similar to: glm.fit: fitted probabilities numerically 0 or 1 occurred?

I have gotten the this error before: "glm.fit: fitted probabilities numerically 0 or 1 occurred" and the problem was usually solved by combining one or more categories were there were no observations. I am now having this error show up for a variable that is continuous (not categorical). What could be the cause of this for a continuous variable?? Thanks, Nick -- View this message

Sorry if this is a duplicate: This is a re-post because the pdf's mentioned below did not go through. Hello, I'm new'ish to R, and very new to glm. I've read a lot about my issue: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred ...including: http://tolstoy.newcastle.edu.au/R/help/05/07/7759.html

Hi, could anyone explain to me what this warning message exactly means and what the consequences are? Is it due to the fact that there are very extreme observations / outliers included or what is the reason for it? Thanks so much, Werner Machen Sie Yahoo! zu Ihrer Startseite. Los geht's:

Hi dear all, suppose that s is a statistic code; i have a matrix (x) which has 7 columns (1=x1,2=x23=x3,4=x4,5=x5,6=x6 and7=y) and has 20 rows. i want to do linear reggression like reg<-lm(x[,7]~1+x[,1]+x[,2]+.......+x[,6]) but i want to do delete i th row for nrows times and create regression model like above and compute each models' "s" statistics and list them. but i could

Hi dear all, It may be a simple question, i have a list output with different number of elements as following; [[1]] [1] 0.86801402 -0.82974691 0.39746666 -0.98566707 -4.96576856 -1.32056754 [7] -5.54093319 -0.07600462 -1.34457280 -1.04080125 1.62843297 -0.20473912 [13] 0.30659907 2.66908117 2.53791837 0.53788013 -0.57463077 0.27708874 [19] -2.94233200 1.54565643 -6.83694100

Hi Dear All, Can someone give me a suggestion about which robust statistics are most appropriate for outlier detection in linear models, and is available with R ? Thanks for any idea. -- View this message in context: http://r.789695.n4.nabble.com/Robust-Statistics-for-Outlier-Detection-tp3438493p3438493.html Sent from the R help mailing list archive at Nabble.com.

Hi dear all, Can aynone help me about delete-d jackknife usually normal jackknife code for my data is: n <- nrow(data) y <- data$y z <- data$z theta.hat <- mean(y) / mean(z) print (theta.hat) theta.jack <- numeric(n) for (i in 1:n) theta.jack[i] <- mean(y[-i]) / mean(z[-i]) bias <- (n - 1) * (mean(theta.jack) - theta.hat) print(bias) but how i can apply delete-d jackknife

Hi Dear All, This is a function which contains Covariance Ratio and Likelihood Distance values (CVRi, LDi). i want to compute the all row's values, that is run this function for nrow(X) times. The X and Y matrices are;

I have a ror project which has been productized. There are several web sites in one ror project I need to set an "enviroment variable" to run different sites, can I do that using mongrel? I tried to use mongrel''s -S option and set the enviroment variable in that file but it seems mongrel runs that file after it calls enviroment.rb I also used apache''s

do i need to provide more information? On 19/10/2016 14:49, Matthew Broadhead wrote: > /var/log/maillog showed this > Oct 19 13:25:41 ns1 postfix/smtpd[1298]: 7599A2C19C6: > client=unknown[127.0.0.1] > Oct 19 13:25:41 ns1 postfix/cleanup[1085]: 7599A2C19C6: > message-id=<edc55a9b-eb49-3945-dc60-0e1d51a78e97 at nbmlaw.co.uk> > Oct 19 13:25:41 ns1 postfix/qmgr[1059]:

is there something more i need to be doing my end? On 25/10/2016 09:11, Matthew Broadhead wrote: > are there any instructions or tests i can make to check the sieve > configuration? or does the magic all happen internally and there are > no settings to change? > > On 21/10/2016 10:22, Matthew Broadhead wrote: >> the server is using CentOS 7 and that is the package that

the server is using CentOS 7 and that is the package that comes through yum. everything is up to date. i am hesitant to install a new package manually as that could cause other compatibility issues? is there another way to test the configuration on the server? On 21/10/2016 01:07, Stephan Bosch wrote: > Op 10/20/2016 om 7:38 PM schreef Matthew Broadhead: >> do i need to provide

Op 19-10-2016 om 13:47 schreef Matthew Broadhead: > i am not 100% sure how to give you the information you require. > > my current setup in /etc/postfix/master.cf is > flags=DRhu user=vmail:mail argv=/usr/libexec/dovecot/deliver -d > ${recipient} > so recipient would presumably be user at domain.tld? or do you want the > real email address of one of our users? is there

Hi dear all, i have a data (data.frame) which contain y and x coloumn(i.e. y x 1 0.58545723 0.15113102 2 0.02769361 -0.02172165 3 1.00927527 -1.80072610 4 0.56504053 -1.12236685 5 0.58332337 -1.24263981 6 -1.70257274 0.46238255 7 -0.88501561 0.89484429 8 1.14466282 0.34193875 9 0.58827457 0.15923694 10 -0.79532232 -1.44193770 ) i changed

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y

Dear R-help, I'm doing conditional logit regression for a discrete choice model. I want to know whether there's a way to get the fitted probabilities. In Stata, "predict" works for clogit, but it seems that in R "predict" does not. Thank you very much! Best wishes. Sincerely, Min -- Min Chen Graduate Student Department of Agricultural,

Hello All, Specifying 'type = "response"' when using predict() on a model fit using glm(...,family="binomial") returns fitted probabilities. Is it possible to get the same from a model object fit using glmmPQL() ? Thanks in advance, Rob _____________________________________________________ Rob Keefe Lab: (208) 885-5165 M.S. student

Dear R-list, Is there a function or technique by which the probability (or log odds) range of a logistic model (fit <- lrm(Y~X)) can be derived? The aim is to obtain min & max of the estimated probabilities of Y. Could summary.Design() be used for that or is there another method/trick? Thanks, Jan _______________________________________________________________________ ir. Jan

I am fitting a logistic binomial model of the form glm(y ~ a*x,family=binomial) where a is a factor (with 5 levels) and x is a continuous predictor. To assess how much ``impact'' x has, I want to compare the fitted success probability when x = its maximum value with the fitted probability when x = its mean value. (The mean and the max are to be taken by level of the factor