ps_trunc() bounds extreme propensity scores to fixed limits, replacing
out-of-range values with the boundary value (a form of winsorizing). The
result is a vector or matrix of the same length and dimensions as
.propensity, with no observations removed. This contrasts with ps_trim(),
which sets extreme values to NA (effectively removing those observations
from analysis).
Usage
ps_trunc(
.propensity,
method = c("ps", "adaptive", "pctl", "cr"),
lower = NULL,
upper = NULL,
.exposure = NULL,
.focal_level = NULL,
.reference_level = NULL,
...,
.treated = NULL,
.untreated = NULL,
ps = lifecycle::deprecated()
)Arguments
- .propensity
A numeric vector of propensity scores in (0, 1) (binary exposures), or a matrix/data.frame where each column contains propensity scores for one level of a categorical exposure. A data frame truncated for a binary exposure is reduced to a single column: the second column of a two column data frame, which is the probability of the second level in the layout model predictions come in, and the first column otherwise. The column taken is announced;
options(propensity.quiet = TRUE)silences the announcement. A vector is held to the open interval, so a score of exactly 0 or 1 is refused. A matrix is held to the closed interval instead: a cell of exactly 0 or 1 is bounded rather than refused, which is how a separated multinomial fit is repaired, and what comes back lies strictly inside (0, 1). Anything outside \([0, 1]\) is refused either way, and so is a truncation whose own computed bounds leave a score at an endpoint; see Propensity scores at 0 and 1 inwt_ate(). A data frame holding a.pred_classcolumn, which a fitted tidymodels classification model returns when no prediction type is named, carries predicted levels rather than probabilities and is refused with an error of classpropensity_df_class_column_error.- method
One of
"ps","adaptive","pctl", or"cr":"ps"(default): Truncate directly on propensity score values. Values outside[lower, upper]are set to the nearest bound. For categorical exposures, applies symmetric truncation usingloweras the threshold (delta) and renormalizes rows to sum to 1."adaptive": Truncate at \([1/c, 1 - 1/c]\) with \(c = \sqrt{n} \log(n) / 5\) (Gruber et al., 2022), where \(n\) is the number of propensity scores present (binary exposures only). See The adaptive bound below."pctl": Truncate at quantiles of the propensity score distribution. Thelowerandupperarguments specify quantile probabilities. For categorical exposures, quantiles are computed across all columns."cr": Truncate to the common range of propensity scores across exposure groups (binary exposures only). Bounds are[min(.propensity[focal]), max(.propensity[reference])]. Requires.exposure. When those bounds cross, so that the two distributions do not overlap at all, there is no common range to bound the scores to and the call errors with classpropensity_no_overlap_error. That differs deliberately fromps_trim(), which trims every observed unit in the same situation, a truthful record of an empty overlap region.
For categorical exposures, only
"ps"and"pctl"are supported.- lower, upper
Bounds for truncation. Interpretation depends on
method:method = "adaptive": Not used. Supplying either is ignored with a warning.method = "ps": Propensity score values. See The"ps"bounds below.method = "pctl": Quantile probabilities (defaults: 0.05 and 0.95; categorical defaults: 0.01 and 0.99).method = "cr": Ignored; bounds are determined by the data.
- .exposure
An exposure vector. Required for method
"cr"(binary exposure vector) and for categorical exposures (factor or character vector) with any method.- .focal_level
The value of
.exposurerepresenting the focal (treated) group, used by"cr". Every binary coding honors it: 0/1 numeric, logical, two-level factor, and two-level character exposures are all coded with the named level as focal, and a level the exposure never takes is an error. With no level named, a binary exposure defaults to its higher level, which is1for a 0/1 exposure,TRUEfor a logical one, and the second of the two levels a factor or character exposure takes. Levels a factor declares but never takes are not candidates. Naming any other level reverses the coding, so.propensitymust then hold the probability of the named level.- .reference_level
The value of
.exposurerepresenting the reference (control) group. Naming it makes the exposure's other level focal, with the same consequence for.propensity, and a level the exposure never takes is an error. Automatically detected if not supplied.- ...
Additional arguments passed to methods.
- .treated
![[Deprecated]](figures/lifecycle-deprecated.svg)
Use .focal_levelinstead.- .untreated
- Use
.reference_levelinstead. - ps
- Use
.propensityinstead. A call that namespsmust name the arguments after it as well, since a positional argument binds to.propensity.
Value
A ps_trunc object (a numeric vector for binary exposures, or a
matrix for categorical exposures). Use ps_trunc_meta() to inspect
metadata including method, lower_bound, upper_bound,
truncated_idx (positions of modified values), and n_obs (the number of
observations those positions describe).
Details
Unlike ps_trim(), truncation preserves all observations. No NA values
are introduced; out-of-range scores are replaced with the boundary value.
For binary exposures, each propensity score \(e_i\) is bounded:
If \(e_i < l\), set \(e_i = l\) (the lower bound).
If \(e_i > u\), set \(e_i = u\) (the upper bound).
For categorical exposures, values below the threshold are set to the threshold and each row is renormalized to sum to 1.
The "ps" bounds
With method = "ps", both paths default to a floor of 0.1. For a binary
exposure, each bound supplied must be a single number strictly between 0
and 1, and a bound supplied alone implies its mirror: lower alone bounds
the scores at [lower, 1 - lower], and upper alone at
[1 - upper, upper]. With neither supplied, the bounds are [0.1, 0.9];
with both supplied, both are used as written, so an asymmetric bound must be
given in full. The mirror follows from the weights: an untreated unit's ATE
weight is \(1/(1 - e)\), so a floor on \(e\) alone would bound the
treated weights and leave the untreated ones unbounded. A lone lower of
0.5 or more, or a lone upper of 0.5 or less, meets or crosses its own
mirror and is an error of class propensity_range_error. lower = 1/c gives the bounds
method = "adaptive" computes for the same \(c\).
For a categorical exposure, lower is the threshold \(\delta\)
(default 0.1) applied to every column, after which each row is renormalized,
and upper is not read and is recorded as NA. With k exposure levels, a
threshold of 1/k or larger cannot be met by every column of a row that
sums to one and is an error, so an exposure with ten or more levels needs an
explicit lower below 1/k.
The adaptive bound
method = "adaptive" bounds a binary propensity score at
\([1/c, 1 - 1/c]\), where \(c = \sqrt{n} \log(n) / 5\) is the weight
bound of Gruber et al. (2022) and \(n\) counts the scores that are present,
as in wt_trunc(). At \(n = 1000\), \(c\) is 43.7 and the bounds are
0.023 and 0.977. The bound adapts to the sample size alone, loosening as
\(n\) grows, and leaves the estimand alone. ps_trim(method = "adaptive")
instead adapts to the estimated scores, tightening under poor overlap, and
changes the estimand to the population it keeps.
For unstabilized ATE weights the bound is the weight bound written on the
score scale. A treated unit's weight is \(1/e\), so the floor \(1/c\)
caps the treated weights at \(c\); an untreated unit's weight is
\(1/(1 - e)\), so the ceiling \(1 - 1/c\) caps the untreated weights at
\(c\). wt_ate() on the result therefore gives the weights
wt_trunc(wt_ate(ps, .exposure), method = "adaptive") gives, with one
difference, provided every unit's exposure is observed. (A unit with a
present score and a missing exposure counts toward \(n\) here but has no
weight for wt_trunc() to count, so the two bounds then differ.) The
difference is this: a treated unit above \(1 - 1/c\) or an untreated unit below
\(1/c\), whose weight is already close to 1, is moved to that bound too,
and its weight rises to \(c / (c - 1)\). Every weight above
\(c / (c - 1)\) is the same under either route. A lower bound alone would
not do the same work, since no floor on \(e\) bounds an untreated unit's
weight.
For stabilized ATE weights the equivalence does not carry over as it stands. Stabilizing multiplies each arm's weights by that arm's prevalence, so the treated weights are capped at \(P(A = 1) c\) and the untreated weights at \(P(A = 0) c\), which agree only when the two arms are equally common.
The bounds cross when \(c < 2\), which holds for fewer than 15 scores, so
"adaptive" needs at least 15 scores present and raises an error of class
propensity_range_error otherwise. It is refused for categorical exposures
with an error of class propensity_method_error: truncating a categorical
score renormalizes each row, which moves scores the bound never reached, so
the bound no longer caps the weights. A two-level nnet::multinom() fit is
read as one binary score and accepted.
Arithmetic behavior: Arithmetic operations on ps_trunc objects return
plain numeric vectors. Once propensity scores are transformed (e.g., into
weights), the result is no longer a propensity score.
Combining behavior: Combining ps_trunc objects with c() requires
matching truncation parameters. Mismatched parameters produce a warning and
return a plain numeric vector.
Fitted models
.propensity can be the fitted propensity score model instead of the scores
it reports. A binomial stats::glm() and a two-level nnet::multinom() are
read as one score per unit; a nnet::multinom() of three or more levels is
read as one column per level. Those are the shapes predict(fit, type = "response") and fitted(fit) give, and truncating a fit bounds exactly what
bounding those values would. A model of a continuous exposure's conditional
mean, such as a stats::lm() fit or a gaussian stats::glm(), has no
propensity score to bound and raises an error of class
propensity_model_family_error. A dose has two routes instead: trim the
dose model with ps_trim(method = "density"), which sets aside the units
whose dose is implausible under the model, or build the weights with
wt_ate() and bound them with wt_trunc(), which keeps every unit. There
is no density method here: a floor on a dose's conditional density is a
bound on its unstabilized weights, the ones wt_ate(stabilize = FALSE)
builds, which wt_trunc() already applies.
The methods that read an exposure ("cr", and every method on the
categorical route) take it from the model when .exposure is not supplied,
announcing the variable they read; options(propensity.quiet = TRUE)
silences the announcement. An .exposure you supply is used instead, and a
categorical model's columns are matched to its levels by name, so an exposure
whose levels are ordered differently is still bounded against the right
column.
Missing values
A propensity score that arrives missing is not one this function pinned to a
bound, so it takes no part in the truncation record: it never joins
truncated_idx, and is_unit_truncated() reports FALSE for it. The value
stays missing in the result. For a matrix of categorical propensity scores, a
row with a missing cell comes back missing throughout, because renormalizing
a row divides by its sum and that sum is unknown.
The methods that read their bounds off the data read them off the data they
have. "pctl" and "cr" work their bounds out from the complete scores, and
the categorical "pctl" bounds come from the cells of the complete rows, so
the bounds are the ones the same call would produce with the missing
observations dropped.
A missing exposure is a different matter, and "cr" refuses one with an
error of class propensity_missing_value_error. Its bounds come from the
exposure groups, and a unit that belongs to neither leaves them undefined.
Remove or impute the missing exposure values first.
The truncation record
A ps_trunc records which units were winsorized as positions among the
observations it was written for, along with how many observations that was.
Operations that hand this package the subscript re-index those positions onto
the result: subsetting with [, sort(), and rep() all return a record
written for what they return, and a subscript naming a position more than
once reports that unit at every place it now holds. unique() does the same
when it can, as described below.
Operations that change how many observations there are without supplying a
subscript cannot re-index the record, and it is dropped rather than worked
out from the values, since a score that arrived equal to a bound is
indistinguishable from one this function pinned there. vctrs::vec_slice(),
which is how filtering, joining, and grouped verbs in dplyr reach a column,
is the usual route, and combining two or more vectors with c() is another,
because concatenation appends one set of observations to another. The record
is dropped without comment on every route, since most of these length changes
build vectors the caller never holds, such as the pieces a grouped verb
slices a column into. The values, the class, and the method and its bounds
are untouched.
A combine drops the positions even when it is handed a single vector. c()
of one ps_trunc returns it unchanged, record included, but
vctrs::vec_c(x), dplyr::bind_rows(df), vctrs::vec_rbind(df), and an
ungrouped dplyr::reframe() rebuild the column, so a later
is_unit_truncated() on the result refuses it.
unique() keeps one element for each distinct value, or one row for each
distinct row of a matrix of scores, and that element or row stands for every
unit holding the same scores. A matrix comes back as a matrix of the same
class with its column names. The record is re-indexed onto the result when
all of the merged units share one status, and dropped otherwise: a score or
row that arrived where truncation would put it and one truncation moved
there merge into a single element or row that neither status describes.
as.data.frame() and tibble::as_tibble() turn a matrix of scores into a
data frame whose columns are ps_trunc vectors carrying the matrix's record, one
row per unit, so the weight functions read the data frame as they read the
matrix and a subset of its rows re-indexes the record. A data frame whose
columns are not all truncated with the same record is refused by the weight
functions with an error of class propensity_matrix_type_error. A matrix
without column names gives columns named V1, V2, and so on, which the
weight functions then refuse because they name no exposure level, so name
the columns after the exposure levels before converting.
A record can also outlive the observations it describes, because it travels
by routes vctrs does not see: growing a ps_trunc by subassignment carries
it across the length change. is_unit_truncated() therefore checks that the
record covers the object it is given and raises an error of class
propensity_missing_meta_error when it does not, rather than name truncated
units at stale positions.
That check compares how many observations the record was written for against
how many the object holds, which a reordering does not change, so a route
that could reorder the observations without saying how drops the positions
instead, at any length: vctrs::vec_slice(), dplyr::arrange(),
dplyr::filter(), and vctrs::vec_assign() and the helpers built on it
return a ps_trunc whose record keeps its method and bounds and names no
units, and is_unit_truncated() refuses it. Subsetting with [, sort(),
unique(), and rep() know where the units went and re-index the record,
and [<- and is.na<- move no unit and keep it, so reorder with [, or
put the propensity scores in the order you want before truncating them.
Casting a numeric vector into a ps_trunc with vctrs::vec_cast() is a type
operation and not a truncation. The result is described by the method and
bounds of the target and records that none of the arriving values was pinned
to a bound, so it can hold scores outside those bounds, including 0 and 1.
Call ps_trunc() on the scores to truncate them.
References
Crump, R. K., Hotz, V. J., Imbens, G. W., & Mitnik, O. A. (2009). Dealing with limited overlap in estimation of average treatment effects. Biometrika, 96(1), 187–199.
Gruber, S., Phillips, R. V., Lee, H., & van der Laan, M. J. (2022). Data-adaptive selection of the propensity score truncation level for inverse-probability-weighted and targeted maximum likelihood estimators of marginal point treatment effects. American Journal of Epidemiology, 191(9), 1640–1651.
Walker, A. M., Patrick, A. R., Lauer, M. S., et al. (2013). A tool for assessing the feasibility of comparative effectiveness research. Comparative Effectiveness Research, 3, 11–20.
See also
ps_trim() for removing (rather than bounding) extreme values,
ps_refit() for refitting the propensity model after trimming,
is_ps_truncated(), is_unit_truncated(), ps_trunc_meta()
Examples
set.seed(2)
n <- 200
x <- rnorm(n)
z <- rbinom(n, 1, plogis(1.2 * x))
fit <- glm(z ~ x, family = binomial)
ps <- predict(fit, type = "response")
# Truncate to [0.1, 0.9]
ps_t <- ps_trunc(ps, method = "ps", lower = 0.1, upper = 0.9)
ps_t
#> <ps_trunc{[0.1,0.9], method=ps}[200]>
#> 1 2 3 4 5 6 7 8
#> 0.2913095 0.5707511 0.8590561 0.2418820 0.4993060 0.5567591 0.7011122 0.4561604
#> 9 10 11 12 13 14 15 16
#> 0.9000000 0.4834312 0.6312438 0.7594616 0.4153613 0.2603881 0.8827176 0.1000000
#> 17 18 19 20 21 22 23 24
#> 0.7384237 0.5307543 0.7655683 0.6349278 0.9000000 0.2283122 0.8592915 0.9000000
#> 25 26 27 28 29 30 31 32
#> 0.5224033 0.1000000 0.6461643 0.3628349 0.7199125 0.5983593 0.7081103 0.6059826
#> 33 34 35 36 37 38 39 40
#> 0.7776782 0.4442186 0.3189618 0.3630602 0.1432262 0.2900408 0.3722937 0.4543264
#> 41 42 43 44 45 46 47 48
#> 0.4177638 0.1148898 0.3038315 0.8956760 0.6813225 0.9000000 0.4385124 0.4964323
#> 49 50 51 52 53 54 55 56
#> 0.4711469 0.2285337 0.3046166 0.9000000 0.3714863 0.8128663 0.2587396 0.1141443
#> 57 58 59 60 61 62 63 64
#> 0.4338453 0.7502468 0.7892860 0.8697086 0.1351329 0.9000000 0.3365403 0.5636424
#> 65 66 67 68 69 70 71 72
#> 0.6533256 0.3088378 0.1105762 0.3927369 0.5438044 0.2916295 0.2858818 0.6089556
#> 73 74 75 76 77 78 79 80
#> 0.4826524 0.6355772 0.5065031 0.2890305 0.8174117 0.7154239 0.7732114 0.1906359
#> 81 82 83 84 85 86 87 88
#> 0.7622717 0.1472971 0.3788316 0.1970403 0.1000000 0.8871259 0.3486998 0.4440783
#> 89 90 91 92 93 94 95 96
#> 0.4168763 0.6233901 0.8606965 0.8708768 0.2314475 0.1994226 0.1740398 0.2183041
#> 97 98 99 100 101 102 103 104
#> 0.9000000 0.5231365 0.3036226 0.3616811 0.7773582 0.5907629 0.4361655 0.3253709
#> 105 106 107 108 109 110 111 112
#> 0.2991480 0.9000000 0.7510710 0.9000000 0.4078242 0.4264336 0.2629645 0.4532487
#> 113 114 115 116 117 118 119 120
#> 0.6448288 0.8262184 0.6674247 0.6408723 0.8053646 0.7907095 0.5498332 0.3173982
#> 121 122 123 124 125 126 127 128
#> 0.8071017 0.5584826 0.8744315 0.4053976 0.2594361 0.6609892 0.3447196 0.6851535
#> 129 130 131 132 133 134 135 136
#> 0.1434914 0.1410494 0.6969682 0.6090883 0.7368408 0.1087328 0.8022199 0.8001309
#> 137 138 139 140 141 142 143 144
#> 0.7692948 0.7186431 0.9000000 0.1834141 0.3662171 0.6292392 0.3118604 0.5441734
#> 145 146 147 148 149 150 151 152
#> 0.7097460 0.3486308 0.6894218 0.6639810 0.3119192 0.2693231 0.7582975 0.4751339
#> 153 154 155 156 157 158 159 160
#> 0.7043397 0.3032090 0.8131267 0.2020948 0.7139969 0.6429234 0.5927018 0.6918371
#> 161 162 163 164 165 166 167 168
#> 0.6263848 0.3524856 0.4486289 0.6165339 0.2074392 0.2942181 0.9000000 0.1003085
#> 169 170 171 172 173 174 175 176
#> 0.2210197 0.7611782 0.7800143 0.7302196 0.5364399 0.6072561 0.2895753 0.3489979
#> 177 178 179 180 181 182 183 184
#> 0.4500408 0.2829251 0.7262051 0.1573127 0.2623292 0.2166743 0.6247878 0.2416706
#> 185 186 187 188 189 190 191 192
#> 0.6625838 0.7959533 0.5278943 0.6555095 0.3485232 0.6526880 0.2148033 0.5002503
#> 193 194 195 196 197 198 199 200
#> 0.2017086 0.4490035 0.7798096 0.6994296 0.4022315 0.3161761 0.3003833 0.3261363
# Bound at [1/c, 1 - 1/c], c = sqrt(n) log(n) / 5, which caps the
# unstabilized ATE weights at c
ps_trunc(ps, method = "adaptive")
#> <ps_trunc{[0.0667,0.933], method=adaptive}[200]>
#> 1 2 3 4 5 6 7
#> 0.29130945 0.57075108 0.85905610 0.24188201 0.49930600 0.55675915 0.70111217
#> 8 9 10 11 12 13 14
#> 0.45616038 0.90360175 0.48343119 0.63124383 0.75946160 0.41536130 0.26038808
#> 15 16 17 18 19 20 21
#> 0.88271762 0.08138334 0.73842370 0.53075430 0.76556830 0.63492785 0.91319684
#> 22 23 24 25 26 27 28
#> 0.22831221 0.85929151 0.90074563 0.52240328 0.07067804 0.64616425 0.36283492
#> 29 30 31 32 33 34 35
#> 0.71991248 0.59835929 0.70811028 0.60598256 0.77767823 0.44421858 0.31896179
#> 36 37 38 39 40 41 42
#> 0.36306016 0.14322616 0.29004079 0.37229372 0.45432638 0.41776382 0.11488981
#> 43 44 45 46 47 48 49
#> 0.30383148 0.89567605 0.68132246 0.90420941 0.43851237 0.49643227 0.47114693
#> 50 51 52 53 54 55 56
#> 0.22853370 0.30461661 0.91106441 0.37148631 0.81286633 0.25873963 0.11414425
#> 57 58 59 60 61 62 63
#> 0.43384531 0.75024682 0.78928598 0.86970860 0.13513287 0.90793306 0.33654034
#> 64 65 66 67 68 69 70
#> 0.56364237 0.65332558 0.30883775 0.11057616 0.39273691 0.54380444 0.29162947
#> 71 72 73 74 75 76 77
#> 0.28588182 0.60895559 0.48265241 0.63557725 0.50650310 0.28903045 0.81741170
#> 78 79 80 81 82 83 84
#> 0.71542388 0.77321135 0.19063586 0.76227170 0.14729713 0.37883165 0.19704028
#> 85 86 87 88 89 90 91
#> 0.09015377 0.88712592 0.34869977 0.44407832 0.41687627 0.62339015 0.86069650
#> 92 93 94 95 96 97 98
#> 0.87087680 0.23144748 0.19942258 0.17403983 0.21830407 0.90120123 0.52313647
#> 99 100 101 102 103 104 105
#> 0.30362260 0.36168114 0.77735817 0.59076288 0.43616553 0.32537093 0.29914802
#> 106 107 108 109 110 111 112
#> 0.90944557 0.75107099 0.90586644 0.40782418 0.42643359 0.26296452 0.45324867
#> 113 114 115 116 117 118 119
#> 0.64482877 0.82621843 0.66742471 0.64087231 0.80536462 0.79070955 0.54983317
#> 120 121 122 123 124 125 126
#> 0.31739822 0.80710169 0.55848264 0.87443145 0.40539762 0.25943605 0.66098916
#> 127 128 129 130 131 132 133
#> 0.34471960 0.68515350 0.14349137 0.14104940 0.69696824 0.60908833 0.73684079
#> 134 135 136 137 138 139 140
#> 0.10873282 0.80221987 0.80013088 0.76929476 0.71864309 0.91483892 0.18341413
#> 141 142 143 144 145 146 147
#> 0.36621714 0.62923918 0.31186040 0.54417339 0.70974601 0.34863085 0.68942181
#> 148 149 150 151 152 153 154
#> 0.66398099 0.31191915 0.26932313 0.75829753 0.47513393 0.70433975 0.30320896
#> 155 156 157 158 159 160 161
#> 0.81312672 0.20209485 0.71399686 0.64292343 0.59270175 0.69183710 0.62638476
#> 162 163 164 165 166 167 168
#> 0.35248559 0.44862887 0.61653394 0.20743922 0.29421813 0.91200893 0.10030845
#> 169 170 171 172 173 174 175
#> 0.22101971 0.76117824 0.78001428 0.73021961 0.53643993 0.60725607 0.28957526
#> 176 177 178 179 180 181 182
#> 0.34899794 0.45004081 0.28292513 0.72620510 0.15731275 0.26232917 0.21667429
#> 183 184 185 186 187 188 189
#> 0.62478778 0.24167062 0.66258378 0.79595325 0.52789428 0.65550948 0.34852324
#> 190 191 192 193 194 195 196
#> 0.65268801 0.21480329 0.50025030 0.20170859 0.44900347 0.77980959 0.69942959
#> 197 198 199 200
#> 0.40223149 0.31617609 0.30038326 0.32613630
# Truncate at the 1st and 99th percentiles
ps_trunc(ps, method = "pctl", lower = 0.01, upper = 0.99)
#> <ps_trunc{[0.0901,0.912], method=pctl}[200]>
#> 1 2 3 4 5 6 7
#> 0.29130945 0.57075108 0.85905610 0.24188201 0.49930600 0.55675915 0.70111217
#> 8 9 10 11 12 13 14
#> 0.45616038 0.90360175 0.48343119 0.63124383 0.75946160 0.41536130 0.26038808
#> 15 16 17 18 19 20 21
#> 0.88271762 0.09006607 0.73842370 0.53075430 0.76556830 0.63492785 0.91202081
#> 22 23 24 25 26 27 28
#> 0.22831221 0.85929151 0.90074563 0.52240328 0.09006607 0.64616425 0.36283492
#> 29 30 31 32 33 34 35
#> 0.71991248 0.59835929 0.70811028 0.60598256 0.77767823 0.44421858 0.31896179
#> 36 37 38 39 40 41 42
#> 0.36306016 0.14322616 0.29004079 0.37229372 0.45432638 0.41776382 0.11488981
#> 43 44 45 46 47 48 49
#> 0.30383148 0.89567605 0.68132246 0.90420941 0.43851237 0.49643227 0.47114693
#> 50 51 52 53 54 55 56
#> 0.22853370 0.30461661 0.91106441 0.37148631 0.81286633 0.25873963 0.11414425
#> 57 58 59 60 61 62 63
#> 0.43384531 0.75024682 0.78928598 0.86970860 0.13513287 0.90793306 0.33654034
#> 64 65 66 67 68 69 70
#> 0.56364237 0.65332558 0.30883775 0.11057616 0.39273691 0.54380444 0.29162947
#> 71 72 73 74 75 76 77
#> 0.28588182 0.60895559 0.48265241 0.63557725 0.50650310 0.28903045 0.81741170
#> 78 79 80 81 82 83 84
#> 0.71542388 0.77321135 0.19063586 0.76227170 0.14729713 0.37883165 0.19704028
#> 85 86 87 88 89 90 91
#> 0.09015377 0.88712592 0.34869977 0.44407832 0.41687627 0.62339015 0.86069650
#> 92 93 94 95 96 97 98
#> 0.87087680 0.23144748 0.19942258 0.17403983 0.21830407 0.90120123 0.52313647
#> 99 100 101 102 103 104 105
#> 0.30362260 0.36168114 0.77735817 0.59076288 0.43616553 0.32537093 0.29914802
#> 106 107 108 109 110 111 112
#> 0.90944557 0.75107099 0.90586644 0.40782418 0.42643359 0.26296452 0.45324867
#> 113 114 115 116 117 118 119
#> 0.64482877 0.82621843 0.66742471 0.64087231 0.80536462 0.79070955 0.54983317
#> 120 121 122 123 124 125 126
#> 0.31739822 0.80710169 0.55848264 0.87443145 0.40539762 0.25943605 0.66098916
#> 127 128 129 130 131 132 133
#> 0.34471960 0.68515350 0.14349137 0.14104940 0.69696824 0.60908833 0.73684079
#> 134 135 136 137 138 139 140
#> 0.10873282 0.80221987 0.80013088 0.76929476 0.71864309 0.91202081 0.18341413
#> 141 142 143 144 145 146 147
#> 0.36621714 0.62923918 0.31186040 0.54417339 0.70974601 0.34863085 0.68942181
#> 148 149 150 151 152 153 154
#> 0.66398099 0.31191915 0.26932313 0.75829753 0.47513393 0.70433975 0.30320896
#> 155 156 157 158 159 160 161
#> 0.81312672 0.20209485 0.71399686 0.64292343 0.59270175 0.69183710 0.62638476
#> 162 163 164 165 166 167 168
#> 0.35248559 0.44862887 0.61653394 0.20743922 0.29421813 0.91200893 0.10030845
#> 169 170 171 172 173 174 175
#> 0.22101971 0.76117824 0.78001428 0.73021961 0.53643993 0.60725607 0.28957526
#> 176 177 178 179 180 181 182
#> 0.34899794 0.45004081 0.28292513 0.72620510 0.15731275 0.26232917 0.21667429
#> 183 184 185 186 187 188 189
#> 0.62478778 0.24167062 0.66258378 0.79595325 0.52789428 0.65550948 0.34852324
#> 190 191 192 193 194 195 196
#> 0.65268801 0.21480329 0.50025030 0.20170859 0.44900347 0.77980959 0.69942959
#> 197 198 199 200
#> 0.40223149 0.31617609 0.30038326 0.32613630
# Use truncated scores to calculate weights
wt_ate(ps_t, .exposure = z)
#> ℹ Treating `.exposure` as binary
#> <psw{estimand = ate; truncated}[200]>
#> [1] 1.411053 2.329651 1.164068 1.319056 2.002780 2.256110 1.426305 1.838777
#> [9] 1.111111 2.068547 2.711819 1.316722 1.710458 1.352060 1.132865 1.111111
#> [17] 1.354236 1.884111 1.306219 2.739185 1.111111 1.295861 1.163749 1.111111
#> [25] 2.093817 1.111111 1.547594 1.569452 1.389058 1.671237 1.412209 2.537959
#> [33] 4.497985 1.799268 1.468346 2.754364 1.167169 3.447791 2.686051 1.832597
#> [41] 2.393697 1.129803 3.291298 1.116475 1.467734 1.111111 2.280437 2.014373
#> [49] 1.890884 1.296233 1.438056 1.111111 1.591055 1.230215 3.864889 1.128852
#> [57] 1.766302 4.003953 4.745769 1.149810 7.400124 1.111111 1.507251 1.774175
#> [65] 1.530630 3.237946 1.124323 2.546234 1.838896 1.411691 1.400328 1.642156
#> [73] 1.932936 1.573373 1.974322 3.459843 1.223374 1.397773 1.293307 1.235538
#> [81] 1.311868 1.172741 2.639695 1.245392 1.111111 1.127236 2.867797 1.798815
#> [89] 2.398793 2.655268 1.161850 1.148268 1.301147 5.014477 1.210712 1.279270
#> [97] 1.111111 2.097036 1.436003 1.566615 1.286408 1.692727 1.773570 1.482296
#> [105] 1.426835 1.111111 1.331432 1.111111 2.452037 2.345031 1.356787 1.828985
#> [113] 1.550799 1.210334 1.498296 1.560373 5.137812 1.264687 1.818733 3.150616
#> [121] 5.184079 1.790566 1.143600 2.466714 1.350322 1.512884 1.526064 3.176151
#> [129] 1.167531 7.089715 1.434786 2.558123 3.799981 1.121998 1.246541 1.249796
#> [137] 1.299892 1.391511 1.111111 5.452143 1.577827 2.697157 1.453194 2.193817
#> [145] 1.408955 1.535228 1.450491 2.976022 1.453318 3.713012 4.137318 1.905248
#> [153] 1.419769 1.435150 5.351220 1.253282 1.400566 1.555395 1.687189 3.245037
#> [161] 2.676550 1.544367 2.229014 1.621971 4.820689 1.416868 1.111111 1.111492
#> [169] 4.524483 1.313753 4.545750 1.369451 2.157218 2.546188 1.407609 2.865346
#> [177] 2.222021 1.394555 1.377021 1.186680 1.355618 1.276608 2.665158 1.318688
#> [185] 1.509243 1.256355 1.894319 1.525531 2.869249 2.879256 1.273566 2.001002
#> [193] 1.252675 1.814893 1.282364 3.327007 1.672888 1.462365 1.429354 1.483980
# Inspect which observations were truncated
is_unit_truncated(ps_t)
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
#> [25] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [49] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [133] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# Bound the scores a fitted model reports, reading the exposure off the model
ps_trunc(fit, method = "cr")
#> ℹ Using exposure variable "z" from the propensity score model
#> <ps_trunc{[0.135,0.813], method=cr}[200]>
#> 1 2 3 4 5 6 7 8
#> 0.2913095 0.5707511 0.8131267 0.2418820 0.4993060 0.5567591 0.7011122 0.4561604
#> 9 10 11 12 13 14 15 16
#> 0.8131267 0.4834312 0.6312438 0.7594616 0.4153613 0.2603881 0.8131267 0.1351329
#> 17 18 19 20 21 22 23 24
#> 0.7384237 0.5307543 0.7655683 0.6349278 0.8131267 0.2283122 0.8131267 0.8131267
#> 25 26 27 28 29 30 31 32
#> 0.5224033 0.1351329 0.6461643 0.3628349 0.7199125 0.5983593 0.7081103 0.6059826
#> 33 34 35 36 37 38 39 40
#> 0.7776782 0.4442186 0.3189618 0.3630602 0.1432262 0.2900408 0.3722937 0.4543264
#> 41 42 43 44 45 46 47 48
#> 0.4177638 0.1351329 0.3038315 0.8131267 0.6813225 0.8131267 0.4385124 0.4964323
#> 49 50 51 52 53 54 55 56
#> 0.4711469 0.2285337 0.3046166 0.8131267 0.3714863 0.8128663 0.2587396 0.1351329
#> 57 58 59 60 61 62 63 64
#> 0.4338453 0.7502468 0.7892860 0.8131267 0.1351329 0.8131267 0.3365403 0.5636424
#> 65 66 67 68 69 70 71 72
#> 0.6533256 0.3088378 0.1351329 0.3927369 0.5438044 0.2916295 0.2858818 0.6089556
#> 73 74 75 76 77 78 79 80
#> 0.4826524 0.6355772 0.5065031 0.2890305 0.8131267 0.7154239 0.7732114 0.1906359
#> 81 82 83 84 85 86 87 88
#> 0.7622717 0.1472971 0.3788316 0.1970403 0.1351329 0.8131267 0.3486998 0.4440783
#> 89 90 91 92 93 94 95 96
#> 0.4168763 0.6233901 0.8131267 0.8131267 0.2314475 0.1994226 0.1740398 0.2183041
#> 97 98 99 100 101 102 103 104
#> 0.8131267 0.5231365 0.3036226 0.3616811 0.7773582 0.5907629 0.4361655 0.3253709
#> 105 106 107 108 109 110 111 112
#> 0.2991480 0.8131267 0.7510710 0.8131267 0.4078242 0.4264336 0.2629645 0.4532487
#> 113 114 115 116 117 118 119 120
#> 0.6448288 0.8131267 0.6674247 0.6408723 0.8053646 0.7907095 0.5498332 0.3173982
#> 121 122 123 124 125 126 127 128
#> 0.8071017 0.5584826 0.8131267 0.4053976 0.2594361 0.6609892 0.3447196 0.6851535
#> 129 130 131 132 133 134 135 136
#> 0.1434914 0.1410494 0.6969682 0.6090883 0.7368408 0.1351329 0.8022199 0.8001309
#> 137 138 139 140 141 142 143 144
#> 0.7692948 0.7186431 0.8131267 0.1834141 0.3662171 0.6292392 0.3118604 0.5441734
#> 145 146 147 148 149 150 151 152
#> 0.7097460 0.3486308 0.6894218 0.6639810 0.3119192 0.2693231 0.7582975 0.4751339
#> 153 154 155 156 157 158 159 160
#> 0.7043397 0.3032090 0.8131267 0.2020948 0.7139969 0.6429234 0.5927018 0.6918371
#> 161 162 163 164 165 166 167 168
#> 0.6263848 0.3524856 0.4486289 0.6165339 0.2074392 0.2942181 0.8131267 0.1351329
#> 169 170 171 172 173 174 175 176
#> 0.2210197 0.7611782 0.7800143 0.7302196 0.5364399 0.6072561 0.2895753 0.3489979
#> 177 178 179 180 181 182 183 184
#> 0.4500408 0.2829251 0.7262051 0.1573127 0.2623292 0.2166743 0.6247878 0.2416706
#> 185 186 187 188 189 190 191 192
#> 0.6625838 0.7959533 0.5278943 0.6555095 0.3485232 0.6526880 0.2148033 0.5002503
#> 193 194 195 196 197 198 199 200
#> 0.2017086 0.4490035 0.7798096 0.6994296 0.4022315 0.3161761 0.3003833 0.3261363
if (rlang::is_installed("nnet")) {
trt <- factor(sample(c("a", "b", "c"), n, replace = TRUE))
multinomial_fit <- nnet::multinom(trt ~ x, trace = FALSE)
ps_trunc(multinomial_fit, method = "ps")
}
#> ℹ Using exposure variable "trt" from the propensity score model
#> <ps_trunc_matrix[200 x 3]; truncated 0 of 200; method=ps[0.1000,Inf]>
#> a b c
#> 1 0.3368013 0.3782680 0.2849307
#> 2 0.3285505 0.3863809 0.2850686
#> 3 0.3179718 0.3969403 0.2850880
#> 4 0.3385922 0.3765210 0.2848868
#> 5 0.3305651 0.3843902 0.2850447
#> 6 0.3289485 0.3859871 0.2850644
#> 7 0.3245896 0.3903135 0.2850969
#> 8 0.3317791 0.3831936 0.2850273
#> 9 0.3150074 0.3999317 0.2850609
#> 10 0.3310106 0.3839508 0.2850386
#> # ... with 190 more rows