I asked a similar question earlier. I'm attempting to fill in missing values such that observations 0-458 are e 0, 445-832 are 1, and 832-850 are 0.
The following code allowed me to replace missing values in observations 1-160 with 1, with the rest of the observations set to 0.
replace myvar = cond(_n <= 160, 1, 0) if missing(myvar)
How can I interpret this command for what my current purpose?
There is no observation 0. I assume you meant observation 1. Your rules are ambiguous otherwise as you give two rules for 445-458 and two rules for 832.
I will give code for a minimal data example.
clear
set obs 6
gen myvar = .
Assume you want myvar in observations 1/2 to be 0, 3/4 to be 1, 5/6 to be 0.
Method 1
replace myvar = inrange(_n, 3, 4) if missing(myvar)
Method 2
replace myvar = cond(_n <= 2, 0, cond(_n <= 4, 1, 0))
Method 3
replace myvar = 0 if missing(myvar) in 1/2
replace myvar = 1 if missing(myvar) in 3/4
replace myvar = 0 if missing(myvar) in 5/6
In general, however, replacing in terms of observation numbers is not best technique. It is utterly dependent on sort order. Also, if there are criteria in terms of other variables, they are preferable as making more and better sense in records of reproducible research, to yourself in the future and to colleagues, reviewers and yet others too.
Related
In my Stata data set, the "alternative" variable consists of 4 modes including pier, private, beach and charter.
I want to generate new variable y as follows:
We collapse the model to three alternatives and order the alternatives, with y = 0 if fishing from a pier or beach, y = 1 if fishing from a private boat and y = 2 if fishing from a charter.
I tried to do this by looking at thetas in this website:
stata tips but I can't solve it.
Note: I don't understand from the dataset. And I get error related to type of the variable while generating variable I download the dataset from the website https://www.stata-press.com/data/musr/musr.zip The data name is mus15data
The variables in the dataset is as follows:
Here, "mode" variable is alternatives.
If I understand correctly, this is
gen y = 0 if inlist(1, dbeach, dpier)
* gen y = 0 if dbeach == 1 | dpier == 1
replace y = 1 if dprivate == 1
replace y = 2 if dcharter == 1
Many other solutions are possible. Here is one more.
gen y = cond(inlist(1, dbeach, pier), 0, 2 * (dcharter == 1) + (dprivate == 1))
If all those variables are only ever 0 or 1 (and never missing) some simplifications are possible.
Go only with code you find clear and can explain to others.
I am assuming that pier, beach, private, charter are mutually exclusive. I've not checked with the dataset.
Goal: perform rolling window calculations on panel data in Stata with variables PanelVar, TimeVar, and Var1, where the window can change within a loop over different window sizes.
Problem: no access to SSC for the packages that would take care of this (like rangestat)
I know that
by PanelVar: gen Var1_1 = Var1[_n]
produces a copy of Var1 in Var1_1. So I thought it would make sense to try
by PanelVar: gen Var1SumLag = sum(Var1[(_n-3)/_n])
to produce a rolling window calculation for _n-3 to _n for the whole variable. But it fails to produce the results I want, it just produces zeros.
You could use sum(Var1) - sum(Var1[_n-3]), but I also want to be able to make the rolling window left justified (summing future observations) as well as right justified (summing past observations).
Essentially I would like to replicate Python's ".rolling().agg()" functionality.
In Stata _n is the index of the current observation. The expression (_n - 3) / _n yields -2 when _n is 1 and increases slowly with _n but is always less than 1. As a subscript applied to extract values from observations of a variable it always yields missing values given an extra rule that Stata rounds down expressions so supplied. Hence it reduces to -2, -1 or 0: in each case it yields missing values when given as a subscript. Experiment will show you that given any numeric variable say numvar references to numvar[-2] or numvar[-1] or numvar[0] all yield missing values. Otherwise put, you seem to be hoping that the / yields a set of subscripts that return a sequence you can sum over, but that is a long way from what Stata will do in that context: the / is just interpreted as division. (The running sum of missings is always returned as 0, which is an expression of missings being ignored in that calculation: just as 2 + 3 + . + 4 is returned as 9 so also . + . + . + . is returned as 0.)
A fairly general way to do what you want is to use time series operators, and this is strongly preferable to subscripts as (1) doing the right thing with gaps (2) automatically working for panels too. Thus after a tsset or xtset
L0.numvar + L1.numvar + L2.numvar + L3.numvar
yields the sum of the current value and the three previous and
L0.numvar + F1.numvar + F2.numvar + F3.numvar
yields the sum of the current value and the three next. If any of these terms is missing, the sum will be too; a work-around for that is to return say
cond(missing(L3.numvar), 0, L3.numvar)
More general code will require some kind of loop.
Given a desire to loop over lags (negative) and leads (positive) some code might look like this, given a range of subscripts as local macros i <= j
* example i and j
local i = -3
local j = 0
gen double wanted = 0
forval k = `i'/`j' {
if `k' < 0 {
local k1 = -(`k')
replace wanted = wanted + L`k1'.numvar
}
else replace wanted = wanted + F`k'.numvar
}
Alternatively, use Mata.
EDIT There's a simpler method, to use tssmooth ma to get moving averages and then multiply up by the number of terms.
tssmooth ma wanted1=numvar, w(3 1)
tssmooth ma wanted2=numvar, w(0 1 3)
replace wanted1 = 4 * wanted1
replace wanted2 = 4 * wanted2
Note that in contrast to the method above tssmooth ma uses whatever is available at the beginning and end of each panel. So, the first moving average, the average of the first value and the three previous, is returned as just the first value at the beginning of each panel (when the three previous values are unknown).
I need to program a nearest neighbor algorithm in stata from scratch because my dataset does not allow me to use any of the available solutions (as far as I am concerned).
To be pecise. I have a dataset that is of similar structure to that of the following (original has around 14k observations)
input id value treatment match
1 0.14 0 .
2 0.32 0 .
3 0.465 1 2
4 0.878 1 2
5 0.912 1 2
6 0.001 1 1
end
I want to generate a variable called match (already included in the example above). For each observation with treatment == 1 the variable match should store the id of another observation from within treatment == 0 whose value is closest to value of the considered observation (treatment == 1).
I am new to stata programming, so I am not yet familiar with the syntax. My first shot is the following however it does not produce any changes to the match variable. I am sure this is a novice question but I am hoping for some advice on how to make the code running.
EDIT: I have changed the code slightly and now it seems to work. Do you see any problems that may arise if I run it on a bigger dataset?
set more off
clear all
input id pscore treatment
1 0.14 0
2 0.32 0
3 0.465 1
4 0.878 1
5 0.912 1
6 0.001 1
end
gen match = .
forval i = 1/`= _N' {
if treatment[`i'] == 1 {
local dist 1
forvalues j = 1/`= _N' {
if (treatment[`j'] == 0) {
local current_dist (pscore[`i'] - pscore[`j'])^2
if `dist' > `current_dist' {
local dist `current_dist' // update smallest distance
replace match = id[`j'] in `i' // write match
}
}
}
}
}
Consider some simulated data: 1,000 observations, 200 of them untreated (treat == 0) and the rest treated (treat == 1). Then the code included below will be much more efficient than the originally posted. (Ties, like in your code, are not explicitly handled.)
clear
set more off
*----- example data -----
set obs 1000
set seed 32956
gen id = _n
gen pscore = runiform()
gen treat = cond(_n <= 200, 0, 1)
*----- new method -----
timer clear
timer on 1
// get id of last non-treated and first treated
// (data is sorted by treat and ids are consecutive)
bysort treat (id): gen firsttreat = id[1]
local firstt = first[_N]
local lastnt = `firstt' - 1
// start loop
gen match = .
gen dif = .
quietly forvalues i = `firstt'/`=_N' {
// compute distances
replace dif = (pscore[`i'] - pscore)^2
summarize dif in 1/`lastnt', meanonly
// identify id of minimum-distance observation
replace match = . in 1/`lastnt'
replace match = id in 1/`lastnt' if dif == r(min)
summarize match in 1/`lastnt', meanonly
// save the minimum-distance id
replace match = r(max) in `i'
}
// clean variable and drop
replace match = . in 1/`lastnt'
drop dif firsttreat
timer off 1
tempfile first
save `first'
*----- your method -----
drop match
timer on 2
gen match = .
quietly forval i = 1/`= _N' {
if treat[`i'] == 1 {
local dist 1
forvalues j = 1/`= _N' {
if (treat[`j'] == 0) {
local current_dist (pscore[`i'] - pscore[`j'])^2
if `dist' > `current_dist' {
local dist `current_dist' // update smallest distance
replace match = id[`j'] in `i' // write match
}
}
}
}
}
timer off 2
tempfile second
save `second'
// check for equality of results
cf _all using `first'
// check times
timer list
The results in seconds to finish execution:
. timer list
1: 0.19 / 1 = 0.1930
2: 10.79 / 1 = 10.7900
The difference is huge, specially considering this data set has only 1,000 observations.
An interesting thing to notice is that as the number of non-treated cases increases relative to the number of treated, then the original method improves, but never reaches the levels of efficiency of the new method. As an example, invert the number of cases, so there is now 800 untreated and 200 treated (change data setup to gen treat = cond(_n <= 800, 0, 1)). The result is
. timer list
1: 0.07 / 1 = 0.0720
2: 4.45 / 1 = 4.4470
You can see that the new method also improves and is still much faster. In fact, the relative difference is still the same.
Another way to do this is using joinby or cross. The problem is they temporarily expand (a lot) the size of your data base. In many cases, they are not feasible due to the hard limit Stata has on the number of possible observations (see help limits). You can find an example of joinby here: https://stackoverflow.com/a/19784222/2077064.
Edit
If there's a large number of treated relative to untreated, your code suffers
because you go through the whole first loop many more times (due to the first if).
Furthermore, going through
that whole loop once, implies going through another loop that
has itself two if conditions, _N more times.
The opposite case in which there are few treated observations means that you go through the whole
first loop only in a small number of occasions, speeding up your code substantially.
The reason my code can maintain its efficiency is due to the use of in. This always
offers speed gains over if. Stata will go directly to those observations with no
logical checking needed. Your problem provides an opportunity for that replacement
and it's wise to seize it.
If my code used if where in is in place, the results would be different.
Your code would be faster for the
case in which there's a large number of untreated relative to treated, and again, that
is because in your code there would not be the need to go through the complete loop,
requiring very little work;
the first loop is short-circuited with the first if. For the opposite case,
my code would still dominate.
The key is to "separate" treated from untreated and work on each group using in.
This is a programming question, but I'll give you a little of the stats background first. This question refers to part of a data sim for a mixed-effects location scale model (i.e., heterogeneous variances). I'm trying to simulate two MVN variance components using the RANDNORMAL function in IML. Because both variance components are heterogeneous, the variances used by RANDNORMAL will differ across people. Thus, I need IML to select the specific row (e.g., row 1 = person 1) and use the RANDNORMAL function before moving onto the next row, and so on.
My example code below is for 2 people. I use DO to loop through each person's specific variance components (VC1 and VC2). I get the error: "Module RANDNORMAL called again before exit from prior call." I am assuming I need some kind of BREAK or EXIT function in the DO loop, but none I have tried work.
PROC IML;
ColNames = {"ID" "VC1" "VC2"};
A = {1 2 3,
2 8 9};
PRINT A[COLNAME=ColNames];
/*Set men of each variance component to 0*/
MeanVector = {0, 0};
/*Loop through each person's data using THEIR OWN variances*/
DO i = 1 TO 2;
VC1 = A[i,2];
VC2 = A[i,3];
CovMatrix = {VC1 0,
0 VC2};
CALL RANDSEED(1);
U = RANDNORMAL(2, MeanVector, CovMatrix);
END;
QUIT;
Any help is appreciated. Oh, and I'm using SAS 9.4.
You want to move some things around, but mostly you don't want to rewrite U twice: you need to write U's 1st row, then U's 2nd row, if I understand what you're trying to do. The below is a bit more efficient also, since I j() the U and _cv matrices rather than constructing then de novo every time through the loop (which is slow).
proc iml;
a = {1 2 3,2 8 9};
print(a);
_mv = {0,0};
U = J(2,2);
_cv = J(2,2,0);
CALL RANDSEED(1);
do i = 1 to 2;
_cv[1,1] = a[i,2];
_cv[2,2] = a[i,3];
U[i,] = randnormal(1,_mv, _cv);
end;
print(u);
quit;
Your mistake is the line
CovMatrix = {VC1 0, 0 VC2}; /* wrong */
which is not valid SAS/IML syntax. Instead, use #Joe's approach or use
CovMatrix = (VC1 || 0) // (0 || VC2);
For details, see the article "How to build matrices from expressions."
You might also be interested in this article that describes how to carry out this simulation with a block-diagonal matrix: "Constructing block matrices with applications to mixed models."
What is the preferred and best way in C++ to do this: Split the letters of the alphabeth into 7 groups so I can later ask if a char is in group 1, 3 or 4 etc... ? I can of course think of several ways of doing this myself but I want to know the standard and stick with it when doing this kinda stuff.
0
AEIOUHWY
1
BFPV
2
CGJKQSXZ
3
DT
4
MN
5
L

6
R
best way in C++ to do this: Split the letters of the alphabeth into 7 groups so I can later ask if a char is in group 1, 3 or 4 etc... ?
The most efficient way to do the "split" itself is to have an array from letter/char to number.
// A B C D E F G H...
const char lookup[] = { 0, 1, 2, 3, 0, 1, 2, 0...
A switch/case statement's another reasonable choice - the compiler can decide itself whether to create an array implementation or some other approach.
It's unclear what use of those 1-6 values you plan to make, but an enum appears a reasonable encoding choice. That has the advantage of still supporting any use you might have for those specific numeric values (e.g. in < comparisons, streaming...) while being more human-readable and compiler-checked than "magic" numeric constants scattered throughout the code. constant ints of any width are also likely to work fine, but won't have a unifying type.
Create a lookup table.
int lookup[26] = { 0, 1, 2, 3, 0, 1, 2, 0 .... whatever };
inline int getgroup(char c)
{
return lookup[tolower(c) - 'a'];
}
call it this way
char myc = 'M';
int grp = lookup(myc);
Error checks omitted for brevity.
Of course, depending on what the 7 groups represent , you can make enums instead of using 0, 1, 2 etc.
Given the small amount of data involved, I'd probably do it as a bit-wise lookup -- i.e., set up values:
cat1 = 1;
cat2 = 2;
cat3 = 4;
cat4 = 8;
cat5 = 16;
cat6 = 32;
cat7 = 64;
Then just create an array of 26 values, one for each letter in the alphabet, with each containing the value of the category for that letter. When you want to classify a letter, you just categories[ch-'A'] to find it.