similar to: Powerful PC to run R

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, 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 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 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'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 would like to solve a problem similar to a multiple knapsack problem and am looking for a function in R that can help me. Specifically, my situation is as follows: I have a list of n items which I would like to allocate to m groups with fixed size. Each item has a certain profit value and this profit depends on the type of group the item is in. My problem is to allocate the items

Dear all, I'm using a while loop in the context of an iterative optimization procedure. Within my while loop I have a counter variable that helps me to determine how long the loop has been running. Before the loop I initialize it as counter <- 0 and the last condition within my loop is counter <- counter + 1. I'd like to print out the current status of "counter" while the

Dear all, I am running a simulation in which I randomly generate a series of vectors to test whether they fulfill a certain condition. In most cases, there is no problem. But from time to time, the (randomly) generated vectors are too large for my system and I get the error message: "Cannot allocate vector of size x". The problem is that in those cases my simulation stops and I have to

Dear all, I'm working with two data frames. The first frame (agg_data) consists of two columns. agg_data[,1] is a unique ID for each row and agg_data[,2] contains a continuous variable. The second data frame (geo_data) consists of several columns. One of these columns (geo_data$ZCTA) corresponds to the unique ID in the first data frame. The problem is that only a subset of the unique ID

Dear all, I have a continuous variable that can take on values between 0 and 100, for example: x<-runif(100,0,100) I also have a second variable that defines a series of thresholds, for example: y<-c(3, 4.5, 6, 8) I would like to convert my continuous variable into a discrete one using the threshold variables: If x is between 0 and 3 the discrete variable should be 1 If x is between 3

Dear all, I would like to run a simple regression model y~x1+x2+x3+... The problem is that I have a lot of independent variables (xi) -- around one hundred -- and that some of them are categorical with a lot of categories (like, for example, ZIP code). One straightforward way would be to (a) transform all categorical variables into 1/0 dummies and (b) enter all the variables into an lm model.

Dear all, I'm working with a code that consists of two parts: In Part 1 I'm generating a random graph using the igraph library (which represents the relationships between different nodes) and a vector (which represents a certain characteristic for each node): library(igraph) g <- watts.strogatz.game(1,100,5,0.05) z <- rlnorm(100,0,1) In Part 2 I'm iteratively changing the

Dear all, This is probably more related to statistics than to [R] but I hope someone can give me an idea how to solve it nevertheless: Assume I have a variable y that is a function of x: y=f(x). I know the average value of y for different intervals of x. For example, I know that in the interval[0;x1] the average y is y1, in the interval [x1;x2] the average y is y2 and so forth. I would like to

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 have a data.frame that includes a series of demographic variables for a set of respondents plus a dependent variable (Theta). For example: Age Education Marital Familysize Income Housing Theta 1: 50 Associate degree Divorced 4 70K+ Owned with mortgage 9.147777 2: 65

Dear all, I have one dependent variable y and two independent variables x1 and x2 which I would like to use to explain y. x1 and x2 are design factors in an experiment and are not correlated with each other. For example assume that: x1 <- rbind(1,1,1,2,2,2,3,3,3) x2 <- rbind(1,2,3,1,2,3,1,2,3) cor(x1,x2) The problem is that I do not only want to analyze the effect of x1 and x2 on y but

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, 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 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 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