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Hello I am using R 2.2.0 with Windows XP. I've got a five element list object, each element containing two dataframes of equivalent size. > str(mylist) List of 1 $ data1:List of 2 ..$ data1a :`data.frame': 77 obs. of 63 variables: .. ..$ var1 : num [1:77] 0.41375 0.00056 1.43040 1.43528 0.61730 ... .. ..$ var2 : num [1:77] 1.154 1.686 0.673 0.800 0.760 ... ..

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi Assuming the following: > x <- data.frame(a=1:10, b=runif(10)) > str(x) 'data.frame': 10 obs. of 2 variables: $ a: int 1 2 3 4 5 6 7 8 9 10 $ b: num 0.692 0.325 0.634 0.16 0.873 ... > write.csv(x, "x.csv") > x2 <- read.csv("x.csv") > str(x2) 'data.frame': 10 obs. of 3 variables: $ X:

Hi, I have the following file, and I need to work out the linear regression for each sample. I tried the model(*) and receive the error message (**): > data=split(mydata,rep(1:(nrow(mydata)/6),each=6)) > arrang.linear=lapply(data,lm,formula=KA~PA) Erro em storage.mode(y) <- "double" : invalid to change the storage mode of a factor Al?m disso: Warning message: In

Dear useRs, I am trying to model a time series with a transfer function. I think it can be put into the ARMA framework, and estimated with the 'arima' function (and others have made similar comments on this list). I have tried to do that, but the results have so far been disappointing. Maybe I am trying to make 'arima' do something it can't... The data are time series of

Hello all, I have just started using fitting the PCM (Partial Credit Model) using eRm and have 2 problems which I cannot solve, I have checked everywhere on the net, but no joy: Firstly, I have fit a PCM model to 10 variables with differing response categories (3 for the first 6 items, 4 for the following 2 and 2 for the last two items). mat1 <- matrix(c(rasch_bart$bart_bowel,

Hi all, i have a data set cintaining 14 columns and 11 rows. Rows represent single point and columns represent the parameter measured. I wiuld like to compare the data to see which are more alike. I used the cluster analysis, but now i ma wondering if there are some other methods, since the cluster analysis did not give me the result i like. I tried to use factanal() for factor analysis

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

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

Hello, I’m trying to follow the syntax of a script from a journal website. In order to create a regression formula used later in the script, the regression matrix must have column names “X1”, “X2”, etc. I have tried to assign these column names to my matrix ScoutRSM.mat using a for loop, but I don’t know how to interpret the error message. Suggestions? Thanks, Paul

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

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

Dear list, I made survival analysis using Weibull regression. I got significance in the analysis: >anova(m1) Df Deviance Resid. Df -2*LL P(>|Chi|) NULL NA NA 2158 4933.109 NA tratt -8 577.0669 2150 4356.042 1.988317e-119 and > summary(m1) Call: survreg(formula = Surv(tempo, sensore) ~ tratt) Value Std. Error z p

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Hello, R version 2.4.1 (2006-12-18) i386-pc-mingw32 Calling serialize() with a NULL connection serializes it to a raw vector. However, when the object to be serialized is large, it takes a very long time: > system.time( serialize(matrix(0, 1000, 1000), NULL) ) [1] 38.25 40.73 81.54 NA NA > system.time( serialize(matrix(0, 2000, 2000), NULL) ) [1] 609.72 664.75 1318.57 NA

I performed the update yesterday that included installing kernel 2.6.9-42.0.2.EL and replacing ethereal with wireshark. First, the trivial... Wireshark would not run from the KDE menu without editing the menu to change the command from "wireshark" to "kdesu wireshark" because dumpcap is at usr/sbin/dumpcap instead of /usr/bin/dumpcap . Making the change causes wireshark to

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

... and the mailing list is picky about attachments... whatever you attached did not conform to the stringent requirements mentioned in the Posting Guide. Pasting the code right into the email is usually safest, though you DO have to post using plain text (as the Posting Guide indicates) or your code may get mangled by the automatic html format removal. On May 15, 2018 7:04:31 AM PDT, Bert Gunter

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")

Unless there is good reason not to, always cc the list -- there are lots of smarter folks than I on it who can help. I may or may not have time to look at this. Hopefully someone else will. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip