Hi,
I had the same necessity for my work. Could not find any solution so I tried to write down the code myself.
Possibly you don't need it any longer, but just in case...I report my code it here. It seems that the UQR has been also used in health statistics: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282843/
The Unconditional Quantile Regression by Firpo Fortin And Lemieux (2009) can be calculated as an OLS regression of a transformation of the dependent variable. They call this transformation Recentered Influence Function (RIF).
RIF(y_i;t)=q_t+([t-1(y_i<=q_t )])/(f_y (q_t))
where y_i is the dependent variable, q_t is the t quantile of the distribution of y_i , 1(.) is the indicator function and f_y(q_t) is the pdf of y_i evaluated at q_t.
the main problem is to estimate f_y(q_t).
The authors suggest to evaluate a kernel density at that point. In the code below, the density is estimated through proc kde, then interpolated to find the value at the target quantile. The interpolation algorithm is taken from the great blog of Rick Wicklin .
Any comment or suggestion to improve the code is very welcome.
*some data from http://www.economicswebinstitute.org/ecdata.htm;
data temp;
infile DATALINES;
input ID WAGE OCCUPATION SECTOR UNION EDUCATION EXPERIENCE AGE SEX MARR RACE SOUTH;
CARDS;
1 5.10 6 1 0 8 21 35 1 1 2 0
2 4.95 6 1 0 9 42 57 1 1 3 0
3 6.67 6 1 0 12 1 19 0 0 3 0
4 4.00 6 0 0 12 4 22 0 0 3 0
5 7.50 6 0 0 12 17 35 0 1 3 0
6 13.07 6 0 1 13 9 28 0 0 3 0
7 4.45 6 0 0 10 27 43 0 0 3 1
8 19.47 6 0 0 12 9 27 0 0 3 0
9 13.28 6 1 0 16 11 33 0 1 3 0
10 8.75 6 0 0 12 9 27 0 0 3 0
11 11.35 6 0 1 12 17 35 0 1 3 0
12 11.50 6 1 1 12 19 37 0 0 3 0
13 6.50 6 0 0 8 27 41 0 1 3 1
14 6.25 6 0 1 9 30 45 0 0 3 1
15 19.98 6 0 0 9 29 44 0 1 3 1
16 7.30 6 2 0 12 37 55 0 1 3 0
17 8.00 6 0 0 7 44 57 0 1 3 1
18 22.20 6 1 1 12 26 44 0 1 3 0
19 3.65 6 0 0 11 16 33 0 0 3 0
20 20.55 6 0 0 12 33 51 0 1 3 0
21 5.71 6 1 1 12 16 34 1 1 3 0
22 7.00 6 1 1 7 42 55 0 1 1 0
23 3.75 6 0 0 12 9 27 0 0 3 0
24 4.50 6 0 0 11 14 31 0 1 1 1
25 9.56 6 0 0 12 23 41 0 1 3 0
26 5.75 6 1 0 6 45 57 0 1 3 1
27 9.36 6 1 0 12 8 26 0 1 3 0
28 6.50 6 0 0 10 30 46 0 1 3 0
29 3.35 6 1 0 12 8 26 1 1 3 0
30 4.75 6 0 0 12 8 26 0 1 3 0
31 8.90 6 0 0 14 13 33 0 0 3 0
32 4.00 6 0 0 12 46 64 1 0 3 1
33 4.70 6 0 0 8 19 33 0 1 3 0
34 5.00 6 0 0 17 1 24 1 0 3 1
35 9.25 6 1 0 12 19 37 0 0 3 0
36 10.67 6 0 0 12 36 54 0 0 1 0
37 7.61 6 2 0 12 20 38 0 1 1 1
38 10.00 6 2 1 12 35 53 0 1 1 0
39 7.50 6 0 0 12 3 21 0 0 3 0
40 12.20 6 1 0 14 10 30 0 1 3 1
41 3.35 6 0 0 12 0 18 0 0 3 0
42 11.00 6 1 1 14 14 34 0 1 3 1
43 12.00 6 1 0 12 14 32 0 1 3 0
44 4.85 6 1 0 9 16 31 1 1 3 0
45 4.30 6 2 0 13 8 27 0 0 3 1
46 6.00 6 1 0 7 15 28 1 1 3 1
47 15.00 6 1 0 16 12 34 0 1 3 0
48 4.85 6 0 0 10 13 29 0 0 3 1
49 9.00 6 0 1 8 33 47 0 1 3 0
50 6.36 6 1 0 12 9 27 0 1 3 0
51 9.15 6 0 0 12 7 25 0 1 3 0
52 11.00 6 1 1 16 13 35 0 1 3 0
53 4.50 6 1 0 12 7 25 1 1 3 0
54 4.80 6 1 0 12 16 34 1 1 3 0
55 4.00 6 0 0 13 0 19 0 0 3 0
56 5.50 6 1 0 12 11 29 1 0 3 0
57 8.40 6 1 0 13 17 36 0 0 3 0
58 6.75 6 1 0 10 13 29 0 1 3 0
59 10.00 6 1 1 12 22 40 0 0 1 0
60 5.00 6 1 0 12 28 46 1 1 3 0
61 6.50 6 0 0 11 17 34 0 0 3 0
62 10.75 6 2 1 12 24 42 0 1 3 0
63 7.00 6 1 0 3 55 64 0 1 2 1
64 11.43 6 2 0 12 3 21 0 0 3 1
65 4.00 6 1 1 12 6 24 0 0 1 0
66 9.00 6 2 0 10 27 43 0 1 3 0
67 13.00 6 1 1 12 19 37 0 1 1 1
68 12.22 6 2 1 12 19 37 0 1 3 0
69 6.28 6 1 0 12 38 56 1 1 3 0
70 6.75 6 1 1 10 41 57 0 1 1 1
71 3.35 6 1 0 11 3 20 0 0 1 1
72 16.00 6 0 1 14 20 40 0 1 3 0
73 5.25 6 0 0 10 15 31 0 1 3 0
74 3.50 6 1 0 8 8 22 0 1 2 1
75 4.22 6 1 0 8 39 53 1 1 3 1
76 3.00 6 1 1 6 43 55 1 1 2 0
77 4.00 6 1 1 11 25 42 1 1 3 1
78 10.00 6 0 1 12 11 29 0 1 3 0
79 5.00 6 0 0 12 12 30 0 1 1 0
80 16.00 6 1 1 12 35 53 0 1 3 1
81 13.98 6 0 0 14 14 34 0 0 3 0
82 13.26 6 0 1 12 16 34 0 1 3 0
83 6.10 6 1 1 10 44 60 1 0 3 0
84 3.75 6 0 0 16 13 35 1 0 3 1
85 9.00 6 1 1 13 8 27 0 0 1 0
86 9.45 6 1 0 12 13 31 0 0 3 0
87 5.50 6 0 1 11 18 35 0 1 3 0
88 8.93 6 0 0 12 18 36 1 1 3 0
89 6.25 6 0 0 12 6 24 1 0 3 1
90 9.75 6 1 1 11 37 54 0 1 3 1
91 6.73 6 1 0 12 2 20 0 1 3 1
92 7.78 6 1 0 12 23 41 0 1 3 0
93 2.85 6 0 0 12 1 19 0 0 3 0
94 3.35 6 1 0 12 10 28 1 1 1 1
95 19.98 6 1 0 12 23 41 0 1 3 0
96 8.50 6 0 1 12 8 26 0 1 1 0
97 9.75 6 1 0 15 9 30 1 1 3 0
98 15.00 6 2 1 12 33 51 0 1 3 0
99 8.00 6 1 0 12 19 37 1 1 3 0
100 11.25 6 0 0 13 14 33 0 1 3 0
101 14.00 6 0 1 11 13 30 0 1 3 0
102 10.00 6 2 0 10 12 28 0 1 3 0
103 6.50 6 0 0 12 8 26 0 0 3 0
104 9.83 6 1 0 12 23 41 0 1 3 0
105 18.50 6 1 0 14 13 33 1 0 3 0
106 12.50 6 0 0 12 9 27 0 1 3 1
107 26.00 6 0 1 14 21 41 0 1 3 0
108 14.00 6 2 0 5 44 55 0 1 3 1
109 10.50 6 0 1 12 4 22 0 1 3 0
110 11.00 6 1 0 8 42 56 0 1 3 0
111 12.47 6 0 1 13 10 29 0 1 3 0
112 12.50 6 2 0 12 11 29 0 0 3 0
113 15.00 6 2 1 12 40 58 0 1 3 0
114 6.00 6 2 0 12 8 26 0 0 3 0
115 9.50 6 2 0 11 29 46 0 1 3 1
116 5.00 6 0 1 16 3 25 0 0 3 0
117 3.75 6 2 0 11 11 28 0 0 3 0
118 12.57 6 0 1 12 12 30 0 1 3 0
119 6.88 6 0 0 8 22 36 1 1 2 0
120 5.50 6 0 0 12 12 30 0 1 3 0
121 7.00 6 0 1 12 7 25 0 1 3 0
122 4.50 6 1 0 12 15 33 1 0 3 0
123 6.50 6 0 0 12 28 46 0 1 3 0
124 12.00 6 1 1 12 20 38 0 1 3 1
125 5.00 6 2 0 12 6 24 0 0 3 1
126 6.50 6 1 0 12 5 23 0 0 3 1
127 6.80 6 1 0 9 30 45 1 1 3 1
128 8.75 6 0 0 13 18 37 0 1 3 0
129 3.75 6 1 0 12 6 24 1 1 1 1
130 4.50 6 0 0 12 16 34 0 0 2 1
131 6.00 6 0 1 12 1 19 0 0 2 1
132 5.50 6 1 0 12 3 21 0 0 3 0
133 13.00 6 0 0 12 8 26 0 1 3 0
134 5.65 6 1 0 14 2 22 0 0 3 0
135 4.80 6 1 0 9 16 31 0 0 1 0
136 7.00 6 2 0 10 9 25 0 1 3 1
137 5.25 6 0 0 12 2 20 0 0 3 0
138 3.35 6 1 0 7 43 56 0 1 3 1
139 8.50 6 1 0 9 38 53 0 1 3 0
140 6.00 6 0 0 12 9 27 0 1 3 0
141 6.75 6 0 0 12 12 30 0 1 3 1
142 8.89 6 1 0 12 18 36 0 1 3 0
143 14.21 6 1 1 11 15 32 0 0 3 0
144 10.78 6 2 1 11 28 45 0 1 1 1
145 8.90 6 2 1 10 27 43 0 1 3 1
146 7.50 6 0 0 12 38 56 0 1 3 1
147 4.50 6 1 0 12 3 21 1 0 3 0
148 11.25 6 0 1 12 41 59 0 1 3 0
149 13.45 6 0 1 12 16 34 0 1 3 1
150 6.00 6 1 0 13 7 26 0 1 3 1
151 4.62 6 1 0 6 33 45 1 0 1 1
152 10.58 6 1 0 14 25 45 0 1 3 0
153 5.00 6 0 0 12 5 23 0 1 3 1
154 8.20 6 0 0 14 17 37 0 0 1 1
155 6.25 6 0 0 12 1 19 0 0 3 1
156 8.50 6 1 0 12 13 31 0 1 3 0
157 24.98 1 0 0 16 18 40 0 1 3 0
158 16.65 1 0 0 14 21 41 0 1 3 1
159 6.25 1 0 0 14 2 22 0 0 3 0
160 4.55 1 0 0 12 4 22 1 0 2 1
161 11.25 1 0 0 12 30 48 1 1 2 1
162 21.25 1 0 0 13 32 51 0 0 3 0
163 12.65 1 0 0 17 13 36 1 1 3 0
164 7.50 1 0 0 12 17 35 0 0 3 0
165 10.25 1 0 0 14 26 46 1 1 3 0
166 3.35 1 0 0 16 9 31 0 0 3 0
167 13.45 1 0 0 16 8 30 0 0 1 0
168 4.84 1 0 1 15 1 22 0 1 3 0
169 26.29 1 0 0 17 32 55 0 1 3 1
170 6.58 1 0 0 12 24 42 1 1 3 0
171 44.50 1 0 0 14 1 21 1 0 3 0
172 15.00 1 1 0 12 42 60 0 1 3 0
173 11.25 1 1 0 16 3 25 1 0 1 0
174 7.00 1 0 0 12 32 50 1 1 3 0
175 10.00 1 0 0 14 22 42 0 0 1 0
176 14.53 1 0 0 16 18 40 0 1 3 0
177 20.00 1 0 0 18 19 43 1 1 3 0
178 22.50 1 0 0 15 12 33 0 1 3 0
179 3.64 1 0 0 12 42 60 1 1 3 0
180 10.62 1 0 0 12 34 52 0 1 3 1
181 24.98 1 0 0 18 29 53 0 1 3 0
182 6.00 1 0 0 16 8 30 0 0 3 1
183 19.00 1 1 0 18 13 37 0 0 3 0
184 13.20 1 0 0 16 10 32 0 0 3 0
185 22.50 1 0 0 16 22 44 0 1 3 0
186 15.00 1 0 0 16 10 32 0 1 3 1
187 6.88 1 0 0 17 15 38 1 1 3 0
188 11.84 1 0 0 12 26 44 0 1 3 0
189 16.14 1 0 0 14 16 36 0 0 3 0
190 13.95 1 0 0 18 14 38 1 1 3 0
191 13.16 1 0 0 12 38 56 1 1 3 0
192 5.30 1 0 0 12 14 32 0 1 1 1
193 4.50 1 0 0 12 7 25 1 1 3 0
194 10.00 1 0 0 18 13 37 1 0 3 1
195 10.00 1 0 0 10 20 36 0 1 3 0
196 10.00 1 0 1 16 7 29 0 1 2 0
197 9.37 1 0 0 16 26 48 1 1 3 0
198 5.80 1 0 0 16 14 36 0 1 3 0
199 17.86 1 0 0 13 36 55 0 0 3 0
200 1.00 1 0 0 12 24 42 0 1 3 0
201 8.80 1 0 0 14 41 61 0 1 3 1
202 9.00 1 0 0 16 7 29 0 1 1 0
203 18.16 1 0 0 17 14 37 0 0 3 1
204 7.81 1 0 0 12 1 19 1 0 3 1
205 10.62 1 1 0 16 6 28 1 1 3 0
206 4.50 1 0 0 12 3 21 1 1 3 0
207 17.25 1 0 0 15 31 52 0 1 3 0
208 10.50 1 1 0 13 14 33 1 1 3 0
209 9.22 1 0 0 14 13 33 1 1 3 0
210 15.00 1 1 1 16 26 48 0 1 1 0
211 22.50 1 0 0 18 14 38 0 1 3 0
212 4.55 2 0 0 13 33 52 1 1 3 0
213 9.00 2 0 0 12 16 34 0 1 3 0
214 13.33 2 0 0 18 10 34 0 1 3 0
215 15.00 2 0 0 14 22 42 0 0 3 0
216 7.50 2 0 0 14 2 22 0 0 3 0
217 4.25 2 0 0 12 29 47 1 1 3 1
218 12.50 2 1 0 12 43 61 0 1 3 0
219 5.13 2 0 0 12 5 23 1 1 3 0
220 3.35 2 0 0 16 14 36 1 1 1 1
221 11.11 2 0 0 12 28 46 0 1 3 1
222 3.84 2 0 0 11 25 42 1 1 1 1
223 6.40 2 0 0 12 45 63 1 1 3 0
224 5.56 2 0 0 14 5 25 0 0 3 1
225 10.00 2 1 0 12 20 38 0 1 3 1
226 5.65 2 0 0 16 6 28 1 1 3 0
227 11.50 2 0 0 16 16 38 0 1 3 0
228 3.50 2 0 0 11 33 50 1 1 3 0
229 3.35 2 0 0 13 2 21 1 1 3 1
230 4.75 2 0 0 12 10 28 1 0 3 1
231 19.98 2 0 0 14 44 64 0 1 3 1
232 3.50 2 0 0 14 6 26 1 1 3 1
233 4.00 2 0 0 12 15 33 1 0 3 0
234 7.00 2 0 0 12 5 23 0 1 3 0
235 6.25 2 1 0 13 4 23 1 1 3 0
236 4.50 2 0 0 14 14 34 0 1 3 0
237 14.29 2 0 0 14 32 52 1 1 3 0
238 5.00 2 0 0 12 14 32 1 1 3 0
239 13.75 2 0 0 14 21 41 0 1 3 0
240 13.71 2 0 1 12 43 61 0 1 3 0
241 7.50 2 0 0 12 27 45 1 1 1 1
242 3.80 2 0 0 12 4 22 1 0 3 0
243 5.00 2 0 0 14 0 20 0 0 2 0
244 9.42 2 0 0 12 32 50 0 1 3 1
245 5.50 2 0 0 12 20 38 0 1 3 0
246 3.75 2 0 0 15 4 25 0 0 3 1
247 3.50 2 0 0 12 34 52 0 1 3 0
248 5.80 2 0 0 13 5 24 0 0 3 0
249 12.00 2 1 0 17 13 36 0 1 3 0
250 5.00 3 0 0 14 17 37 1 1 2 0
251 8.75 3 0 0 13 10 29 1 1 3 1
252 10.00 3 0 0 16 7 29 1 1 3 0
253 8.50 3 0 0 12 25 43 1 0 3 0
254 8.63 3 0 0 12 18 36 1 1 1 0
255 9.00 3 1 0 16 27 49 1 1 3 0
256 5.50 3 0 0 16 2 24 1 0 3 0
257 11.11 3 0 0 13 13 32 0 1 3 0
258 10.00 3 0 0 14 24 44 1 0 3 0
259 5.20 3 0 0 18 13 37 1 1 2 1
260 8.00 3 0 1 14 15 35 1 0 3 0
261 3.56 3 0 0 12 12 30 1 0 2 1
262 5.20 3 0 0 12 24 42 1 1 3 0
263 11.67 3 2 0 12 43 61 1 1 3 0
264 11.32 3 1 0 12 13 31 1 1 3 0
265 7.50 3 0 0 12 16 34 1 1 3 1
266 5.50 3 0 0 11 24 41 1 1 3 0
267 5.00 3 0 0 16 4 26 1 1 3 1
268 7.75 3 0 0 12 24 42 1 1 3 0
269 5.25 3 0 0 12 45 63 1 1 3 0
270 9.00 3 0 1 12 20 38 0 1 3 0
271 9.65 3 0 0 12 38 56 1 1 3 0
272 5.21 3 0 0 18 10 34 0 1 3 1
273 7.00 3 0 0 11 16 33 1 1 1 0
274 12.16 3 0 0 12 32 50 1 1 1 1
275 5.25 3 0 0 16 2 24 1 0 3 1
276 10.32 3 0 0 13 28 47 1 0 3 1
277 3.35 3 0 0 16 3 25 0 0 1 0
278 7.70 3 0 1 13 8 27 1 0 3 0
279 9.17 3 1 0 12 44 62 1 1 3 0
280 8.43 3 0 0 12 12 30 0 1 3 1
281 4.00 3 0 0 12 8 26 0 1 1 1
282 4.13 3 0 0 12 4 22 1 1 3 0
283 3.00 3 0 0 12 28 46 1 1 3 1
284 4.25 3 0 0 13 0 19 1 0 3 1
285 7.53 3 0 0 14 1 21 0 0 3 1
286 10.53 3 1 0 14 12 32 1 1 3 0
287 5.00 3 0 0 12 39 57 1 1 3 0
288 15.03 3 0 0 12 24 42 1 1 3 0
289 11.25 3 0 0 17 32 55 1 1 1 0
290 6.25 3 0 0 16 4 26 0 0 1 0
291 3.50 3 0 0 12 25 43 1 0 1 0
292 6.85 3 0 0 12 8 26 0 0 1 0
293 12.50 3 0 0 13 16 35 1 1 3 0
294 12.00 3 0 0 12 5 23 0 0 3 1
295 6.00 3 0 0 13 31 50 0 0 3 0
296 9.50 3 0 0 12 25 43 1 0 3 0
297 4.10 3 0 0 12 15 33 1 1 3 0
298 10.43 3 0 0 14 15 35 1 1 3 1
299 5.00 3 0 0 12 0 18 1 0 3 0
300 7.69 3 0 0 12 19 37 0 1 3 0
301 5.50 3 0 0 12 21 39 1 0 1 0
302 6.40 3 0 0 12 6 24 1 0 3 0
303 12.50 3 0 1 12 14 32 1 1 3 0
304 6.25 3 0 0 13 30 49 1 1 3 0
305 8.00 3 0 0 12 8 26 1 0 3 0
306 9.60 3 0 1 9 33 48 0 0 3 0
307 9.10 3 0 0 13 16 35 0 0 2 0
308 7.50 3 0 0 12 20 38 1 0 3 1
309 5.00 3 0 0 13 6 25 1 1 3 1
310 7.00 3 0 1 12 10 28 1 1 3 0
311 3.55 3 0 0 13 1 20 1 0 3 1
312 8.50 3 0 0 12 2 20 0 0 1 1
313 4.50 3 0 0 13 0 19 1 0 3 1
314 7.88 3 0 0 16 17 39 0 1 1 0
315 5.25 3 0 0 12 8 26 1 0 3 0
316 5.00 3 0 0 12 4 22 0 0 3 1
317 9.33 3 0 0 12 15 33 1 0 3 0
318 10.50 3 0 0 12 29 47 1 1 3 0
319 7.50 3 0 0 12 23 41 1 1 1 1
320 9.50 3 0 0 12 39 57 1 1 3 1
321 9.60 3 0 0 12 14 32 1 1 3 1
322 5.87 3 0 0 17 6 29 1 0 1 1
323 11.02 3 0 1 14 12 32 0 1 3 1
324 5.00 3 0 0 12 26 44 1 0 3 1
325 5.62 3 0 0 14 32 52 1 1 3 0
326 12.50 3 0 0 15 6 27 1 1 3 0
327 10.81 3 0 0 12 40 58 1 1 3 0
328 5.40 3 1 0 12 18 36 1 1 3 0
329 7.00 3 0 0 11 12 29 1 0 3 0
330 4.59 3 2 0 12 36 54 1 1 3 1
331 6.00 3 0 0 12 19 37 1 1 3 0
332 11.71 3 1 0 16 42 64 1 0 3 0
333 5.62 3 0 0 13 2 21 1 1 2 0
334 5.50 3 0 0 12 33 51 1 1 3 0
335 4.85 3 0 0 12 14 32 1 1 3 1
336 6.75 3 0 0 12 22 40 0 0 3 0
337 4.25 3 0 0 12 20 38 1 1 3 0
338 5.75 3 0 0 12 15 33 1 1 3 0
339 3.50 3 0 0 12 35 53 0 1 3 0
340 3.35 3 0 0 12 7 25 1 1 3 0
341 10.62 3 1 0 12 45 63 1 0 3 0
342 8.00 3 0 0 12 9 27 1 0 3 0
343 4.75 3 0 0 12 2 20 1 1 3 1
344 8.50 3 0 0 17 3 26 0 0 3 1
345 8.85 3 0 1 14 19 39 1 1 1 0
346 8.00 3 0 0 12 14 32 1 1 3 1
347 6.00 4 0 0 4 54 64 0 1 3 0
348 7.14 4 0 0 14 17 37 0 1 3 0
349 3.40 4 0 0 8 29 43 1 1 1 0
350 6.00 4 0 0 15 26 47 1 0 3 1
351 3.75 4 0 0 2 16 24 0 0 2 0
352 8.89 4 0 0 8 29 43 1 0 1 0
353 4.35 4 0 0 11 20 37 1 1 3 0
354 13.10 4 0 0 10 38 54 1 1 1 1
355 4.35 4 0 0 8 37 51 1 1 1 1
356 3.50 4 0 0 9 48 63 0 0 3 0
357 3.80 4 0 0 12 16 34 1 0 3 0
358 5.26 4 0 0 8 38 52 1 1 3 0
359 3.35 4 0 0 14 0 20 0 0 1 0
360 16.26 4 0 1 12 14 32 0 0 1 0
361 4.25 4 0 0 12 2 20 1 1 3 0
362 4.50 4 0 0 16 21 43 0 1 3 0
363 8.00 4 0 0 13 15 34 1 1 3 0
364 4.00 4 0 0 16 20 42 1 0 3 0
365 7.96 4 0 0 14 12 32 1 1 3 0
366 4.00 4 0 0 12 7 25 0 0 2 1
367 4.15 4 0 0 11 4 21 0 1 3 0
368 5.95 4 0 0 13 9 28 0 1 3 1
369 3.60 4 0 0 12 43 61 1 1 2 1
370 8.75 4 0 0 10 19 35 0 0 3 1
371 3.40 4 0 0 8 49 63 1 0 3 0
372 4.28 4 0 0 12 38 56 1 1 3 0
373 5.35 4 0 0 12 13 31 1 1 3 0
374 5.00 4 0 0 12 14 32 1 1 3 0
375 7.65 4 0 0 12 20 38 0 0 3 0
376 6.94 4 0 0 12 7 25 1 0 3 0
377 7.50 4 1 1 12 9 27 1 1 3 0
378 3.60 4 0 0 12 6 24 1 0 3 0
379 1.75 4 0 0 12 5 23 1 1 3 1
380 3.45 4 0 0 13 1 20 1 0 1 1
381 9.63 4 0 1 14 22 42 0 1 3 0
382 8.49 4 0 0 12 24 42 1 1 3 0
383 8.99 4 0 1 12 15 33 1 0 3 0
384 3.65 4 0 0 11 8 25 1 1 3 1
385 3.50 4 0 0 11 17 34 1 1 3 1
386 3.43 4 0 0 12 2 20 0 0 1 1
387 5.50 4 0 0 12 20 38 0 1 3 1
388 6.93 4 0 1 12 26 44 0 1 3 0
389 3.51 4 0 0 10 37 53 1 1 1 1
390 3.75 4 0 0 12 41 59 1 0 3 0
391 4.17 4 0 0 12 27 45 1 1 3 0
392 9.57 4 0 1 12 5 23 1 1 3 0
393 14.67 4 0 0 14 16 36 0 1 1 0
394 12.50 4 0 0 14 19 39 1 1 3 0
395 5.50 4 0 0 12 10 28 0 1 3 0
396 5.15 4 0 1 13 1 20 0 0 3 1
397 8.00 4 0 1 12 43 61 1 1 1 0
398 5.83 4 0 0 13 3 22 0 0 1 0
399 3.35 4 0 0 12 0 18 1 0 3 0
400 7.00 4 0 0 12 26 44 1 1 3 1
401 10.00 4 0 1 10 25 41 1 1 3 0
402 8.00 4 0 0 12 15 33 1 1 3 0
403 6.88 4 0 0 14 10 30 1 0 3 1
404 5.55 4 0 1 11 45 62 1 0 3 0
405 7.50 4 0 0 11 3 20 0 0 1 0
406 8.93 4 0 1 8 47 61 0 1 2 0
407 9.00 4 0 0 16 6 28 1 1 1 0
408 3.50 4 0 0 10 33 49 1 0 3 1
409 5.77 4 1 0 16 3 25 0 0 3 0
410 25.00 4 0 1 14 4 24 0 0 2 0
411 6.85 4 0 1 14 34 54 0 1 1 0
412 6.50 4 0 0 11 39 56 0 1 3 1
413 3.75 4 0 0 12 17 35 1 1 3 1
414 3.50 4 0 1 9 47 62 0 1 3 0
415 4.50 4 0 0 11 2 19 0 0 3 0
416 2.01 4 0 0 13 0 19 0 0 3 1
417 4.17 4 0 0 14 24 44 1 0 3 0
418 13.00 4 0 1 12 25 43 0 1 1 0
419 3.98 4 0 0 14 6 26 1 0 3 0
420 7.50 4 0 0 12 10 28 1 0 3 0
421 13.12 4 0 0 12 33 51 1 1 1 0
422 4.00 4 0 0 12 12 30 0 0 3 0
423 3.95 4 0 0 12 9 27 1 1 3 1
424 13.00 4 0 1 11 18 35 0 1 3 1
425 9.00 4 0 0 12 10 28 0 1 3 0
426 4.55 4 0 0 8 45 59 1 0 3 1
427 9.50 4 0 1 9 46 61 1 1 3 0
428 4.50 4 0 0 7 14 27 0 1 2 1
429 8.75 4 0 0 11 36 53 1 0 3 0
430 10.00 5 2 1 13 34 53 0 1 3 0
431 18.00 5 0 0 18 15 39 0 1 3 0
432 24.98 5 1 0 17 31 54 0 1 3 0
433 12.05 5 1 0 16 6 28 1 0 3 0
434 22.00 5 0 0 14 15 35 0 1 3 1
435 8.75 5 0 0 12 30 48 0 1 3 0
436 22.20 5 0 0 18 8 32 0 1 3 0
437 17.25 5 1 0 18 5 29 0 1 3 0
438 6.00 5 0 1 17 3 26 1 0 3 0
439 8.06 5 0 0 13 17 36 0 1 3 1
440 9.24 5 1 1 16 5 27 0 1 1 0
441 12.00 5 0 0 14 10 30 1 1 3 0
442 10.61 5 0 0 15 33 54 1 0 3 0
443 5.71 5 0 0 18 3 27 0 1 3 0
444 10.00 5 0 0 16 0 18 1 0 3 0
445 17.50 5 0 0 16 13 35 0 1 1 1
446 15.00 5 0 0 18 12 36 0 1 3 0
447 7.78 5 0 0 16 6 28 1 1 3 0
448 7.80 5 0 0 17 7 30 0 1 3 0
449 10.00 5 0 1 16 14 36 0 1 3 1
450 24.98 5 0 0 17 5 28 1 0 3 0
451 10.28 5 0 0 15 10 31 1 1 3 1
452 15.00 5 0 0 18 11 35 1 1 3 0
453 12.00 5 0 0 17 24 47 1 1 3 0
454 10.58 5 1 0 16 9 31 0 0 3 0
455 5.85 5 0 0 18 12 36 0 1 3 1
456 11.22 5 0 0 18 19 43 0 1 3 0
457 8.56 5 0 0 14 14 34 1 1 3 0
458 13.89 5 1 0 16 17 39 1 0 3 0
459 5.71 5 0 0 18 7 31 0 0 3 1
460 15.79 5 0 0 18 7 31 0 1 3 0
461 7.50 5 0 0 16 22 44 1 1 3 0
462 11.25 5 0 0 12 28 46 1 1 3 0
463 6.15 5 0 0 16 16 38 1 0 3 0
464 13.45 5 0 0 16 16 38 0 0 1 1
465 6.25 5 0 0 16 7 29 1 1 3 0
466 6.50 5 0 0 12 11 29 1 0 3 0
467 12.00 5 0 0 12 11 29 1 1 3 0
468 8.50 5 0 0 12 16 34 1 0 3 0
469 8.00 5 0 1 18 33 57 0 0 3 0
470 5.75 5 0 0 12 21 39 1 1 3 1
471 15.73 5 1 0 16 4 26 0 1 3 0
472 9.86 5 0 0 15 13 34 0 1 3 0
473 13.51 5 0 1 18 14 38 0 1 3 0
474 5.40 5 0 0 16 10 32 1 1 3 0
475 6.25 5 0 0 18 14 38 0 1 3 1
476 5.50 5 0 0 16 29 51 0 1 3 1
477 5.00 5 0 0 12 4 22 0 0 2 0
478 6.25 5 0 0 18 27 51 0 1 1 0
479 5.75 5 0 0 12 3 21 0 1 3 0
480 20.50 5 0 1 16 14 36 0 1 3 1
481 5.00 5 2 0 14 0 20 0 1 3 0
482 7.00 5 0 0 18 33 57 0 1 3 0
483 18.00 5 0 0 16 38 60 0 1 3 1
484 12.00 5 0 1 18 18 42 1 1 3 0
485 20.40 5 1 0 17 3 26 0 0 3 0
486 22.20 5 0 0 18 40 64 1 0 3 0
487 16.42 5 1 0 14 19 39 0 0 3 0
488 8.63 5 0 0 14 4 24 1 0 3 0
489 19.38 5 0 0 16 11 33 1 1 3 0
490 14.00 5 0 0 16 16 38 1 1 3 0
491 10.00 5 0 0 14 22 42 0 1 3 0
492 15.95 5 0 1 17 13 36 1 0 3 0
493 20.00 5 0 1 16 28 50 1 1 3 1
494 10.00 5 0 0 16 10 32 1 1 3 0
495 24.98 5 0 0 16 5 27 1 0 3 1
496 11.25 5 0 0 15 5 26 0 0 3 0
497 22.83 5 1 0 18 37 61 1 0 3 0
498 10.20 5 0 1 17 26 49 1 1 3 0
499 10.00 5 0 0 16 4 26 1 1 3 1
500 14.00 5 0 1 18 31 55 1 0 3 0
501 12.50 5 0 1 17 13 36 1 1 3 0
502 5.79 5 0 0 12 42 60 1 1 3 0
503 24.98 5 0 0 17 18 41 0 1 2 0
504 4.35 5 0 0 12 3 21 1 1 3 0
505 11.25 5 0 0 17 10 33 1 0 3 0
506 6.67 5 0 1 16 10 32 1 0 3 0
507 8.00 5 0 0 16 17 39 1 1 2 0
508 18.16 5 0 0 18 7 31 0 1 3 0
509 12.00 5 0 0 16 14 36 1 1 3 0
510 8.89 5 0 1 16 22 44 1 1 3 0
511 9.50 5 0 0 17 14 37 1 1 3 0
512 13.65 5 0 0 16 11 33 0 1 3 0
513 12.00 5 0 1 18 23 47 0 1 3 0
514 15.00 5 0 1 12 39 57 0 1 3 0
515 12.67 5 0 0 16 15 37 0 1 3 0
516 7.38 5 0 0 14 15 35 1 0 2 0
517 15.56 5 0 0 16 10 32 0 0 3 0
518 7.45 5 0 0 12 25 43 1 0 3 1
519 6.25 5 0 0 14 12 32 1 1 3 0
520 6.25 5 0 0 16 7 29 1 1 2 1
521 9.37 5 0 1 17 7 30 0 1 3 0
522 22.50 5 1 0 16 17 39 0 1 3 0
523 7.50 5 0 1 16 10 32 0 1 3 0
524 7.00 5 0 0 17 2 25 0 1 3 1
525 5.75 5 0 1 9 34 49 1 1 1 1
526 7.67 5 0 0 15 11 32 1 1 3 0
527 12.50 5 0 0 15 10 31 0 0 3 0
528 16.00 5 0 0 12 12 30 0 1 3 1
529 11.79 5 0 1 16 6 28 1 0 3 0
530 11.36 5 0 0 18 5 29 0 0 3 0
531 6.10 5 0 0 12 33 51 1 1 1 0
532 23.25 5 0 1 17 25 48 1 1 1 0
533 19.88 5 0 1 12 13 31 0 1 3 1
534 15.38 5 1 0 16 33 55 0 1 3 0
;
run;
data temp1;
set temp;
lwage=log(wage);
age2=age*age;
run;
*calculate the density;
proc kde data=temp1;
univar lwage/out=kde;
run;
*calculate and transpose the percentiles;
proc univariate data=temp1 noprint;
var lwage;
output out=p pctlpre=P_
pctlpts=1 to 99 by 1;
run;
proc transpose data=p out=pt(rename=(col1=value));
run;
data pt(drop=_name_ _label_);
set pt;
retain percentile;
if _n_=1 then percentile=1;
else percentile+1;
run;
*interpolate the density to evaluate it at the percentiles value;
* adapted from https://blogs.sas.com/content/iml/2012/03/16/linear-interpolation-in-sas.html;
proc iml;
start Last(x); /* Helper module: return last element in a vector */
return( x[nrow(x)*ncol(x)] );
finish;
/* Interpolate such that (x,y) is on line segment between (x1,y1) and (x2,y2) */
start LinInterpPt(x1, x2, y1, y2, x);
m = (y2-y1)/(x2-x1); /* slope */
return( m#(x-x1) + y1 ); /* point-slope formula */
finish;
/* Linear interpolation: simple version */
start LinInterp1(x, y, v);
/* Given column vectors (x, y), interpolate values for column vector v.
Assume: 1. no missing values in x, y, or v
2. the values of x are unique and sorted
3. each element of v is in the interval [minX, maxX) */
fv = j(nrow(v),1); /* allocate return vector */
do i = 1 to nrow(v);
k = Last( loc(x <= v[i] )); /* largest x less than v[i] */
fv[i] = LinInterpPt(x[k], x[k+1], y[k], y[k+1], v[i]);
end;
return( fv );
finish;
/* test it on some data */
*xy = {0 1, 1 2, 2 4, 4 0 };
*v = {0.1, 1.1, 0.5, 2.7, 3}; /* interpolate at these points */
use kde(keep=value density);
read all var _ALL_ into xy;
close kde;
use pt;
read all var _ALL_ into v;
close pt;
fv = LinInterp1(xy[,1], xy[,2], v[ ,1]);
v2=insert(v, fv, 0, 3);
*print xy;
print v fv v2;
/** create SAS data set from a matrix **/
create pt2 from v2[colname={"value" "percentile" "density"}];
append from v2;
close pt2;
quit;
proc transpose data=pt2 out=p2 prefix=P_;
id percentile;
var value density;
run;
*add the values of the 10,50,90 percentiles and the density evaluated at them to the dataset;
*calculate the the rif transformation;
data temp2;
set temp1;
if _n_ eq 1 then do;
set p2(where=(_NAME_='value') keep=_NAME_ P_10 P_50 P_90);
set p2(where=(_NAME_='density') keep=_NAME_ P_10 P_50 P_90 rename=(P_10=D_10 P_50=D_50 P_90=D_90));
end;
under10=(lwage le P_10);
under50=(lwage le P_50);
under90=(lwage le P_90);
RIF10=P_10+(0.1-under10)/D_10;
RIF50=P_50+(0.5-under50)/D_50;
RIF90=P_90+(0.9-under90)/D_90;
label RIF10='P10'
RIF50='P50'
RIF90='P90'
;
run;
*perform the RIF-OLS regression;
ods graphics off;
ods output ParameterEstimates (persist) = parmrif fitstatistics (persist) = fitrif nobs(persist) = nobsrif;
proc reg data=temp2 ;
model rif10=sex age age2 education;
model rif50=sex age age2 education;
model rif90=sex age age2 education;
run;
quit;
ods output close;
ods graphics on;
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