adjust | R Documentation |

This function can be used to adjust the data for the effect of other variables present in the dataset. It is based on an underlying fitting of regressions models, allowing for quite some flexibility, such as including factors as random effects in mixed models (multilevel partialization), continuous variables as smooth terms in general additive models (non-linear partialization) and/or fitting these models under a Bayesian framework. The values returned by this function are the residuals of the regression models. Note that a regular correlation between two "adjusted" variables is equivalent to the partial correlation between them.

adjust( data, effect = NULL, select = NULL, exclude = NULL, multilevel = FALSE, additive = FALSE, bayesian = FALSE, keep_intercept = FALSE, ignore_case = FALSE, regex = FALSE, verbose = FALSE ) data_adjust( data, effect = NULL, select = NULL, exclude = NULL, multilevel = FALSE, additive = FALSE, bayesian = FALSE, keep_intercept = FALSE, ignore_case = FALSE, regex = FALSE, verbose = FALSE )

`data` |
A data frame. |

`effect` |
Character vector of column names to be adjusted for (regressed
out). If |

`select` |
Variables that will be included when performing the required tasks. Can be either a variable specified as a literal variable name (e.g., `column_name` ),a string with the variable name (e.g., `"column_name"` ), or a character vector of variable names (e.g.,`c("col1", "col2", "col3")` ),a formula with variable names (e.g., `~column_1 + column_2` ),a vector of positive integers, giving the positions counting from the left (e.g. `1` or`c(1, 3, 5)` ),a vector of negative integers, giving the positions counting from the right (e.g., `-1` or`-1:-3` ),one of the following select-helpers: `starts_with()` ,`ends_with()` ,`contains()` , a range using`:` or`regex("")` .`starts_with()` ,`ends_with()` , and`contains()` accept several patterns, e.g`starts_with("Sep", "Petal")` .or a function testing for logical conditions, e.g. `is.numeric()` (or`is.numeric` ), or any user-defined function that selects the variables for which the function returns`TRUE` (like:`foo <- function(x) mean(x) > 3` ),ranges specified via literal variable names, select-helpers (except `regex()` ) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a`-` , e.g.`-ends_with("")` ,`-is.numeric` or`-Sepal.Width:Petal.Length` .**Note:**Negation means that matches are*excluded*, and thus, the`exclude` argument can be used alternatively. For instance,`select=-ends_with("Length")` (with`-` ) is equivalent to`exclude=ends_with("Length")` (no`-` ). In case negation should not work as expected, use the`exclude` argument instead.
If |

`exclude` |
See |

`multilevel` |
If |

`additive` |
If |

`bayesian` |
If |

`keep_intercept` |
If |

`ignore_case` |
Logical, if |

`regex` |
Logical, if |

`verbose` |
Toggle warnings. |

A data frame comparable to `data`

, with adjusted variables.

adjusted_all <- adjust(attitude) head(adjusted_all) adjusted_one <- adjust(attitude, effect = "complaints", select = "rating") head(adjusted_one) adjust(attitude, effect = "complaints", select = "rating", bayesian = TRUE) adjust(attitude, effect = "complaints", select = "rating", additive = TRUE) attitude$complaints_LMH <- cut(attitude$complaints, 3) adjust(attitude, effect = "complaints_LMH", select = "rating", multilevel = TRUE) # Generate data data <- simulate_correlation(n = 100, r = 0.7) data$V2 <- (5 * data$V2) + 20 # Add intercept # Adjust adjusted <- adjust(data, effect = "V1", select = "V2") adjusted_icpt <- adjust(data, effect = "V1", select = "V2", keep_intercept = TRUE) # Visualize plot(data$V1, data$V2, pch = 19, col = "blue", ylim = c(min(adjusted$V2), max(data$V2)), main = "Original (blue), adjusted (green), and adjusted - intercept kept (red) data" ) abline(lm(V2 ~ V1, data = data), col = "blue") points(adjusted$V1, adjusted$V2, pch = 19, col = "green") abline(lm(V2 ~ V1, data = adjusted), col = "green") points(adjusted_icpt$V1, adjusted_icpt$V2, pch = 19, col = "red") abline(lm(V2 ~ V1, data = adjusted_icpt), col = "red")

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