# Methods for class condList The output of the `condition` (aka `condList`) function is a nested list of class condList that contains one or several data frames. The utilities in `condList-methods` are suited for rendering or reshaping these objects in different ways. ```r ## S3 method for class 'condList' summary(object, n = 6, ...) ## S3 method for class 'condList' as.data.frame(x, row.names = attr(x, "cases"), optional = TRUE, nobs = TRUE, ...) group.by.outcome(object, cases = TRUE) ``` ## Arguments - `object,x`: Object of class condList as output by the `condition` function. - `n`: Positive integer: the maximal number of conditions to be printed. - ``...``: Not used. - `row.names,optional`: As in `as.data.frame`. - `nobs`: Logical; if `TRUE`, the returned data frame has a column named n.obs indicating how many cases instantiate a given configuration in the data. - `cases`: Logical; if `TRUE`, the returned data frame has a column named cases . ## Details The `summary` method for class condList prints the output of `condition` in a condensed manner. It is identical to `print`ing with `print.table = FALSE` (but with a different default of argument `n`), see `print.condList`. The output of `condition` is a nested list of class condList that contains one or several data frames. The method `as.data.frame` is a variant of the base method `as.data.frame`. It offers a convenient way of combining the columns of the data frames in a `condList` into one regular data frame. Columns appearing in several tables (typically the modeled outcomes) are included only once in the resulting data frame. The output of `as.data.frame` has syntactically invalid column names by default, including operators such as `"->"` or `"+"`. Setting `optional = FALSE` converts the column names into syntactically valid names (using `make.names`). `group.by.outcome` takes a `condList` as input and combines the entries in that nested list into a data frame with a larger number of columns, combining all columns concerning the same outcome into the same data frame. The additional attributes (measures, info, etc.) are thereby removed. ## See Also `condition`, `condList`, `as.data.frame`, `make.names` ## Examples ```r # Analysis of d.irrigate data with standard evaluation measures. ana1 <- cna(d.irrigate, ordering = "A, R, L < F, C < W", con = .9) (ana1.csf <- condition(csf(ana1)$condition, d.irrigate)) # Convert condList to data frame. as.data.frame(ana1.csf) as.data.frame(ana1.csf[1]) # Include the first condition only as.data.frame(ana1.csf, row.names = NULL) as.data.frame(ana1.csf, optional = FALSE) as.data.frame(ana1.csf, nobs = FALSE) # Summary. summary(ana1.csf) # Analyze atomic solution formulas. (ana1.asf <- condition(asf(ana1)$condition, d.irrigate)) as.data.frame(ana1.asf) summary(ana1.asf) # Group by outcome. group.by.outcome(ana1.asf) # Analyze minimally sufficient conditions. (ana1.msc <- condition(msc(ana1)$condition, d.irrigate)) as.data.frame(ana1.msc) group.by.outcome(ana1.msc) summary(ana1.msc) # Print more than 6 conditions. summary(ana1.msc, n = 10) # Analysis with different evaluation measures. ana2 <- cna(d.irrigate, ordering = "A, R, L < F, C < W", con = .9, cov = .9, measures = c("PAcon", "PACcov")) (ana2.csf <- condition(csf(ana2)$condition, d.irrigate)) print(ana2.csf, add.data = d.irrigate, n=10) as.data.frame(ana2.csf, nobs = FALSE, row.names = NULL) summary(ana2.csf, n = 10) ```

condList-methods function