These functions convert incidence objects into other classes.
# S3 method for incidence as.data.frame(x, ..., long = FALSE) as.incidence(x, ...) # S3 method for matrix as.incidence( x, dates = NULL, interval = NULL, standard = TRUE, isoweeks = standard, ... ) # S3 method for data.frame as.incidence(x, dates = NULL, interval = NULL, isoweeks = TRUE, ...) # S3 method for numeric as.incidence(x, dates = NULL, interval = NULL, isoweeks = TRUE, ...)
Arguments
| x | An |
|---|---|
| ... | Further arguments passed to other functions (no used). |
| long | A logical indicating if the output data.frame should be 'long', i.e. where a single column containing 'groups' is added in case of data computed on several groups. |
| dates | A vector of dates, each corresponding to the (inclusive) lower limit of the bins. |
| interval | An integer indicating the time interval used in the computation of the incidence. If NULL, it will be determined from the first time interval between provided dates. If only one date is provided, it will trigger an error. |
| standard | A logical indicating whether standardised dates should be
used. Defaults to |
| isoweeks | Deprecated. Use standard. |
Details
Conversion to incidence objects should only be done when the
original dates are not available. In such case, the argument x
should be a matrix corresponding to the $counts element of an
incidence object, i.e. giving counts with time intervals in rows and
named groups in columns. In the absence of groups, a single unnamed columns
should be given. data.frame and vectors will be coerced to a matrix.
See also
the incidence() function to generate the 'incidence' objects.
Examples
## create fake data data <- c(0,1,1,2,1,3,4,5,5,5,5,4,4,26,6,7,9) sex <- sample(c("m","f"), length(data), replace=TRUE) ## get incidence per group (sex) i <- incidence(data, groups = sex) i#> <incidence object> #> [17 cases from days 0 to 26] #> [2 groups: f, m] #> #> $counts: matrix with 27 rows and 2 columns #> $n: 17 cases in total #> $dates: 27 dates marking the left-side of bins #> $interval: 1 day #> $timespan: 27 days #> $cumulative: FALSE #>plot(i)#> dates f m #> 1 0 0 1 #> 2 1 1 2 #> 3 2 1 0 #> 4 3 1 0 #> 5 4 1 2 #> 6 5 3 1 #> 7 6 0 1 #> 8 7 1 0 #> 9 8 0 0 #> 10 9 1 0 #> 11 10 0 0 #> 12 11 0 0 #> 13 12 0 0 #> 14 13 0 0 #> 15 14 0 0 #> 16 15 0 0 #> 17 16 0 0 #> 18 17 0 0 #> 19 18 0 0 #> 20 19 0 0 #> 21 20 0 0 #> 22 21 0 0 #> 23 22 0 0 #> 24 23 0 0 #> 25 24 0 0 #> 26 25 0 0 #> 27 26 0 1#> dates counts groups #> 1 0 0 f #> 2 1 1 f #> 3 2 1 f #> 4 3 1 f #> 5 4 1 f #> 6 5 3 f #> 7 6 0 f #> 8 7 1 f #> 9 8 0 f #> 10 9 1 f #> 11 10 0 f #> 12 11 0 f #> 13 12 0 f #> 14 13 0 f #> 15 14 0 f #> 16 15 0 f #> 17 16 0 f #> 18 17 0 f #> 19 18 0 f #> 20 19 0 f #> 21 20 0 f #> 22 21 0 f #> 23 22 0 f #> 24 23 0 f #> 25 24 0 f #> 26 25 0 f #> 27 26 0 f #> 28 0 1 m #> 29 1 2 m #> 30 2 0 m #> 31 3 0 m #> 32 4 2 m #> 33 5 1 m #> 34 6 1 m #> 35 7 0 m #> 36 8 0 m #> 37 9 0 m #> 38 10 0 m #> 39 11 0 m #> 40 12 0 m #> 41 13 0 m #> 42 14 0 m #> 43 15 0 m #> 44 16 0 m #> 45 17 0 m #> 46 18 0 m #> 47 19 0 m #> 48 20 0 m #> 49 21 0 m #> 50 22 0 m #> 51 23 0 m #> 52 24 0 m #> 53 25 0 m #> 54 26 1 m## conversion from a matrix of counts to an incidence object i$counts#> f m #> [1,] 0 1 #> [2,] 1 2 #> [3,] 1 0 #> [4,] 1 0 #> [5,] 1 2 #> [6,] 3 1 #> [7,] 0 1 #> [8,] 1 0 #> [9,] 0 0 #> [10,] 1 0 #> [11,] 0 0 #> [12,] 0 0 #> [13,] 0 0 #> [14,] 0 0 #> [15,] 0 0 #> [16,] 0 0 #> [17,] 0 0 #> [18,] 0 0 #> [19,] 0 0 #> [20,] 0 0 #> [21,] 0 0 #> [22,] 0 0 #> [23,] 0 0 #> [24,] 0 0 #> [25,] 0 0 #> [26,] 0 0 #> [27,] 0 1new_i <- as.incidence(i$counts, i$dates) new_i#> <incidence object> #> [17 cases from days 0 to 26] #> [2 groups: f, m] #> #> $counts: matrix with 27 rows and 2 columns #> $n: 17 cases in total #> $dates: 27 dates marking the left-side of bins #> $interval: 1 day #> $timespan: 27 days #> $cumulative: FALSE #>#> [1] TRUE
