similar to: how to keep very small or large number?

Dear R-helpers, I want to merge several data sets into one single data set. For example, there are three separate data sets like: Set 1: id age gender 01 12 M 03 15 F 04 19 M ... Set 2: id time x1 01 1 0.25 01 2 0.27 01 3 0.29 03 1 0.15 03 2 0.18 04 2 0.22 04 3 0.54 ... Set 3: id time x2 01 1 0.34 01 2 0.55 01 3 0.79 03 1 0.12 03 2 0.23 04 2 0.45 04 3 0.56 ...... I want to merge

Dear R-helpers, I have the following data: y happenat x 5185 (07/22/05 00:05:14) 14 5186 (07/22/05 00:15:14) 14 5187 (07/22/05 00:25:14) 14 5188 (07/22/05 00:35:14) 14 ...... I want to choose between 07/25/05 15:30:00 and 07/26/05 12:30:00. Anybody had experience in handling this kind of data? Is there a simple way to subset by the variable

Dear list, I'm a bit perplexed why the 95% confidence bands for the predicted probabilities for units where x=0 and x=1 overlap in the following instance. I've simulated binary data to which I've then fitted a simple logistic regression model, with one covariate, and the coefficient on x is statistically significant at the 0.05 level. I've then used two different methods to

The usual background of xyplot is grey. Is there any advantage of using this color instead of white? I don't know how to change this specification into other colors. Anybody knows?

Dear helpers in this forum, I have the following question: Suppose I have the following data set: id x y 023 1 2 023 2 5 023 4 6 023 5 7 412 2 5 412 3 4 412 4 6 412 7 9 220 5 7 220 4 8 220 9 8 ...... and i want to calculate sum_{i=1}^k sum_{j=1}^{n_i}x_{ij}*y_{ij} is there a simple way to do this within and between subject summation in R?

Dear R-helpers, In my data set, I have a time variable 'RecordTime' whose class property is 'times'. When I list my data set, I see the values of RecordTime is like 10:20:30 in a 'h:m:s' format. Suppose I want to choose all the data after 10 o'clock, then use subset(data,RecordTime>10:20:30) just doesn't work. I noticed that all the values of RecordTime seem

I am wondering if there is any package in R that can fit a nonparametric regression model with monotone constraints on the fitted results. --------------------------------- [[alternative HTML version deleted]]

Hello, I think I am beginning to understand what is involved in the so-called "Type-I, II, ..." ANOVAS (thanks to all the replies I got for yesterday's post). I have a question that will help me (and others?) understand it better (or remove a misunderstanding): I know that ANOVA is really a special case of regression where the predictor variable is categorical. I know that there

suppose I have the following data x<-c(rep(.1,5),rep(.2,6),rep(.4,10),rep(.5,20)) y<-c(rep(.5,3),rep(.6,8),rep(1.2,8),rep(2.5,18),rep(3,4)) If I plot(x,y) in R, I will only get seven distinct points. What I want to do is to use different symbols to show the frequency at each point. e.g. if the frequncey is between 1 and 5, then I plot the point as a circle; if the frequency is between 6

Suppose I have the following data: y x id 44 0 104 48 58 104 48 55 204 47 105 204 41 275 206 18 67 209 ....... I fit the model >fit=lme(y~x+I(x^2),random=~1|id) Now I want to make a prediction plot: >time=seq(0,300,len=100) >plot(predict(fit,data.frame(x=time),level=0)) Very fine. It gives me the prediction curve based on the model. My further request is to make a confidence bands

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

  Dear R helpers   Suppose I have two sets of ranges (interest rates) as   Range 1 : (7 – 7.50, 7.50 – 8.50, 8.50 – 10.00) with respective probabilities 0.42, 0.22 and 0.36.     Range II : (11-12, 12-14, 14-21) with respective probabilities 0.14, 0.56 and 0.30 respectively.     My problem is to form the combinations of these ranges in a decreasing order of joint probabilities. It is assumed that

a closer look to the help on predict.glm will reveal that the function accepts a 'type' argument. In you case 'type = response' will give you the results in probabilities (that it seems to be what you are looking for). There also is an example on use of the 'type' argument at the end of the page. Stefano -----Messaggio originale----- Da: r-help-bounces at r-project.org

Suppose I make two discrete variables -- > D <- data.frame(f1=sample(1:5,100,replace=T), f2=sample(1:5,100,replace=T)) I know I can do: > table(D$f1, D$f2) 0 1 2 3 4 0 5 5 5 5 4 1 4 2 6 7 3 2 5 3 5 3 6 3 3 1 3 1 2 4 6 4 3 3 6 > table(D$f1) 0 1 2 3 4 24 22 22 10 22 > table(D$f2) 0 1 2 3 4 23 15 22 19 21 which is all great. But how do I produce the

Dear all, Given rr<-data.frame(r1<-rnorm(1000,10,5),r2<-rnorm(1000,220,5)) How can I add a column (rr$p) for the joint probability of each r1 & r2 pair? I know how to add the column.. I just dont know how to compute the p value for joint probabilities given the two samples. //M

Greetings all, I have two logistic plots coming from two calls to plogis. The code is .x <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x, location=0, scale=1), xlab="x", ylab="Density", main="Logistic Distribution: location = 0, scale = 1", type="l") abline(h=0, col="gray") .y <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x,

Hello colleagues, This is a follow up to a question I posed in November regarding an analysis I was working on. Thank you to Dr. Brian Ripley and Dr. John Fox for helping me out during that time. I am conducting logistic regression on data set on psi (ESP) ganzfeld trials. The response variable is binary (correct/incorrect), with a 25% guessing base rate. Dr. Ripley suggested that I

Hi all R users I did a logistic regression with my binary variable Y (0/1) and 2 explanatory variables. Now I try to draw my nomogram with predictive value. I visited the help of R but I have problem to understand well the example. When I use glm fonction, I have a problem, thus I use lrm. My code is: modele<-lrm(Y~L+P,data=donnee) fun<- function(x) plogis(x-modele$coef[1]+modele$coef[2])

I added "plogis" to the derivative table in the development version of R; the patch against yesterday's R-devel src/deriv/main.c is available at http://www.zoology.ufl.edu/bolker/deriv_patch.txt . I pretty much followed the framework of the other symbols; here was my incantation - } else if (CAR(expr) == PlogisSymbol) { - ans = simplify(TimesSymbol, -

dear£º in the example£¨nomogram£©£¬I don't understand the meanings of the program which have been marked by red line.And how to compile the program(L <- .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))). n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10)