search for: 0.069

The file TENSILE.DAT from the Hand et al "Handbook of Small Data Sets" looks like this: 0.023 0.032 0.054 0.069 0.081 0.094 0.105 0.127 0.148 0.169 0.188 0.216 0.255 0.277 0.311 0.361 0.376 0.395 0.432 0.463 0.481 0.519 0.529 0.567 0.642 0.674 0.752 0.823 0.887 0.926 except that my mail client has replaced the tab separators by blanks. If I paste this data into R 2.2.1 what I get is

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")

Sadly you failed to set your email program to send plain text and the data is corrupted at my end. I also think you need to reduce the size of the data set... the intent here is to increase your understanding, not debug your particular analysis. I will say that I am having a very challenging time understanding what you are trying to accomplish though. What are the equations that you think need

I want to paste in the following numbers into a scan: 0.023 0.032 0.054 0.069 0.081 0.094 0.105 0.127 0.148 0.169 0.188 0.216 they are separated by tabs alone, unless my mailer has done something to the tabs. Now have a look at this: > scan() 1: 0.0230.0320.0540.0690.0810.094 1: 0.1050.1270.1480.1690.1880.216 Error in scan() : "scan" expected a real, got

Here is the file minimal.Snw: \documentclass[a4paper]{article} \title{R tips and tricks} \author{Murray Jorgensen} \usepackage{Sweave} \begin{document} \maketitle \section*{Entering data from a single variable} The following data are transformed tensile strength measurements on polyester fibres. They may be found on the file \texttt{TENSILE.DAT}. We may enter this data into R using the

... 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

Hi. I'm attempting to fit a logistic/binomial model so I can determine the influence of landscape on the probability that a box gets used by a bird. I've looked at a few sources (MASS text, Dalgaard, Fox and google) and the examples are almost always based on tabular predictor variables. My data, however are not. I'm not sure if that is the source of the problems or not because the

En innebygd og tegnsett-uspesifisert tekst ble skilt ut... Navn: ikke tilgjengelig Nettadresse: https://stat.ethz.ch/pipermail/r-help/attachments/20060921/b479a8df/attachment.ksh

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

Dear All, I am trying to calculate a 95% confidence interval for the difference in two c statistics (or equivalently D statistics). In Stata I gather that this can be done using the lincom command. Is there anything similar in R? As you can see below I have two datasets (that are actually two independent subsets of the same data) and the respective c statistics for the variables in both cases.

Dear All, Apologies if you have a seen a question like this from me before. I am hoping that if I re-word my question more carefully someone may be able to offer more help than the last time I asked something similar. I am using R 2.9.2 and Windows XP. I am trying to determine if there is a statistically significant difference between two c-statistics (or equivalently D statistics). In Stata

I was wondering if anyone knows how accurate the values are when you do a "sip show peers" from the CLI. My configuration is: Asterisk box (192.168.1.102) -> gigabit switch <- PC running x-lite (192.168.1.100) the CLI reports 101 ms delay however, ping is showing <1ms delay Where is the extra 100ms coming from? The softphone response? Here is a dump of some data: CLI>

I have a table like the following. I want to fit Cm to Vm like this: Cm ~ Cl+Q1*b1*38.67*exp(-b1*(Vm-Vp1)*0.03867)/(1+exp(-b1*(Vm-Vp1)*0.03867))^2+Q2*b2*38.67*exp(-b2*(Vm-Vp2)*0.03867)/(1+exp(-b2*(Vm-Vp2)*0.03867))^2 I use nls, with start=list(Q1=2e-3, b1=1, Vp1=-25, Q2=3e-3, b2=1, Vp2=200). But I always get 'singlular gradient' error like this. But in SigmaPlot I can get the result. How

https://bugzilla.netfilter.org/show_bug.cgi?id=837 Summary: Large ICMP packets are lost Product: libnetfilter_queue Version: unspecified Platform: x86_64 OS/Version: other Status: NEW Severity: normal Priority: P5 Component: libnetfilter_queue AssignedTo: netfilter-buglog at lists.netfilter.org

I'm doing: > alyL32007z <- zoo(alyL32007,alyL32007$time) > range(time(alyL32007z)) [1] "2007-01-01 00:00:00 UTC" "2007-12-31 23:30:00 UTC" But then, while the original variable is: > summary(alyL32007$NEE_st) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's -15.340 -1.615 -0.054 -0.814 0.750 8.965 11124 the variable within the zoo object

Dear list, I am trying to replicate some Stata results but having a tough time doing it in R. The goal is to obtain a difference-in-difference estimate in a model with simple state fixed effects. The "state" variable is a factor, but some levels are missing. It appears that Stata automatically recognizes this and works around it. It also automatically pick "ME" as the base

Dear R-helpers, I'm stuck with a little problem that surely has an easy solution but I can't think of a way to solve it. I'd really appreciate any help you can offer me! I'll provide a small example. Given a dataframe data.txt that looks like this: ID freq Var Var_mean Ratio_mean Var_median Ratio_median Var_sum Ratio_min Var_max Ratio_max Var_min

Dear all, can somebody please help me how to calculate "McFadden R-square" for a LOGIT model? Corresponding definition can be found here: http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_plum_statistics_rsq_mcfadden.htm Here is my data: Data <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,

Hi all, I have a dataset in which the output Y is observed on two groups of patients (treatment factor T with 2 levels). Every subject in each group is observed three times (not time points but just technical replication). I am interested in estimating the treatment effect and take into account the fact that I have repeated measurements for every subject. If I do this with repeated measures

I am trying to investigate the interaction term in the below. The paradigm in aquatic systems is that algal production is either nitrogen (TIN) or Phosphorus limited, and I am trying to investigate this- what is the best way to go about investigating the interaction term. I have some thoughts on the above, but I will withhold them to see what others think. Thanks for your help. d <-