similar to: Re: R 1.7.x and inaccurate log1p() on OpenBSD 3.2 and NetBSD 1.6 (PR#3979)

Dear ReXperts, I have the below text file output. I need to extract the T, QC, QO, QO-QC and WT columns for the data between T = 10 and T=150. Any ideas? Thanks in advance. ======================================================================================== 1 D C ---CAT-- T THETA QC QO QO-QC QC/QO WT FSD 8 1 0 1.0000E+01

Dear Wiza[R]ds, I have the following data in a data.frame. I need to do an anova with multiple comparison but I don't know how to factor the groups for analysis. There are 3 groups, 1,2 and 3 labelled in column 1. Help appreciated with thanks in advance. Group SI Sif SG Io I2 lol2 1 9.08e-05 9.08e+00 0.060842287 1.798556446 32.574500

Upgrading an ancient customer installation... was running 1.4.23.1 (Trixbox) with Zaptel 1.4.12.9 and a Sangoma A102D, which has been running fine for 5+ years. Customer getting anxious about hardware failure, so we built a new box and installed 1.8.24.0, Dahdi 2.7.0.1, and a new Sangoma A104D. The single active span is an RBS T1 B8ZS/ESF/E&M Wink. I tried to move one span over one

Hi all, I am trying to run Weibull PH model in R. Assume in the data set I have x1 a continuous variable and x2 a categorical variable with two classes (0= sick and 1= healthy). I fit the model in the following way. Test=survreg(Surv(time,cens)~ x1+x2,dist="weibull") My questions are 1. Is it Weibull PH model or Weibull AFT model? Call: survreg(formula = Surv(time, delta) ~ x1

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

Hi all, I've been trying to learn more about memory profiling in R and I've been trying memory profiling out on read.table. I'm getting a bit of a strange result, and I hope that someone might be able to explain why. After running Rprof("read-table.prof", memory.profiling = TRUE, line.profiling = TRUE, gc.profiling = TRUE, interval = interval) diamonds <-

I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm

Hi r users,   I would like sort of   cdf         seq   rand 0.00E+00    0     0.262123478 1.56E-03    20    0.964293344 1.55E-02    40    0.494827113 5.30E-02    60    0.733726005 1.16E-01    80    0.800408948 1.97E-01    100   0.925748466 2.88E-01    120   0.047578356 3.80E-01    140   0.266060366 4.68E-01    160   0.125522629 5.48E-01    180   0.701193274 6.18E-01    200   0.915799432

Hi R-users,   I would like to search for the values of seq that match my rand values.  In excel I will use =VLOOKUP(G2,$E$2:$F$32,2).  For example, for rand=.262 it will give me approximately seq=120 and rand=0.964293344, seq=460 and etc.   E           F     G cdf         seq   rand 0.00E+00    0     0.262123478 1.56E-03    20    0.964293344 1.55E-02    40    0.494827113 5.30E-02    60   

Hi, I'm estimating a loglogistic aft (accelerated failure time) model, just a simple plain vanilla one (without time dependent covariates), I'm comparing the results that I obtain between aftreg (eha package) and survreg(surv package). If I don't use any covariate the results are identical , if I add covariates all the coefficients are the same until a precision of 10^4 or 10^-5 except

I have a fairly large set of data with the following attributes: >str(raw.data) `data.frame': 1429 obs. of 16 variables: $ TStamp :`POSIXlt', format: chr "2001-11-25 02:00:00" "2001-11-25 01:55:00" "2001-11-25 01:50:00" "2001-11-25 01:45:00" ... $ iPDT.AHU14.14: num 0.0122 0.0125 0.0120 0.0120 0.0122 ... $ iPDT.AHU14.15: num 0.0121

Hi List, My goal is to force R not to print in scientific notation in the sixth column (rel_diff - for the p-value) of my data frame (not a matrix). I have used the format.pval () and printCoefmat () functions on the data frame. The R script is appended below. This issue is that use of the format.pval () and printCoefmat () functions on the data frame gives me the desired results, but coerces

Hello, I fear its a stupid question,..but here it is: If I do this simple calculation with the R console, I surprisingly do not get a zero. Why? -1.1-0.1+1.2 [1] -2.220446e-16 greetings, Ole -- View this message in context: http://r.789695.n4.nabble.com/1-1-0-1-1-2-is-NOT-null-Why-tp4636053.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version

Hello, I have conducted an SEM in which the resultant standardized path coefficients are much higher than would be expected from the raw correlation matrix. To explore further, I stripped the model down to a simple bivariate relationship between two variables (NDVI, and species richness), where it's my understanding that the SEM's standardized path coefficient should equal the correlation

Dear list, dear Frank, I try to fit a Weibull survival regression model with package Design: sclear <- psm(sobj~V1+V2,dist="weibull") sobj is a one-dimensional survival object (no event indicators), V1 and V2 are factors. I get the following result: Parametric Survival Model: Weibull Distribution psm(formula = sobj ~ V1 + V2, dist = "weibull") Obs Events

Dear R-list users, I'm new to principal components and factor analysis. I thought this method can be very useful for me to find relationships between several variables (which I know there is, only don't know which variables exactly and what kind of relation), so as a structure detection method. Now, I'm experimenting with the function prcomp from the mva package. In my source code

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 all, I'm from Brazil. I fit a Tobit model to FLUID MILK CONSUMPTION (DEPENDENT VARIABLE) data using survreg (attached). I am confused about the output interpretation and I would like yours explanations. Thanks, Marcio Roberto Silva -------------- next part -------------- A non-text attachment was scrubbed... Name: Tobit model.pdf Type: application/pdf Size: 9018 bytes Desc: not

Full_Name: M Welinder Version: 1.4 OS: (src) Submission from: (NULL) (192.5.35.38) It seems to me that pexp can be improved in the lower_tail=TRUE and log_p=FALSE case by using expm1. Something like -expm1 (-x / scale); I think. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

The derivative table resides in the function D. In S+ that table is extensible because it is written in the S language. R is faster but less flexible, since that table is programmed in C. It would be useful if R provided a mechanism for extending the derivative table, or barring that, provided a broader table. Currently unsupported mathematical functions of one argument include expm1, log1p,