First stack exchange post so please bear with me. I'm trying to automate the creation of a list, and the list will be made up of many empty vectors of various, known lengths. The empty vectors will then be filled with simulated data. How can I automate creation of this list using a for loop in R?
In this simplified example, fish have been caught by casting a net 4 times, and their abundance is given in the vector "abundance" (from counting the number of total fish in each net). We don't have individual fish weights, just the mean weight of all fish each net, so I need to simulate their weights from a lognormal distribution. So, I'm then looking to fill those empty vectors for each net, each with a length equal to the number of fish caught in that net, with weight data simulated from a lognormal distribution with a known mean and standard deviation.
A simplified example of my code:
abundance <- c(5, 10, 9, 20)
net1 <- rep(NA, abundance[1])
net2 <- rep(NA, abundance[2])
net3 <- rep(NA, abundance[3])
net4 <- rep(NA, abundance[4])
simulated_weights <- list(net1, net2, net3, net4)
#meanlog vector for each net
weight_per_net
#meansd vector for each net
sd_per_net
for (i in 1:4) {
simulated_weights[[i]] <- rlnorm(n = abundance[i], meanlog = weight_per_net[i], sd = sd_per_net[i])
print(simulated_weights_VM)
}
Could anyone please help me automate this so that I don't have to write out each net vector (e.g. net1) by hand, and then also write out all the net names in the list() function? There are far more nets than 4 so it would be extremely time consuming and inefficient to do it this way. I've tried several things from other posts like paste0(), other for loops, as.list(c()), all to no avail.
Thanks!
HM
Turns out you don't need the net1, net2, etc variables at all. You can just do
abundance <- c(5, 10, 9, 20)
simulated_weights <- lapply(abundance, function(x) rep(NA, x))
The lapply function will return the list you need by calling the function once for each value of abundance
We could create the 'simulated_weights' with split and rep
simulated_weights <- split(rep(rep(NA, length(abundance)), abundance),
rep(seq_along(abundance), abundance))
Related
I am here to ask you about R language and how to construct a loop to iterate some functions several times.
Here is my problem: I have a numeric matrix obtained from previous analyses (matrix1) that I want to compare (using the overlap function that results in a single value) with another numeric matrix that I get by extracting values of a given raster with a set of randomly created points, as many as the values in the first numeric matrix.
I want to repeat the random sampling procedure 1'000 times, in order to get 1'000 different sets of random points, then repeat the comparison with matrix1 1'000 times (one for each set of random points), and, in the end, calculate the mean of the results to get a single value.
Hereafter I give you an example of the functions I want to use:
#matrix1 is the first matrix, obtained before starting the potential loop;
#LineVector is a polyline shapefile that has to be used within the loop and downloaded before it;
#Raster is a raster from which I should extract values at points location;
#The loop should start from here:
Random_points <- st_sample(LineVector, size = 2000, exact = TRUE, type = "random")
Random_points <- Random_points[!st_is_empty(Random_points)]
Random_points_vect <- vect(Random_points)
Random_values <- terra::extract(Raster, Random_points_vect, ID = F, raw = T)
Random_values <- na.omit(Random_values[, c("Capriolo")])
Values_list <- list(matrix1, Random_values)
Overlapping_value <- overlap(Values_list, type = "2")
#This value, obtained 1'000 times, has then to be averaged into a single number.
I hope I have posed my question in a clear and understandable manner, and I hope you can help me with this problem.
Thanks to everyone in advance, I wish you a good day!
Easy way i can figure out is to use "replicate":
values <- replicate(1000, {
Random_points <- st_sample(LineVector, size = 2000, exact = TRUE, type = "random")
Random_points <- Random_points[!st_is_empty(Random_points)]
Random_points_vect <- vect(Random_points)
Random_values <- terra::extract(Raster, Random_points_vect, ID = F, raw = T)
Random_values <- na.omit(Random_values[, c("Capriolo")])
Values_list <- list(matrix1, Random_values)
Overlapping_value <- overlap(Values_list, type = "2")
Overlapping_value
})
mean(values)
I am performing a PLS-DA analysis in R using the mixOmics package. I have one binary Y variable (presence or absence of wetland) and 21 continuous predictor variables (X) with values ranging from 1 to 100.
I have made the model with the data_training dataset and want to predict new outcomes with the data_validation dataset. These datasets have exactly the same structure.
My code looks like:
library(mixOmics)
model.plsda<-plsda(X,Y, ncomp = 10)
myPredictions <- predict(model.plsda, newdata = data_validation[,-1], dist = "max.dist")
I want to predict the outcome based on 10, 9, 8, ... to 2 principal components. By using the get.confusion_matrix function, I want to estimate the error rate for every number of principal components.
prediction <- myPredictions$class$max.dist[,10] #prediction based on 10 components
confusion.mat = get.confusion_matrix(truth = data_validatie[,1], predicted = prediction)
get.BER(confusion.mat)
I can do this seperately for 10 times, but I want do that a little faster. Therefore I was thinking of making a list with the results of prediction for every number of components...
library(BBmisc)
prediction_test <- myPredictions$class$max.dist
predictions_components <- convertColsToList(prediction_test, name.list = T, name.vector = T, factors.as.char = T)
...and then using lapply with the get.confusion_matrix and get.BER function. But then I don't know how to do that. I have searched on the internet, but I can't find a solution that works. How can I do this?
Many thanks for your help!
Without reproducible there is no way to test this but you need to convert the code you want to run each time into a function. Something like this:
confmat <- function(x) {
prediction <- myPredictions$class$max.dist[,x] #prediction based on 10 components
confusion.mat = get.confusion_matrix(truth = data_validatie[,1], predicted = prediction)
get.BER(confusion.mat)
}
Now lapply:
results <- lapply(10:2, confmat)
That will return a list with the get.BER results for each number of PCs so results[[1]] will be the results for 10 PCs. You will not get values for prediction or confusionmat unless they are included in the results returned by get.BER. If you want all of that, you need to replace the last line to the function with return(list(prediction, confusionmat, get.BER(confusion.mat)). This will produce a list of the lists so that results[[1]][[1]] will be the results of prediction for 10 PCs and results[[1]][[2]] and results[[1]][[3]] will be confusionmat and get.BER(confusion.mat) respectively.
For a paper I'm writing I have subsetted a larger dataset into 3 groups, because I thought the strength of correlations between 2 variables in those groups would differ (they did). I want to see if subsetting my data into random groupings would also significantly affect the strength of correlations (i.e., whether what I'm seeing is just an effect of subsetting, or if those groupings are actually significant).
To this end, I am trying to generate n new data frames by randomly sampling 150 rows from an existing dataset, and then want to calculate correlation coefficients for two variables in those n new data frames, saving the correlation coefficient and significance in a new file.
But, HOW?
I can do it manually, e.g., with dplyr, something like
newdata <- sample_n(Random_sample_data, 150)
output <- cor.test(newdata$x, newdata$y, method="kendall")
I'd obviously like to not type this out 1000 or 100000 times, and have been trying things with loops and lapply (see below) but they've not worked (undoubtedly due to something really obvious that I'm missing!).
Here I have tried to assign each row to a different group, with 10 groups in total, and then to do correlations between x and y by those groups:
Random_sample_data<-select(Range_corrected, x, y)
cat <- sample(1:10, 1229, replace=TRUE)
Random_sample_cats<-cbind(Random_sample_data,cat)
correlation <- function(c) {
c <- cor.test(x,y, method="kendall")
return(c)
}
b<- daply(Random_sample_cats, .(cat), correlation)
Error message:
Error in cor.test(x, y, method = "kendall") :
object 'x' not found
Once you have the code for what you want to do once, you can put it in replicate to do it n times. Here's a reproducible example on built-in data
result = replicate(n = 10, expr = {
newdata <- sample_n(mtcars, 10)
output <- cor.test(newdata$wt, newdata$qsec, method="kendall")
})
replicate will save the result of the last line of what you did (output <- ...) for each replication. It will attempt to simplify the result, in this case cor.test returns a list of length 8, so replicate will simplify the results to a matrix with 8 rows and 10 columns (1 column per replication).
You may want to clean up the results a little bit so that, e.g., you only save the p-value. Here, we store only the p-value, so the result is a vector with one p-value per replication, not a matrix:
result = replicate(n = 10, expr = {
newdata <- sample_n(mtcars, 10)
cor.test(newdata$wt, newdata$qsec, method="kendall")$p.value
})
As someone relatively new to R I'm having an issue with creating a for loop.
I have a very large data set with 9000 observations and 25 categorical variables, which I've transformed into binary data and preformed hierarchical clustering. Now I want to try K-Modes clustering to produce an Elbow Plot using the "within-cluster simple-matching distance for each cluster", which is outputted from kmodes$withindiff. I can sum this for each of the k in 1:8 clusters to get the Elbow Plot.
library(klaR)
for(k in 1:8)
{
WCSM[k] <- sum(kmodes(data,k,iter.max=100)$withindiff)
}
plot(1:8,WCSM,type="b", xlab="Number of Clusters",ylab="Within-Cluster
Simple-Matching Distance Summed", main="K-modes Elbow Plot")
My issue is that I want further output from k-modes. For each k in 1:8 I would like to get the vector of integers indicating the cluster to which each object is allocated to given by kmodes$cluster. I need to create a for loop that loops through each k in 1:8 and saves each of the outputs into 8 separate vectors. But I don't know how to do such a for loop. I could just run the 8 lines of code separately but they each take 15mins to run with iter.max=10 so increasing this to iter.max=100 will need to be left running overnight so a loop would be useful.
cl.kmodes2=kmodes(data, 2,iter.max=100)
cl.kmodes3=kmodes(data, 3,iter.max=100)
cl.kmodes4=kmodes(data, 4,iter.max=100)
cl.kmodes5=kmodes(data, 5,iter.max=100)
cl.kmodes6=kmodes(data, 6,iter.max=100)
cl.kmodes7=kmodes(data, 7,iter.max=100)
cl.kmodes8=kmodes(data, 8,iter.max=100)
Ultimately I want to compare the results from the hierarchical binary clustering to the k-modes clustering by getting the Adjusted Rand Index. For example, cutting the tree at k=4 for the hierarchical cluster and comparing this to a 4 cluster solution from k-modes:
dist.binary = dist(data, method="binary")
cl.binary = hclust(dist.binary, method="complete")
hcl.4 = cutree(cl.binary, k = 4)
tab = table(hcl.4, cl.kmodes4$cluster)
library(e1071)
classAgreement(tab)
I agree with Imo, using a list is the best solution.
If you don't want to do that, you could also use assign() to create a new vector in every iteration:
library(klaR)
for(k in 1:8) {
assign(paste("cl.kmodes", k, sep = ""), kmodes(data, k, iter.max = 100))
}
The best method is to put the output from your clusters into a named list:
library(klaR)
myClusterList <- list()
for(k in 1:8) {
myClusterList[[paste0("k.", i)]] <- kmodes(data, i,iter.max=100)
}
You can then pull out the any of the contents easily:
sum(myClusterList[["k.1"]]$withindiff)
or
sum(myClusterList[[1]]$withindiff)
You can also save the list to use in future R sessions, see ?save.
I am trying to implement genetic algorithm in R. I found out that r has 'GA' and 'genalg' packages for genetic algorithm implementation. I encountered the example i the link http://www.r-bloggers.com/genetic-algorithms-a-simple-r-example/. They tried solving the Knapsack problem. The problem can be briefly explained as:
"You are going to spend a month in the wilderness. You’re taking a backpack with you, however, the maximum weight it can carry is 20 kilograms. You have a number of survival items available, each with its own number of 'survival points'. You’re objective is to maximize the number of survival points"
The problem is easily solved using 'genalg' package for a single person and the output is binary string. Now i have a doubt, lets say instead of one person there are 2 or more i.e multiple persons and we need to distribute the survival points. The weight constraints apply for each person. Then how can we solve this problem? Can we use 'genalg' or 'GA' package? If so how can we apply them? Are there any examples on this that are solved in R or other software's?
Thanks
The R package adagio (https://cran.r-project.org/web/packages/adagio/index.html) comes with two functions (knapsack and mknapsack) which solves this type of problem more efficient by dynamic programming.
A simple approach could be to have one chromosome containing all individuals in the group and have the evaluation function split this chromosome in multiple parts, one for each individual and then have these parts evaluated. In the example below (based on the example in the question) I have assumed each individual has the same weight limit and multiple individuals can bring the same item.
library(genalg)
#Set up the problem parameters
#how many people in the group
individual_count <-3
#The weight limit for one individual
weightlimit <- 20
#The items with their survivalpoints
dataset <- data.frame(item = c("pocketknife", "beans", "potatoes", "unions",
"sleeping bag", "rope", "compass"), survivalpoints = c(10, 20, 15, 2, 30,
10, 30), weight = c(1, 5, 10, 1, 7, 5, 1))
#Next, we choose the number of iterations, design and run the model.
iter <- 100
#Our chromosome has to be large enough to contain a bit for all individuals and for all items in the dataset
chromosomesize <- individual_count * nrow(dataset)
#Function definitions
#A function to split vector X in N equal parts
split_vector <- function(x,n) split(x, cut(seq_along(x), n, labels = FALSE))
#EValuate an individual (a part of the chromosome)
evalIndividual <- function(x) {
current_solution_survivalpoints <- x %*% dataset$survivalpoints
current_solution_weight <- x %*% dataset$weight
if (current_solution_weight > weightlimit)
return(0) else return(-current_solution_survivalpoints)
}
#Evaluate a chromosome
evalFunc <- function(x) {
#First split the chromosome in a list of individuals, then we can evaluate all individuals
individuals<-split_vector(x,individual_count)
#now we need to sapply the evalIndividual function to each element of individuals
return(sum(sapply(individuals,evalIndividual)))
}
#Run the Genetic Algorithm
GAmodel <- rbga.bin(size = chromosomesize, popSize = 200, iters = iter, mutationChance = 0.01,
elitism = T, evalFunc = evalFunc)
#First show a summary
summary(GAmodel,echo=TRUE)
#Then extract the best solution from the GAmodel, copy/paste from the source code of the summary function
filter = GAmodel$evaluations == min(GAmodel$evaluations)
bestSolution = GAmodel$population[filter, , drop= FALSE][1,]
#Now split the solution in the individuals.
split_vector(bestSolution,individual_count)