search for: 0.503

Hello, I'm a new user to R and need some help coding a mathmatically simple aggregation of normal distributions. I have three normal distributions: A ~ N(8.51, 4.24^2) B ~ N(7.57, 3.62^2) C ~ N(10.84, 6.59^2) with correlation coefficients of: rho(AB) = 0.710 rho(AC) = 0.263 rho(BC) = 0.503 and I want to simulate Z = A + B + C, showing the results on a plot and fitting a distribution to the

I don't understand which is the problem Could you help me please? Thanks in advance Marta > str(data_indiciN2) 'data.frame': 200 obs. of 36 variables: $ gruppo: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... $ I001N2: num 19.32 8.22 28.35 7.24 14.7 ... $ I002N2: num 2.92 2.54 0.11 1.6 7.12 ... $ I003N2: num -22.362 -0.222 -19.291

Dear Sir or Madam, I am a student at MSc Probability and Finance at Paris 6 University/ Ecole Polytechnique. I am using R and I can't find an answer to the following question. I will be very thankful if you can answer it. I have two vectors rendements_CAC40 and rendements_AlcatelLucent. I use the lm function as follows, and then the sumarry function: regression=lm(rendements_CAC40 ~

Hi, I have a Community (COM) composed of 6 species: A, B, C, D, E & F. The density of my Community is thus (Eq.1): dCOM = dA + dB + dC + dE + dF I would like to calculate and plot a linear regression between the density of each of my species and the density of the whole community (illustrating how the density of each species varies with variations of the whole community). For example, I would

From: Eric Dumazet <edumazet at google.com> Straightforward patch to add GRO processing to virtio_net. napi_complete_done() usage allows more aggressive aggregation, opted-in by setting /sys/class/net/xxx/gro_flush_timeout Tested: Setting /sys/class/net/xxx/gro_flush_timeout to 1000 nsec, Rick Jones reported following results. One VM of each on a pair of OpenStack compute nodes with

From: Eric Dumazet <edumazet at google.com> Straightforward patch to add GRO processing to virtio_net. napi_complete_done() usage allows more aggressive aggregation, opted-in by setting /sys/class/net/xxx/gro_flush_timeout Tested: Setting /sys/class/net/xxx/gro_flush_timeout to 1000 nsec, Rick Jones reported following results. One VM of each on a pair of OpenStack compute nodes with

Dear Madam or Sir I am writing you hoping, that you can help me with a problem concerning the output of regressions done with the function lme in R. I would need the standard deviations for intercepts and predictors, but in the output I can only find those for the intercepts. Could it be, that this is my fault? (I am just a beginner with R and multilevel modeling). I am sorry to annoy you with

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

Hi all, I have a package that is specific to a task I was repetitively using a few years ago. I now needed to run it again with new data. However I am told it was built with an older version or R and will not work. How can I tweak the package so it will run on 11.1? It was a one-off product and has not been maintained. Is there a way to "unpackage" it and repackage it to work? I

On Fri, Jul 31, 2015 at 06:25:17PM +0200, Eric Dumazet wrote: > From: Eric Dumazet <edumazet at google.com> > > Straightforward patch to add GRO processing to virtio_net. > > napi_complete_done() usage allows more aggressive aggregation, > opted-in by setting /sys/class/net/xxx/gro_flush_timeout > > Tested: > > Setting /sys/class/net/xxx/gro_flush_timeout to

> Subject: [R] Question on running tseries::garch on Mac OSX > Date: Sat, 18 Jan 2003 15:58:50 -0800 > From: Nicholas Waltner <nwaltner at attbi.com> > To: <R-help at stat.math.ethz.ch> > > Hello, > > When I run the garch examples, I get the following output: > > > dax.garch <- garch(dax) > > ***** ESTIMATION WITH ANALYTICAL GRADIENT *****

I am trying to fit a non linear mixed effect model but I also want to do a post hoc comparison. My data is binary and consist of recording mice track prints on plates plates in plots that submited to one of 4 different treatments (fruits and vegetation complexity manipulated for two levels each. The design is random blocks repeated measures with presence or absence of track prints as a response

hi everyone! i want to check the autocorrelation function for a univariate time series (streamflow) in a data frame as below: < DF <- read.table("D:/file path....") < DF year jan feb mar apr ...... dec 1966 0.504 0.406 0.740 0.241 0.429 1967 0.683 0.529 0.780 0.443 0.503 . . . . what i first tried is: acf (DF, plot = TRUE)

R Users, I was wondering if someone might point me in the right direction. I am using a Cox model (survival package) to evaluate survival of pen-reared birds (time to event data collected daily) and I have been trying to determine how I can estimate a 'daily' survival rate and std error from the results of a Cox model? Using survfit (see input data below) I computed the predicted

Dear R-help, Hi I've got data table with variation and freqeuncy. I don't know how to get mean and sd. Please help me. Cheers. ========== variation frequency 0.503 79930 0.174 291140 -0.444 95916 -0.731 11451 0.453 102899 0.596 46133 -0.295 204859 0.013 390121 0.311 187552 -0.085 378902 -0.633 28164 0.175 291411 0.611 41903 0.318 183254 -0.661 22580 0.149 312574 0.594 46903 -0.557

I have used the greedy.wilks to stepwise discriminant analysis, but it doesn't work with my dataset. I don't understand which is the problem Could you help me please? Thanks in advance Marta > str(data_indiciN2) 'data.frame': 200 obs. of 36 variables: $ gruppo: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... $ I001N2: num 19.32 8.22 28.35 7.24

Hi: I am using a subset of the below dataset to predict PRED_SUIT for the whole dataset but I am having trouble with 'newdata'. The model was created with 153 records and want to predict for 208 records. wolf2 <- structure(list(gridcell = c(367L, 444L, 533L, 587L, 598L, 609L, 620L, 629L, 641L, 651L, 662L, 674L, 684L, 695L, 738L, 748L, 804L, 805L, 872L, 919L, 929L, 938L, 950L, 958L,

I'm an R novice and my colleagues are about to convince me to get my data into SPSS, which will presumably be easier for someone who doesn't live in R to point and click his way into some kind of analysis that might be meaningful. I've got two groups of subjects (classkey in the table below). They've each received several different treatments. One measure is a 1-7 rating taken

I am getting an unexpected gap in a simple plot of monthly data series. I have created a csv file of monthly climate observations that I store on-line. When I download the csv file and plot one of the series, I get a gap even though there is data for the missing point. Here is a snippet to show the problem. ## Strange plot results link <-

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