similar to: Wine and ALSA

Hello rsync users, I'm trying to copy every file which belongs to my Ubuntu 23.04 installation (stored on an ext4 fs to a ZFS storage disk. The command that I've used is : # rsync -avxHAX * /mnt/zroot2/zroot2/OS/Linux/EVO sending incremental file list rsync: [generator] set_acl: sys_acl_set_file(dev/kvm, ACL_TYPE_ACCESS): Operation not supported (95) rsync: [generator] set_acl:

Hello everyone, I'm streaming audio on CentOS 5.8 with no problem, even on a cheap sound card using DarkIce as the input tool. For the input under CentOS5, I use: device = /dev/dsp # OSS DSP soundcard device for the audio input But under CentOS 6.2, there is no such device. I see /dev/snd, and it has: controlC0 hwC0D2 midiC0D1 pcmC0D0p pcmC0D2p pcmC1D0p seq controlC1 hwC1D0

The bio.infer package contains a data frame /usr/lib/R/library/bio.infer/data/itis.ttable.rda that needs to be modified. After loading the bio.infer package and attaching the data frame with the data() function, I wrote the data frame to a text file. After adding another row to the data frame I applied read.table() to create a data frame, but it's in my pwd, not the R library data

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Hi, I want to run t.test() for several variables among two groups, and I would like to skip the tedious process of collecting information to assemble a table, but I am not sure if the function I want already exists. Any suggestion would be appreciated. I have a working example, as required by the posting guide: my_swiss = swiss[-1,] my_swiss$facto = rep(1:2,nrow(my_swiss)/2)

I don't see what's missing in my statements to add rows to a data frame and someone else will probably see what needs to be added to the statements. The data frame has this structure (without any data): $ PHYLUM : chr $ SUBPHYLUM : chr $ SUPERCLASS : chr $ CLASS : chr $ SUBCLASS : chr $ INFRACLASS : chr $ SUPERORDER : chr $ ORDER : chr $ SUBORDER :

Hi, I am running a mixed-effect model with a nested-random effect. I am interested in gut parasites in moose. I has three different type of treatment that I applied to moose which are from different "families". My response variable is gut parasites and the factors are moose families which is nested within treatment. My data is balanced. To answer this question, I used the lme function

Dear helpers, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear R-list I have a question concerning the argument "adjustSigma" in the function "lme" of the package "nlme". The help page says: "the residual standard error is multiplied by sqrt(nobs/(nobs - npar)), converting it to a REML-like estimate." Having a look into the code I found: stdFixed <- sqrt(diag(as.matrix(object$varFix))) if (object$method

I wantto run sequential ANOVAs (i.e. type I sums of squares), and trying to getresults including ANOVA tables and associated coefficients for predictive variables(I am using the R 3.4.2 version). I think ANOVA tables look right, but believecoefficients are wrong. Specifically, it looks like that the coefficients arefrom ANOVA with ?marginal? (type III sums of squares). I have tried both lme

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

I am wondering how to obtain SE estimates for fixed effects from a nonlinear mixed effects model? I have fixed effects corresponding to three factors A, B and C with 2, 3 and 3 levels respectively. I have fit a model of the following general form: nlme1<-nlme(y~ SasympOrig(x, Asym, lrc), data=df, fixed=list(Asym~A*B*C, lrc~A*B*C), start=c(fixef(ETR.nlme)[1], rep(0,17), fixef(ETR.nlme)[2],

Hello, Is there an easy way to format the output of a result table that R generates from a regression? I like the table, but would like to limit the number of decimal points in the entries if possible. For instance I would like only 3 digits of precision for the Value, Std.Error. (And if it would be easy to get rid of scientific notation, that would be good to know too). So ideally keep the

Hi all, I'm hoping someone might help with a query about conditionally applying formulas to a dataframe. In essence I have 3 lookup tables (Table A, B & C) and a dataframe with a variable Type.Code, which identifies the Lookup Table to which each record belongs. The lookup tables reference different sensor types for which I need apply a different formula to values in Column3 in each row

Hi all, I understand that gls() uses generalized least squares, but I thought that maybe optimum weights from gls might be used as weights in lm (as shown below), but apparently this is not the case. See: library(nlme) f1 <- gls(Petal.Width ~ Species / Petal.Length, data = iris, weights = varIdent(form = ~ 1 | Species)) aa <- attributes(summary(f1)$modelStruct$varStruct)$weights f2 <-

Hello I am analysing aboveground biomass data from revegetation testplots which I constructed in a split-plot design using the function lme. For the experiment, the three factors are amelioration (2 levels), fertilizer (2 levels) and treatment (7 levels). Each testplot (block) has a singlereplicate of each treatment (total of 8 testplots). The blocks were constructed of topsoil. Each block was

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not

Behavioral science data is often collected from nested structures (students in schools, in districts, etc.). This can produce nonindependence among responses from individuals in the same groups. Consequently, researchers are advised to model the nested nature of the data to avoid biases in SE estimates. Failing to account for nonindependence can lead to SE estimates that are too large or too