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HI I use glm in R to do logistic regression. and treat both response and predictor as factor In my first try: ******************************************************************************* Call: glm(formula = as.factor(diagnostic) ~ as.factor(7161521) + as.factor(2281517), family = binomial()) Deviance Residuals: Min 1Q Median 3Q Max -1.5370 -1.0431 -0.9416 1.3065 1.4331 Coefficients:

Hi R-Help, I am using R to do functional outlier detection (using PCA to reduce to 2 dimensions - the functional boxplot methodology used in the Rainbow package), and using Hscv.diag function to calculate the bandwidth matrix where this line of code is run: result <- optim(diag(Hstart), scv.mat.temp, method = "Nelder-Mead", control = list(trace = as.numeric(verbose))) Within the

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

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

Hello, I am trying to filter a data set like below so that the peaks in the Phase value are more obvious and can be identified by a peak finding function following the useful advise of Carl Witthoft. I have written the following for(i in length(data$Phase)){ newphase=if(abs(data$Phase[i+1]-data$Phase[i])>6){ data$Phase[i+1] }else{data$Phase[i] } } I get the following error which I have not

I am using acf() to get the autocorrelations of a time series. It works but I want to then get the autocorrelations into a simple list of numbers. > x <- acf(price_changes, lag.max = 12,type = "correlation",plot = FALSE) > x Autocorrelations of series ‘price_changes’, by lag 0 1 2 3 4 5 6 7 8 9 10 11 12 1.000

Dear R users, Is it possible to use a generalized linear model to do a binomial comparison of one list of proportions with a matched list of proportions to test for a difference? So, for example: list 1 list 2 a1 | b1 a2 | b2 3 | 4 7 | 9 6 | 7 5 | 1 9 | 1 3 | 1 I want to compare list 1 with list 2 and the samples

Hi, I'm sure this is simple, but I haven't been able to find this in TFM, say I have some data in R like this (pasted here: http://pastebin.com/raw.php?i=sjS9Zkup): > head(df) gender age smokes disease Y 1 female 65 ever control 0.18 2 female 77 never control 0.12 3 male 40 state1 0.11 4 female 67 ever control 0.20 5 male 63 ever state1 0.16

Dear All, I like to simulate a physiologically based pharmacokinetics model using R but am having a problem with the daspk routine. The same problem has been implemented in Berkeley madonna and Winbugs so that I know that it is working. However, with daspk it is not, and the numbers are everywhere! Please see the following and let me know if I am missing something... Thanks a lot in advance,

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

Hi, how can ip instances be used with vlans ? Is that possible ? I don''t know how to configure this, if I only have one interface (e.g. bge0), and want to assign e.g. bge1000 to a zone. Any cookbooks available ? Thanks Detlef

R 2.12 windows 7 I am summary data that I would like to make into a 2x2 table representing counts positive vs. negative counts: 28/289 20/276 My table should look something like the following: group1 group2 Positive 28 20 Negative 289 276 How can a (1) create the 2x2 table (2) run a chi square test on the table? I have tried the following code, but I

Hi, I want to set a make a contingency table which will look like this.. The problem is that I can't  set the table like the following.                      col1     col2       Total     row1              a         b         n10                      rp1       rp2        100                      cp1       cp2       rtp1   ---------------------------------------------     row2            

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Dear R helpers, I have been using the loadings function from the multiv library and I get the typical output (see below). When I try to export these results to a file using a write.table() I get the following error message "Error in as.data.frame.default(x[[i]], optional = TRUE) : can't coerce loadings into a data.frame" Any idea why write.table is doing that and any

Hello, Thank you to all those great folks that have helped me in the past (especially Dennis Murphy). I have a new challenge. I often generate time-series data sets that look like the one below, with a variable ("Phase") which has a series of flat-topped peaks (sample data below with 5 "peaks"). I would like to calculate the phase value for each peak. It would be great to

> -----Original Message----- > From: Rock > Sent: Tuesday, July 23, 2013 17:46 > > QUESTION: > Why does my Centos 6.4 laptop keep wiping out my eth0 IP address? Googling https://www.google.com/search?q=rhel+6+network+configuration&oq=rhel+6+network https://access.redhat.com/site/documentation/en-US/Red_Hat_Enterprise_Linux/6/ht

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

Hello R users I'm using MuMIn but for some reason I'm not getting the adjusted confidence interval and uncoditional SE whe I use model.avg(). I took into consideration the steps provided by Grueber et al (2011) Multimodel inference in ecology and evolution: challenges and solutions in JEB. I created a global model to see if malaria prevalence (binomial distribution) is related to any

OK, Let's try this again! Here is the reproducible script; it is long because I had to copy the panel dataset here. My question is related to systemfit; I don't know how to get the result for the entire panel. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) install.packages("systemfit")