similar to: histogram - density on y axis and restriction to interval [0, 1]

Hello, I really wonder how to distinguish between secs and mins in the example below. In other terms, how can I access the time unit in variable d ? start1 <- Sys.time(); stop1 <- Sys.time(); d <- stop1-start1; print(d); v<-unlist(strsplit(as.character(d), split=" ")); print(v) Time difference of 3.024054 secs [1] "3.02405381202698" stop1 <- Sys.time(); d

Hello, I would like to use the ROCR package to draw ROC curves and compute AUC values. However, in the specific context of my application, the true positive rates and false positive rates are already provided by some upstream method. Of course, I can draw a ROC plot with the following command : plot(x=FPrate, y=TPrate, "o", xlab="false positive rate", ylab="true

> Dear All > > I have data as follows. > > D T M L > 0.20 1 03 141 > 0.32 1 07 62 > 0.50 1 05 49 > 0.80 1 04 46 > 0.20 2 14 130 > 0.32 2 17 52 > 0.50 2 13 41 > 0.80 2 14 36 > 0.20 3 24 120 > 0.32

Dear R gurus I am analysing data from a study of behaviour and shade utilization of chimpanzees. I am using GLMs in R (version 2.13.0) to test whether shade/sun utilization is predicted by behaviour observed. I am thus interested in whether an interaction of behaviour (as a predictor) and presence in the sun/shade (also predictor) predicts the counts I have for the respective categories. I have

Hi, R-users, data is a matrix like this AMR BS GE HR MO UK SP500 1974 -0.3505 -0.1154 -0.4246 -0.2107 -0.0758 0.2331 -0.2647 1975 0.7083 0.2472 0.3719 0.2227 0.0213 0.3569 0.3720 1976 0.7329 0.3665 0.2550 0.5815 0.1276 0.0781 0.2384 1977 -0.2034 -0.4271 -0.0490 -0.0938 0.0712 -0.2721 -0.0718 1978 0.1663 -0.0452 -0.0573 0.2751 0.1372 -0.1346

Greetings everyone, I'm trying to add a specific table or a specific number of rows (e.g.44) to a table with no success. This is my basic table > head(dataA) year plot spp prop.B DCA1 DCA2 DCA3 DCA4 1 2000 1 a1 0.031079 -0.0776 -0.0009 0.0259 -0.0457 2 2000 1 a2 0.968921 -0.0448 0.1479 -0.1343 0.1670 3 2000 2 a1 0.029218

Dear all, I am trying to fit a nonlinear model with a autocorrelation term, but everytime I type in the command, I got an error message from Winwows and R closes itself. The command line is as follows: mod1<-nlme(V~A*exp(-B*A.O)*Vac.t.1.,data,fixed=A+B~1,random=A+B~1|ORDINAL,+ correlation=corCAR1(0.3179,~A.O|ORDINAL,TRUE),start=c(A=1.2,B=0.2)) I have already fitted this model allowing Phi to

Dear list, I have a question regarding the meaning of intercept term in a two-way anova model without interaction term. for example (let's assume there is no interaction between factor1 and factor2) : > df         val        factor1 factor2 1  48.61533       A      t1 2 171.13535       B      t1 3  65.96884       C      t1 4  63.71222       A      t2 5  80.22049       B      t2 6 

Dear list members, I am using Mclust in order to deconvolute a distribution that I believe is a sum of two gaussians. First I can make a model: > my.data.model = Mclust(my.data, modelNames=c("E"), warn=T, G=1:3) But then, when I try to plot the result, I get the following error: > mclust1Dplot(my.data.model, parameters = my.data.model$parameters, what = "density")

Hello, I have a data set which I am using to find a model with the most significant parameters included and most importantly, the p-values. The full model is of the form: sad[,1]~b_1 sad[,2]+b_2 sad[,3]+b_3 sad[,4]+b_4 sad[,5]+b_5 sad[,6]+b_6 sad[,7]+b_7 sad[,8]+b_8 sad[,9]+b_9 sad[,10], where the 9 variables on the right hand side are all indicator variables. The thing I don't understand

Hi, I am experiencing a baffling behaviour of stepAIC(), and I hope to get any advice/help on what went wrong or I'd missed. I greatly appreciate any advice given. I am using stepAIC() to, say, select a model via stepwise selection method. R Version : 1.7.1 Windows ME Many thanks and best regards, Siew-Leng ***Issue : When stepAIC() is placed within a function, it seems

Hi, I had a logit regression, but don't really know how to handle the "Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos)" problem. I had the same logit regression without weights and it worked out without the warning, but I figured it makes more sense to add the weights. The weights sum up to one. Could anyone give me some hint? Thanks a lot!

Hi folks, I suspect this is a request for a sanity check than a bug report: I've been successfully compiling an optimised version of R for several years using the Intel compiler and MKL. I've just test-run the new Intel 15.0 compiler suite, and I'm seeing a few numeric failures that I don't see using the same build method with Intel 13.0. I've attached the output of

Hello, I have some trouble using step() and stepAIC() functions. I'm predicting recruitment against several factors for different plant species using a negative binomial glm. Sometimes, summary(step(model)) or summary(stepAIC(model) does not select the best model (lowest AIC) but just stops before. For some species, step() works and stepAIC don't and in others, it's the opposite.

I'm trying to run "rfe" for variable selection in the caret package, and am getting an error. My data frame includes a dummy variable with 3 levels. x <- chlDescr y <- chl #crate dummy variable levels(x$State) <- c("AL","GA","FL") dummy <- model.matrix(~State,x) z <- cbind(dummy, x) #remove State category variable w <- z[,c(-4)]

Hi everyone, I am performing the time series regression analysis on a series of data sets. A few data sets followed an ARMA(1,1) process. However, they all had a same value of moving average MA coefficients = -1, constantly, from output of function “arima" . Example: > arima(residuals, order=c(1,0,1)) Call: arima(residuals, order = c(1, 0, 1)) Coefficients:          ar1      ma1  intercept

Hi, I am having a hard time getting what the summary.lm-output for an ANOVA / ANCOVA means. Examples I find always seem to deal with simpler cases than what I meet in my data. My main problem is understanding the output when getting significant INTERACTION TERMS (what never occurs in examples :(). The following is the output after summary.lm(ancova) where "week" is continuous,

Hello R users, I have been using R for a while now for basic stats but I'm now trying to get my head around looping scripts and in some places I am failing! I have a data set with c. 1200 data points on 98 individual animals with data on each row representing a daily measure and I am asking the question "what variables affect the animal's behaviour?" the dataset includes

Hi, This report deals with p-values coming from chisq.test using the simulate.p=TRUE option. The issue is numerical accuracy and was brought up in previous bug reports 3486 and 3896. The bug was considered fixed but apparently was only mostly fixed. Just the typical problem of two values that are mathematically equal not ending up numerically equivalent. Consider this series of three 2x2

Hi Everyone, I'm continuing to run into trouble with polyfit. I'm using the fitting function of the form; fit <- lm(y ~ poly(x,degree,raw=TRUE)) and I have found that in some cases a polynomial of certain degree can't be fit, the coefficient won't be calculated, because of a singularity. If I use orthogonal polynomials I can fit a polynomial of any degree, but I don't get