similar to: How do anova() and Anova(type="III") handle incomplete designs?

Hi all: I'm attempting to create a faceted plot with ggplot2 and I'm having issues with a factor's order that is used to define the facet_grid(). The factor (named total.density) has three levels - 8, 16, and 32 - and I would like them presented in that order. Running order(levels(total.density)) yields the incorrect order of the facet grid - 2 3 1, corresponding with 16, 32, and 8.

Hi all: There was a concern raised by reviewers of a manuscript of mine over the proper execution of a Pearson's correlation. In brief, this was undertaken in order to determine the relationship between the extent of wheel running (y axis) and ethanol intake (x axis) across three, separate 10 day periods in 7 animals. In the paper, the correlational plots for each 10 day-period had 70 data

Hi ! I have a vector: vec= TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE and I'm looking for a method which let me get only the first values equal TRUE from this vector. It means that I want to get a vector: vec_out = TRUE TRUE TRUE TRUE or posictions values = TRUE: vec_out = 1,2,3,4 -- View this message in context:

Hello all: I would like to test whether there are treatment effects on decomposition rate, and I would like to inquire about the best, most appropriate means using R. I have plant decomposition data that is generally considered to follow an exponential decay model as follows: Wt = Wi * exp(-k * t) Where Wt and Wi are the weights of the plant material at time t and 0, respectively. k is a

I am trying to use t.test on the following data: date type INTERVAL nCASES MTF SDF MTO SDO nFST MF nOBS MO MB BIASCV BIASEV ME MAE RMSE CRCF 2001-06-15 avn GE1.00 4385 0.246 0.300 1.502 0.556 1367 1.373 4385 1.502 1.471 0.285 0.164 -1.256 1.266 1.399 0.056 2001-06-15 avn

I have a file, each column of which is a separate year, and each row of each column is mean precipitation for that month. Looks like this (except it goes back to 1964). month X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009 1 1.600 1.010 4.320 2.110 0.925 3.275 3.460 0.675 1.315 2.920 2 2.960 3.905 3.230 2.380 2.720 1.880 2.430 1.380

Dear all here is my data called "rglp" structure(list(vzorek = structure(1:17, .Label = c("179/1/1", "179/2/1", "180/1", "181/1", "182/1", "183/1", "184/1", "185/1", "186/1", "187/1", "188/1", "189/1", "190/1", "191/1", "192/1",

Dear all,I am a new user of WinBUGS and need your help. After running the following code, I got parameters of beta0 through beta4 (stats, density), but I don't know how to get the prediction of the last value of h, the variable I set to NA and want to model it using the following code.Does anyone can given me a hint? Any advice would be greatly appreciated.Best

I should also mention that Accounting rules are not stateful -- each rule only handles traffic in one direction. So for example, if eth0 is your internet interface and you have a web server in your DMZ connected to eth1 then to measure HTTP traffic in both directions requires two rules: DONE eth0 eth1 tcp 80 DONE eth1 eth0 tcp - 80 Associating a counter with a chain allows for aggregation.

Dear R users, I am new in R and I want to use the nls package to analyze some experimental data. The data is in the attached file "data". It is the response "Sav" measured at different "C0". Basically, the "C0" is a function of C1, K2, and r, and the "Sav" is a function of C0, C1, K2, and r. The math equations are shown in the attached

Hello: I would like to run a Cox proportional hazards regression on crayfish dislodgement at different water velocities by crayfish size class and substrate (rock) type. Additionally, there is a covariate variable, rock movement that may be influencing crayfish dislodgment. So... I have crayfish size class (CFSZCL) and substrate type (SUBSZ) as fixed factors influencing the dislodgment of

Hello all: I'm attempting to run a Cox proportional hazards function on survival data from insects and I have a few questions. My current command that I'm using to call the model is as follows (using coxph() from the survival library): coxph(Surv(day, censor) ~ treatment + room + chamber %in% treatment, data = data.table) Day indicates which day a particular observation occurred, and

Justin, You have an interesting problem, and a serious (reliable) consultation would take more time than I have to give at the moment. Which is to say that you should take these comments with a grain of salt. First, I don't think that you have censored data. You have 2 subdistribution functions F1(t) and F2(t), F1(t) + F2(t) = F(t) = the "time to endpoint" distribution.

Hi R users: How can I obtain the same "anova" table for the effects for a 2^k experiment design that MINITAB shows (and authors recommends Box, Hunter, & Hunter). http://www.stat.psu.edu/online/development/stat503/06_2k/04_2k_unreplicate.html Here is the code that I use for this case: D<-C<-B<-A<-c("-","+") design<-expand.grid(A=A,B=B,C=C,D=D)

Hi R users: I want to know if there is any package that makes the specific computation 2^k factorial designs as in: George E. P. Box, J. Stuart Hunter and William G. Hunter. Statistics for Experimenters. Second Edition. 2005. John Wiley & Sons. They advice about the misuse of the ANOVA table in this kind of designs in page 188. Thank you for your help. Kenneth

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Hello R users, I am looking for R (or S) code related to group sequential or adaptive designs for clinical trials. (The most prominent examples are the designs of Pocock or O'Brien/Fleming, the alpha-spending function approach, or Fisher's combination test and the inverse normal method.) I am particularly interested in the calculation of the critical boundaries, the handling of spending

Hi All, recently I used Design Expert for some Design Of Experiment work. I was happy with the interface to select which effects I want to see in my experiment, and which not. For example: I can select of course my main effects, but also if I want to see interaction A:B, B:C, A:B:C,but not A:C. This was very interessting as you can end up with fewer runs, especially in cases of 10 factors with

I am wondering if in a block experimental design (ex. triple square lattice), the block effect was not significant, is it all right not to include the block effect in an empirical model (even though the sampling was done from different blocks)? Or we are forced to control for the block effect in block designs anyway? Thanks, Julia