ps_trim() applies trimming methods to a propensity-score vector, returning
a new vector of the same length, with trimmed entries replaced by NA. You
can inspect further metadata in ps_trim_meta(x). After running ps_trim(),
you should refit the model with ps_refit().
Usage
ps_trim(
ps,
method = c("ps", "adaptive", "pctl", "pref", "cr"),
lower = NULL,
upper = NULL,
.exposure = NULL,
.treated = NULL,
.untreated = NULL
)Arguments
- ps
The propensity score, a numeric vector between 0 and 1.
- method
One of
c("ps", "adaptive", "pctl", "pref", "cr").- lower, upper
Numeric cutoffs or quantiles. If
NULL, defaults vary by method.- .exposure
For methods like
"pref"or"cr", a vector for a binary exposure.- .treated
The value representing the treatment group. If not provided, it is automatically detected.
- .untreated
The value representing the control group. If not provided, it is automatically detected.
Details
The returned object is a ps_trim vector of the same length as
ps, but with trimmed entries replaced by NA. An attribute ps_trim_meta
contains:
method: Which trimming method was usedkeep_idx: Indices retainedtrimmed_idx: Indices replaced byNAPossibly other fields such as final cutoffs, etc.
See also
ps_trunc() for bounding/winsorizing instead of discarding,
is_refit(), is_ps_trimmed()
Examples
set.seed(2)
n <- 300
x <- rnorm(n)
z <- rbinom(n, 1, plogis(1.3 * x))
fit <- glm(z ~ x, family = binomial)
ps <- predict(fit, type = "response")
ps_trim(ps, method = "adaptive")
#> <ps_trim; trimmed 44 of [300]>
#> 1 2 3 4 5 6 7 8
#> 0.1780112 0.4934483 0.8725819 0.1353577 0.4025855 0.4752489 0.6683913 0.3506150
#> 9 10 11 12 13 14 15 16
#> NA 0.3831809 0.5738044 0.7467782 0.3038553 0.1508037 0.8997256 NA
#> 17 18 19 20 21 22 23 24
#> 0.7187207 0.4419184 0.7548592 0.5787647 NA 0.1244366 0.8728587 NA
#> 25 26 27 28 29 30 31 32
#> 0.4313639 NA 0.5939254 0.2474276 0.6938170 0.5298266 0.6778670 0.5399668
#> 33 34 35 36 37 38 39 40
#> 0.7707828 0.3366773 0.2037945 0.2476600 NA 0.1768609 0.2572600 0.3484614
#> 41 42 43 44 45 46 47 48
#> 0.3065403 NA 0.1895191 NA 0.6415565 NA 0.3300895 0.3990495
#> 49 50 51 52 53 54 55 56
#> 0.3683877 0.1246121 0.1902500 NA 0.2564150 0.8160957 0.1494022 NA
#> 57 58 59 60 61 62 63 64
#> 0.3247367 0.7345271 0.7859021 0.8849770 NA NA 0.2208817 0.4841803
#> 65 66 67 68 69 70 71 72
#> 0.6036086 0.1941979 NA 0.2790100 0.4585602 0.1783019 0.1731103 0.5439312
#> 73 74 75 76 77 78 79 80
#> 0.3822372 0.5796397 0.4114856 0.1759469 0.8218240 0.6877562 0.7649259 NA
#> 81 82 83 84 85 86 87 88
#> 0.7505008 NA 0.2641422 0.1005949 NA NA 0.2330120 0.3365148
#> 89 90 91 92 93 94 95 96
#> 0.3055473 0.5632496 0.8745082 0.8863194 0.1269292 0.1023453 NA 0.1166039
#> 97 98 99 100 101 102 103 104
#> NA 0.4322875 0.1893249 0.2462384 0.7703638 0.5197606 0.3273939 0.2099624
#> 105 106 107 108 109 110 111 112
#> 0.1851823 NA 0.7356255 NA 0.2954896 0.3163021 0.1530042 0.3471981
#> 113 114 115 116 117 118 119 120
#> 0.5921212 0.8328274 0.6227067 0.5867797 0.8065734 0.7877456 0.4663064 0.2023006
#> 121 122 123 124 125 126 127 128
#> 0.8087856 0.4774812 0.8903829 0.2928150 0.1499937 0.6139848 0.2290136 0.6467536
#> 129 130 131 132 133 134 135 136
#> NA NA 0.6627758 0.5441083 0.7165979 NA 0.8025574 0.7998822
#> 137 138 139 140 141 142 143 144
#> 0.7597742 0.6921037 NA NA 0.2509264 0.5711077 0.1970442 0.4590332
#> 145 146 147 148 149 150 151 152
#> 0.6800801 0.2329425 0.6525433 0.6180388 0.1970997 0.1584870 0.7452343 0.3731672
#> 153 154 155 156 157 158 159 160
#> 0.6727629 0.1889404 0.8164247 0.1043218 0.6858279 0.5895482 0.5223260 0.6558189
#> 161 162 163 164 165 166 167 168
#> 0.5672709 0.2368400 0.3418011 0.5540597 0.1083159 0.1806594 NA NA
#> 169 170 171 172 173 174 175 176
#> 0.1187105 0.7490531 0.7738375 0.7077039 0.4491471 0.5416643 0.1764396 0.2333126
#> 177 178 179 180 181 182 183 184
#> 0.3434474 0.1704628 0.7023006 NA 0.1524604 0.1153463 0.5651259 0.1351849
#> 185 186 187 188 189 190 191 192
#> 0.6161453 0.7945146 0.4382952 0.6065642 0.2328341 0.6027459 0.1139088 0.4037497
#> 193 194 195 196 197 198 199 200
#> 0.1040353 0.3422376 0.7735701 0.6661116 0.2893391 0.2011360 0.1863223 0.2107038
#> 201 202 203 204 205 206 207 208
#> 0.5327159 0.1544197 NA 0.5052145 0.1642856 0.5622725 0.3887452 0.3163883
#> 209 210 211 212 213 214 215 216
#> 0.6341619 0.5095796 0.7582940 0.2665601 NA NA 0.4774214 0.5852567
#> 217 218 219 220 221 222 223 224
#> 0.8030404 0.1067663 0.1338595 0.8855459 0.5671613 0.6707177 0.2000938 NA
#> 225 226 227 228 229 230 231 232
#> 0.4538222 0.5912550 0.3993802 0.7230688 0.3094663 NA 0.8702859 NA
#> 233 234 235 236 237 238 239 240
#> 0.2415827 0.4195113 NA 0.1483657 0.2541910 0.5957602 0.4626159 0.2496911
#> 241 242 243 244 245 246 247 248
#> 0.4020747 NA 0.7936546 0.6179515 0.6899355 0.2556513 0.4650462 0.5270157
#> 249 250 251 252 253 254 255 256
#> 0.2803714 0.2850014 0.6020472 0.3002118 0.3705819 0.3257820 0.7087374 0.5960756
#> 257 258 259 260 261 262 263 264
#> 0.3306472 0.3363861 0.7464321 0.3725508 0.8297224 0.3965473 0.3250342 NA
#> 265 266 267 268 269 270 271 272
#> 0.2345937 NA NA 0.5886632 0.6106387 0.4172303 NA 0.4137080
#> 273 274 275 276 277 278 279 280
#> 0.4312953 0.1206438 0.5164038 0.8752400 0.4176263 0.7591012 0.3016993 0.4527210
#> 281 282 283 284 285 286 287 288
#> NA 0.6501483 0.8168531 0.2393292 0.8311549 0.8851143 0.7936328 0.4317872
#> 289 290 291 292 293 294 295 296
#> 0.8222307 0.3983355 0.1356224 0.6302455 0.4602807 0.3527491 0.8562989 0.3153233
#> 297 298 299 300
#> 0.5741435 NA 0.1008201 0.2253829