similar to: dynlm and lm: should they give same estimates?

Dear List, Wonder if you have some thoughts on the following question using lsoda in desolve: I have the following data and function: require(deSolve) times <- c(0:24) tin  <- 0.5 D <- 400 V    <- 26.3 k <-0.056 k12  <- 0.197118 k21  <- 0.022665 yini <- c(dy1 = 0,dy2 = 0)  events <- data.frame(var = "dy1",time = c(10,15),value = c(200,100),method =

I need to solve a system of non-linear fourth-order differential equations. Is there a command which solves this system? Thanks in advance.

Dear All wonder if you could provide some insights on the following: currently I have this code which produces the expected results: require(deSolve) pars <- list(k = 0.08,v=15) intimes <- c(0,0.5,12) input <- c(800,0,0) forc <- approxfun(intimes, input, method="constant", rule=2) derivs <- function(t, state, pars) { inp <- forc(t) dy1 <- - pars$k * state[1]

Hi, there. Suppose I have a bivariate data set y1 and y2. Can anybody tell me how to estimate the conditional density of f(y1|y2) and vice versa? Thanks. Yulei $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ Yulei He 1586 Murfin Ave. Apt 37 Ann Arbor, MI 48105-3135 yuleih at umich.edu 734-647-0305(H) 734-763-0421(O) 734-763-0427(O) 734-764-8263(fax) $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

As this forum proved to be very helpful, I got another question... I'd like to fit data points on which I have an error, dx and dy, on each x and y. What would be the common procedure to fit this data by a linear model taking into account uncertainty on each point? Would weighting each point by 1/sqrt(dx2+dy2) (and taking dx and dy as relative errors) in a lm() fit do the job? I would like to

Full_Name: Albrecht Gebhardt Version: 0.99.0 OS: alpha, osf4.0 Submission from: (NULL) (143.205.180.40) DEC cc doesn't like c++ style comments // like this one Please use standard cc comments or #if 0 ... #endif A quite large patch follows (I hope it will pass mailing with not too much wrapped lines): --- ./src/main/g_her_glyph.c.dec-cc.patch Mon Feb 7 14:48:10 2000 +++

Dear All I am trying to use svychisq with a two-dimensional table 4 x 5. The command I am using is summary(svytable(~dietperception+dietstatus,dudleyls1rake,na.rm=TRUE),"C hisq") It is throwing up an error message as follows: Error in NCOL(y) : only 0's may be mixed with negative subscripts In addition: Warning messages: 1: is.na() applied to non-(list or vector) in:

I have some output from a survey shown below and would like to insert total rows for each category to then be able to use it as a data set for further analysis. Is it possible to do this in R. In this example I would like to insert a row between rows 5 and 6, 10 and 11, 15 and 16, 20 and 21 and put in the sum of the rows 1-5, 6-10, 11-15, 16-20 and to delete row 21 which has NA in it. I would

Dear Professor Lumley I am relatively new to using R and also to logistic regression. We have analysed our Dudley Health Survey using the survey package. I am now trying to look at associations using svyglm but I am unsure of how to interpret the output and present the resulting model or whether there are any other things I should do to check the validity of the model. Below is an example of

Dear All,   please help with some thoughts on overcoming the following issues, if possible:   #R Code   require(deSolve) require(FME) pars <- list(k = 0.06,v=18) intimes <- c(0,0.5,12,12.5,50) input   <- c(800,0,800,0,0) forc <- approxfun(intimes, input, method="constant") model <- function(pars, times=seq(0, 50, by = 1)) {   derivs <- function(t, state, pars) {     

disable bogus touchpad device model, which cause annoying dmesg on 2.6 kernel Signed-off-by: Edwin Zhai <edwin.zhai@intel.com> diff -r 7c951e3eb5ab -r 14d3890843d0 tools/ioemu/hw/pckbd.c --- a/tools/ioemu/hw/pckbd.c Wed Oct 19 11:53:00 2005 +0100 +++ b/tools/ioemu/hw/pckbd.c Thu Oct 20 18:28:18 2005 +0800 @@ -29,6 +29,9 @@ /* debug PC keyboard : only mouse */ //#define DEBUG_MOUSE +/*

The data set consists of two sets of matrices, as labelled by the columns, T's and C's. > xy x T1 T2 T3 T4 T5 C1 C2 C3 C4 C5 [1,] 50 0.00 0.00 33.75 0.00 0.00 0.00 36.76 0.00 35.26 0.00 [2,] 13 34.41 0.00 0.00 36.64 32.86 34.11 35.80 37.74 0.00 0.00 [3,] 14 35.85 0.00 33.88 36.68 34.88 34.58 0.00 32.75 37.45 0.00 [4,] 33 34.56

I have very large csv files (up to 1GB each of ASCII text). I'd like to be able to read them directly in to R. The problem I am having is with the variable length of the data in each record. Here's a (simplified) example: $ cat foo.csv Name,Start Month,Data Foo,10,-0.5615,2.3065,0.1589,-0.3649,1.5955

Hi R- User I have very big data set (5000 rows). I wanted to make classes based on a column of that table (that column has the data which is continuous .) After converting into different class, this class would be Unique ID. I want to run regression for each ID. For example I have a data set > dput(dat) structure(list(ID = c(0.1, 0.8, 0.1, 1.5, 1.1, 0.9, 1.8, 2.5, 2, 2.5, 2.8, 3, 3.1, 3.2,

Dear all I have found that the two "equivalent" commands do not produce the same results. 1. (I wrote this command by hand, this is what I would do usually) >summary(aov(eduyrs ~ cntry * edf, data=ESS1)) Df Sum Sq Mean Sq F value Pr(>F) cntry 1 257 256.65 21.2251 4.243e-06 *** edf 4 11010 2752.42 227.6296 <

Dear experts, I just noticed that I get different results conducting type III anova using drop1 or the Anova command from the car package. I suppose I made a mistake and hope you can offer me some help. I have no idea where I got wrong and would be very grateful for explaination as R is new terrain for me. If I run the commands in line, they produce the same results. But if I run them in

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Dear All: In the attached file, I have 3 group patients, and there are 5 in each group (the groups are decided by the prefix of the idno). I want draw a repeat measurement comparison figure. My goal is to list 5 patients from same group on one horizontal line. But xyplot sounds pick them randomly (or I was confused?). Could you please help me modify the following code to accomplish this?

Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.