similar to: Just about "approximate F-test"

Hola a todos. Estoy utilizando la función lmer del paquete lme4 para ajustar un modelo mixto. Tengo varias variables en mi data.frame, unas son a nivel individual y otras a nivel de comarcas. Listo algunas. ingre_6 : Ingresos (nivel individual) iscs_a : un indicador sintético resumen de otras variables, calculado mediante componentes principales. sau_com : superficie agraria útil de cada una

Estimados usuarios: Estoy intentando reproducir el ejemplo 6.4 de Thomas Lumley. Complex Survey. Editorial Wiley. 2010 (ver la página en google:

Hi, I ran the example on pp. 799-800 from Machael Crawley's "The R Book" using package survival v. 2.36-5, R 2.13.0 and RStudio 0.94.83. The model is a Cox's Proportional Hazards model. The result was quite different compared to the R Book. I have compared my code to the code in the book but can not find any differences in the function call. My results are attached as well as a

Hello to all the people in the forum, I'm trying to perform a Scheirer-Ray-Hare test in R, and despite I've found some posts talking about this test, there no one in which I can found how to do so. Can anyone explain me how to do the test? Thanks Pablo -- View this message in context: http://r.789695.n4.nabble.com/Scheirer-Ray-Hare-tp3818476p3818476.html Sent from the R help mailing

Dear Sir, I am working with one example in R donlp2. How I can get the Hessian matrix from the output. p = c(10,10) par.l = c(-100,-100) par.u = c(100,100) nlin.l = nlin.u = 2 fn = function(x) { x[1]^2+x[2]^2 } dfn = function(x){ c(2*x[1], 2*x[2]) } attr(fn, "gr") = dfn nlcon = function(x){ x[1]*x[2] } dnlcon = function(x){ c(x[2], x[1]) } attr(nlcon, "gr") = dnlcon

Hi, All: I try to type some long string in R console, whenever I like to change to a new line, I get error message. The following is my simple code -------------------------------------- h3 <- sqlQuery(myConnect, "select * from console where Error: syntax error byday = 'dd1'group by by hour") Error: syntax error --------------------------------------- Any Hints? Thanks.

Hello, I have used R in the past to conduct multiple comparisons on standard linear models, but am a bit confused as to how to go about doing it with a mixed effects model. I am conducting a bioindication study using carabid beetles in which I have four treatment types (forest harvest types with varying levels of canopy structure retention), and am using canopy closure percent as a covariate in

Hi Everyone, Can anyone share their experience with Motorola Canopy solution deployment and Asterisk? Is this a good fit? Thanks, Bruce -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20100817/09e5cb7b/attachment.htm

I am running a 3-parameter nonlinear fit using the default Gauss-Newton method of nls. initialValues.L = list(b=4,d=0.04,t=180); fit.nls.L = nls( myModel.nlm , fData.L, start = initialValues.L, control = nls.control(warnOnly = TRUE), trace=T ); summary.nls.L = summary(fit.nls.L); I run the same analysis in SAS proc NLIN. proc nlin data=apples outest=a; parms b=4 d=.04 t=180; model Y =

Hi everyone, I'm witnessing an odd modelling phenomenon that I can't explain. If anyone has seen this before, or can explain what's going on would let me know, I'd be very grateful! Especially if I'm just being dim. I'm fitting a pair of continuous variates and their interaction to some residuals from another model. The sequential anova statement changes with the term

Hi, I am trying to make use of "approximate" option to obtain null distribution through Monte-Carlo resampling, as described in coin library documentation. Unfortunately, this does not play well with my data -- permutation process swallows astonishingly large amounts of RAM (4-5Gb) and runs far too long (30 min for B=10). Apparently, this is caused by the size of my dataset (see

Hello! I am using relatively simple linear model. By applying fastbw() on ols() results from rms package I would like to get subtable "Approximate Estimates after Deleting Factors". However, it seems this is not possible. Am I right? I can only get coefficients for variables kept in the model (for example: x$coefficients), but not S.E., Wald's Z and P? Is there any easy way to

Hi All, Say I have the values of function f(x1,x2,x3,x4) for each values of x1,x2,x3,x4 but not complete. But the functional form is not known. Techniques like regression, etc. are not able to give me satisfactory results and msy be more complex than we thought. I wanted to use Taylor's approximation to continuous function, to approximate a functional form using the given data. But failed

Hey all, Roughly, how much disk space would I need on my server to mirror the entire ISO collection and the repository files. Also, how would I tell my server to only mirror CentOS 5 and 6? -- If you have any questions, please do not hesitate to contact me on +61 478 241 896. Regards, Christopher Hawker

Hi all, If possible, I would like to ask for an approximate Samba 3 stable release date. I have heard that the alpha code is already quite stable; however, I would prefer to use the official release. Any estimate? September? October? Thank you in advance, Michel DESOIZE ___________________________________________________________ Do You Yahoo!? -- Une adresse @yahoo.fr gratuite et en

Is there any existing implementation in R/S of : 1] Pincus & Kalman's approximate entropy (ApEn) measure 2] Total Correlation / Multiinformation 3] Information Interaction A search doesn't quite reveal anything, but I'd be keen to not reinvent in case someone has worked on it. Many thanks in anticipation. Best, Vishal Belsare

AFAIK, R only has "pnorm" which computes the probability of getting a value smaller or equal to "x" from a normal distribution N[mean,stdev]. For example: R> pnorm(0, 4, 10) [1] 0.3446 means there is 34.46% chance of getting a value equal to or smaller than 0 from a N(4, 10) distribution. What I intend to get is: given the observed value "x", mean, and stdev

Is there R software available for doing approximate matching of personal names? I have data about the same people produced by different organizations and the only matching key I have is the name. I know that commercial solutions exist, and I know I code code this from scratch, but I'd prefer to build on some existing free solution if it exists. Unfortunately, the names are not standardized,

Hi, I have a data-matrix: > CB                    Zeit                Low 2   2011-05-02 08:05:05 7596.0 3   2011-05-02 08:10:06 7593.5 4   2011-05-02 08:15:11 7594.5 5   2011-05-02 08:20:15 7597.5 6   2011-05-02 08:25:18 7595.0 7   2011-05-02 08:30:20 7593.5 8   2011-05-02 08:35:21 7593.0 9   2011-05-02 08:40:21 7593.0 10  2011-05-02 08:45:25 7599.0 11  2011-05-02 08:50:34 7596.0 12 

Hi I am trying to generate a design using Algdesign and came across terms of "exact design" and "approxiamte theory design", I did not find a reference to explain what they are, could some one shed some light about this on me? Another question is, I want to measure the main effects and at least two interactions, variables are factors, how do I ensure this in formula,