similar to: block averaging data frames

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Olá pessoal, estou realizando uma ANOVA com medidas repetidas e estou utilizando a função "Anova" do pacote "car". Medi o biovolume de algas a cada dois dias durante 10 dias (no banco de dados abaixo só coloquei até o 4° dia). Tenho 2 tratamentos ("c","t") e o experimento foi realizado em tréplicas ("A","B","C"). > Pa2

I have a series of numbers I'm wanting to plot. They come from a nanodrop machine, which graphs with a specific x and y indices. X goes from 220nm to 350nm, which I can set. But the y axis should go from -5 to 65, but I'm finding it impossible to hardcode that. I've looked. I've typed ?plot at the R prompt. Google has not been my friend. _R Graphics_, if it holds the key, has not

Dear r-helpers, I have a graph created using the library 'spatgraphs'. library(spatstat) library(spatgraphs) xy <- rbind(c(28.39, -16.27), c(30.62, -20.13), c(32.25, -28.7), c(22.43, -27.22), c(27.5, -21.17), c(31.22, -24.52), c(17.93, -26.92), c(18.72, -17.95), c(24.15, -17.82), c(29.23, -22.85)) ow <- owin(xrange=range(xy[,1]), yrange=range(xy[,2])) pp <-

Hi Hemant, As I suspected, the code broke when I got to the line: result <- rfm_auto(df, id="user_id", payment ="subtotal_amount", date="created_at") Error in rfm_auto(df, id = "user_id", payment = "subtotal_amount", date = "cr eated_at") : could not find function "rfm_auto" It looks like you are using the hoxo-m/easyRFM

Dear List, At 32 degrees Celsius in the office, I was too lazy to figure out the correct xapplytion for a simple lookup problem and regressed to well-known c-style. Only to see my computer hang forever doing 10000 indexed offset calculation. Boiled down, the problem is shown below; needs a few milliseconds in c. Looking at the timing results of n=2000 and n=4000, this is not linear in time, so

I'm trying to perform an RFM analysis on the attached dataset, I'm able to get the results using the auto_rfm function but i want to define my own breaks for RFM. as follow r <-c(30,60,90) f <-c(2,5,8) m <-c(10,20,30) but when i tried to define my own breaks i got the identical result for RFM i.e 111 for every ID. please help me with this with working R script so that i can get

On 2020-04-30 22:56, Jeremy Allison via samba wrote: > On Thu, Apr 30, 2020 at 10:25:49AM +0200, A L wrote: > >> So I did some more tests. smbclient mget does not copy in the same way >> Windows Explorer does. When copying in Windows Explorer, there are many >> multiple concurrent threads used to transfer the files. With smbclient mget >> there are no corruptions,

Hi, folks, I''m attempting to run an e-mail server on Xen. The e-mail system is Novell GroupWise, and it serves about 250 users. The disk volume for the e-mail is on my SAN, and I''ve attached the FC LUN to my Xen host, then used the "phy:/dev..." method to forward the disk through to the domU. I''m running into an issue with high I/O wait on the box (~250%)

Greetings! As part of my research project I am using R to study temperature data collected by a network. Each node (observation point) records temperature of its surroundings throughout the day and generates a dataset. Using the recorded datasets for the past 7 days I need to build a prediction model for each node that would enable it to check the observed data against the predicted data. How can

On Thu, Apr 30, 2020 at 10:25:49AM +0200, A L wrote: > So I did some more tests. smbclient mget does not copy in the same way > Windows Explorer does. When copying in Windows Explorer, there are many > multiple concurrent threads used to transfer the files. With smbclient mget > there are no corruptions, both locally and over the network from another > Linux machine. > > I

While I was testing this new series (v2) I discovered an huge use of memory and a memory leak in the virtio-vsock driver in the guest when I sent 1-byte packets to the guest. These issues are present since the introduction of the virtio-vsock driver. I added the patches 1 and 2 to fix them in this series in order to better track the performance trends. v1:

While I was testing this new series (v2) I discovered an huge use of memory and a memory leak in the virtio-vsock driver in the guest when I sent 1-byte packets to the guest. These issues are present since the introduction of the virtio-vsock driver. I added the patches 1 and 2 to fix them in this series in order to better track the performance trends. v1:

I am struggling to get GAM model predictions from the top models calculated using model.avg in the package "MuMIn". My model looks something like the following: gamp <- gam(log10(y)~s(x1,bs="tp",k=3)+s(x2,bs="tp",k=3)+ s(x3,bs="tp",k=3)+s(x4,bs="tp",k=3)+s(x5,bs="tp",k=3)+ s(x6,bs="tp",k=3)+x7,data=dat,

Dear All, I'm using AIC-weighted model averaging to calculate model averaged parameter estimates and partial r-squares of each variable in a 10-variable linear regression. I've been using the MuMIn package to calculate parameter estimates, but am struggling with partial r-squares. There doesn't seem to be any function in the MuMIn package dealing with partial r-square

Dear all, does anyone have r code to perform model-averaging of regression parameters by Akaike weights, and/or to do all-possible-subsets lm modelling that reports parameter estimates, AICc and number of parameters for each model? I have been looking for these in the archive but found none. (I am aware that many of you would warn me against these methods advocated by Burnham and Anderson

Hi, What's the best way to average dates? I though mean.POISXct would work fine but... > a [1] "2007-04-02 19:22:00 WEST" > b [1] "2007-03-17 16:23:00 WET" > class(a) [1] "POSIXt" "POSIXct" > class(b) [1] "POSIXt" "POSIXct" > mean(a,b) [1] "2007-04-02 19:22:00 WEST" > mean(b,a) [1] "2007-03-17

Dear R-users, I've spent most of the day reading R documentation at length but couldn't find something perhaps obvious. I have a dataset made of 3 morphometric variables for a series of watershed [log(slope); log(drainage_area); distance_to_outlet] My aim is to predict the value of log(slope) for pairs of [drainage_area; distance_to_outlet] (sounds like a plain linear model fitting,

I have a dataframe with names in the first column and wait times between decisions in the second column. Since individuals make multiple decisions, I want the average for each individual. For example, the data might look like this name wtime jo 1 jo 2 jo 1 jo 3 tim 3 tim 2 tim 2 ro 1 ro 2 etc. I'm hoping there is something like mean(dataname$wtime[name]) which will just

HI, List I want to know weather any body has used BMA Package for model averaging for prediction of future values.....What are the paprmeters we have to suplly to the model. Any script for the same. thanks in advance ANIL KUMAR( METEOROLOGIST) LRF SECTION NATIONAL CLIMATE CENTER ADGM(RESEARCH) INDIA METEOROLOGICAL DEPARTMENT SHIVIJI NAGAR PUNE-411005 INDIA MOBILE +919422023277