similar to: conversion from SAS

Thank you very much for the example. I think interactively I could get something. But my obstacle is to write an R script that processes my set of data automatically. My difficulty is to extract the information that appears on the screen, when R is operated interactively, from a scripts. Let me go over some steps to make sure I am doing things right. Assume my data have been read into the matrix

Dear package mgcv users, I am using package mgcv to describe presence of a migratory bird species as a function of several variables, including year, day number (i.e. day-of-the-year), duration of survey, latitude and longitude. Thus, the "global model" is: global_model<-gam(present ~ as.factor(year) + s(dayno, k=5) + s(duration, k=5) + s(x, k=5) + s(y, k=5), family =

hi, R-users I have a data.frame for example test$newdataday24 and test$newdataday48 I can plot them by plot(test$newdataday24) but now i want to plot different data by define a variable to describe them dayno<-c(24,48) newnam<-paste("test$newdataday",dayno,sep="") plot(newnam[1]) but i failed,the error message said that something wrong with plot.window what can i do

Hello there fellow R-users, I have received some data which comes in the following format: example1<-"200301" The first 4 digits correspond to the year and the remaining 2 digits correspond to the week of the year. I have tried to convert this to a date by using strptime as follows: strptime(example1,format="%Y%U") where U (looking up strptime) is the week of the

In the following, I'm fitting a logistic regression model, and using car:::influencePlot. When I run the latter with output to the screen, it calls identify() that lets me label observations with large CookD. However, if I use postscript() to get .eps output, identify() seems not to be called at all. If instead, I use dev.copy2eps() after getting output to the screen, the point labels

Dear list, I've noticed that influence.measures and cooks.distance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome +