perturb)

Friends I am new to R (and statistics) so am struggling a bit. Briefly... I am interested in getting the P-Value from cor(X) where X is a matrix. I have found cor.test. Verbosely... I have 4 vectors and can generate the corellation matrix... > cor(cbind(X1, X2, X3, X4)) X1 X2 X3 X4 X1 1.00000000 -0.06190365 -0.156972795 0.182547517 X2

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,

Hi everyone. Based on a dependent variable (y), I'm trying to generate some independent variables with a specified correlation. For this there's no problems. However, I would like that have all my "regressors" to be orthogonal (i.e. no correlation among them. For example, y = x1 + x2 + x3 where the correlation between y x1 = 0.7, x2 = 0.4 and x3 = 0.8. However, x1, x2 and x3

Dear all, mydata<-data.frame(x1=c(1,4,6),x2=c(3,1,2),x3=c(2,1,3)) cor(mydata) x1 x2 x3 x1 1.0000000 -0.5960396 0.3973597 x2 -0.5960396 1.0000000 0.5000000 x3 0.3973597 0.5000000 1.0000000 I wonder if it is possible to fill only lower triangle of this correlation matrix? Using 'dist' doesn't seem to be useful as it doesnt allow to convert this table