My Stata code is complicated that it has many clear all commands.
Is it possible to store eststo so that it does not wiped out by clear command?
I want those stored eststo to use later.
Related
I want to create in Stata a coefplot variable: however, in one of the models I want to show there is no value for the estimate which I report, but instead I want to report the constant.
How is that possible?
sysuse auto, clear
regress price weight
coefplot, drop(weight) rename(_cons = abcdef)
I am trying to tabulate frequencies for a variable divided in two groups. That is, I would like to see how much a variable takes value "Yes" divided by both region and sex. Now, this is easy to do in Stata using "tab" and option row, but I have trouble exporting it. To make it clear, I am able to build the table with absolute frequencies in this way:
eststo formalyes: estpost tab regionwb_c female if fin22a==1
eststo formalno: estpost tab regionwb_c female if fin22a==0
eststo formalt: estpost tab regionwb_c female
estout formalyes formalno formalt using summformal.tex, replace varlabels(`e(labels)') unstack booktabs ///
mgroups("Yes" "No" "Tot", pattern(1 1 1) prefix(\multicolumn{#span}{c}{) suffix(}) span erepeat(\cmidrule(lr){#span})) fragment
This, put in my latex code, produces this relatively nice table:table1
Now what I would like to do is to reproduce the exact same table, but to have the relative and not absolute frequencies there. Now normally to my understanding if you want the relative frequencies you can have
tab x y, row nofreq
but if you try to combine this with estpost it does not work. Are there any hints? I tried working it out with tabout, but all i was able to produce is this:
tabout regionwb_c female using trial.tex, replace percent style(tex) c(mean fin22a) sum
Which gives this:table2
Where, as you can see, I am pretty lost. I am sorry if the question sounds silly but I struggled finding results online or on the tabout manual. I hope somebody can help me.
I have not worked with tabout before, but maybe one way to work around it could be to just program new variables containing the male and female relative frequencies by regionwb_c using the egen command for example (like in this link enter link description here. Then you could just pass these relative frequencies variables in your table.
Could that maybe help you? Good Luck!
I'm trying to collapse only a subset of my data using if, but it seems to be dropping / collapsing much more than I expect.
With every other command with which I have used an if qualifier, the command applies only to the subset of the data that meets the if criteria and leaves the rest of the data alone.
For example, replace does not alter the data for which foreign != 1:
. sysuse auto, clear
(1978 Automobile Data)
. replace mpg = 16 if foreign == 1
(22 real changes made)
However, it appears that collapse applies to the data that meets the if criteria and drops the rest:
. count if mpg > -1
74
. * all the data has mpg > -1
. count if foreign == 1
22
. collapse (mean) mpg if foreign == 1
. count if mpg > -1
1
There is no reason why collapse could not in theory work the same way as replace. It could leave all the foreign != 1 intact, while collapsing all foreign == 1 data to one observation.
That is in fact what I want to do with my data, so what should I do differently?
#NickCox helpfully suggested something like this:
. save "temp/whatever"
file temp/whatever.dta saved
. sysuse auto, clear
(1978 Automobile Data)
. drop if foreign == 1
(22 observations deleted)
. append using "temp/whatever"
(note: variable mpg was int, now float to accommodate using data's values)
That works in this sandbox, but my dataset has 10 million observations. If I can avoid having to re-load it, I can save myself a half hour. More if I have to do this for multiple cases.
Any other suggestions would be appreciated.
collapse with if works this way:
Those observations selected by the if condition are collapsed, typically (but not necessarily) into a new dataset with fewer observations.
Those observations not selected disappear.
It's incorrect to say that this command is unusual, let alone unique, in that respect. contract and keep also work in this way: whatever is not selected disappears.
(The community has often asked for save with if: savesome from SSC is one work-around.)
If you want to collapse some of the observations but leave the others unchanged, then you can try
A. this strategy
A1. use your dataset
A2. keep if what you want unchanged and save those observations
A3. use your dataset again
A4. collapse to taste
A5. append the dataset from A2
sysuse auto, clear
keep if !foreign
save domestic
sysuse auto, clear
collapse mpg if foreign
gen make = "All foreign"
append using domestic
or B. this one:
B1. work with (if needed create) an identifier that is unique (distinct) for the observations you want unchanged but takes on a single value for the observations you want collapsed
B2. collapse feeding that identifier to by().
sysuse auto, clear
replace make = "All foreign" if foreign
collapse mpg, by(make)
Although B looks trivial for this example, it is far from obvious to me that it is always superior for large datasets and if you want to continue to work with many variables. I have not experimented with timing or memory comparisons for large datasets or even any datasets, as I haven't encountered this wish before.
Is it possible to change the confidence interval level (say from the default 95% to 90%) when using Stata's marginsplot command? It does not seem to accept the level(90) option or keep information from a preceding margins, level(90) command.
Please post exact code along with your explanation of what went wrong. It's difficult to assess what the problem is if you don't do that. This works fine (from the help file):
clear
set more off
webuse nhanes2
regress bpsystol agegrp##sex
margins agegrp
marginsplot, level(80)
I want to create a table in Stata with the estout package to show the mean of a variable split by 2 groups (year and binary indicator) in an efficient way.
I found a solution, which is to split the main variable cash_at into 2 groups by hand through the generation of new variables, e.g. cash_at1 and cash_at2. Then, I can generate summary statistics with tabstat and get output with esttab.
estpost tabstat cash_at1 cash_at2, stat(mean) by(year)
esttab, cells("cash_at1 cash_at2")
Link to current result: http://imgur.com/2QytUz0
However, I'd prefer a horizontal table (e.g. year on the x axis) and a way to do it without splitting the groups by hand - is there a way to do so?
My preference in these cases is for year to be in rows and the statistic (e.g. mean) in the columns, but if you want to do it the other way around, there should be no problem.
For a table like the one you want it suffices to have the binary variable you already mention (which I name flag) and appropriate labeling. You can use the built-in table command:
clear all
set more off
* Create example data
set seed 8642
set obs 40
egen year = seq(), from(1985) to (2005) block(4)
gen cash = floor(runiform()*500)
gen flag = round(runiform())
list, sepby(year)
* Define labels
label define lflag 0 "cash0" 1 "cash1"
label values flag lflag
* Table
table flag year, contents(mean cash)
In general, for tables, apart from the estout module you may want to consider also the user-written command tabout. Run ssc describe tabout for more information.
On the other hand, it's not clear what you mean by "splitting groups by hand". You show no code for this operation, but as long as it's general enough for your purposes (and practical) I think you should allow for it. The code might not be as elegant as you wish but if it's doing what it's supposed to, I think it's alright. For example:
clear all
set more off
set seed 8642
set obs 40
* Create example data
egen year = seq(), from(1985) to (2005) block(4)
gen cash = floor(runiform()*500)
gen flag = round(runiform())
* Data management
gen cash0 = cash if flag == 0
gen cash1 = cash if flag == 1
* Table
estpost tabstat cash*, stat(mean) by(year)
esttab, cells("cash0 cash1")
can be used for a table like the one you give in your original post. It's true you have two extra lines and variables, but they may be harmless. I agree with the idea that in general, efficiency is something you worry about once your program is behaving appropriately; unless of course, the lack of it prevents you from reaching that state.