search for: 0.351

I wonder if the 'sep' argument in reshape() is being ignored unintentionally: ## From example(reshape) df <- data.frame(id=rep(1:4,rep(2,4)), visit=I(rep(c("Before","After"),4)), x=rnorm(4), y=runif(4)) reshape(df, timevar="visit", idvar="id", direction="wide", sep = "_") id x.Before

Hi there, >library(faraway) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6 0.627 50 1 2 1 85 66 29 0 26.6 0.351 31 0 >data(pima) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6

I am trying to it a particular nonlinear model common in Soil Science to moisture release data from soil. I have written the function as shown below according to the logist example in Ch8 of Pinheiro & Bates. I am getting the following error (R version 2.1.1) *Error in qr(attr(rhs, "gradient")) : NA/NaN/Inf in foreign function call (arg 1)* Below is the function and data. /#

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

Hi, I hope some one can help. I need to compute Thurston's case 5 on a large set of data. I have gotten as far as computing the proportional preference matrix but the next math is beyond me. Here us my matrix 0.500 0.472 0.486 0.587 0.366 0.483 0.496 0.434 0.528 0.500 0.708 0.578 0.633 0.554 0.395 0.620 0.514 0.292 0.500 0.370 0.557 0.580 0.615 0.329 0.413 0.422 0.630 0.500 0.783 0.641 0.731

I am unable to get the basehaz function to apply a proportional hazards model to a new data frame. I replicated my specific situation with the example for coxph in the help, where I changed the x value of the first record from 0 to 1. Is there something incorrect in the syntax that I am using? Thanks in advance! test1 <- list(time= c(4, 3,1,1,2,2,3), status=c(1,NA,1,0,1,1,0),

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

We are two french students and we have a problem concerning an exercize. We don't know how to resolve it. It would be fantastic if someone can help us. Thanks. Description: This study examined how the metabolic cost of locomotion varied with speed, stride frequency and body mass. Cost was determined by measuring oxygen consumption (?vo2?), analyzing the oxygen content in air inhaled and

On Tue, Jul 30, 2019 at 11:54:53AM -0400, Michael S. Tsirkin wrote: > On Tue, Jul 30, 2019 at 05:43:29PM +0200, Stefano Garzarella wrote: > > This series tries to increase the throughput of virtio-vsock with slight > > changes. > > While I was testing the v2 of this series I discovered an huge use of memory, > > so I added patch 1 to mitigate this issue. I put it in this

Hello all! I have a problem to arrange data in another form. My initial data is like this: 'data.frame': 421 obs. of 58 variables: $ 01A: num NA NA NA NA NA NA NA NA NA NA ... $ 01B: num NA NA NA NA NA NA NA NA NA NA ... $ 03A: num NA NA NA NA NA NA NA NA NA NA ... $ 03B: num NA NA NA NA NA NA NA NA NA NA ... $ 05A: num NA NA NA NA NA NA NA NA NA NA ... $ 05B: num NA NA NA NA

Dear R list members, I've turned up a strange discrepancy between results obtained from the lme function in the nlme package in R and results obtained with lme in S-PLUS. I'm using version 3.1-24 of nlme in R 1.4.1 under Windows 2000, and both S-PLUS 2000 and 6.0, again under Windows 2000. I've noticed discrepancies in a couple of instances. Here's one, using data from Bryk

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

[code]>require("survival") > coxph(Surv(futime,fustat)~age + strata(rx),ovarian) Call: coxph(formula = Surv(futime, fustat) ~ age + strata(rx), data = ovarian) coef exp(coef) se(coef) z p age 0.137 1.15 0.0474 2.9 0.0038 Likelihood ratio test=12.7 on 1 df, p=0.000368 n= 26, number of events= 12 > coxph(Surv(futime,fustat)~age, ovarian, subset=rx==1)

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

Answering on a mail from >From Keith Wong <keithw_at_med.usyd.edu.au> Date Sun 04 Jul 2004 - 17:21:36 EST Subject [R] Random intercept model with time-dependent covariates, results different from SAS Hi all I've got a question about the degrees of freedom in a mixed model, calculated with lme from the lme4 package. Since I've no access to the original data

Does anybody understand what's going on here: I have the following ascii file called "y.txt" "USD-DEM" "USD-JPY" "DEM-JPY" 0.344901 4.78712 4.442 0.345715 4.7882 4.44189 0.350657 4.79065 4.44177 0.35347 4.79065 4.43616 0.35368 4.78957 4.43622 0.35361 4.78982 4.43669 0.353821 4.79036 4.43622 0.352767 4.78899 4.43634 0.353119 4.78916 4.43545

Following the update this morning, NTP is failing with the same symptoms that I saw several years ago (pre-CentOS, I believe) when Anaconda mistakenly blessed my ASUS A7N8X, Athlon (CPU: AMD Athlon(tm) XP 2600+ stepping 00) based system with an SMP kernel. The symptom is that NTPD never establishes sync with a server, showing over the top jitter values. [root at mavis log]# uname -r

On Wed, Jul 17, 2019 at 01:30:25PM +0200, Stefano Garzarella wrote: > This series tries to increase the throughput of virtio-vsock with slight > changes. > While I was testing the v2 of this series I discovered an huge use of memory, > so I added patch 1 to mitigate this issue. I put it in this series in order > to better track the performance trends. Series: Acked-by: Michael S.