similar to: summarise subsets of a vector

Hello, I have a dataframe (test.df) with intervals that were generated by table (see below). I would prefer a dataframe labeled like this: > test.tab <- table(cut(skn.test$density, seq (0,1, by=0.05))) > test.tab (0,0.05] (0.05,0.1] (0.1,0.15] (0.15,0.2] (0.2,0.25] (0.25,0.3] (0.3,0.35] 68 87 85 72 65 73 74 (0.35,0.4] (0.4,0.45]

Hello, I have a dataframe with positive integers and for every value > 0 I would like to have 1 and the rest should be zero. For instance 1 0 1 0 1 1 with df as reference. > df V1 V2 V3 yes 23 0 5 no 0 5 7 > dput (df) structure(list(V1 = c(23, 0), V2 = c(0, 5), V3 = c(5, 7)), .Names = c("V1", "V2", "V3"), row.names = c("yes",

Hello, I have a question about modelling via glm. I have a dataset (see dput) that looks like as if it where poisson distributed (actually I would appreciate that) but it isnt because mean unequals var. > mean (x) [1] 901.7827 > var (x) [1] 132439.3 Anyway, I tried to model it via poisson and quasipoisson. Actually, just to get an impression how glm works. But I dont know how to

Dear R-helpers, I have produced several models, named model1, model2, model3, etc... I would like to extract several elements from each model's object, e.g. at minimum the estimates, SEs, and P values of each model's intercept and slopes, model R-squared, and AIC... ...and then produce a new object (a table) that summarises all of my models, with M\models in rows and extractd model

Having read the list posting guidelines I fear my first post is about to break the rules. Apologies in advance. We have been asked to produce some graphs of relative performance of 3 groups of people in relation to the trend of their previous performance. I am neither a mathematician or a statistician, but wondered if R (which I have been using as a desktop calculator!) and some knowledge from

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.

Hi Lars I am not very sure what you really want. However, I am suggesting the following code that enables (1) to obtain the full summary of your data and (2) retrieve only mean of X values as function of factors f1 and f2. library(tidyverse) library(psych) df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 = gl(3, 10, labels

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 =

Hi Lars, Two comments: 1. You can achieve what you want with a slight modification of your definition of s(), using the hint from the error message that you need an argument '.': s <- function(.) { dplyr::summarise(., x1m = mean(X1), x2m = mean(X2), x3m = mean(X3), x4m = mean(X4)) } 2. You have not given a great test case in

Hi, I'm having trouble using the constrOptim function to generate the 9-component vector argmin of the function ELfsds: ELfsds <- function(pvechat){ LG=0 for(i in 1:9){ LG=LG+log(pvechat[i]) } return(-LG) } with accompanying gradient function: gradfunc <- function(thetavec){ g=1/(9*thetavec) return(g) } The constraints on the optimization problem are: 1 - components of

Dear R users I have a really simple question (hoping for a really simple answer :-): Having estimated the spectral density of a time series "x" (heart rate data) with: x.pgram <- spectrum(x,method="pgram") I would like to compute the power in a specific energy band. Assuming that frequency(x)=4 (Hz), and that I am interested in the band between f1 and f2, is the

Hi, In Splus7 this statement xlrmN1 <- sample(c(0,1,2),400 ,prob=c(0.02 ,0.93 ,0.05 )) worked fine, but in R the interpreter reports that the length of the vector to chose c(0,1,2) is shorter than the size of many times I want to be selected from the vector c(0,1,2). Any good reason? See below the error. > xlrmN1 <- sample(c(0,1,2),400 ,prob=c(0.02 ,0.93 ,0.05 )) Error in

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>

Hello R Folks, I am new to R. I have been struggling to overlay a barchart with a xyplot together on one plot but did not get this worked out. Any help and idea are greatly appreciated. I attached R scripts for barchart and xyplot below and also data I used. What I am trying to do is just to put the barchart and xyplot together on one plot. Huapeng <<dispersal_infestation.csv>>

Hi all - I'm on R 2.5.1 for XP. in the systemfit package, the summary is set to print the McElroy's measure of fit unless it's NULL. When the option saveMemory = TRUE, the McElroy isn't included, instead it defaults to NA. Thus I am unable to use summary.systemfit. > library(systemfit) > example(systemfit) > surfit2 <-

I have some summarised data from a 2D pivot table which I want to visualise in R. How can I read in the data as a R table so I can use mosaicplot()? Dirk -- View this message in context: http://r.789695.n4.nabble.com/read-in-summarised-data-as-table-tp3442283p3442283.html Sent from the R help mailing list archive at Nabble.com.

I tried to post this a few times last week and it seems to have got stuck somehow so I'm trying from a different email in the hope that works. If somehow this has appeared on the list 20 tiems and I never saw any of them I apologize ;-) I'm basically an R-newbie. But I am VERY computer literate. But this has me stumped... All the examples for using the rmeta package to create a

I am an R beginner and trying to run a market model using event study in R framework. First, I run a market model, that is lm(stock security~SP500 index, subset=Obs[197, 396]) ->result1 Then I get predict results for a new dataset using predict (result1, newdata=Obs[397,399]) ->pred1 Pred1 should have three numbers. Now I need to calculate abnormal return by the formula stock

To everyone, I am currently doing analysis through Splus but am having severe problems. I am trying to fit all subset weighted regression to a multivariate data set and get PRESS statistics, along with Rsquare, Rsquare adjusted and Mallows Cp for all possible models. Splus, will give me these statistics but the leaps procedure is highly sensitive to nearly singular data matrices (which mine is)

Hi, I am trying to fit all subsets for a vector autoregression with exogenous variables. I have been looking at the 'leaps' function but I not sure how to get it to work when lags for each variable are included in the model. I would be really appreciative if someone could provide some links to examples. Thanks in advance! -- View this message in context: