similar to: testing correlated proportions

Hi, I am trying to analyze a data set when nematodes were killed after a drug administration. We have counted the number of nematode died and the number of nematode survival at three time points. So, there are 100% died in some plot and could be found zero percent in another. Then, the data set have a lot of zeros. I have googled and found a lot of information. Moreover, my data isn't

Does anyone know how I can generate a 2x2 table in a format where in each cell of the table, it contains a) count (frequency) b) total percentage c) row percentage d) column percentage. SAS can generate this format easily, is there a R package that can do this? Frequency | Percent | Row Pct | Col Pct | positive negative | Total --------------------------------|------- disease |

Hej, i need the which() funktion to find the positions of an entry in a matrix. the entries i'm looking for are : seq(begin,end,0.01) and there are no empty spaces i'm searching in the right range. so i was looking for the results R can find and i recieved this answer. for (l in

R community, I am trying to write a code that fills in data gaps in a time series. I have no R or statistics background at all but the use of R is proving to be a large portion of my PhD research. So far my code identifies where and the number of new entries required but I do not know how to add additional rows or columns into an array. Any advice on how this can be done? Here is an example:

Hello, The problem is that the factors are not orderd by the row number. If you want to check their order, use str(sortdata) and you'll see Santa-Rosa was attributed factor level 4 (in the output, first variable, the 3rd and 4th). Try the following. sortdata <- read.table(text=" county year x1 x2 x3 x4 x5 x6 x7 rank 141 Escambia 2002 6.50

This may not actually be an R/Splus problem, but it started off that way ..... ===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+=== Executive summary: ================== Simulations involving extreme value distributions seem to ``work'' when the underlying distribution is exponential(1) or exponential(2) == chi-squared_2, but NOT when the underlying distribution is

I have question: how can I put the value on the bar chart. This my code: barchart(Tuberize~Family|factor(Year)*factor(Hr),data=tuber) This's my data:    Year Hr Family Tuberize 1  2007 20      A     0.26 2  2007 20      B     6.08 3  2007 20      C     0.00 4  2007 20      D     0.27 5  2008 20      A     1.18 6  2008 20      B     9.17 7  2008 20      C     0.00 8  2008 20      D     2.13 9 

Nate Homier wrote: > I think I get it. So I added the modeline: > Modeline "1024x768 at 50" 51.85 1024 1056 1248 1280 768 784 791 807 > > But it still don't work. Running winedebug gives: > trace:x11settings:X11DRV_ChangeDisplaySettingsEx width=1024 height=768 > bpp=32 freq=50 (XRandR) > err:x11settings:X11DRV_ChangeDisplaySettingsEx No matching mode found

I have a multidimensional array "a", for example, > a , , 1 [,1] [,2] [1,] 1 3 [2,] 2 4 , , 2 [,1] [,2] [1,] 5 7 [2,] 6 8 So a is 2x2x2. I have another array "b", for example, > b [,1] [,2] [1,] 9 11 [2,] 10 12 So b is 2x2. I want to "tack" b onto a so that b becomes a new "plane", so to speak,

Not sure what I'm doing wrong. Can't seem to get loess values. It looks like loess is returning the same values as the input. j <-loess(x1$total~as.numeric(index(x1) plot(x1$total,type='l', ylab='M coms/y global',xlab='') lines(loess(total~as.numeric(index(x1)),x1)) The plot statement works fine No errors with the "lines" statement But I don't

Hello, I have a hierarchical dataset of this form and am trying to analyze it in R. 1 subject Tested under 2 conditions: A and B 10 sesssions in each condition In each session, 2 kinds of tests: Test 1 and Test 2 200 independent repetitions of each test-type, with 200 Yes/No answers So I think this is a 2 x 2 x 10 x 2 setup What I want to know is whether the difference in percentage of yes

rucker222 wrote: > The drop down layer selection menu at the top left of the screen directly above where the drawing 1 tab is opens upwards off of the screen once the drawing has a few layers on it. jjmckenzie wrote: > Log file please. I loaded a drawing with plenty of layers and then clicked on the layer drop down menu a couple of times before exiting ProgeCAD. Log file below. (sorry

Hi, i am working in the forecast of the daily price crude . The last prices of this data are the following: 100.60 101.47 100.20 100.06 98.68 101.28 101.05 102.13 101.70 98.27 101.00 100.50 100.03 102.23 102.68 103.32 102.67 102.23 102.14 101.25 101.11 99.90 98.53 96.76 96.12 96.54 96.30 95.92 95.92 93.45 93.71 96.42 93.99 93.76 95.24 95.63 95.95 95.83 95.65

I can use data() to find the available datasets in a package, but I'd like to extract and display some additional information for each dataset than what is provided by data(), e.g., class() and dim() for datasets for which these are available. I'm stuck on using using lapply properly with objects, rather than the names of objects that I get from data() Example: > DS <-

Hi [macOSX 10.4.6; R-2.3.0] I have encountered a difference in behaviour between R-2.2.1 and R-2.3.0 when performing a linear model. Transcript follows for R-2.3.0 (R-2.2.1 worked as expected). How to make R-2.3.0 perform as R-2.2.1 did? > dput(x) c(29.13, 29.88, 30.09, 29.99, 29.74, 29.64, 29.65, 29.7, 30.04, 29.89, 29.96, 29.65, 28.76, 28.41, 28.38, 29.55, 29.76, 29.75, 29.84,

Hi R list, I imported values from Excel, there is a column with numbers like 45%, 65%, 12%. I want to find its mean. What should I use? strisplit() split() parse() Data from dput(), structure(c(78L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "-0.15%", "-0.34%", "-1.3%", "-10.77%", "-100.00%", "-11.45%",

Hi, Here is the summary-output of the Coxph-model I used (the output is based on the best final model i.e. all significant explanatory variables and their interactions are included): coxph(formula = Y ~ LT + Food + Temp2 + LT:Food + LT:Temp2 + Food:Temp2 + LT:Food:Temp2) n= 555 coef exp(coef) se(coef) z

Hello all, Is AIC calculated incorrectly in lmer? It appears as though it uses AIC = -2*logLik - 2*#parms, instead of -2*LogLik + 2*#parms? Below is output from one of many models I have tried: Generalized linear mixed model fit using PQL Formula: cswa ~ pcov.ess1k + (1 | year) Data: ptct50.5 Family: poisson(log link) AIC BIC logLik deviance 224.8466 219.19 -114.4233 228.8466

Hello, I am running a model with four categories and want predicted probabilities in each category. Now for this example I wont give a counterfactual just the training data is fine but is there anyway to get a confidence interval around the predicted probabilities in each group? I have tried but it gives me probabilities and I have used interval="confidence", level=.095 and then interval

Hi all, We're playing around with new versions and uprading options. We currently have a 2x2x2 stripped-distributed-replicated volume based on 3.3.0 and we're planning to upgrade to 3.4 version. We've tried upgrading fist the clients and we've tried with 3.4.0, 3.4.1 and 3.4.2qa2 but all of them caused the same error: Failed to get stripe-size So it seems as if 3.4 clients are