MatchIt：完全匹配 - 长向量错误
我正在进行一项分析，以评估土地保护政策对包裹级别土地使用变化的影响。为了解决保护计划注册的非随机性质，我正在处理经过处理和未处理的包裹级数据之间的匹配分析。当我尝试使用Matchit软件包运行完整的匹配时，我会遇到此错误。
Error in cbind(treatmentids, controlids) :
long vectors not supported yet: ../include/Rinlinedfuns.h:535
我使用的配置是：
m1.out <- matchit(formula = Y ~ X1 + X2 + ..... Xn, data = dataframe,
method = "full", distance = 'glm', link = 'logit',
estimand = 'ATT', ratio = 1, pop.size = 16)
其中x1 .. xn是连续的协变量，y是二进制处理变量。该数据集包含121226行，其中51693是处理，其余为对照样品。
我正在Windows机器上使用Matchit（4.3.4）运行R（4.0.2）。遗传或最近的邻居匹配方法正在运行没有任何问题。感谢对此的任何帮助。
I am running an analysis to assess the land conservation policy impact on land use change at parcel level. To address the non-random nature of conservation program enrollment, I am running a matching analysis between treated and non-treated parcel level data. I am getting this error when I am trying to run a full matching using MatchIt package.
Error in cbind(treatmentids, controlids) :
long vectors not supported yet: ../include/Rinlinedfuns.h:535
The configuration I am using is:
m1.out <- matchit(formula = Y ~ X1 + X2 + ..... Xn, data = dataframe,
method = "full", distance = 'glm', link = 'logit',
estimand = 'ATT', ratio = 1, pop.size = 16)
Where X1 .. Xn are continuous covariates, Y is a binary treatment variable. The dataset contains 121226 rows out of which 51693 are treatment and the rest are control samples.
I am running R (4.0.2) with MatchIt(4.3.4) on a windows machine. Genetic or nearest neighbor matching methods are running without any issues. I appreciate any help on this.
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这是来自
optmatch的错误。对于使用method = "full"调用matchit()函数的optmatch::fullmatch()来说，这个问题太大了，无法处理。这是因为fullmatch()对所有成对距离进行搜索，在本例中，成对距离的数量超过 25 亿。该问题可能根本无法实现完全匹配。对于同样的问题，请参阅此处。您可以尝试的一些方法是施加非常严格的卡尺，这会减少合格节点的数量，或者使用
exact参数添加精确匹配约束，这会将问题分割成更小的块，这些块可能会更复杂。易于管理。您还可以尝试对大量子类使用子分类，这近似于完全匹配。另请注意，
ratio和pop.size参数对于完全匹配没有任何作用，因此您应该将它们从对matchit()的调用中排除。This is an error from
optmatch. The problem is too big foroptmatch::fullmatch(), the functionmatchit()calls withmethod = "full", to handle. This is becausefullmatch()does a search over all pairwise distances, which in this case is over 2.5 billion in number. The problem may simply be infeasible for full matching. See here for the same problem.Some things you can try are to impose a very strict caliper, which reduces the number of eligible nodes, or add an exact matching constraint using the
exactargument, which splits the problem into smaller chunks that may be more manageable. You can also try using subclassification with a large number of subclasses, which approximates full matching.Also note that the
ratioandpop.sizearguments do nothing with full matching, so you should exclude them from your call tomatchit().