ps_trim() applies trimming methods to a propensity-score vector or matrix,
returning a new vector/matrix of the same length/dimensions, 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", "optimal"),
lower = NULL,
upper = NULL,
.exposure = NULL,
.focal_level = NULL,
.reference_level = NULL,
...,
.treated = NULL,
.untreated = NULL
)Arguments
- ps
The propensity score, either a numeric vector between 0 and 1 for binary exposures, or a matrix/data.frame where each column represents propensity scores for each level of a categorical exposure.
- method
One of
c("ps", "adaptive", "pctl", "pref", "cr", "optimal"). For categorical exposures, only"ps"and"optimal"are supported.- lower, upper
Numeric cutoffs or quantiles. If
NULL, defaults vary by method. For categorical exposures with method"ps",lowerrepresents the symmetric trimming threshold (delta).- .exposure
For methods like
"pref"or"cr", a vector for a binary exposure. For categorical exposures with method"optimal", must be a factor or character vector.- .focal_level
For binary exposures, the value representing the focal group (typically the treatment group). For categorical exposures with ATT or ATU estimands, specifies the focal category. Must be one of the levels of the exposure variable. Required for
wt_att()andwt_atu()with categorical exposures.- .reference_level
For binary exposures, the value representing the reference group (typically the control group). If not provided, it is automatically detected.
- ...
Additional arguments passed to methods
- .treated
![[Deprecated]](figures/lifecycle-deprecated.svg)
Use .focal_levelinstead.- .untreated
- Use
.reference_levelinstead.
Details
The returned object is a ps_trim vector/matrix of the same
length/dimensions 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.
For categorical exposures:
Symmetric trimming (
method = "ps"): Removes observations where any propensity score falls below the threshold delta (specified vialower).Optimal trimming (
method = "optimal"): Uses the Yang et al. (2016) approach for multi-category treatments.
Arithmetic behavior: Arithmetic operations on ps_trim objects return
numeric vectors, not ps_trim objects. This is intentional - once you
transform propensity scores (e.g., 1/ps for weights), the result is no
longer a propensity score.
NA handling: Trimmed values are set to NA. Operations that don't handle
NA values will propagate them (e.g., sum() returns NA unless
na.rm = TRUE).
Metadata tracking: The trimmed_idx and keep_idx are updated when
subsetting or reordering:
Subsetting with
[updates indices to new positionssort()reorders data and updates indices accordinglyunique()may change lengths but preserves the classna.omit()removes trimmed values and updates indices
Combining behavior: When combining ps_trim objects with c(), metadata
must match (same trimming parameters). Mismatched metadata triggers a warning
and returns a numeric vector.
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
# Coercion behavior with ps_trim objects
ps_trim1 <- ps_trim(ps, method = "ps", lower = 0.1, upper = 0.9)
ps_trim2 <- ps_trim(ps, method = "ps", lower = 0.1, upper = 0.9)
# Compatible objects combine silently
c(ps_trim1[1:50], ps_trim2[51:100]) # Returns ps_trim object
#> <ps_trim; trimmed 20 of [100]>
#> 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
#> NA 0.4322875 0.1893249 0.2462384
# Different trim parameters trigger warning
ps_trim3 <- ps_trim(ps, method = "ps", lower = 0.2, upper = 0.8)
c(ps_trim1[1:50], ps_trim3[51:100]) # Warning: returns numeric
#> Warning: Converting ps_trim to numeric: different trimming parameters
#> ℹ Metadata cannot be preserved when combining incompatible objects
#> ℹ Use identical objects or explicitly cast to numeric to avoid this warning
#> 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 NA NA 0.2564150 NA NA NA
#> 57 58 59 60 61 62 63 64
#> 0.3247367 0.7345271 0.7859021 NA NA NA 0.2208817 0.4841803
#> 65 66 67 68 69 70 71 72
#> 0.6036086 NA NA 0.2790100 0.4585602 NA NA 0.5439312
#> 73 74 75 76 77 78 79 80
#> 0.3822372 0.5796397 0.4114856 NA NA 0.6877562 0.7649259 NA
#> 81 82 83 84 85 86 87 88
#> 0.7505008 NA 0.2641422 NA NA NA 0.2330120 0.3365148
#> 89 90 91 92 93 94 95 96
#> 0.3055473 0.5632496 NA NA NA NA NA NA
#> 97 98 99 100
#> NA 0.4322875 NA 0.2462384
# Cross-class combinations warn and return numeric
psw_obj <- psw(ps[1:50], estimand = "ate")
c(ps_trim1[1:50], psw_obj) # Warning: returns numeric
#> Warning: Converting ps_trim and psw to numeric
#> ℹ Class-specific attributes and metadata have been dropped
#> ℹ Use explicit casting to numeric to avoid this warning
#> 1 2 3 4 5 6 7
#> 0.17801124 0.49344831 0.87258189 0.13535765 0.40258554 0.47524889 0.66839132
#> 8 9 10 11 12 13 14
#> 0.35061504 NA 0.38318089 0.57380442 0.74677823 0.30385529 0.15080370
#> 15 16 17 18 19 20 21
#> 0.89972561 NA 0.71872070 0.44191837 0.75485924 0.57876473 NA
#> 22 23 24 25 26 27 28
#> 0.12443658 0.87285871 NA 0.43136394 NA 0.59392536 0.24742762
#> 29 30 31 32 33 34 35
#> 0.69381700 0.52982663 0.67786695 0.53996676 0.77078278 0.33667732 0.20379449
#> 36 37 38 39 40 41 42
#> 0.24766004 NA 0.17686094 0.25726004 0.34846140 0.30654026 NA
#> 43 44 45 46 47 48 49
#> 0.18951912 NA 0.64155648 NA 0.33008953 0.39904946 0.36838768
#> 50
#> 0.12461207 0.17801124 0.49344831 0.87258189 0.13535765 0.40258554 0.47524889
#>
#> 0.66839132 0.35061504 0.92239228 0.38318089 0.57380442 0.74677823 0.30385529
#>
#> 0.15080370 0.89972561 0.02943822 0.71872070 0.44191837 0.75485924 0.57876473
#>
#> 0.93233516 0.12443658 0.87285871 0.91937240 0.43136394 0.02433818 0.59392536
#>
#> 0.24742762 0.69381700 0.52982663 0.67786695 0.53996676 0.77078278 0.33667732
#>
#> 0.20379449 0.24766004 0.06402613 0.17686094 0.25726004 0.34846140 0.30654026
#>
#> 0.04714018 0.18951912 0.91394741 0.64155648 0.92303132 0.33008953 0.39904946
#>
#> 0.36838768 0.12461207