similar to: logistic regression - glm.fit: fitted probabilities numerically 0 or 1 occurred

Hello, How do I plot a gam fit object on probability (Y axis) vs raw values (X axis) axis and include the confidence plot lines? Details... I'm using the gam function like this: l_yx[,2] = log(l_yx[,2] + .0004) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) And I want to plot it so that probability is on the Y axis and values are on the X axis (i.e. I don't want log

Hello, I'd like to understand 'what' is predicting the response for library(mgcv) gam? For example: library(mgcv) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) xx <- seq(min(l_yx[,2]),max(l_yx[,2]),len=101) plot(xx,predict(fit,data.frame(x=xx),type="response"),type="l") I want to see the generalized function(s) used to predict the response

Hello, I don't understand what went wrong or how to fix this. How do I set qr=TRUE for gam? When I produce a fit using gam like this: fit = gam(y~s(x),data=as.data.frame(l_yx),family=family,control = list(keepData=T)) ...then try to use predict: (see #1 below in the traceback() ) > traceback() 6: stop("lm object does not have a proper 'qr' component.\n Rank zero or should

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

Hi all, I am doing bootstrap with logistic regression by using glm function, and I get the errors; glm.fit: fitted probabilities numerically 0 or 1 occurred and glm.fit: algorithm did not converge I have read some things about this issue in the mailing list. I can guess what was the problem. My data contains one or may be two outliers. Does the error occur due to these extreme values or

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:

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

I was hoping for some advice regarding possible explanations for the fitted probability values I obtained for a multinomial logistic regression. The analysis aims to predict whether Capgras delusions (present/absent) are associated with group (ABH, SV, homicide; values = 1,2,3,), controlling for previous violence. What has me puzzled is that for each combination the fitted probabilities are

(corrected version of previous posting) I fit a GAM to turtle growth data following methods in Limpus & Chaloupka 1997 (http://www.int-res.com/articles/meps/149/m149p023.pdf). I want to obtain figures similar to Fig 3 c & f in Limpus & Chaloupka (1997), which according to the figure legend are "expected size-at-age functions" obtained by numerically integrating

Dear Users, I am a new user of the sem package. I have a model that is being flagged by sem as "S is numerically singular: expect problems" I have checked John Fox's response to a similar problem. Obviously the variance-covariance matrix is singular, but none of the possible reasons seems to hold in my case. Any leads how I could get the model to work? from Prof. John Fox That

Dear List, I am not sure how should i optimize a log-likelihood numerically: Here is a Text book example from Statistical Inference by George Casella, 2nd Edition Casella and Berger, Roger L. Berger (2002, pp. 355, ex. 7.4 # 7.2.b): data = x = c(20.0, 23.9, 20.9, 23.8, 25.0, 24.0, 21.7, 23.8, 22.8, 23.1, 23.1, 23.5, 23.0, 23.0) n <- length(x) # likelihood from a 2 parameter Gamma(alpha,

Hi everyone, I've been trying to find a function that outputs the Pearson and/or Spearman correlation coefficients for several variables with the associated statistics in one single table/matrix. For what I've been able to understand the Stats package is only able to compute these coeficients/statistics only in defined pairs. This becomes time consuming when we want to determine these

Dear All, I am using "Caret"package for SVM regression and elastic net regression . I can get the final fiited vs observed values. How can I get the coefficients? Any ideas? Thanks Alex [[alternative HTML version deleted]]

hi everybody I have to do a lot of Anova with R and I would like to have another type of coefficients coding.. I explain. by default if I have 2 temperatures for an experience. 100°C or 130°C and I want to see the temperature effect on the presure I want to estimate the coefficient of each temperature. I will obtain ,with the anova, juste one coefficients for example +3,56 (for 100°C), and the

I'm trying to read in a perfectly good Stata data file created using Stata 10 in Linux and saved in the version 8/9 format using "saveold". R fails to read in the file: aa<-read.dta("myfile.dta") Error in read.dta("myfile.dta"): a binary read error occurred If I resave the file in Stata using the "nolabels" option of saveold, I get: There were

I have an index method with a respond_to block that produces this error: NoMethodError (You have a nil object when you didn''t expect it! The error occurred while evaluating nil.accepts): The show method has the same respond_to block but doesn''t error out. If I take the respond_to block out of the index method, it works. If I give index params[:keywords] I get this error:

Hi, I tested xm block-attach command with a wrong parameter(file:). Naturally a command error occurred. Then I retested xm block-attach command with a correct parameter(phy:). But a command error occurred again. The second command error occurred because Xend did not remove device information from self.info when the first command error occurred. # xm block-attach vm1 file:/dev/hda4 hdb1 w