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))
}
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 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)
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 got warnings when running this code.
For example, when I put
tm1<- summary(tmfit)[c(4,8,9)],
I can get the result, but I need to run this code for each $i$.
Why do I get this error?
Is there any way to do this instead of via a for loop?
Specifically, I have many regressants ($y$) with the same two regressors ($x$'s).
How I can get these results of regression analysis(to make some comparisons)?
dreg=read.csv("dayreg.csv")
fundr=read.csv("fundreturnday.csv")
num=ncol(fundr)
exr=dreg[,2]
tm=dreg[,4]
for(i in 2:num)
{
tmfit=lm(fundr[,i]~exr+tm)
tm1[i]<- summary(tmfit)[c(4,8,9)]
}
Any help is highly appreciated
Try storing your result into a list instead of a vector.
dreg=read.csv("dayreg.csv")
fundr=read.csv("fundreturnday.csv")
num=ncol(fundr)
exr=dreg[,2]
tm = list()
for(i in 2:num)
{
tmfit=lm(fundr[,i]~exr+tm)
tm1[[i]]<- summary(tmfit)[c(4,8,9)]
}
You can look at an element in the list like so
tm1[[2]]
I am trying to write a function that reads a vector of numbers element wise and then storing them into a container. This is meant as a practice before I code something with if conditions.
My approach has failed so far, as the function returns a null statement, instead of what I wanted. I tried writing it in script form and it it worked, but somehow it malfunctioned when written as a function.
Here's the code I used.
amieven<-function(x){
flag<-numeric()
for(i in 1:length(x)){
flag[i]=x[i]
}
}
The script version that worked fine looks like this:
flag<-numeric()
for (i in 1:length(x))
flag[i]=x[i]
Assuming your goal is to return the container called flag, then you simply need to specify flag as the return value.
amieven<-function(x){
flag<-numeric()
for(i in 1:length(x)){
flag[i]=x[i]
}
return(flag)
}
or simply
amieven<-function(x){
flag<-numeric()
for(i in 1:length(x)){
flag[i]=x[i]
}
flag
}