similar to: Bootstrap pvalues

I have a list of dataframes i.e. each list element is a dataframe with three columns and differing number of rows. The third column takes on only two values. I wish to split the list into two sublists based on the value of the third column of the list element.  Second issue with lists as well. I would like to reduce each of the sublist based on the range of the second column, i.e. if the range of

Given a vector;> ab = seq(0.5,1, by=0.1)> ab[1] 0.5 0.6 0.7 0.8 0.9 1.0 The euclidean distance between the vector elements is given by the lower triangular matrix > dd1 = dist(ab,"euclidean")> dd1    1   2   3   4   52 0.1                3 0.2 0.1            4 0.3 0.2 0.1        5 0.4 0.3 0.2 0.1    6 0.5 0.4 0.3 0.2 0.1 Convert the lower triangular matrix to a full

I have a matrix, called data. I used the code below to rearrange the data such that the first column remains the same, but the y value falls under either columns 2, 3 or 4, depending on the value of z. If z=1 for example, then the value of y will fall under column 2, if z=2, the value of y falls under column 3, and so on. data x y z [1,] 50 13 1 [2,] 14 8 2 [3,] 3 7 3 [4,] 4 16 1 [5,] 6

I wish to rearrange the matrix, df, such that all there are not repeated x values. Particularly, for each value of x that is reated, the corresponded y value should fall under the appropriate column. For example, the x value 3 appears 4 times under the different columns of y, i.e. y1,y2,y3,y4. The output should be such that for the lone value of 3 selected for x, the corresponding row entries

Greetings, I would like to use the "boot" function to generate a bootstrap confidence interval for the slope in a SLR that has a zero intercept. My attempt to do this is shown below. Is this the correct implementation of the boot function to solve this problem? In particular, should I be doing anything with the residuals in the "bs" function (e.g., using weighted residuals)?

I wish to perform moving tiles bootstrap resampling on some gridded data meteorological data. I've many years experience with S-Plus, but it has no way to perform a moving-tiles bootstrap. Within R I've learned how to use quadratresample() with the spatstats package and would be happy to simply use empirical percentiles if generating the replicates were fast, but it isn't. So,

I have read in a file (call it myData). The actual file is about 3000x30,000 columns and object.size() says myData takes: > 737910472/(1024^2) [1] 703.7263 Unfortunately, my program ends up using 40GB as indicated by maxvmem on Unix, which causes my department's cluster to stop working. Perhaps, I have some copying going on that I cannot find. I have created an example below that mimics

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

I forgot, you should also set.seed() before calling boot() to make the results reproducible. Rui Barradas On 5/22/2018 10:00 AM, Rui Barradas wrote: > Hello, > > If you want to bootstrap a statistic, I suggest you use base package boot. > You would need the data in a data.frame, see how you could do it. > > > library(boot) > > bootMedianSE <- function(data,

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-

mat is a matrix with X and Y values. > mat X Y [1,] 56 20 [2,] 56 21 [3,] 2 50 [4,] 3 46 [5,] 18 77 [6,] 57 12 [7,] 57 36 [8,] 95 45 [9,] 65 23 [10,] 33 25 [11,] 33 98 [12,] 63 96 [13,] 66 75 [14,] 99 54 [15,] 78 65 [16,] 75 69 [17,] 54 68 [18,] 54 67 [19,] 0 22 [20,] 14 74 [21,] 15 52 [22,] 46 10 [23,] 6 20 [24,] 6 30 As you can see, some of the X values repeat. I wish

Hello, I am using R.12.2.0. I am trying to generate bootstrap confidence intervals using bootcov from the rms package. I am able to impute the missing data using aregImpute and to perform a linear regression on the imputed datasets using fit.mult.impute, but I am unable to use bootcov to generate the confidence intervals for the R-squared. Here is a small example that should duplicate the

Hello R-List I use DClusters package (I work in a cancer regestry). I have 2 questions about it: 1-how is it possible to get back the bootstrap pValue? I mean the pValue of the calculated statistic with respect of the distribution of this statistic under the null hypothesis. 2-how is it possible to test an overdispersion in the poisson model? for choosing a best model I need this mesure of

Dear R users, I have developed a model I have compared several options of obtaining p-values for poisson lmer model including Marlov chain monty carlo methods, single term deletions and summary. > > However, I encountered several problems that can be classified as > (1) the p values from the summary command are total different from those derived from Marlov chain monty carlo methods >

Hi I was wondering whether anyone can provide any help or suggestions on the boostrap method for latent class analysis please. My main question is: can the bootstrap procedure sometimes be less precise than the non-bootstrap procedure when carrying out latent class analysis? I am asking this question because I carried out some analyses and when I *did not* use the bootstrap procedure I found

Dear R community, Please, can you help me with a problem concerning bootstrap. The data table called «RMika», contained times (Tps) and corresponding concentration of a chemical in a soil (SolA). I would like to get, by bootstraping, 10 estimations of the parameters C0 and k from the function: SolA = C0*exp(-k*Tps). # First, I fit the data and all is OK >

Dear R-experts, I am trying to bootstrap (and average) the median squared error evaluation metric for a robust regression. I can't get it. What is going wrong ? Here is the reproducible example. ############################# install.packages( "quantreg" ) library(quantreg) crp <-c(12,14,13,24,25,34,45,56,25,34,47,44,35,24,53,44,55,46,36,67) bmi

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

Dear R-helpers and Klaus, I would like to know how to write out a tree file with bootstrap from phangorn package. That tree file could be in newick format or others. I am new for phylogenetic operation in R. Could you please give me any directions on that? Thanks in advance. Best wishes, Jian-Feng, ######## # as a example # I accomplished 1000 bootstrap simulation on a fit object (a maximum

I'm trying to find a bootstrap based confidence band for a linear model. I have created a data set with X and Y X=runif(n,-1.25,1.25) e=rnorm(n,0,1) Y=exp(3*X)+5*sin((30*X)/(2*pi))+2*e fit=lm(Y~X) summary(fit)   I define a bootstrap function named PairedBootstrap which is not listed here. Than I try many ways to find the confidence band. One way is to predict Y using the model I get above for