similar to: Standard errors of the predicted values from a lme (or lmer)

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Hi does somebody know how to plot single letters in a text in different colours? example 1: I would like to add the word "ABC" to a figure. Thereby each letter should have a different colour. text(x,y,"ABC", col=c(1,2,3)) # this does not work example 2: I would like to add the name of a parameter p with an index i to a figure. The index i should be in red, whereas the

--Apple-Mail-27-953181986 Content-Transfer-Encoding: 7bit Content-Type: text/plain; charset=US-ASCII; format=flowed a possible bug with survival analysis - either in R or in SPSS... find more details in bug.doc, and the data in bug.txt best Pius Korner --Apple-Mail-27-953181986 Content-Disposition: attachment Content-Type: multipart/appledouble; boundary=Apple-Mail-28-953181987

I'm looking for a book for someone completely ignorant of statistics who wishes to learn both statistics and R. I've found three possibilities, one by Verzani ("Using R for Introductory Statistics"), one by Crawley ("Statistics: An Introduction using R"), and one by Dalgaard ("Introductory Statistics with R"). Do these books have different emphases,

Hi, `model.matrix' might kill R with a segfault (on a illposed problem, but anyway): mydf <- as.data.frame(sapply(1:40, function(i) gl(2, 100))) f <- as.formula(paste("~ - 1 + ", paste(names(mydf), collapse = ":"), sep = "")) X <- model.matrix(f, data = mydf) *** caught segfault *** address 0x18, cause 'memory not mapped' Segmentation fault

Dear List, My first R function is a rip-off bagging algorithm from pg. 138 of Everitt and Hothorn's "Handbook of Statistical Analyses using R" (HSAUR). I'm using recursive partitioning to develop a set of useful variables in diagnosing ADHD. I'm running this in ESS in XEmacs 21.4.19, R 2.4.1 on Slackware Linux 11.0 with a 2.6 kernel. This is almost an entire script,

Can anyone give advice how to interactively exchange data between R and SQL-Databases like DB2, ORACLE, MS-SQL-Server ? If the answer is: 'currently not', this would be information for me as well. I will summarize to the list. Best regards -- Dr. Jens Oehlschl?gel-Akiyoshi MD FACTORY GmbH Bayerstrasse 21 80335 M?nchen Tel.: 089 545 28-27 Fax.: 089 545 28-10 http://www.mdfactory.de

I tried to use the effect() to get predicted marginals for multinomial logistic as I did for general logistic regression, but failed. Is there anyway to do that? Thx! -- View this message in context: http://www.nabble.com/How-to-get-predicted-marginal-%28aka-predicted-mean%29-after-multinomial-logistic--tp17200114p17200114.html Sent from the R help mailing list archive at Nabble.com.

Hi, stepAIC generic plot function creates useful graphics for the diagnosis of multiple regressions. To create predicted versus observed plots, I use to look for the coefficients, copy them by hand, calculate R?, then plot. Is there a more automated way to plot predicted versus observed with its associated R? output using stepAIC, or another function? Kind regards, S.-?. Parent Universit?

Hello I need to use a coxme model with my data (survival analysis with right-censoring and hierarchical nesting), but I cant find a way to get predicted values from a new data table (or even from the original one). Has anyone had this problem before? I cant find anything about that anywhere. Thanks, Allan [[alternative HTML version deleted]]

I estimated a negative binomial model using zelig. z.out<- zelig(NEWBHC~ PW80 + CHNGBLK + XBLK,data=data, model="negbin") How do I calculate predicted probabilities for this model? Is it the same process as a poisson regression? Thanks in advance Joe [[alternative HTML version deleted]]

Hello all, I am using the hurdle model for fitting my count data using the pscl package which is working fine. However, I am stuck with the problem of calculating the percent correctly predicted (PCP) zeros for hurdle model. The method I am trying to use to achieve this is 'hitmiss' in the pscl package (ref: http://www.inside-r.org/packages/cran/pscl/docs/hitmiss). When I do: >

I ran three logit models in R with the Zelig package and I'm trying to compute the predicted probabilities for a number of different values on the independent variable. My dep variable was accepted or decline and my indep variable is bid amount, and varies. So for a bid amount of 3, what's the expected probability of winning. For a bid amount of 5, what's the expected probability of

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Dear all I generated predicted risk of death for each subject in the study by means of Cox proportional hazards model at 8 year of follow-up, a time point at which follow-up was more than 90% complete. It is possible to extrapolate to 10-year the predicted risk of each subjet by assuming an exponential distribution? Any help would be greatly appreciated. Thanks for your consideration.

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

All, I have several years of longitudinal test scores for students (many who switch schools at various points in time). I am using a mixed-effects model with crossed random effects to model student trajectories. The model includes time at level 1 and students crossed with schools at level 2. When I run the model I get the posterior variances on the intercepts and slopes for students and schools,

I want to construct a logit model, plot the probability curve with the confidence intervals, and then I want to print out a data frame with the predictor, response value, predicted value, the low ci predicted value, and the high ci predicted value. So it should look something like: value low_ci prob hi_ci 5 0.10 0.12 0.13 6 0.11 0.13 0.16 7 0.13 0.15

Hi! I want to use the DRC package in order to calculate the IC50 value of an enzyme inhibition assay. The problem is that the estimated ED50, is always out of the fitted curve. In the example below, I had a ED50 value of 2.2896, But when I predict the response level for this concentration I get a value of 45.71 instead of the expected value of 50. This is my data: #Dose unit is concentration

Hi, I've built a linear model using multiple linear regression which leads me a R-squared value of 73.58%. After that, I used this model to predicted some values based on the test data. Now I'm wondering how: 1. can I measure de R-squared value between the predicted(by the model) and real (observed) values.? 2. Measure the RMSE error . Example: suppose my data its below: REAL