This is my code:
#Start of my Code#
test1<-function(c,x){
high=0
low=0
samp=NULL
samp=sample(c,x)
for(i in 1:x){
if(samp[i]>1){high=high+1}
else if (samp[i]<0){low=low+1}}
c(high,low,mean(samp),var(samp),samp)
}
sim1 <-function(c,x){
replicate(nsim,{test1(c,x)})}
size=10
a<-sim1(overall,size)
listnormwor=NULL
countnormwor=0
meannormwor=NULL
for(i in 0:nsim-1){
**if (a[1+(size+4)*i]+a[2+(size+4)*i]==0)**{
countnormwor=countnormwor +1
for (z in 5:(size+4)){
listnormwor=c(listnormwor, a[z+(size+4)*i])}
meannormwor=c(meannormwor,a[3+(size+4)*i])}
}
countnormwor
mean(meannormwor)
var(listnormwor)
Simply, I want to say if there are no outliers (indicated as '0' in first and second value of every 14 data points), count it into a normal bucket and keep its values to calculate variance and mean later.
But the problem is that, it generates all values from a and at the very end, it provides actual values I want.
For example, it must satisfy length(listnormwor) = 10 * countnormwor
But it gives me a ridiculous amount of data and when I play around with the if statement, it says "missing value where TRUE/FALSE needed."
I'd suggest stepping through the code (sending each line to the interpreter) one line at a time. Inspect the value of variables by calling them in the interpreter. I bet this will lead you to the source of your problem. To start, create the values x and c inside the function then work from there. Instead of running the for loop, create your own index variable i. Again, the point is to work line by line and carefully check your expectations against the values that variables take at each point.
Related
I'm trying to create a list of conjoint cards using R.
I have followed the professor's introduction, with my own dataset, but I'm stuck with this issue, which I have no idea.
library(conjoint)
experiment<-expand.grid(
ServiceRange = c("RA", "Active", "Passive","Basic"),
IdentProce = c("high", "mid", "low"),
Fee = c(1000,500,100),
Firm = c("KorFin","KorComp","KorStrt", "ForComp")
)
print(experiment)
design=caFactorialDesign(data=experiment, type="orthogonal")
print(design)
at the "design" line, I'm keep getting the following error message:
Error in optFederov(~., data, nTrials = i, approximate = FALSE, nRepeats = 50) :
nTrials must not be greater than the number of rows in data
How do I address this issue?
You're getting this error because you have 144 rows in experiment, but the nTrials mentioned in the error gets bigger than 144. This causes an error for optFederov(), which is called inside caFactorialDesign(). The problem stems from the fact that your Fee column has relatively large values.
I'm not familiar with how the conjoint package is set up, but I can show you how to troubleshoot this error. You can read the conjoint documentation for more on how to select appropriate experimental data.
(Note that the example data in the documentation always has very low numeric values, usually values between 1-10. Compare that with your Fee vector, which has values up to 1000.)
You can see the source code for a function loaded into your RStudio namespace by highlighting the function name (e.g. caFactorialDesign) and hitting Command-Return (on a Mac - probably something similar on PC). You can also just look at the source code on GitHub.
The caFactorialDesign is implemented here. That link highlights the line (26) that is throwing the error for you:
temp.design<-optFederov(~., data, nTrials=i, approximate=FALSE, nRepeats=50)
Recall the error message:
nTrials must not be greater than the number of rows in data
You've passed in experiment as the data parameter, so nrow(experiment) will tell us what the upper limit on nTrials is:
nrow(experiment) # 144
We can actually just think of the error for this dataset as:
nTrials must not be greater than 144
Ok, so how is the value for nTrials determined? We can see nTrials is actually an argument to optFederov(), and its value is set as i - often a sign that there's a for-loop wrapping an operation. And in fact, that's what we see:
for (i in ca.number: profiles.number)
{
temp.design<-optFederov(~., data, nTrials=i, approximate=FALSE, nRepeats=50)
...
}
This tells us that optFederov() is going to get called for each value of i in the loop, which will start at ca.number and will go up to profiles.number (inclusive).
How are these two variables assigned? If we look a little higher up in the caFactorialDesign() definition, ca.number is defined on lines 5-9:
num <- data.frame(data.matrix(data))
vars.number<-length(num)
levels.number<-0
for (i in 1:length(num)) levels.number<-levels.number+max(num[i])
ca.number<-levels.number-vars.number+1
You can run these calculations outside of the function - just remember that data == experiment. So just change that first line to num <- data.frame(data.matrix(experiment)), and then run that chunk of code. You can see that ca.number == 1008!!
In other words, the very first value of i in the for-loop which calls optFederov() is already way bigger than the max limit: 1008 >> 144.
It's possible you can include these numeric values as factors or strings in your definition of experiment - I'm not sure if that is an appropriate way to do this analysis. But I hope it's clear that you won't be able to use such large values in caFactorialDesign(), unless you have a much larger number of total observations in your data.
I'm creating a Monte Carlo model using R. My model creates matrices that are filled with either zeros or values that fall within the constraints. I'm running a couple hundred thousand n values thru my model, and I want to find the average of the non zero matrices that I've created. I'm guessing I can do something in the last section.
Thanks for the help!
Code:
n<-252500
PaidLoss_1<-numeric(n)
PaidLoss_2<-numeric(n)
PaidLoss_3<-numeric(n)
PaidLoss_4<-numeric(n)
PaidLoss_5<-numeric(n)
PaidLoss_6<-numeric(n)
PaidLoss_7<-numeric(n)
PaidLoss_8<-numeric(n)
PaidLoss_9<-numeric(n)
for(i in 1:n){
claim_type<-rmultinom(1,1,c(0.00166439057698873, 0.000810856947763742, 0.00183509730283373, 0.000725503584841243, 0.00405428473881871, 0.00725503584841243, 0.0100290201433936, 0.00529190850119495, 0.0103277569136224, 0.0096449300102424, 0.00375554796858996, 0.00806589279617617, 0.00776715602594742, 0.000768180266302492, 0.00405428473881871, 0.00226186411744623, 0.00354216456128371, 0.00277398429498122, 0.000682826903379993))
claim_type<-which(claim_type==1)
claim_Amanda<-runif(1, min=34115, max=2158707.51)
claim_Bob<-runif(1, min=16443, max=413150.50)
claim_Claire<-runif(1, min=30607.50, max=1341330.97)
claim_Doug<-runif(1, min=17554.20, max=969871)
if(claim_type==1){PaidLoss_1[i]<-1*claim_Amanda}
if(claim_type==2){PaidLoss_2[i]<-0*claim_Amanda}
if(claim_type==3){PaidLoss_3[i]<-1* claim_Bob}
if(claim_type==4){PaidLoss_4[i]<-0* claim_Bob}
if(claim_type==5){PaidLoss_5[i]<-1* claim_Claire}
if(claim_type==6){PaidLoss_6[i]<-0* claim_Claire}
}
PaidLoss1<-sum(PaidLoss_1)/2525
PaidLoss3<-sum(PaidLoss_3)/2525
PaidLoss5<-sum(PaidLoss_5)/2525
PaidLoss7<-sum(PaidLoss_7)/2525
partial output of my numeric matrix
First, let me make sure I've wrapped my head around what you want to do: you have several columns -- in your example, PaidLoss_1, ..., PaidLoss_9, which have many entries. Some of these entries are 0, and you'd like to take the average (within each column) of the entries that are not zero. Did I get that right?
If so:
Comment 1: At the very end of your code, you might want to avoid using sum and dividing by a number to get the mean you want. It obviously works, but it opens you up to a risk: if you ever change the value of n at the top, then in the best case scenario you have to edit several lines down below, and in the worst case scenario you forget to do that. So, I'd suggest something more like mean(PaidLoss_1) to get your mean.
Right now, you have n as 252500, and your denominator at the end is 2525, which has the effect of inflating your mean by a factor of 100. Maybe that's what you wanted; if so, I'd recommend mean(PaidLoss_1) * 100 for the same reasons as above.
Comment 2: You can do what you want via subsetting. Take a smaller example as a demonstration:
test <- c(10, 0, 10, 0, 10, 0)
mean(test) # gives 5
test!=0 # a vector of TRUE/FALSE for which are nonzero
test[test!=0] # the subset of test which we found to be nonzero
mean(test[test!=0]) # gives 10, the average of the nonzero entries
The middle three lines are just for demonstration; the only necessary lines to do what you want are the first (to declare the vector) and the last (to get the mean). So your code should be something like PaidLoss1 <- mean(PaidLoss_1[PaidLoss_1 != 0]), or perhaps that times 100.
Comment 3: You might consider organizing your stuff into a dataframe. Instead of typing PaidLoss_1, PaidLoss_2, etc., it might make sense to organize all this PaidLoss stuff into a matrix. You could then access elements of the matrix with [ , ] indexing. This would be useful because it would clean up some of the code and prevent you from having to type lots of things; you could also then make use of things like the apply() family of functions to save you from having to type the same commands over and over for different columns (such as the mean). You could also use a dataframe or something else to organize it, but having some structure would make your life easier.
(And to be super clear, your code is exactly what my code looked like when I first started writing in R. You can decide if it's worth pursuing some of that optimization; it probably just depends how much time you plan to eventually spend in R.)
I scripted a simple for-loop to iterate over each row of a data set to calculate the distance between two coordinates. The code uses the 'geosphere' package and the 'distm' function which takes two sets of coordinates and returns the distance in meters (which I convert to miles by multiplying by 0.00062137).
Here is my loop:
##For loop to find distance in miles for each coordinate pair
miles <- 0
for (i in i:3303) {
miles[i] <- distm(x = c(clean.zips[i,4], clean.zips[i,3]), y = c(clean.zips[i,7], clean.zips[i,6]))[,1] * 0.00062137
}
However, when I run it I receive an error:
Error: object 'i' not found
The thing is, I've run this code before and it worked. Other times, I get this error. I'm not changing any code, it just seems to randomly work only some of the times. I feel the loop must be constructed correctly if it does what I want on occasion, but why would it only work sometimes?
OK, I'm not certain what justifies the down votes on this, but guess I apologize to whomever thought that necessary.
The issue seems to have just been starting the indexing with an actual numeric value like Zheyuan suggested (i.e. using '1:3303' rather than 'i:3303'). I feel like I've created loops before using 'i in i:xxx' without first defining 'i' but maybe not. Anyway, it's solved and thank you!
I want to run the following regressions, the variable which has the problem is EP, is a dummy variable and I must to check different cases, z (lenght=1000) is the threshold variable. Ι want to crate 1000 different variables of EP from z variable and save the coefficients. I use a loop in loop but the results are completely wrong.The code runs properly and does not make an error. The square brackets and parentheses are the code I run. The problem is that there is a huge delay and the results after two hours still running.
I reduced the sample by 99% and again I did not get a result, the code ran without problem .
I do not want anything special, just for each value of z to run a different regression and end up to stored the estimates. I can not understand why take so long. Any idea?
for (k in 1:1000){
z<-u[k]
for (i in 1:length(dS)){
if (dS[i]>=z) {
EP[i]=1
} else {
EP[i]=0
}
fitT <- dynlm(dR ~ L(dR,1)+L(EN)+L(EP)+L(ΚΜ,1)
prob[[k]] <- summary(fitT)$coefficients[1, 2]
}
You don't have a closing } for the i-loop; you also don't have a closing ) for dynlm.
Note, you can really replace your i-loop by
EP <- as.integer(dS >= z)
Next time when asking question, be clear and specific. What do you mean by "I use a loop in loop but the results are completely wrong"? Error message, etc?
I'm trying to figure out how to observe just one particular section of the data in the graph below (e.g. 5pm onwards). I know there are basically two methods of doing this:
1) Method 1: Limiting the window size, which requires the following function:
< symbols(Data$Times, Data$y, circles=Data$z, xlim=c("5:00pm","10:00pm"))
The problem is, I get an "invalid 'xlim' value" error when I try to input the two time endpoints.
2) Method 2: Clearing out the rows in Data$Times that have values over 5pm.
The problem here is that I'm not sure how to sort the rows by earliest time -> latest time OR how to define a new variable such that TimesPM <- Data$Times>"5pm" (what I typed just now obviously did not work.)
Any ideas? Thanks in advance.
ETA: This is what I plotted:
Times<-strptime(DATA$Time,format="%I:%M%p")
symbols(Times, y, circles=z, xaxt='n', inches=.4, fg="3", bg=(a), xlab="Times", ylab="y")
axis.POSIXct(1, at=Times, format="%I:%M%p")
Both approaches have the problem that in all likelihood your datetime format will not equal the values expressed just as a character vector like "5:00pm" even after coercion with the ">" comparison operator. To get the best advice you need to present str(DATA$Times) or dput(head(DATA$Times)) or class(Data$Times) . Generally plotting functions recognize either valid date or datetime classes or their numeric representation. If the ordering operation is not working, then it raises the question whether you have a proper class. But you appear to have an axis labeling that suggests a date-time format of some sort, and that we just need to figure out what class it really is.
Because you are creating a character vector from you Time column, you probably want to apply the restriction before you send the DATA$Time vector to strptime(). You still have not offered the requested clarifications, so I have no way to give tested or even very specific code, but you might be doing something like
Times<-strptime(DATA$Time[ as.POSIXlt(DATA$Time)$hour >= 17 &
as.POSIXlt(DATA$Time)$hour <= 22 ] ,
format="%I:%M%p")