How to test the accuracy of predictions?

[lime-n]

I am rather new to random forests and especially imputations and I would like to know how can I get an estimate of the accuracy of these predictions?

For example, if I have a matrix with species in columns and abundance in rows and se generateNA to create NA values for imputation. How can I test the accuracy of the predicted and actual observed values?

For example, I have the following dataset and want to test how well the imputation works against the abundance of these species over these selected two years, relative to the actual values:

#data
# A tibble: 12 x 7
   eventDate   year Hirundorustica Himantopushimantopus Gallinulachloropus Fulicaatra Spilopeliachinensis
   <date>     <dbl>          <int>                <int>              <int>      <int>               <int>
 1 2019-01-01  2019         375087               275213             337709    1638522               81054
 2 2019-02-01  2019         245500               174385             230240     864141               72817
 3 2019-03-01  2019         478287               169552             207516     509113               59389
 4 2019-04-01  2019        1149118               146255             162036     371454               58119
 5 2019-05-01  2019        1777995                84937             132554     290331               43462
 6 2019-06-01  2019         674044                52308             101186     255249               24479
 7 2019-07-01  2019        1114053                75779             107368     377148               23558
 8 2019-08-01  2019        2091425                81571             133904     535402               31321
 9 2019-09-01  2019        1834696               105622             141551     659778               46775
10 2019-10-01  2019         676342               111289             174135     737695               76354
11 2019-11-01  2019         322766               143620             302165     869143               63237
12 2019-12-01  2019         359126               193387             281926    1299738               66995

#Generate NAs
mimNA[, -c(1, 2)]<- generateNA(mimNA[, -c(1, 2)], seed=5)

#then create imputations
mRan <- missRanger(mimNA[, -c(1, 2)], pmm.k=3, num.trees = 100)

[mayer79]

In practice, one does not have access to the true values, so this question pops up only for simulation studies.

Since missRanger ends up with a predictive model for each column, you can evaluate its "accuracy" column by column on the missing values, using a suitable scoring function (e.g. rmse for continuous columns).