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Is there a way to have printing data.frames with POSIXct to display milliseconds if digits.secs is set as a default? You can use the digits argument in print, such as print(df, digits = 3) to get the intended output, but I assumed it was done with the option digits.secs set. Tibbles by default do this printing, which is shown below, but I was unsure if digits.secs should affect printing

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,] 

I currently work on a draft of an aquatic bioassessment. The conditions tested are the following: ER river water T dechlorinated water control 0.5 + 0.5mg / L of malate T + 1 dechlorinated water control + 1g / L of malate T ED dechlorinated water control SED + ER + river water sediment SED ED + sediment + water dechlorinated. It is the result of AChE in muscle (fillet of fish). The production of

I ran two separate hours-long projects. Results of each were saved to two separate .RData files. Content of each includes, among others, the following: ?????????????????? me??? se????? t???? p sig pc21.age??????? 0.640 0.219? 2.918 0.004 *** pc21.agesq????? 0.000 0.000??? NaN?? NaN pc21.inc??????? 0.903 0.103? 8.752 0.000 *** pc21.incsq????? 0.000 0.000??? NaN?? NaN pc21.sei10????? 0.451 0.145?

Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables &

Hi R users, I need some help in the followings: I'm doing factor analysis and I need to extract the loading values and the Proportion Var and Cumulative Var values one by one. Here is what I am doing: > fact <- factanal(na.omit(gnome_freq_r2),factors=5); > fact$loadings Loadings: Factor1 Factor2 Factor3 Factor4 Factor5 b1freqr2 0.246 0.486 0.145

Dear R help I am having difficulty doing a biplot of the first two factors of a factor analysis. I presume it is because the values in factor 2 for Milk and NUTS are not displayed in the component loadings. Loadings: Factor1 Factor2 RedMeat 0.561 -0.112 WhiteMeat 0.593 -0.432 Eggs 0.839 -0.195 Milk 0.679 Fish 0.300 0.951 Cereals -0.902 -0.267

Hi all, I have been testing network throughput and latency and I was wondering if my measurements are as expected. For the test, I used Fedora 17 for both host and guest, using kernel 3.5.2-3.fc17.86_64. Pinging an external server on the LAN from the host, using a gigabit interface, the results are: # ping -c 10 172.16.1.1 PING 172.16.1.1 (172.16.1.1) 56(84) bytes of data. 64 bytes from

Hi all, I have been testing network throughput and latency and I was wondering if my measurements are as expected. For the test, I used Fedora 17 for both host and guest, using kernel 3.5.2-3.fc17.86_64. Pinging an external server on the LAN from the host, using a gigabit interface, the results are: # ping -c 10 172.16.1.1 PING 172.16.1.1 (172.16.1.1) 56(84) bytes of data. 64 bytes from

?load Read this carefully. Pay attention to its instructions re: overwriting existing objects. Cheers, 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 ) On Tue, Jan 16, 2018 at 12:43 AM, Steven Yen <styen at ntu.edu.tw> wrote: >

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

Hello, list. Yesterday I tried changing the instance type of my c1.medium instances on AWS to c3.large and I wasn't able to do so. It looks like the official Centos AMI on market place is still not ready for c3.large. This is the error message I received: "The instance configuration for this AWS Marketplace product is not supported. Please see

I've just found the lavaan package, and I really appreciate it, as it seems to succeed with models that were failing in sem::sem. I need some clarification, however, in the output, and I was hoping the list could help me. I'll go with the standard example from the help documentation, as my problem is much larger but no more complicated than that. My question is, why is there one latent

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

Hi. I'm using lmer() from the lme4 package (version 0.8-3) and I can't get anova() to group variables properly. I'm fitting the mixed model Response ~ Weight + Experimenter + (1|SUBJECT.NAME) + (1|Date.StudyDay) where Weight is numeric and Experimenter is a factor, ie, > str(data.df) `data.frame': 4266 obs. of 5 variables: $ SUBJECT.NAME : Factor w/ 2133 levels

I have a file, each column of which is a separate year, and each row of each column is mean precipitation for that month. Looks like this (except it goes back to 1964). month X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009 1 1.600 1.010 4.320 2.110 0.925 3.275 3.460 0.675 1.315 2.920 2 2.960 3.905 3.230 2.380 2.720 1.880 2.430 1.380

Hi R users: What is the difference between in the computation of the diag of the "hat" matrix in: "lm.influence" and the matrix operations with "solve()" and "t()"? I mean, this is my X matrix x1 x2 x3 x4 x5 [1,] 0.297 0.310 0.290 0.220 0.1560 [2,] 0.360 0.390 0.369 0.297 0.2050 [3,] 0.075 0.058 0.047 0.034 0.0230 [4,] 0.114 0.100

Having spent the last few weeks trying to decipher R, I feel I may finally be getting somewhere, but i'M still in need of some advice and all my tutors seem to be on holiday! Basically a bit of background, I have data collected on a population of Lizards which includes age,sex, and body condition. I collected data myself this year and I have data previously collected from 1999, 2002 and

Dear list members, First of all thank you for your helpful advices. After your answeres to my firt mail I studied a lot (R-News n?5) and I tried to perform my analysis: First, to fit a GLM with a nested design I decided to use the function "lmer" in package "lme4" as suggested by Spencer Graves and Filippo Piro. I remember to you that my data were: land use classes, 3 levels

I am trying to do some verification across a large dataset, cuData, that has 23 columns. Column 23 (similarity) is the outcome 0 or 1 and the other columns are the features. I do this: verificationglm.model <- glm(formula = similarity ~ ., family=binomial, data=cuData[1:1000,]) and produce the model: > summary(verificationglm.model) Call: glm(formula = similarity ~ ., family =