similar to: confidential interval of correlation coefficient using bootstrap

I just corrected std.error of my 'model'(Multi Regression). Then how can I get new t and p-values? Isn't there any R command which shows new t and p values? -- View this message in context: http://r.789695.n4.nabble.com/After-heteroskedasticity-correction-how-can-I-get-new-confidential-interval-tp3095643p3095643.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I want to use smooth.spline to construct a cubic smoothing spline and its first derivative to my data. However, the predict.smooth.spline does not seem to provide a SE for both the fitted values and their derivatives. How should I calculate it? Thank you very much, Bingzhang

Hi, We are pleased to announce the call for presentations for this years Confidential Computing MC at the Linux Plumbers Conference. In this microconference we want to discuss ongoing developments around Linux support for memory encryption and support for confidential computing in general. Topics of interest include: * Support for unaccepted memory * Attestation workflows * Confidential

Dear R-help listers, Under the helps from the ggplot2 list, I have set up method of drawing a graph with multiple density plots arranged one by one in one page. Now, I want to add 95% confidential interval as vertical lines to x axe in density plot. I have found the library(hdrcde) can do this work, but I do not know how to handle functions of this library when I used ggplot2 to draw the graph.

We keep our puppet configuration in a revision control system and are trying to facilitate developers using the same modules as production/ staging servers. We define a variable ($servergroup) as test/staging/production and use fileserver defaults from hostname to servergroup to generic. The hostname and $servergroup versions need to be confidential. We have modules specific to an application,

Dear R-help listers, I want to add 95% confidential interval as vertical lines to x axe in density plot. I have found the library(hdrcde) can do this work, but I do not know how to handle functions of this library when I used ggplot2 to draw the graph. Thank you in advance. The data and codes followed: # dummy data factor<-rep(c("Alice","Jone","Mike"),each=100)

Hi I am trying to do a logistic regression on a small data file yet when i get up to plotting the first set of graphs instead of 4 I only get one graph, and some error messages. Yet it still looks like the program is doing something due to the "blue wheel" of the mouse. Below is the script copied from R and where I get up to before the message occurs. The data set is based on how

Hi all, To compound my problems, I am now unable to change the password on the problem user. The following error message is displayed. [root@gatekeeper samba]# smbpasswd michael New SMB password: Retype new SMB password: ldapsam_modify_entry: LDAP Password could not be changed for user michael: Confidentiality required Operation requires a secure connection. Failed to modify entry

Hi list! I am trying to get dovecot to authenticate users against Novell eDirectory via LDAP. I have successfully gotten open-xchange to authenticate, and I have written a PHP module that authenticates, however I cannot seem to get Dovecot working. If I turn on TLS and restart dovecot, I get: dovecot: 2010-05-20 09:22:05 Error: auth(default): LDAP: ldap_start_tls_s() failed: Connect error

Hello - I am trying to figure out R's transformation for interaction terms in a linear regression. My simple background understanding is that interaction terms are generally calculated by multiplying the centred (0-mean) variables with each other and then doing the regression. However, in this regard I would have expected to see the same p-value when I calculate summary(lm(Y~A:B)) for A:B

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Are these 225 compile time regressions real? It sure looks bad! Ciao, Duncan. On 01/12/11 09:39, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/380/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 380