search for: lin1

...t and server. I tweaked the LDAP and Kerberos settings using the CentOS/RH GUIs, and have the clients looking to the RH box for authentication. I also have the firewall enabled, but am letting kerberos and ldap ports through as tcp. During a login test, /var/log/messages on the client showed: lin1 gdm[pid]: nss_ldap: failed to bind to LDAP server ldap://192.168.1.100: Can't contact LDAP server lin1 gdm[pid]: nss_ldap: reconnecting to LDAP server (sleeping 32 seconds)... lin1 dbus-daemon: nss_ldap: failed to bind to LDAP server ldap://192.168.1.100: Can't contact LDAP server lin...

...and-in-hand with my studies. Basically it looks like this ('scuse ascii-art) INTERNET GATEWAY (ADSL-DHCP) / /eth0 (213.x.x.x) | winbox1 -- eth1 (192.x.x.1) [SNAT for incoming] | / /eth1 (192.x.x.10) [SNAT for incoming from eth0] | lin1 -- eth0 (192.x.x.20) | lin2 -- eth0 (192.x.x.21) | lin3 -- eth0 (192.x.x.22) I decided to use the same subnet and simply SNAT the connections from lin2 and lin3 which works a treat, I can access (outgoing) anything I like from all the lin boxes, having cleverly <g> set...

...t;-1000 a<--0.03 b<--0.02 t<-seq(0:144);t y<-p*exp(a*t) + q*exp(b*t)+rnorm(t,sd=0.3*(p* exp(a*t) + q*exp(b*t))) fittA <- nls(y~cbind(exp(a*t), exp(b*t)), algorithm="plinear",start=list(a=-.1, b=-0.2), data=list(y=y, t=t), trace=FALSE);fittA # a b .lin1 .lin2 # -0.003074 -2.777 4512 -2399 fittB <- nls(y~cbind(exp(a*t), exp(b*t)), algorithm="plinear",start=list(a=-.1, b=-0.3), data=list(y=y, t=t), trace=FALSE);fittB # a b .lin1 .lin2 # -0.02248 -0.04684 2414.86017 2052.96601...

Dear R specialists, I'm working with time series and want to investigate the relationship between two time series by correlation analysis or by fitting a gen. lineair model to the plot of x(timeserie1) and y(timeserie2). Lin1 <- data.frame( Nr = c(1:lengte), NDII = window(ts.mNDII,c(1998,10),c(2003,11)), InvERC = window(Inv.ERC,c(1998,10),c(2003,11)) ) summary(glm(NDII ~ InvERC, data=Lin1, family=gaussian(link ="identity"))) Error in "storage.mode<-"(`*tmp*`, v...

...del" Now, recode trial from 1-15 and run the same model. >fit.pw <-nls(RT ~ cbind(1,trl, trl^p), start = c(p = -.2), algorithm = "plinear", data=df.one) Seems to work fine now... Nonlinear regression model model: RT ~ cbind(1, trl, trl^p) data: df.one p .lin1 .lin.trl .lin3 -0.2845 200.3230 -8.9467 904.7582 residual sum-of-squares: 555915 Number of iterations to convergence: 11 Any idea why having a zero for the first value of X causes this problem? Thanks in advance, Rick DeShon [[alternative HTML version deleted]]

hi! when i have busy signal on analog line (zap card) it doesn't detect that line is busy ? is it possible to change detected sequence (frequency) of busy tones on line (zapata.conf ??) tnx, Thomas my zapata.conf [channels] language=en context=lin1 signalling=fxs_ks channel => 1 group=1 echocancel=yes echocancelwhenbridged=yes rxgain=3.0 txgain=3.0 busydetect=yes

I am using Microsoft .NET (2.0) PrintPreviewDialog to preview reports, and all works well when the Windows default printer is set to a local printer or a network printer on a Windows server, but if I set it to a Samba network printer, it slows down to a crawl. With a Windows server network printer, it takes about 10 seconds to render 100 pages. With a Samba network printer, it takes over 5

Hi all, I am not a mathematician and I am trying to fit a function which could fit my observed data. Which function should I use and how could I fit it to data in R? Below are the data: x <- c(0, 9, 17, 24, 28, 30) y <- c(500, 480, 420, 300, 160, 5) I use R for Mac OS, version 2.10-1 2009-08-24 Thank you for your help. Vincent. [[alternative HTML version deleted]]

Hi, I have a problem fitting the following Weibull Model to a set of data. The model is this one: a-b*exp(-c*x^d) If I fitted the model with CurveExpert I can find a very nice set of coefficients which create a curve very close to my data, but when I use the nls.lm function in R I can't obtain the same result. My data are these: X Y 15 13 50 13 75 9 90 4 With the commercial

This data are kilojoules of energy that are consumed in starving fish over a time period (Days). The KJ reach a lower asymptote and level off and I would like to use a non-linear plot to show this leveling off. The data are noisy and the sample sizes not the largest. I have tried selfstarting weibull curves and tried the following, both end with errors. Days<-c(12, 12, 12, 12, 22, 22, 22,

...scal 0.974952 1.123036 #R> b1 2.193703 2.600172 #R> b2 -0.001597 0.002978 f2 <- nls(density ~ cbind(1/(1 + exp((xmid - log(conc))/scal)), x), data = DNase1, start = list(xmid = 0, scal = 1), algorithm = "plinear") coef (f2) #R> xmid scal .lin1 .lin.x #R> 1.461636 1.028726 2.323707 0.008807 confint(f2) # this fails #R> Waiting for profiling to be done... #R> Error in attr(ans, "gradient")[c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, : #R> (subscript) logical subscript too long traceback() # [output output abbrevia...

...for the linear parameters: > DF <- data.frame(X = c(15, 50, 75, 90), Y = c(13, 13, 9, 4)) > > nls(Y ~ cbind(1, exp(-c*X^d)), DF, start = list(c = 1, d = 1), alg = "plinear") Nonlinear regression model model: Y ~ cbind(1, exp(-c * X^d)) data: DF c d .lin1 .lin2 1.000e+00 1.000e+00 8.667e+00 1.417e+07 residual sum-of-squares: 40.67 Number of iterations to convergence: 0 Achieved convergence tolerance: 0 --Forwarded Message Attachment-- From: pmilin at ff.uns.ac.rs To: r-help at stat.math.ethz.ch Date: Mon, 23 Aug 2010 21:33:19 +0200 Subject...