similar to: aggregate

Dear All- I am trying to merge two data files - they have different date formats and different times zones. I need to match up the date/time of the datasets and then invoke a conditional statement, such as: if dataC$mph is >= 12 then keep dataM$co23 for the corresponding time/date stamp. snippets of data files: *dataC.txt* LST in mph Deg DegF DegF2 % volts Deg

Hello, It's an alternative to use SAS by function in R? I want to plot d histograms by plot.from example bellow: Thank you! plot d 1 1 16.3 2 1 25.0 3 1 57.8 4 1 17.0 5 2 10.8 13 2 96.4 17 3 76.0 18 3 32.0 19 3 11.0 20 3 11.0 24 3 106.0 25 3 12.5 21 4 19.3 22 4 12.0 26 4 15.0 27 5 99.3 32 7 11.0 36

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on

Hii, I will save the elements of the vector median<-with(x, tapply(V3, grup, median)). The output of this vector is: 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400 425 450 475 500 17.8 17.8 17.5 17.8 17.7 17.6 17.7 17.6 17.8 17.7 17.6 17.7 17.8 17.7 17.8 17.8 17.8 17.8 17.7 17.7 Can anybody help me how to do it. I will save it to a text file...

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

Dear all: Could anyone please provide some R codes to plot the below survival data to compare two groups (0 vs 1) after 2 simulations (TRL)? need 95% prediction interval on the plot from these 2 trials. I would like to simulate 1000 trials later. Thanks a lot for your great help and consideration! yan TRL ID ECOG BASE PTR8 GROUP POP ST ind 1 1 1 1 2.2636717 0.255634126 1 1 99.4 F 3 1 2 1

Hi all: I have been using ggplot2 graphics for quite some time now and I really like it. However, I haven't used barplots enough to understand the arquitecture behind it. Can someone show me how to make this simple barplot graph with ggplot2? I want "PondName" along the x axis and "avgWt" along the Y axis which represents the avgWt by each Pond. PondName avgWt Pond01 21.5

Hello, I am new to R. I often collect data at multiple sites and need to create separate graphs (such as scatterplots or histograms) of specific variables for each site. I have tried to do this by splitting the data frame and then using lapply, but it seems that the graphing commands cannot be called as functions. Here is a sample of my data, called "seeddist2": site

HI >i have a problem please help me to solve it: http://r.789695.n4.nabble.com/file/n4639434/aj.pdf aj.pdf >i want to calculate the vecteur a[j] where j: 1...8 >this is the code in R: >aj.fun <- function(j, i, X, z, E, beta0, beta1){ + n <- length(X) + iX <- order(X) + iz <- order(z) + e1 <- -(beta)*z[ iz[1:(i - 1)] ] + numer <- E[j] - sum( X[ iX[1:(i - 1)] ]

Thanks, that was a really helpful suggestion. If you're curious- here are some of the high cost areas: ===-------------------------------------------------------------------------=== DWARF Emission ===-------------------------------------------------------------------------=== Total Execution Time: 2.0117 seconds (2.0185 wall clock) ---User Time---

I have the following: Tout = c(". ", ". ", + "-51.0", " -9.6", " -9.6", " -9.6", " -9.6", " -9.6", " -9.6", + " -9.6", " -9.5", " -9.5", " -9.6", " -9.5", " -9.6", " -9.6", + " -9.5", " -9.4", "

Dear R helpers I have following dataset rate_number = matrix(c(5, 15, 60, 15, 5, 0, 20, 60, 20,0, 10, 20, 40, 20, 10), nrow = 5, ncol = 3) range_mat = matrix(c(6.25, 6.75, 7.25, 8.75, 9.25, 9.75, 8.5, 9, 9.5, 10.5, 11, 11.5, 4.25, 4.75, 5.25, 5.75, 6.25, 6.75), nrow = 6, ncol = 3) > rate_number        [,1]   [,2]   [,3] [1,]    5     0      10 [2,]   15   20      20 [3,]   60   60      40

Dear R-experts, The forest.rma() function from the metafor package creates nice forest plots for presenting the results of a meta-analysis. These plots can be annotated for e.g. giving names to the columns. E.g. as in the documentation of the package: data(dat.bcg) ### meta-analysis of the log relative risks using a random-effects model res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg,

Hello I?m need fitt growth curve with data length-age. I want to evaluate which is the function that best predicts my data, to do so I compare the Akaikes of different models. I'm now need to evaluate if changing the initial values changes the parameters and which do not allow to estimate the model. To do this I use the function nls(); and I randomize the initial values (real positive number).

Hello, I found that round() does not behave as I expected. Have you had similar experience as following? > x<-seq(0.5,10.5,by=1) > x [1] 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 > round(x) [1] 0 2 2 4 4 6 6 8 8 10 10 > cbind(x,round(x)) x [1,] 0.5 0 [2,] 1.5 2 [3,] 2.5 2 [4,] 3.5 4 [5,] 4.5 4 [6,] 5.5 6 [7,] 6.5 6

Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables

Hi, For example, the data.frame like: origdata.long <- read.table(header=T, con <- textConnection(' subject sex condition measurement 1 M control 7.9 1 M first 12.3 1 M second 10.7 2 F control 6.3 2 F first 10.6 2 F second 11.1 3 F control 9.5 3

#subject: type III sum of squares - anova() Anova() AnovaM() #R-version: 2.12.2 #Hello everyone, #I am currently evaluating experimental data of a two factor experiment. to illustrate de my problem I will use following #dummy dataset: Factor "T1" has 3 levels ("A","B","C") and factor "T2" has 2 levels "E" and "F". The

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

Hi, Can someone explain this error below, I don't understand why the 2 int columns are associated to make a row name > horse1.data<-read.csv("cte2.csv", header=TRUE, sep=";") > h1.dat<-mlogit.data(data=horse1.data,choice="win",chid.var="raceid",alt.var="nbChev",shape="long") Error in `row.names<-.data.frame`(`*tmp*`,