Im having some issues with some SPSS code. Im new to SPSS and still trying to figure out the syntax. I'm trying to get my code to count the sum of two dice equal to 7. I cant get the count function to work the way I want it. Below is my code. Any tips would be greatly appreciated.
INPUT PROGRAM.
LOOP #I=1 TO 100000.
COMPUTE case = 1.
END CASE.
END LOOP.
END FILE.
END INPUT PROGRAM.
EXECUTE.
COMPUTE Dice_1 = TRUNC (RV.UNIFORM(1,7)).
COMPUTE Dice_2 = TRUNC (RV.UNIFORM(1,7)).
COMPUTE total = Dice_1+Dice_2.
COMPUTE Number_Sum7= Dice_1+Dice_2 = 7.
COUNT Num= case TO Number_Sum7(1).
SAVE outfile = 'my file path'.
count function counts across a list of variables, in each line separately.
What you seem to be looking for is to count over rows.
You can start with:
frequencies total. /* see counts of all possible totals.
means Number_Sum7/cells=sum. /* count only the cases where total=7.
These will give you the answers in the output window.
If you want the answers in data for further analysis, look up the aggregate function.
For example, the following will give you the same results but in a new datasets:
DATASET NAME ORIG.
DATASET DECLARE freqs.
AGGREGATE /OUTFILE='freqs' /BREAK=total /Mycount=N.
DATASET ACTIVATE ORIG.
DATASET DECLARE only7.
AGGREGATE /OUTFILE='only7' /BREAK= /only7=sum(Number_Sum7).
Or, instead, you can add the results to your present data:
AGGREGATE /OUTFILE=* MODE=ADDVARIABLES /BREAK=total /TotalCount=N.
AGGREGATE /OUTFILE=* MODE=ADDVARIABLES /BREAK= /total7=sum(Number_Sum7).
Related
Given a dataset of blood results, say cholesterol level, and knowing that the instrument that produced those results is subject to a known degree of variability, how would I add that variability back into the dataset? i.e. I want to assume the result in the original dataset is the true/mean value, and then produce new results that are subject to the known variability of the instrument.
In Excel you use =NORM.INV(RAND(), mean, std_dev), where RAND() provides a random value between 0 and 1, "mean" will be the original value and I have the CV so I can calculate the SD. NORM.INV then provides the inverse of the cumulative normal distribution function.
I've done the following to create a new column with my new values, but would like to know if it is valid (i.e., will each row have a different random number between 0 and 1 as the probability? and is this formula equivalent to NORM.INV?
df8000['HDL_1'] = norm.ppf(random(), loc = df8000['HDL_0'], scale = TAE_df.loc[0,'HDL'])
Thanks in advance!
I am trying to get a value from this equation
--counted gives the total row count in a file
samplecount = counted*(10/100);
How to sample data according to this
--Load data
examples = LOAD '/home/sreeveni/myfiles/PE/USCensus1990New.csv' ;
--Group data
groupedByUser = group examples all;
--count no of lines in the file
counted = FOREACH groupedByUser generate COUNT(examples) ;
--sampling
sampled = SAMPLE examples counted*(10/100);
store sampled into '/home/sreeveni/myfiles/OUT/samplesout';
Showing error in above line
Invalid scalar projection: counted : A column needs to be projected
from a relation for it to be used as a scalar
Please advice.
Am I doing anything wrong.
i guess sample works with a number between [0,1]. In your case, its exceeding the required value. If you want just 10% of the data, pass 0.1 directly and to get that in a code, find this percentage in a FOREACH statement only.
If you are trying to generate a sample of "examples" with 10% of the total number of rows, all you have to do is:
SAMPLE examples 0.1;
Read the documentation for SAMPLE command here.
I have output = A(:,Nout) Nout = points along the array..... : = all points in column
So, it is saying the values in the last column.
How do I use output as A at the first column for the next iteration?
Your question is not clear. You may mean a variety of things.
If you want to loop through values in the first column in some order specified by your last column you can:
Asort = A ( A (:, end), :);
and then loop through Asort.
You may also mean to loop N times for each row where N is defined by last column of A.
You can do it using a nested loop:
for Arow = A(:, end)
for ii = 1:Arow
% your code here
end
end
You may also mean several other things, but instead me guessing you could try clarifing a bit. :)
(it should be a comment but I can't add comments yet, sorry)
I have input records of the form
2013-07-09T19:17Z,f1,f2
2013-07-09T03:17Z,f1,f2
2013-07-09T21:17Z,f1,f2
2013-07-09T16:17Z,f1,f2
2013-07-09T16:14Z,f1,f2
2013-07-09T16:16Z,f1,f2
2013-07-09T01:17Z,f1,f2
2013-07-09T16:18Z,f1,f2
These represent timestamps and events. I have written these by hand, but actual data should be sorted based on time.
I would like to generate a set of records which would be input to graph plotting function which needs continuous time series. I would like to fill in missing values, i.e. if there are entries for "2013-07-09T19:17Z" and "2013-07-09T19:19Z", I would like to generate entry for "2013-07-09T19:18Z" with predefined value.
My thoughts on doing this:
Use MIN and MAX to find the start and end date in the series
Write UDF which takes min and max and returns relation with missing
timestamps
Join above 2 relations
I cannot get my head around on how to implement this in PIG though. Would appreciate any help.
Thanks!
Generate another file using a script (outside pig)with all time stamps between MIN and MAX , including MIN and MAX. Load this as a second data set. Here is a sample that I used from your data set. Please note I filled in only few gaps not all.
2013-07-09T01:17Z,d1,d2
2013-07-09T01:18Z,d1,d2
2013-07-09T03:17Z,d1,d2
2013-07-09T16:14Z,d1,d2
2013-07-09T16:15Z,d1,d2
2013-07-09T16:16Z,d1,d2
2013-07-09T16:17Z,d1,d2
2013-07-09T16:18Z,d1,d2
2013-07-09T19:17Z,d1,d2
2013-07-09T21:17Z,d1,d2
Do a COGROUP on the original dataset and the generated dataset above. Use a nested FOREACH GENERATE to write output dataset. If first dataset is empty, use the values from second set to generate output dataset else the first dataset. Here is the piece of code I used on these two datasets.
Org_Set = LOAD 'pigMissingData/timeSeries' USING PigStorage(',') AS (timeStamp, fl1, fl2);
Default_set = LOAD 'pigMissingData/timeSeriesFull' USING PigStorage(',') AS (timeStamp, fl1, fl2);
coGrouped = COGROUP Org_Set BY timeStamp, Default_set BY timeStamp;
Filled_Data_set = FOREACH coGrouped {
x = COUNT(times);
y = (x == 0? (Default_set.fl1, Default_set.fl2): (Org_Set.fl1, Org_Set.fl2));
GENERATE FLATTEN(group), FLATTEN(y.$0), FLATTEN(y.$1);
};
if you need further clarification or help let me know
In addition to #Rags answer, you could use the STREAM x THROUGH command and a simple awk script (similar to this one) to generate the date range once you have the min and max dates. Something similar to (untested! - you might need to single line the awk script with semi-colon command delimitation, or better to ship it as a script file)
grunt> describe bounds;
(min:chararray, max:chararray)
grunt> dump bounds;
(2013/01/01,2013/01/04)
grunt> fullDateBounds = STREAM bounds THROUGH `gawk '{
split($1,s,"/")
split($2,e,"/")
st=mktime(s[1] " " s[2] " " s[3] " 0 0 0")
et=mktime(e[1] " " e[2] " " e[3] " 0 0 0")
for (i=st;i<=et;i+=60*24) print strftime("%Y/%m/%d",i)
}'`;
This is similar to a question I asked before, but is slightly different:
So I have a very large structure array in matlab. Suppose, for argument's sake, to simplify the situation, suppose I have something like:
structure(1).name, structure(2).name, structure(3).name structure(1).returns, structure(2).returns, structure(3).returns (in my real program I have 647 structures)
Suppose further that structure(i).returns is a vector (very large vector, approximately 2,000,000 entries) and that a condition comes along where I want to delete the jth entry from structure(i).returns for all i. How do you do this? or rather, how do you do this reasonably fast? I have tried some things, but they are all insanely slow (I will show them in a second) so I was wondering if the community knew of faster ways to do this.
I have parsed my data two different ways; the first way had everything saved as cell arrays, but because things hadn't been working well for me I parsed the data again and placed everything as vectors.
What I'm actually doing is trying to delete NaN data, as well as all data in the same corresponding row of my data file, and then doing the very same thing after applying the Hampel filter. The relevant part of my code in this attempt is:
for i=numStock+1:-1:1
for j=length(stock(i).return):-1:1
if(isnan(stock(i).return(j)))
for k=numStock+1:-1:1
stock(k).return(j) = [];
end
end
end
stock(i).return = sort(stock(i).return);
stock(i).returnLength = length(stock(i).return);
stock(i).medianReturn = median(stock(i).return);
stock(i).madReturn = mad(stock(i).return,1);
end;
for i=numStock:-1:1
for j = length(stock(i+1).volume):-1:1
if(isnan(stock(i+1).volume(j)))
for k=numStock:-1:1
stock(k+1).volume(j) = [];
end
end
end
stock(i+1).volume = sort(stock(i+1).volume);
stock(i+1).volumeLength = length(stock(i+1).volume);
stock(i+1).medianVolume = median(stock(i+1).volume);
stock(i+1).madVolume = mad(stock(i+1).volume,1);
end;
for i=numStock+1:-1:1
for j=stock(i).returnLength:-1:1
if (abs(stock(i).return(j) - stock(i).medianReturn) > 3*stock(i).madReturn)
for k=numStock+1:-1:1
stock(k).return(j) = [];
end
end;
end;
end;
for i=numStock:-1:1
for j=stock(i+1).volumeLength:-1:1
if (abs(stock(i+1).volume(j) - stock(i+1).medianVolume) > 3*stock(i+1).madVolume)
for k=numStock:-1:1
stock(k+1).volume(j) = [];
end
end;
end;
end;
However, this returns an error:
"Matrix index is out of range for deletion.
Error in Failure (line 110)
stock(k).return(j) = [];"
So instead I tried by parsing everything in as vectors. Then I decided to try and delete the appropriate entries in the vectors prior to building the structure array. This isn't returning an error, but it is very slow:
%% Delete bad data, Hampel Filter
% Delete bad entries
id=strcmp(returns,'');
returns(id)=[];
volume(id)=[];
date(id)=[];
ticker(id)=[];
name(id)=[];
permno(id)=[];
sp500(id) = [];
id=strcmp(returns,'C');
returns(id)=[];
volume(id)=[];
date(id)=[];
ticker(id)=[];
name(id)=[];
permno(id)=[];
sp500(id) = [];
% Convert returns from string to double
returns=cellfun(#str2double,returns);
sp500=cellfun(#str2double,sp500);
% Delete all data for which a return is not a number
nanid=isnan(returns);
returns(nanid)=[];
volume(nanid)=[];
date(nanid)=[];
ticker(nanid)=[];
name(nanid)=[];
permno(nanid)=[];
% Delete all data for which a volume is not a number
nanid=isnan(volume);
returns(nanid)=[];
volume(nanid)=[];
date(nanid)=[];
ticker(nanid)=[];
name(nanid)=[];
permno(nanid)=[];
% Apply the Hampel filter, and delete all data corresponding to
% observations deleted by the filter.
medianReturn = median(returns);
madReturn = mad(returns,1);
for i=length(returns):-1:1
if (abs(returns(i) - medianReturn) > 3*madReturn)
returns(i) = [];
volume(i)=[];
date(i)=[];
ticker(i)=[];
name(i)=[];
permno(i)=[];
end;
end
medianVolume = median(volume);
madVolume = mad(volume,1);
for i=length(volume):-1:1
if (abs(volume(i) - medianVolume) > 3*madVolume)
returns(i) = [];
volume(i)=[];
date(i)=[];
ticker(i)=[];
name(i)=[];
permno(i)=[];
end;
end
As I said, this is very slow, probably because I'm using a for loop on a very large data set; however, I'm not sure how else one would do this. Sorry for the gigantic post, but does anyone have a suggestion as to how I might go about doing what I'm asking in a reasonable way?
EDIT: I should add that getting the vector method to work is probably preferable, since my aim is to put all of the return vectors into a matrix and get all of the volume vectors into a matrix and perform PCA on them, and I'm not sure how I would do that using cell arrays (or even if princomp would work on cell arrays).
EDIT2: I have altered the code to match your suggestion (although I did decide to give up speed and keep with the for-loops to keep with the structure array, since reparsing this data will be way worse time-wise). The new code snipet is:
stock_return = zeros(numStock+1,length(stock(1).return));
for i=1:numStock+1
for j=1:length(stock(i).return)
stock_return(i,j) = stock(i).return(j);
end
end
stock_return = stock_return(~any(isnan(stock_return)), : );
This returns an Index exceeds matrix dimensions error, and I'm not sure why. Any suggestions?
I could not find a convenient way to handle structures, therefore I would restructure the code so that instead of structures it uses just arrays.
For example instead of stock(i).return(j) I would do stock_returns(i,j).
I show you on a part of your code how to get rid of for-loops.
Say we deal with this code:
for j=length(stock(i).return):-1:1
if(isnan(stock(i).return(j)))
for k=numStock+1:-1:1
stock(k).return(j) = [];
end
end
end
Now, the deletion of columns with any NaN data goes like this:
stock_return = stock_return(:, ~any(isnan(stock_return)) );
As for the absolute difference from medianVolume, you can write a similar code:
% stock_return_length is a scalar
% stock_median_return is a column vector (eg. [1;2;3])
% stock_mad_return is also a column vector.
median_return = repmat(stock_median_return, stock_return_length, 1);
is_bad = abs(stock_return - median_return) > 3.* stock_mad_return;
stock_return = stock_return(:, ~any(is_bad));
Using a scalar for stock_return_length means of course that the return lengths are the same, but you implicitly assume it in your original code anyway.
The important point in my answer is using any. Logical indexing is not sufficient in itself, since in your original code you delete all the values if any of them is bad.
Reference to any: http://www.mathworks.co.uk/help/matlab/ref/any.html.
If you want to preserve the original structure, so you stick to stock(i).return, you can speed-up your code using essentially the same scheme but you can only get rid of one less for-loop, meaning that your program will be substantially slower.