I am trying to plot a lorenz curve, using the following command:
glcurve drugs, sortvar(death) pvar(rank) glvar(yord) lorenz nograph
generate rank1=rank
label variable rank "Cum share of mortality"
label variable rank1 "Equality Line"
twoway (line rank1 rank, sort clwidth(medthin) clpat(longdash))(line yord rank , sort clwidth(medthin) clpat(red)), ///
ytitle(Cumulative share of drug activity, size(medsmall)) yscale(titlegap(2)) xtitle(Cumulative share of mortality (2012), size(medsmall)) ///
legend(rows(5)) xscale(titlegap(5)) legend(region(lwidth(none))) plotregion(margin(zero)) ysize(6.75) xsize(6) plotregion(lcolor(none))
However, in the resultant curves, the Line of equality does not start from 0, is there a way to fix this?
Is it recommended to use the following in order to get the perfect 45 degree line of equality:
(function y=x, range(0 1)
Also, how many minimum observations are required to plot the above graph? Does it work well with 2 observations as well?
The reason your Line of Perfect Equality does not pass through (0,0) is because the values for your variable do not contain 0.
The smallest value you will have for rank will be 1/_N. Although this value will asymptotically approach 0, it will never actually reach 0.
To see this, try:
quietly sum rank
di r(min)
di 1/_N
Further, by applying the program code to your data (beginning around line 152 in the ado file and removing unnecessary bits), one can easily see that yord cannot take on a value of 0 without values of 0 for drugs:
glcurve drugs, sortvar(death) pvar(rank) glvar(yord) lorenz nograph
sort death drugs , stable
gen double rank1 = _n / _N
qui sum drugs
gen yord1= (sum(drugs) / _N) / r(mean)
The best way to plot your Equality would be the method from your edit, namely:
twoway(function y = x, ra(0 1))
One quick yet (very) crude fix to force the lorenz curve to start at the origin (if it doesn't already) is to add an observation to the data after obtaining rank and yord, and then deleting it after you have your curve:
glcurve drugs, sortvar(death) pvar(rank) glvar(yord) lorenz nograph
expand 2 in 1
replace yord = 0 in 1
replace rank = 0 in 1
twoway (function y = x, ra(0 1)) ///
(line yord rank)
drop in 1
Like I said, this is admittedly crude and even somewhat ill advised, but I can't see a much better alternative at the moment, and with this method you will not be altering any of the other values of yord by running glcurve on the extrapolated data.
Related
I am trying to run a multinomial logit with year fixed effects in mlogit in Stata (panel data: year-country), but I do not get standard errors for some of the models. When I run the same model using multinom in R I get both coefficients and standard errors.
I do not use Stata frequently, so I may be missing something or I may be running different models in Stata and R and should not be comparing them in the first place. What may be happening?
So a few details about the simple version of the model of interest:
I created a data example to show what the problem is
Dependent variable (will call it DV1) with 3 categories of -1, 0, 1 (unordered and 0 as reference)
Independent variables: 2 continuous variables, 3 binary variables, interaction of 2 of the 3 binary variables
Years: 1995-2003
Number of observations in the model: 900
In R I run the code and get coefficients and standard errors as below.
R version of code creating data and running the model:
## Fabricate example data
library(fabricatr)
data <- fabricate(
N = 900,
id = rep(1:900, 1),
IV1 = draw_binary(0.5, N = N),
IV2 = draw_binary(0.5, N = N),
IV3 = draw_binary(0.5, N = N),
IV4 = draw_normal_icc(mean = 3, N = N, clusters = id, ICC = 0.99),
IV5 = draw_normal_icc(mean = 6, N = N, clusters = id, ICC = 0.99))
library(AlgDesign)
DV = gen.factorial(c(3), 1, center=TRUE, varNames=c("DV"))
year = gen.factorial(c(9), 1, center=TRUE, varNames=c("year"))
DV = do.call("rbind", replicate(300, DV, simplify = FALSE))
year = do.call("rbind", replicate(100, year, simplify = FALSE))
year[year==-4]= 1995
year[year==-3]= 1996
year[year==-2]= 1997
year[year==-1]= 1998
year[year==0]= 1999
year[year==1]= 2000
year[year==2]= 2001
year[year==3]= 2002
year[year==4]= 2003
data1=cbind(data, DV, year)
data1$DV1 = relevel(factor(data1$DV), ref = "0")
## Save data as csv file (to use in Stata)
library(foreign)
write.csv(data1, "datafile.csv", row.names=FALSE)
## Run multinom
library(nnet)
model1 <- multinom(DV1 ~ IV1 + IV2 + IV3 + IV4 + IV5 + IV1*IV2 + as.factor(year), data = data1)
Results from R
When I run the model using mlogit (without fixed effects) in Stata I get both coefficients and standard errors.
So I tried including year fixed effects in the model using Stata three different ways and none worked:
femlogit
factor-variable and time-series operators not allowed
depvar and indepvars may not contain factor variables or time-series operators
mlogit
fe option: fe not allowed
used i.year: omits certain variables and/or does not give me standard errors and only shows coefficients (example in code below)
* Read file
import delimited using "datafile.csv", clear case(preserve)
* Run regression
mlogit DV1 IV1 IV2 IV3 IV4 IV5 IV1##IV2 i.year, base(0) iterate(1000)
Stata results
xtmlogit
error - does not run
error message: total number of permutations is 2,389,461,218; this many permutations require a considerable amount of memory and can result in long run times; use option force to proceed anyway, or consider using option rsample()
Fixed effects and non-linear models (such as logits) are an awkward combination. In a linear model you can simply add dummies/demean to get rid of a group-specific intercept, but in a non-linear model none of that works. I mean you could do it technically (which I think is what the R code is doing) but conceptually it is very unclear what that actually does.
Econometricians have spent a lot of time working on this, which has led to some work-arounds, usually referred to as conditional logit. IIRC this is what's implemented in femlogit. I think the mistake in your code is that you tried to include the fixed effects through a dummy specification (i.year). Instead, you should xtset your data and then run femlogit without the dummies.
xtset year
femlogit DV1 IV1 IV2 IV3 IV4 IV5 IV1##IV2
Note that these conditional logit models can be very slow. Personally, I'm more a fan of running two one-vs-all linear regressions (1=1 and 0/-1 set to zero, then -1=1 and 0/1 set to zero). However, opinions are divided (Wooldridge appears to be a fan too, many others very much not so).
I have the following model:
Y_{it} = alpha_i + B1*weight_{it} + B2*Dummy_Foreign_{i} + B3*(weight*Dummy_Foreign)_ {it} + e_{it}
and I am interested on the effect on Y of weight for foreign cars and to graph the evolution of the relevant coefficient across quantiles, with the respective standard errors. That is, I need to see the evolution of the coefficients (B1+ B3). I know this is a non-linear effect, and would require some sort of delta method to obtain the variance-covariance matrix to obtain the standard error of (B1+B3).
Before I delve into writing a program that attempts to do this, I thought I would try and ask if there is a way of doing it with grqreg. If this is not possible with grqreg, would someone please guide me into how they would start writing a code that computes the proper standard errors, and graphs the quantile coefficient.
For a cross section example of what I am trying to do, please see code below.
I use grqred to generate the evolution of the separate coefficients (but I need the joint one)-- One graph for the evolution of (B1+B3) with it's respective standard errors.
Thanks.
(I am using Stata 14.1 on Windows 10):
clear
sysuse auto
set scheme s1color
gen gptm = 1000/mpg
label var gptm "gallons / 1000 miles"
gen weight_foreign= weight*foreign
label var weight_foreign "Interaction weight and foreign car"
qreg gptm weight foreign weight_foreign , q(.5)
grqreg weight weight_foreign , ci ols olsci reps(40)
*** Question 1: How to constuct the plot of the coefficient of interest?
Your second question is off-topic here since it is statistical. Try the CV SE site or Statalist.
Here's how you might do (1) in a cross section, using margins and marginsplot:
clear
set more off
sysuse auto
set scheme s1color
gen gptm = 1000/mpg
label var gptm "gallons / 1000 miles"
sqreg gptm c.weight##i.foreign, q(10 25 50 75 95) reps(500) coefl
margins, dydx(weight) predict(outcome(q10)) predict(outcome(q25)) predict(outcome(q50)) predict(outcome(q75)) predict(outcome(q95)) at(foreign=(0 1))
marginsplot, xdimension(_predict) xtitle("Quantile") ///
legend(label(1 "Domestic") label(2 "Foreign")) ///
xlabel(none) xlabel(1 "Q10" 2 "Q25" 3 "Q50" 4 "Q75" 5 "Q95", add) ///
title("Marginal Effect of Weight By Origin") ///
ytitle("GPTM")
This produces a graph like this:
I didn't recast the CI here since it would look cluttered, but that would make it look more like your graph. Just add recastci(rarea) to the options.
Unfortunately, none of the panel quantile regression commands play nice with factor variables and margins. But we can hack something together. First, you can calculate the sums of coefficients with nlcom (instead of more natural lincom, which the lacks the post option), store them, and use Ben Jann's coefplot to graph them. Here's a toy example to give you the main idea where we will look at the effect of tenure for union members:
set more off
estimates clear
webuse nlswork, clear
gen tXu = tenure*union
local quantiles 1 5 10 25 50 75 90 95 99 // K quantiles that you care about
local models "" // names of K quantile models for coefplot to graph
local xlabel "" // for x-axis labels
local j=1 // counter for quantiles
foreach q of numlist `quantiles' {
qregpd ln_wage tenure union tXu, id(idcode) fix(year) quantile(`q')
nlcom (me_tu:_b[tenure]+_b[tXu]), post
estimates store me_tu`q'
local models `"`models' me_tu`q' || "'
local xlabel `"`xlabel' `j++' "Q{sub:`q'}""'
}
di "`models'
di `"`xlabel'"'
coefplot `models' ///
, vertical bycoefs rescale(100) ///
xlab(none) xlabel(`xlabel', add) ///
title("Marginal Effect of Tenure for Union Members On Each Conditional Quantile Q{sub:{&tau}}", size(medsmall)) ///
ytitle("Wage Change in Percent" "") yline(0) ciopts(recast(rcap))
This makes a dromedary curve, which suggests that the effect of tenure is larger in the middle of the wage distribution than at the tails:
I'm trying to create a bar chart in which the frequency is outside the bar and the percentage inside, is it possible? Would post a picture but the system doesn't allow for it yet.
As others pointed out, this is a poor question without code.
It is possible to guess that you are using graph bar. That makes you choose at most one kind and position of bar labels. Much more is possible with twoway bar so long as you do a little work.
sysuse auto, clear
contract rep78 if rep78 < .
su _freq
gen _pc = 100 * _freq / r(sum)
gen s_pc = string(_pc, "%2.1f") + "%"
gen one = 1
twoway bar _freq rep78, barw(0.9) xla(1/5, notick) bfcolor(none) ///
|| scatter one _freq rep78, ms(none ..) mla(s_pc _freq) mlabcolor(black ..) ///
mlabpos(0 12) scheme(s1color) ysc(r(0 32)) yla(, ang(h)) legend(off)
In short:
contract collapses to a dataset of frequencies.
Calculation of percents is trivial, but you need a formatted version in a string variable if the labels are not to look silly. Precise format is at choice.
The frequency scale on the axis is arguably redundant given the bar labels, and could be omitted.
The example puts labels within the bar just above its base at the level of frequency equal to 1. That's a choice for this example and would be too close to the axis if the typical frequencies were much higher.
I am trying to run a random intercept, random coefficient (usually referred to as random slope) multilevel logit model for cross-sectional data with cross-level interactions in Stata with the command gllamm. So, my code would be something like this;
> gen cons = 1
> gen inter = cons
> gen slope = IV3
> gllamm dv iv1 iv2 iv3 iv3iv4 iv4, i(country) link(logit) family(binomial) adapt nrf(2) eqs(inter slope)
where iv1 - iv3 are the level-1 variables, iv3 is dichotomous and its slope / coefficient is let to vary, iv4 is the level-2 variable and iv3iv4 is the cross-level interactions.
After running gllamm, what I actually wanted was something like the Stata command
> margins, dydx (iv3) at(iv4=(1(10)100))
would do. If I am correct, something similar can be obtain with the command
> gllapred prob, mu marg
However, here comes the problem. When I want to plot the marginal predicted probabilities as a function of my level-2 iv4 for the two groups of the dichotomous iv3 separately using the code
> twoway (line prob iv4 if iv3==0, sort) (line prob iv4 if iv3==1, sort),
> ytitle(Predicted marginal probability) xtitle(iv4)
> legend(order(1 "no" 2 "yes"))
what I obtain is not a nice plot with a smooth curve, but with a line that goes up and down at each value of iv4!
I saw people using the
> sort
command after gllapred and before twoway, but I am not sure I understand how it works. Nevertheless, I tried for example
> sort iv3 country iv4
and then
> twoway
but the plot does NOT look the same!
My main question is; is there a problem with my data, or is it about how I rearrange the syntax or the data? What does my plot actually say? Is there a way for me to obtain a nice smooth line?
margins averages over the values of all other variables. Since iv1 and iv2 vary from observation to observation, your line is jiggly. You may want to replace the remaining variables at their means before gllapred (backing up the original values, of course).
I need to calculate the Gini coefficient from disposable personal income data at LIS. According to a LIS training document, the Stata code to do this is:
di "** INCOME DISTRIBUTION II – Exercise 13 **"
program define bottop
qui sum ey [w=hweight*d4]
replace ey = .01*r(mean) if ey<.01*r(mean)
qui sum dpi [w=hweight*d4], de
replace ey = (10*r(p50)/(d4^.5)) if dpi>10*r(p50)
end
foreach file in $us00h $fi00h {
display "`file'"
use hweight d4 dpi if (!mi(dpi) & !(dpi==0)) using "`file'", clear
gen ey=dpi/(d4^0.5)
bottop
ineqdeco ey [w=hweight*d4]
}
I have simply copied and pasted this code from the training document. The snippets
qui sum ey [w=hweight*d4]
replace ey=0.01*r(mean) if ey<0.01*r(mean)
and
qui sum dpi [w=hweight*d4], de
replace ey=(10*r(p50)/(d4^0.5)) if dpi>10*r(p50)
are bottom and top coding, respectively.
When I tried to run this code, the variable hweight was not found. Does anyone know what the new name of hweight is at LIS? Or can anyone suggest how I might otherwise overcome this impasse?
I'm familiar with stata, but the sophistication of this code is beyond my ken.
Much appreciated.
Based on the varaiable definition list at the LIS Documentation page, it looks like the variable is now called HWGT
This is more of a second-best solution. However, the census of population provides income by brackets. If you are willing to do that, you can get the counts for every bracket. Have a top-coded bracket for the last one. Use the median income value within each bracket. Then you can directly apply the formula for the Gini coefficient. It is a second best because it is an approximation for the individaul-level data.
Why don't you try the fastgini command:
http://www.stata.com/statalist/archive/2007-02/msg00524.html
ssc install fastgini
fastgini income
return list
this should give you the gini for the variable income.
This package also allows for weights. Type
help fastgini
for more information