I am working with a int (%8.0g) variable called timeinsecond that was badly coded. For example, a value for this variable 12192 should mean 3h 23min 12s. I'm trying to create a new variable that based on the value of time would give me the total time expressed in HH:MM:SS.
In the example I mentioned, the new variable would be 03:23:12.
Stata uses the units of milliseconds for date-times, so assuming that no time here is longer than 24 hours, you can use the principle here:
. clear
. set obs 1
number of observations (_N) was 0, now 1
. gen timeinsecond = 12192
. gen double wanted = timeinsecond * 1000
. format wanted %tcHH:MM:SS
. list
+---------------------+
| timein~d wanted |
|---------------------|
1. | 12192 03:23:12 |
+---------------------+
All documented at help datetime.
Related
I have a data frame with the following two variables:
amount: num 1213.5 34.5 ...
txn_date: POSIXct, format "2017-05-01 12:13:30" ...
I want to transform it in a time series using ts().
I started using this code:
Z <- zoo(data$amount, order.by=as.Date(as.character(data$txn_date), format="%Y/%m/%d %H:%M:%S"))
But the problem is that in Z I loose the dates. In fact, all the dates are reported as NA.
How can I solve it?
For my analysis is important to have date in the format:%Y/%m/%d %H:%M:%S
for example 2017-05-01 12:13:30. I don't want to remove the time component in the variable txn_date.
Yhan you for your help,
Andrea
I think your prolem comes from the way you're manipulating your data frame, could post more details about it please ?
I think i have a fix for you.
Data frame I used :
> df1
$data
value
1 1.9150
2 3.1025
3 6.7400
4 8.5025
5 11.0025
6 9.8025
7 9.0775
8 7.0900
9 6.8525
10 7.4900
$date
%Y-%m-%d
1 1974-01-01
2 1974-01-02
3 1974-01-03
4 1974-01-04
5 1974-01-05
6 1974-01-06
7 1974-01-07
8 1974-01-08
9 1974-01-09
10 1974-01-10
> class(df1$data$value)
[1] "numeric"
> class(df1$date$`%Y-%m-%d`)
[1] "POSIXct" "POSIXt"
Then I can create a time serie by calling zoo like that :
> Z<-zoo(df1$data,order.by=(as.POSIXct(df1$date$`%Y-%m-%d`)))
> Z
value
1974-01-01 1.9150
1974-01-02 3.1025
1974-01-03 6.7400
1974-01-04 8.5025
1974-01-05 11.0025
1974-01-06 9.8025
1974-01-07 9.0775
1974-01-08 7.0900
1974-01-09 6.8525
1974-01-10 7.4900
The important thing here is that I use df1$date$%Y-%m-%d instead of just
df1$date
In fact if I try the way you did it I get NA values too :
> Z<-zoo(df1$data,order.by=as.POSIXct(as.Date(as.character(df1$date),format("%Y-%m-%d"))))
> Z
value
<NA> 1.915
To get the name of data$txn_date you can use the following command : names(data$txn_date) and try my solution with your data frame and name.
> names(df1$date)
[1] "%Y-%m-%d"
This document explains that the values of AIC and BIC are stored in r(S), but when I try display r(S), it returns "type mismatch" and when I try sum r(S), it returns "r ambiguous abbreviation".
Sorry for my misunderstanding this r(S), but I'll appreciate it if you let me know how I can access the calculated BIC value.
The document you refer to mentions that r(S) is a matrix. The display command does not work with matrices. Try matrix list. Also see help matrix.
For example:
clear
sysuse auto
regress mpg weight foreign
estat ic
matrix list r(S)
matrix S=r(S)
scalar aic=S[1,5]
di aic
The same document that you cited explains that r(S) is a matrix. That explains the failure of your commands, as summarize is for summarizing variables and display is for displaying strings and scalar expressions, as their help explains. Matrices are neither.
Note that the document you cited
http://www.stata.com/manuals13/restatic.pdf
is at the time of writing not the most recent version
http://www.stata.com/manuals14/restatic.pdf
although the advice is the same either way.
Copy r(S) to a matrix that will not disappear when you run the next r-class command, and then list it directly. For basic help on matrices, start with
help matrix
Here is a reproducible example. I use the Stata 13 version of the dataset because your question hints that you may be using that version:
. use http://www.stata-press.com/data/r13/sysdsn1
(Health insurance data)
. mlogit insure age male nonwhite
Iteration 0: log likelihood = -555.85446
Iteration 1: log likelihood = -545.60089
Iteration 2: log likelihood = -545.58328
Iteration 3: log likelihood = -545.58328
Multinomial logistic regression Number of obs = 615
LR chi2(6) = 20.54
Prob > chi2 = 0.0022
Log likelihood = -545.58328 Pseudo R2 = 0.0185
------------------------------------------------------------------------------
insure | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Indemnity | (base outcome)
-------------+----------------------------------------------------------------
Prepaid |
age | -.0111915 .0060915 -1.84 0.066 -.0231305 .0007475
male | .5739825 .2005221 2.86 0.004 .1809665 .9669985
nonwhite | .7312659 .218978 3.34 0.001 .302077 1.160455
_cons | .1567003 .2828509 0.55 0.580 -.3976773 .7110778
-------------+----------------------------------------------------------------
Uninsure |
age | -.0058414 .0114114 -0.51 0.609 -.0282073 .0165245
male | .5102237 .3639793 1.40 0.161 -.2031626 1.22361
nonwhite | .4333141 .4106255 1.06 0.291 -.371497 1.238125
_cons | -1.811165 .5348606 -3.39 0.001 -2.859473 -.7628578
------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 615 -555.8545 -545.5833 8 1107.167 1142.54
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
. ret li
matrices:
r(S) : 1 x 6
. mat S = r(S)
. mat li S
S[1,6]
N ll0 ll df AIC BIC
. 615 -555.85446 -545.58328 8 1107.1666 1142.5395
The BIC value is now in S[1,6].
I've been melting my brains over a peculiar request: execute every two minutes a certain query and if it returns rows, send an e-mail with these. This was already done and delivered, so far so good. The result set of query is like this:
+----+---------------------+
| ID | last_update |
+----+---------------------|
| 21 | 2011-07-20 13:03:21 |
| 32 | 2011-07-20 13:04:31 |
| 43 | 2011-07-20 13:05:27 |
| 54 | 2011-07-20 13:06:41 |
+----+---------------------|
The trouble starts when the user asks me to modify it so the solution so that, e.g., the first time that ID 21 is caught being more than 5 minutes old, the e-mail is sent to a particular set of recipients; the second time, when ID 21 is between 5 and 10 minutes old another set of recipients is chosen. So far it's ok. The gotcha for me is from the third time onwards: the e-mails are now sent each half-hour, instead of every five minutes.
How should I keep track of the status of Mr. ID = 43 ? How would I know if he has already received an e-mail, two or three? And how to ensure that from the third e-mail onwards, the mails are sent each half-hour, instead of the usual 5 minutes?
I get the impression that you think this can be solved with a simple mathematical formula. And it probably can be, as long as your system is reliable.
Every thirty minutes can be seen as 360 degrees, or 2 pi radians, on a harmonic function graph. That's 12 degrees = 1 minute. Let's take cosin for instance:
f(x) = cos(x)
f(x) = cos(elapsedMinutes * 12 degrees)
Where elapsed minutes is the time since the first 30 minute update was due to go out. This should be a constant number of minutes added to the value of last_update.
Since you have a two minute window of error, it will be time to transmit the 30 minute update if the the value of f(x) (above) is between the value you would get at less than one minute before or after the scheduled update. Which would be = cos(1* 12 degrees) = 0.9781476007338056379285667478696.
Bringing it all together, it's time to send a thirty minute update if this SQL expression is true:
COS(RADIANS( 12 * DATEDIFF(minutes,
DATEADD(minutes, constantNumberOfMinutesBetweenSecondAndThirdUpdate, last_update),
CURRENT_TIMESTAMP))) > 0.9781476007338056379285667478696
If you need a wider window than exactly two minutes, just lower this number slightly.
I am trying to figure out how to essentially create a "floor" call based on a specific decimal place as opposed to a whole value. Below is a table of actual values and the desired result:
=========|=========
3.125 | 3.12
4.187 | 4.18
1.212 | 1.21
5.999 | 5.99
Is this possible with mysql? using the round function to the 2nd decimal place returns "bad" data and rounding to the third does not reach the goal either.
Use the TRUNCATE function:
SELECT TRUNCATE(3.125, 2)
Output:
3.12
Could you multiply by 100 and floor and then divide by 100? Like
floor(value*100)/100
I have to declare n = 01. But whenever I try it's getting changed to 1.
What should I try?
If this is just for display purposes then I would use the .ToString("0#"), unless you really need to do calculations based on two significant figures.
For index As Integer = 1 To 100
Console.WriteLine(index.ToString("0#"))
Next
Gives you
01
02
.
.
100