search for: results2

#I wish to create a "list of three dataframes" ("results2") from a "list of two dataframes" (temp) and a dataframe ("c")? #Please advise. a <- data.frame(x=c(1,2,3),y=c(5,7,9)) b <- data.frame(x=c(2,4,7,9),y=c(2,3,5,4)) c <- data.frame(x=c(22,34,7,9),y=c(52,63,5,4)) results1 <- list(a,b,c) #what I want #but this is...

...to inform: I posted that before in R-sig-ecology but as it might be interesting also for other useRs, I post it also to the general r-user list: Hello Alexandre, thank you very much. I also found another way to extract summarizing information from lm results over e.g. 1000 repeated model runs: results2 <- t(as.data.frame(results)) summary(results2) Although some questions popped up in my analysis (procedure see below) : 1) it seems that I can't get an element with the usual command like results2$intercept ... that doesn't work here, why? That would be useful for plotting an element l...

...deltamethod( ~(-2)*sqrt(Q)*cos(theta/3)+x1/3,m,cov) stderrr1 <- deltamethod( ~(-2)*sqrt(Q)*cos((theta+2*pi)/3)+x1/3, m, cov) stderrr2 <- deltamethod( ~(-2)*sqrt(Q)*cos((theta-2*pi)/3)+x1/3, m, cov) stderr <- c(stderrb,stderrr1,stderrr2) results <- c(beta,rho1,rho2,stderr) results2 <- t(results) results2 } When I call the function in an IF statement like if (Q^3>R^2) results2 <- case1() else print('ciccio') I get Error in eval(expr, envir, enclos) : Object "theta" not found I do not understand why, any help? Thanks -- ===================...

...######################### acts_as_ferret :fields=> [''short_description'',...,''date_registered''] def VoObject.find_results(query,page) sort_fields = [] sort_fields << Ferret::Search::SortField.new("date_registered", :reverse => :false) results2 = VoObject.find_by_contents(query,:num_docs=> 2000000,:sort =>sort_fields ) num = results2.size if page == 1 page = 0 else page = (page-1)*20 end results = VoObject.find_by_contents(query,:first_doc=>page, :num_docs=> 20,:sort => sort_fields) [num,results,results2...

...) require(boot) set.seed(123) results1 <- boot(data= AA, statistic=boot1, R=100) (2) The alternative way by calculating "indices" myself: boot2 <- function (data,indices) { indices <- sample(data, replace=TRUE) d <- data[indices] return(d) } AA <- c(1:10) set.seed(123) results2 <- boot(data= AA, statistic=boot2, R=100) When I looked up using results1$t and results2$t, I had totoally different bootstrap samples. I found this even had great impacts on the results in my study. Does the second approach have any problem? Anyone could provide any inputs on this? Thank you v...

...ations for which x1>=x2 mydata2 <- mydata[mydata$x1>=mydata$x2,] # calculate bandwidth bandwidth2 <- npregbw(formula=y ~ x1 + x2, regtype="ll", bwmethod="cv.aic", data=mydata2) summary(bandwidth2) # perform nonparametric regression using bandwidth2 results2 <- npreg(bws=bandwidth2) summary(results2) # use plot to plot results # note that the plot is not limited to points where x1>=x2 plot(results2,view="fixed",theta=300) evaluate <- data.frame(x1=mydata2$x1>=mydata2$x2) # use exdata option to specify the set of (x1,x2) points #...

I believe this may be more related to analysis than it is to R, per se. Suppose I have the following function that I wish to integrate: ff <- function(x) pnorm((x - m)/sigma) * dnorm(x, observed, sigma) Then, given the parameters: mu <- 300 sigma <- 50 m <- 250 target <- 200 sigma_i <- 50 I can use the function integrate as: > integrate(ff, lower= -Inf, upper=target)

Hello, I have a data set which is similar to the following data mice <- rep(letters[1:4],10) outcome <- sample(c(0,1),length(mice),replace=T) group <- c(rep("A",length(mice)/2),rep("B",length(mice)/2)) my.data <- data.frame(mice,outcome,group) my.sort.data <- my.data[order(my.data[,1]),] I would like to test wether there is a different between group A and B

...That's difficult of course when the values in question are only "0" and "1"... Has anybody any ideas? Thanks a lot, Mika PS: The data I have looks something like this (of course I actually have 500 values, not only 10): results1: 0,1,1,1,0,1,1,0,1,0 accuracy: 0.6 results2: 0,0,1,1,0,0,1,1,1,0 accuracy: 0.5 -- View this message in context: http://www.nabble.com/Help-with-significance.-T-test--tp24699690p24699690.html Sent from the R help mailing list archive at Nabble.com.

Hello, I have a ferret index with 2 fields: Acts_as_ferret :fields => [:client, :content] If I do model.find_by_contents(query) I obtain all results by the query but I would like to obtain 3 results for each client. Any ideas? Thanks for all. -- Posted via http://www.ruby-forum.com/.

...d programming to me. It's needed when comparing, say, the time to convergence (if at all) of various iterative processes, for each of a few hundred simulated values. For example one wants basically function(data){ summary<-rep(NA,1000) for(i in 1:1000){ results1<-prog1(data[i]) results2<-prog2(data[i]) . . . summary[i] <- f(results1,results2,...) } summary } where prog1, say, can sometimes fail fatally, for certain data values. The *object of the exercise* is to find out what values cause program i to crash but program j to run ok. One can use immediate assigns...

...31.59 33.78 0m4 2 32.21 33.25 0m4 3 31.78 NA 0m4 4 31.34 32.05 0m5 5 31.61 32.59 0m5 6 31.61 NA 0m5 results1 <- aggregate(. ~ id ,data=myData,FUN=mean,na.rm=T) head(results1,1) # id var1 var2 # 1 0m11 30.79 32.27 library(data.table) mydt <- data.table(myData) setkey(mydt,id) results2 <- mydt[,lapply(.SD,mean,na.rm=TRUE),by=id] head(results2,1) # id var1 var2 # [1,] 0m11 30.84 32.27 library(plyr) results3 <- ddply(myData,.(id),colwise(mean),na.rm=TRUE) head(results3,1) # id var1 var2 # 1 0m11 30.84 32.27 > sessionInfo() R version 2.14.0 (2011-10-31) Pla...

I am running a simulation many times changing one parameter each time and recording the outcome. I have to produce a plot to make a sense of the bunch of numbers I get from every run. My problem is to insert a multi-line text to keep track of which result correspond to which parameter values. A legend does not work in this case because I will plot 2 variables and the corresponding experimental

Hi All, I'm still pretty new to using R - and I was hoping I might be able to get some advice as to how to use 'apply' or a similar function instead of using nested for loops. Right now I have a script which uses nested for loops similar to this: i <- 1 for(a in Alpha) { for (b in Beta) { for (c in Gamma) { for (d in Delta) { for (e in Epsilon) { Output[i] <-