Comparison of efficiency methods to concatenate data (list, vector and data.frame)

As you can see, the use of c() to concatenate data into an object is not very efficient. It is better to create an object of the final size and replace data. However, there are no difference if the list element is replaced or the content of the list element:

For vector:

> c <- NULL
> system.time(
+ for (i in 1:100000) c <- c(c, rnorm(1, 1, 1))
+ )
utilisateur     système      écoulé 
     26.971       8.214      35.681 

> c <- rep(NA, 100000)
> system.time(
+ for (i in 1:100000) c[i] <- rnorm(1, 1, 1)
+ )
utilisateur     système      écoulé 
      0.479       0.045       0.525 

For list:

> library(embryogrowth)
> 
> c <- list()
> system.time(
+ for (i in 1:100000) c <- c(c, list(resultNest_4p))
+ )
utilisateur     système      écoulé 
     68.412       7.460      76.696 
> 
> c <- rep(list(), 100000)
> system.time(
+   for (i in 1:100000) c[i] <- list(resultNest_4p)
+ )
utilisateur     système      écoulé 
     44.764       6.975      51.899 
> 
> c <- rep(list(), 100000)
> system.time(
+   for (i in 1:100000) c[[i]] <- resultNest_4p
+ )
utilisateur     système      écoulé 
     44.902       6.975      51.996 

For data.frame

> system.time({
+   df <- data.frame(col1=numeric(), col2=character(), stringsAsFactors = FALSE);
+   
+   for (i in 1:10000) {
+     df <- rbind(df, data.frame(col1=i, col2=as.character(i)))
+   }
+ }
+ )
utilisateur     système      écoulé 
     29.227       1.607      30.886 
> system.time({
+   df <- data.frame(col1=rep(NA, 10000), col2=rep(NA, 10000), stringsAsFactors = FALSE);
+   
+   for (i in 1:10000) {
+     df[i, "col1"] <- i
+     df[i, "col2"]  <- as.character(i)
+   }
+ }
+ )
utilisateur     système      écoulé 
      1.822       0.253       2.080 

Commentaires

Enregistrer un commentaire

Posts les plus consultés de ce blog

Standard error from Hessian Matrix... what can be done when problem occurs

An Hessian matrix is a square matrix of partial second order derivative. From the square-root of the inverse of the diagonal, it is possible to estimate the standard error of parameters. Let take an example: val=rnorm(100, mean=20, sd=5) # Return -ln L of values in val in Gaussian distribution with mean and sd in par fitnorm<-function(par, val) {   -sum(dnorm(val, par["mean"], par["sd"], log = TRUE)) } # Initial values for search p<-c(mean=20, sd=5) # fit the model result <- optim(par=p, fn=fitnorm, val=val, method="BFGS", hessian=TRUE) # Inverse the hessian matrix to get SE for each parameters mathessian <- result$hessian inversemathessian <- solve(mathessian) res <- sqrt(diag(inversemathessian)) # results data.frame(Mean=result$par, SE=res)   library(MASS) (r<-fitdistr(val, "normal")) It works well. However, the inverse of the matrix cannot be calculated if the second order derivative for some paramete
Lire la suite

stepAIC from package MASS with AICc

Due to a bug in dropterm() and addterm() in package MASS, it is impossible to use AICc with stepAIC() . Here is a solution. Enter these commands and now you can use: stepAIC(g0, criteria="AICc") or stepAIC(g0, criteria="BIC") For example: datax <- data.frame(y=rnorm(100), x1=rnorm(100),                     x2=rnorm(100), x3=rnorm(100), x4=rnorm(100)) g0 <- glm(y ~ x1+x2+x3+x4, data=datax) gx_AICc <- stepAIC(g0, criteria="AICc") gx_AIC <- stepAIC(g0, criteria="AIC") gx_AIC <- stepAIC(g0, criteria="BIC")
Lire la suite

Install treemix in ubuntu 20.04

Informations are available here  https://bitbucket.org/nygcresearch/treemix/downloads/ In terminal, enter for  Ubuntu 20.04: wget https://bitbucket.org/nygcresearch/treemix/downloads/treemix-1.13.tar.gz tar -xvf treemix-1.13.tar.gz sudo apt-get install libgsl-dev sudo apt install libboost-dev sudo apt install libboost-all-dev cd treemix-1.13/ ./configure make sudo make install And it works: $ treemix TreeMix v. 1.13 $Revision: 231 $ Options: -h display this help -i [file name] input file -o [stem] output stem (will be [stem].treeout.gz, [stem].cov.gz, [stem].modelcov.gz) -k [int] number of SNPs per block for estimation of covariance matrix (1) -global Do a round of global rearrangements after adding all populations -tf [file name] Read the tree topology from a file, rather than estimating it -m [int] number of migration edges to add (0) -root [string] comma-delimited list of populations to set on one side of the root (for migration) -g [vertices file name] [edges file name] read the g
Lire la suite