Importing Model Outputs into R Reliably

Why Reliable Importing Matters

A model output is not just a collection of numbers. Each value has a name, a role, a unit, and often a software-specific meaning. If an identifier is converted from a character string to a number, a parameter name is silently changed, or a sentinel is mistaken for a measurement, the resulting R object may look usable while representing something different from the original output.

Reliable importing therefore means more than successfully reading a file. It means making the import contract explicit: which file and delimiter are used, what each column means, how identifiers and units are represented, and which values count as missing. NONMEM and Monolix commonly write delimited output files whose precise names and layouts depend on the run configuration and software version. An nlmixr2 fit, by contrast, is an R object; its components and available methods should be inspected rather than treated as a generic text table.

Start With the Import Contract

Before reading a file, record its source, delimiter, header status, expected columns, units, and documented missing-value conventions. A useful contract also states which columns identify a row. For a prediction table, that might be subject ID and time; for a parameter table, it might be parameter name. A table that has been read without errors can still violate this contract.

Names deserve particular care. R may alter names to make them syntactically convenient unless instructed otherwise. For example, a source name such as THETA(1) may be changed during import. Keep the original name when it is needed for mapping or auditability; use explicit column indexing such as df[["THETA(1)"]] rather than assuming every name can be used directly as an R symbol.

Identifiers should generally be read as character data unless there is a clear reason to treat them numerically. The strings "001" and "1" may identify different records even though they have the same numeric value. Likewise, an ID is usually a label, not a quantity for which a mean is meaningful. A row key should also be checked for unexpected duplicates: if subject and time are intended to identify a row, repeated subject-time pairs may indicate either legitimate replicates or an import or data-preparation problem that needs explanation.

Importing NONMEM and Monolix Tables

NONMEM and Monolix can produce different kinds of output files, and their layouts can vary with settings and software version. A table may be separated by tabs or whitespace, may have a header or not, and may contain software-specific missing-value codes. Confirm these properties from the run configuration or the file itself before choosing import arguments. Do not assume every file from a given program shares one universal format.

For a whitespace-delimited NONMEM table with a header, base R's read.table allows the delimiter and name handling to be specified directly. Explicit column classes can protect an ID column from numeric conversion. For example, if a documented file contains an ID, time, observation, prediction, and a configured missing observation code, a starting import could be:

nm <- read.table("sdtab.csv", header = TRUE, sep = "", check.names = FALSE, colClasses = c("character", "numeric", "numeric", "numeric", "numeric"))

The filename in this example is illustrative. Use the actual file and its actual column layout; the colClasses entries must match the columns in order. If the file is comma- or tab-delimited, specify that delimiter instead of using whitespace separation. When the file has no header, do not set header = TRUE: provide verified column names separately and check that the number of fields matches.

Missing-value codes must be treated as data conventions, not guessed from appearance. A value such as -9999 could be a missing marker in a particular output, but it is not safe to convert every occurrence of that number to NA unless the file specification says it is a sentinel for that field. It could instead be a legitimate value or have a different meaning in another column. Apply recoding only to the affected columns and record the rule.

For Monolix, exported result tables are also delimited files, but available files, headers, and missing-value representations depend on the export and version. Inspect the exact file and its associated settings. Do not infer units, column meaning, or sentinel codes from a filename alone. If IDs could have leading zeros, protect them as character data in the same way as for a NONMEM table.

Importing an nlmixr2 Result Object

An nlmixr2 fit is not a delimited file. It is an R object that can contain estimates and other fit results in structured components. Inspect the object class and structure, and consult the documentation for the installed nlmixr2 version to identify the supported component or accessor for the quantity you need. Do not scrape printed console output: printed summaries are for display and may change formatting, rounding, or labels.

For fixed-effect estimates, a named estimate vector can be turned into a table without discarding the original parameter names. In versions whose fit object exposes fixed estimates as fit$parFixed, the basic extraction pattern is:

theta <- fit$parFixed
stopifnot(!is.null(names(theta)))
theta_table <- data.frame(parameter = names(theta), estimate = unname(theta), row.names = NULL)

Check that this component is present and has the expected meaning for the fitted object before using the pattern. A fitted object may also provide a formatted parameter table, individual estimates, covariance information, or other results; those are not interchangeable. For example, population fixed-effect estimates are not individual random-effect estimates. If the required values are not in the component you inspected, use the documented method for that result instead of treating another component as equivalent.

Validation: A Short Justification and Practical Checks

The reason for explicit validation can be stated simply. An import maps source records into R values. If the mapping changes an identifier, name, unit, or missing-value meaning, then calculations on the imported object operate on a different representation of the intended data. Therefore, successful parsing is necessary but not sufficient: the import must also preserve the fields that define the interpretation of each record.

This is a conceptual checklist, not a numeric scoring rule. In practice, inspect names, types, and dimensions immediately after import. Compare row counts with the source file or run report; check that IDs and times have plausible values; check missingness by column; and test the intended row key. For a subject-time key, one useful check is that the number of distinct pairs equals the number of rows when duplicates are not expected. If replicates are expected, define the additional key fields rather than deleting duplicate rows automatically.

Units are often absent from a numeric file, so retain them in accompanying metadata or in unambiguous column names. Check units before combining outputs from different tools. Similarly, parameter names should be mapped explicitly when software uses different naming conventions. The earlier material on mapping parameter names and values between tools is useful for defining that mapping; importing should preserve enough information to apply it, not silently perform an undocumented rename.

Alternatives, Diagnostics, and Failure Modes

Base R readers such as read.table and read.csv are useful when the file is simple and the import settings can be specified directly. A package reader can be convenient for repeated workflows and explicit column specifications. Whichever reader is chosen, check its defaults for name repair, missing-value parsing, and type guessing. Automatic type guessing is convenient for exploration but less reliable for a repeatable pipeline, especially when a column contains identifiers or a mixture of numeric values and special codes.

Common warning signs include unexpectedly changed names, IDs with lost leading zeros, numeric columns imported as character, character columns converted to numbers, a sudden change in row count, and implausible values after conversion. A conversion warning is evidence to investigate, not a reason to suppress the warning and continue. Compare the problematic source fields with the import contract and determine whether the cause is a delimiter error, a header mismatch, a documented missing marker, or a genuinely malformed record.

A common mistake is to replace every negative value with missing data because one output column uses a negative sentinel. The reasoning is wrong because missing-value codes are specific to a file and sometimes to a field; a negative estimate or observation may be valid. The correction is to consult the source convention, recode only the specified field and code, and report how many values were changed. Another mistake is to rename or convert columns before recording their original names and units. Preserve the raw import or a clear mapping so that transformations can be reconstructed.

These safeguards cannot establish that a model was correctly fitted or that outputs from separate software are equivalent. They establish that the values being compared or reused in R have been imported and labelled coherently. They also do not replace inspection of software-specific output documentation, which remains essential when file layouts or conventions vary.