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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 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?

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 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

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

When I was computing some joint probabilities, I found that R reported most of the results to to -Inf and thus didn't record the value. I guess it is b/c the joint log(probability) can be extremely small. Is there a way in R to keep the values even if they are small?

Recently I was using GAM and couldn't help noticing the following incoherence in prediction: > data(gam.data) > data(gam.newdata) > gam.object <- gam(y ~ s(x,6) + z, data=gam.data) > predict(gam.object)[1] 1 0.8017407 > predict(gam.object,data.frame(x=gam.data$x[1],z=gam.data$z[1])) 1 0.1668452 I would expect that using two types of predict arguments

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Dear R-helper, I want to plot the following-like data: x y 1 1 1 1 1 2 1 3 1 3 1 4 ...... In the plot that produced, I don't want to show the usual circles or points. Instead, I want to show the number of replicates at that point. e.g. at the position of (1,1), there are 2 obsevations, so a number '2' will be displayed in the plot. Is my narrative clear? Is there a way to make the

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]]

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

I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood: \sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i))) Does anybody know a better way to fit this kind of model in R? FYI: one example of getting error message is like: > x=runif(10,0,3) > y=c(rep(0,5),rep(1,5)) >

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

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > 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 > ...... > Now I want to compute the

I have a very simple problem. When using glm to fit binary logistic regression model, sometimes I receive the following warning: Warning messages: 1: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start,