From the documentation for the
missForest() function, it looks like the first argument is:
xmis a data matrix with missing values. The columns correspond to the variables and the rows to the observations.
If you're starting from a data frame, you might want to look at the example at the bottom of the function reference (see link above, and pasted below)
## Nonparametric missing value imputation on mixed-type data:
## The data contains four continuous and one categorical variable.
## Artificially produce missing values using the 'prodNA' function:
iris.mis <- prodNA(iris, noNA = 0.2)
## Impute missing values providing the complete matrix for
## illustration. Use 'verbose' to see what happens between iterations:
iris.imp <- missForest(iris.mis, xtrue = iris, verbose = TRUE)
## The imputation is finished after five iterations having a final
## true NRMSE of 0.143 and a PFC of 0.036. The estimated final NRMSE
## is 0.157 and the PFC is 0.025 (see Details for the reason taking
## iteration 4 instead of iteration 5 as final value).
## The final results can be accessed directly. The estimated error:
## The true imputation error (if available):
## And of course the imputed data matrix (do not run this):
H16_IC is already a data matrix with missing values, then it'll be much easier to help you if you can supply a reprex (short for reproducible example).
If you've never heard of a reprex before, you might want to start by reading the tidyverse.org help page. The reprex dos and don'ts are also useful.
What to do if you run into clipboard problems
If you run into problems with access to your clipboard, you can specify an outfile for the reprex, and then copy and paste the contents into the forum.
reprex::reprex(input = "fruits_stringdist.R", outfile = "fruits_stringdist.md")
For pointers specific to the community site, check out the reprex FAQ.