I am attempting to combine a series of loops/functions into one all-encompassing function to then be able to see the result for different input values. While the steps work properly when standalone (and when given just one input), I am having trouble getting the overall function to work. The answer I am getting back is a vector of 1s, which is incorrect.
The goal is to count the number of occurrences of consecutive zeroes in the randomly generated results, and then to see how the probability of consecutive zeroes occurring changes as I change the initial percentage input provided.
Does anyone have a tip for what I'm doing wrong? I have stared at this at several separate points now but cannot figure out where I'm going wrong. Thanks for your help.
### Example
pctgs_seq=seq(0.8,1,.01)
occurs=20
iterations=10
iterate_pctgs=function(x) {
probs=rep(0,length(pctgs_seq))
for (i in 1:length(pctgs_seq)) {
all_sims=lapply(1:iterations, function (x) ifelse(runif(occurs) <= i, 1, 0))
totals=sapply(all_sims,sum)
consec_zeroes=function (x) {
g=0
for (i in 1:(length(x)-1))
{ g= g+ifelse(x[i]+x[i+1]==0,1,0) }
return (g) }
consec_zeroes_sim=sapply(all_sims,consec_zeroes)
no_consec_prob=sum(consec_zeroes_sim==0)/length(consec_zeroes_sim)
probs[i]=no_consec_prob }
return (probs)
}
answer=iterate_pctgs(pctgs_seq)
Related
Suppose we have given dataframe in R. By 0--7, it means it is taking integer values from 0-7 i.e. 0,1,2,3,4,5,6,7.
I am interested in making a function such that
If a[1,1]>alpha, it goes and checks its children i.e. 0--7 consists of a[1,2] and a[2,2].
So,
{a[2,1]>alpha
{a[4,1]>alpha
{a[5,1]>alpha
ps=list.append(0)
else ps=list.append(1)
}}}
Here, alpha is a a threshold. The ps is appended from values of 0 to 15 based on this criteria.
My code is
{for (i in 1:2)
{ if (a[j,i]>alpha)
{if (i%%2==1}
{j=j*2
if (a[j,i]>alpha
###here i want to go recursively i think and where and how should i add append values to the list
if a[j,i+1]>alpha}
if{i%%2==0}
{}
}}
I am stuck and confused at the same time. Any help or advices would be greatly appreciated.
Thanks
I'm fairly new to R and I have not been working with functions in R before.
I want to write a program/algorithm (using R) that calculates the square root of a given positive number.
Would anyone mind take the time to give me an example of how this can be achieved?
Thanks a lot in advance!
UPDATE
posNum_to_squaRtNum <- function(posNum) {
if (posNum <= 0)
print("Due to mathmatical principles you have to input a positive number")
else
squaRtNum <- sqrt(posNum)
return(squaRtNum)
}
When I insert a negative number in the function, the output is my print PLUS the error: "Error in posNum_to_squaRtNum(-1) : object 'squaRtNum' not found." It should not go on to the else statement, if the if statement is fulfilled right?
You should wrap your if conditions in brackets:
posNum_to_squaRtNum <- function(posNum) {
if (posNum <= 0) {
print("Due to mathmatical principles you have to input a positive number")
} else {
squaRtNum <- sqrt(posNum)
return(squaRtNum)
}
}
I created a function that adds the square of numbers in vectors, but I can't get the correct result when one of the variables is NA. How can I make this work ? Thank you already
sos=function(){
example=c(1,2,3,4,5,6,7,8,9,NA)
newExample=c()
for (i in 1:10){
count=0
count=count+example[i]^2
newExample=append(newExample,count)
}
print(sum(newExample))
}
I am trying to implement following algorithm in R:
Iterate(Cell: top)
While (top != null)
Print top.Value
top = top.Next
End While
End Iterate
Basically, given a list, the algorithm should break as soon as it hits 'null' even when the list is not over.
myls<-list('africa','america south','asia','antarctica','australasia',NULL,'europe','america north')
I had to add a for loop for using is.null() function, but following code is disaster and I need your help to fix it.
Cell <- function(top) {
#This algorithm examines every cell in the linked list, so if the list contains N cells,
#it has run time O(N).
for (i in 1:length(top)){
while(is.null(top[[i]]) !=TRUE){
print(top)
top = next(top)
}
}
}
You may run this function using:
Cell(myls)
You were close but there is no need to use for(...) in this
construction.
Cell <- function(top){
i = 1
while(i <= length(top) && !is.null(top[[i]])){
print(top[[i]])
i = i + 1
}
}
As you see I've added one extra condition to the while loop: i <= length(top) this is to make sure you don't go beyond the length of the
list in case there no null items.
However you can use a for loop with this construction:
Cell <- function(top){
for(i in 1:length(top)){
if(is.null(top[[i]])) break
print(top[[i]])
}
}
Alternatively you can use this code without a for/while construction:
myls[1:(which(sapply(myls, is.null))[1]-1)]
Check this out: It runs one by one for all the values in myls and prints them but If it encounters NULL value it breaks.
for (val in myls) {
if (is.null(val)){
break
}
print(val)
}
Let me know in case of any query.
I hope everyone is well; I have a question it is may be looked as a dumb one but I really need someone to explain it for me. I also though it will be useful for some, since it has been asked before with no satisfactory answer.
Since , I have mixed data type matrix, I was looking for K-nearst neighbors algorithem that works with gower distance in R. I found the function Knngow under the package dprep that claims to perform this.
http://finzi.psych.upenn.edu/library/dprep/html/knngow.html
The function take three argument knngow( Training_Set, Testing_set, K_number) and return the predicted class.
I was playing around with it and was wondering how the function can recognize what is my target vector? Put differently, how does it return the predicted class, without me acknowledging it in advance with my target column.
please find the source code below ( I retrieved it using the function edit)
function (train, test, k)
{
p = dim(train)[2]
ntest = dim(test)[1]
ntrain = dim(train)[1]
classes = rep(0, ntest)
if (ntest == ntrain) {
for (i in 1:ntest) {
tempo = order(gower.dist(test[i, -p], train[-i,
-p]))[1:k]
classes[i] = moda(train[tempo, p])[1]
}
}
else {
for (i in 1:ntest) {
tempo = order(StatMatch::gower.dist(test[i, -p],
train[, -p]))[1:k]
classes[i] = moda(train[tempo, p])[1]
}
}
classes
}
please can someone explain for me the code?
I hope I have post the question in the correct form, please let me know if I have to move it to somewhere else.
Thank you very much for your time.
knngow function takes the last column of the train as the target attribute. Also p = dim(train)[2]) indicates your column number.
Column p (the last column of your training data) is not used for calculating Gower dist. It is only taken into account when it comes to predict the class label of test samples.