prepare_data() prepares the raw individual time-to-event data
for hazard estimation in 1d or 2d.
Given the raw data, this function first constructs the bins over one or two time axes and then computes the aggregated (or individual) vectors or matrices of exposure times and events indicators. A data.frame with covariates values can be provided by the user.
Usage
prepare_data(
data = NULL,
t_in = NULL,
t_out = NULL,
u = NULL,
s_in = NULL,
s_out,
events,
min_t = NULL,
max_t = NULL,
min_u = NULL,
max_u = NULL,
min_s = NULL,
max_s = NULL,
dt = NULL,
du = NULL,
ds,
individual = FALSE,
covs = NULL
)Arguments
- data
A data frame.
- t_in
(optional) A vector of entry times on the time scale
t.- t_out
(optional) A vector of exit times on the time scale
t.- u
(optional) A vector of fixed-times at entry in the process.
- s_in
(optional) A vector of entry times on the time scale
s.- s_out
A vector of exit times on the time scale
s.- events
A vector of event's indicators (possible values 0/1).
- min_t
(optional) A minimum value for the bins over
t. IfNULL, the minimum oft_inwill be used.- max_t
(optional) A maximum value for the bins over
t. IfNULL, the maximum oft_outwill be used.- min_u
(optional) A minimum value for the bins over
u. IfNULL, the minimum ofuwill be used.- max_u
(optional) A maximum value for the bins over
u. IfNULL, the maximum ofuwill be used.- min_s
(optional) A minimum value for the bins over
s. IfNULL, the minimum ofs_inwill be used.- max_s
(optional) A maximum value for the bins over
s. IfNULL, the maximum ofs_outwill be used.- dt
(optional) A scalar giving the length of the intervals on the
ttime scale.- du
(optional) A scalar giving the length of the intervals on the
uaxis.- ds
A scalar giving the length of the intervals on the
stime scale.- individual
A Boolean. Default is
FALSE: ifFALSEcomputes the matricesRandYcollectively for all observations; ifTRUEcomputes the matricesRandYseparately for each individual record.- covs
A data.frame with the variables to be used as covariates. The function will create dummy variables for any factor variable passed as argument in
covs. If a variable of class character is passed as argument, it will be converted to factor.
Value
A list with the following elements:
binsa list:bins_tift_outis provided, this is a vector of bins extremes for the time scalet.mid_tift_outis provided, this is a vector with the midpoints of the bins overt.ntift_outis provided, this is the number of bins overt.bins_uifuis provided, this is a vector of bins extremes foruaxis.miduifuis provided, this is a vector with the midpoints of the bins overu.nuifuis provided, this is the number of bins overu.bins_sis a vector of bins extremes for the time scales.midsis a vector with the midpoints of the bins overs.nsis the number of bins overs.
bindata:rorRan array of exposure times: if binning the data over one time scale only this is a vector. If binning the data over two time scales and ifindividual == TRUEthenRis an array of dimension nu by ns by n, otherwise it is an array of dimension nu by nsyorYan array of event counts: if binning the data over one time scale only this is a vector. If binning the data over two time scales and ifindividual == TRUEthenYis an array of dimension nu by ns by n, otherwise it is an array of dimension nu by nsZA matrix of covariates' values to be used in the model, of dimension n by p
Details
A few words about constructing the grid of bins.
Bins are containers for the individual data. There is no 'golden rule' or
optimal strategy for setting the number of bins over each time axis, or deciding
on the bins' width. It very much depends on the data structure, however, we
try to give some directions here. First, in most cases, more bins is better
than less bins. A good number is about 30 bins.
However, if data are scarce, the user might want to find a compromise between
having a larger number of bins, and having many bins empty.
Second, the chosen width of the bins (that is du and ds) does depend on
the time unit over which the time scales are measured. For example, if the time
is recorded in days, as in the example below, and several years of follow-up
are available, the user can split the data in bins of width 30 (corresponding
to about one month), 60 (about two months), 90 (about three months), etc.
If the time scale is measured in years, then appropriate width could be 0.25
(corresponding to a quarter of a year), or 0.5 (that is half year). However,
in some cases, time might be measure in completed years, as is often the case
for age. In this scenario, an appropriate bin width is 1.
Finally, it is always a good idea to plot the data first, and explore the range
of values over which the time scale(s) are recorded. This will give insight
about reasonable values for the arguments min_s, min_u, max_s and max_u
(that in any case are optional).
Regarding names of covariates or levels of categorical covariates/factors: When using "LMMsolver" to fit a model with covariates that which have names (or factor labels) including a symbol such as "+", "-", "<" or ">" will result in an error. To avoid this, the responsible names (labels) will be rewritten without mathematical symbols. For example: "Lev+5FU" (in the colon cancer data) is replaced by "Lev&5FU".
Author
Angela Carollo carollo@demogr.mpg.de
Examples
# Bin data over s = time since recurrence only, with intervals of length 30 days
# aggregated data (no covariates)
# The following example provide the vectors of data directly from the dataset
binned_data <- prepare_data(s_out = reccolon2ts$timesr, events = reccolon2ts$status, ds = 30)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
# Visualize vector of event counts
print(binned_data$bindata$y)
#> [1] 14 24 16 11 24 22 20 10 24 26 13 19 10 9 11 11 10 10 9 13 10 7 10 7 4
#> [26] 4 7 5 8 4 1 2 5 5 5 0 0 1 1 1 1 1 1 0 0 0 1 2 1 1
#> [51] 0 1 0 0 0 2 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> [76] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
# Visualize midpoints of the bins
print(binned_data$bins$mids)
#> [1] 15 45 75 105 135 165 195 225 255 285 315 345 375 405 435
#> [16] 465 495 525 555 585 615 645 675 705 735 765 795 825 855 885
#> [31] 915 945 975 1005 1035 1065 1095 1125 1155 1185 1215 1245 1275 1305 1335
#> [46] 1365 1395 1425 1455 1485 1515 1545 1575 1605 1635 1665 1695 1725 1755 1785
#> [61] 1815 1845 1875 1905 1935 1965 1995 2025 2055 2085 2115 2145 2175 2205 2235
#> [76] 2265 2295 2325 2355 2385 2415 2445 2475 2505 2535 2565 2595 2625 2655 2685
#> [91] 2715
# Visualize number of bins
print(binned_data$bins$ns)
#> [1] 91
# Now, the same thing is done by providing a dataset and the name of all relevant variables
binned_data <- prepare_data(data = reccolon2ts, s_out = "timesr", events = "status", ds = 30)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
# Visualize vector of event counts
print(binned_data$bindata$y)
#> [1] 14 24 16 11 24 22 20 10 24 26 13 19 10 9 11 11 10 10 9 13 10 7 10 7 4
#> [26] 4 7 5 8 4 1 2 5 5 5 0 0 1 1 1 1 1 1 0 0 0 1 2 1 1
#> [51] 0 1 0 0 0 2 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> [76] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
# Now using ds = .3 and the same variable measured in years
binned_data <- prepare_data(s_out = reccolon2ts$timesr_y, events = reccolon2ts$status, ds = .3)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
# Visualize vector of exposure timess
print(binned_data$bindata$r)
#> [1] 128.9234086 108.6935661 86.6990418 67.7699521 55.8161533 43.9006160
#> [7] 33.6388090 26.2145791 20.8121150 16.9198494 14.2230664 12.1813142
#> [13] 11.2851472 10.0655715 9.0108145 7.7641342 6.0433949 4.7993155
#> [19] 2.9854209 2.0730322 1.2588638 0.9572895 0.8856947 0.3787817
#> [25] 0.2606434
# Bin data over u = time at recurrence and s = time since recurrence, measured in days
# aggregated data (no covariates)
# Note that if we do not provide du this is taken to be equal to ds
binned_data <- prepare_data(
u = reccolon2ts$timer, s_out = reccolon2ts$timesr,
events = reccolon2ts$status, ds = 30
)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
# Visualize matrix of event counts
print(binned_data$bindata$Y)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 1 0 0 0 1 0 0 0 0 0 0 0 0
#> [2,] 1 2 3 0 0 0 0 0 1 0 0 0 0
#> [3,] 0 5 1 0 2 2 1 0 2 2 0 2 0
#> [4,] 1 4 0 1 1 3 2 1 3 0 1 0 0
#> [5,] 1 1 2 1 1 1 1 0 0 0 0 0 0
#> [6,] 2 1 2 1 1 2 1 0 2 4 2 0 0
#> [7,] 1 0 1 1 1 1 2 0 2 2 1 0 0
#> [8,] 1 3 0 0 1 2 3 0 1 1 0 5 2
#> [9,] 1 1 1 0 1 2 0 0 0 0 2 1 0
#> [10,] 2 0 0 1 2 0 1 1 2 2 2 1 1
#> [11,] 0 2 1 0 0 1 0 2 0 2 0 1 1
#> [12,] 0 0 0 1 1 0 2 0 1 1 1 0 1
#> [13,] 0 1 1 0 4 0 1 0 0 0 0 0 0
#> [14,] 0 0 0 0 0 1 0 0 1 0 1 1 0
#> [15,] 0 1 0 0 0 0 1 0 1 1 0 2 0
#> [16,] 0 0 0 1 1 0 0 1 1 0 0 1 0
#> [17,] 0 0 0 0 1 1 2 0 1 0 1 0 2
#> [18,] 0 0 0 0 3 1 0 2 0 1 0 1 0
#> [19,] 0 0 1 0 0 1 1 0 0 1 0 1 0
#> [20,] 0 0 1 1 0 0 0 0 1 1 1 0 0
#> [21,] 0 1 0 0 0 0 0 0 0 0 0 0 1
#> [22,] 0 0 0 0 1 0 0 0 1 1 0 0 1
#> [23,] 0 0 0 0 0 0 0 0 1 1 0 0 0
#> [24,] 0 1 0 0 1 0 0 0 1 0 0 0 0
#> [25,] 0 0 0 0 0 1 0 1 0 2 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 1 0 0 0 1 0 0 0 0 0 0
#> [28,] 1 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 1 0 0 0 1 0 0 0
#> [30,] 0 1 0 0 0 0 0 1 0 0 1 0 0
#> [31,] 1 0 0 0 0 1 0 0 0 1 0 0 0
#> [32,] 0 0 0 1 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0 1 0 2 0 0 0 0
#> [35,] 0 0 0 0 0 0 0 0 0 1 0 0 0
#> [36,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 1 0 0 0 0 0 0 0
#> [38,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [39,] 0 0 0 0 0 0 0 0 0 0 0 1 0
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 0 0 0 0 0 0 0 0 0 0 0 0 1
#> [42,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [43,] 0 0 0 0 0 0 0 1 0 0 0 0 0
#> [44,] 0 0 0 1 0 0 0 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0 0 0 0 0 0 1 0
#> [46,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [48,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [49,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [50,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [52,] 0 0 0 0 1 0 0 0 0 0 0 0 0
#> [53,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [54,] 0 0 0 0 0 0 0 0 0 1 0 0 0
#> [55,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [58,] 0 0 1 0 0 0 0 0 0 0 0 0 0
#> [59,] 1 0 0 0 0 0 0 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0 0 0 0 0 0 1 0
#> [61,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [64,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [66,] 0 0 0 1 0 0 0 0 0 0 0 0 0
#> [67,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [69,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [76,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,] 2 0 0 0 0 0 0 1 0 0 0 0
#> [2,] 1 0 0 0 1 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [4,] 1 0 0 0 0 1 1 1 0 0 0 0
#> [5,] 0 1 0 1 0 1 1 1 0 0 1 0
#> [6,] 1 1 0 1 1 1 1 0 0 0 1 0
#> [7,] 0 0 1 1 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 0 0 2 0 0
#> [9,] 0 1 1 2 0 0 0 1 1 1 0 0
#> [10,] 0 0 1 0 0 0 0 0 0 1 0 0
#> [11,] 0 0 2 0 0 1 1 0 0 0 1 0
#> [12,] 0 0 0 0 0 0 1 1 0 0 0 0
#> [13,] 1 1 0 0 0 1 0 0 0 0 0 0
#> [14,] 0 1 0 1 0 1 0 0 0 0 1 0
#> [15,] 0 0 0 0 0 0 1 0 0 0 0 0
#> [16,] 0 0 0 0 1 0 0 0 0 1 0 1
#> [17,] 1 0 0 1 1 0 0 0 0 1 0 0
#> [18,] 0 1 0 0 0 0 1 0 0 0 0 0
#> [19,] 0 0 1 0 0 0 1 1 0 0 0 1
#> [20,] 0 0 0 0 0 0 0 1 1 0 0 1
#> [21,] 0 0 1 0 3 0 0 0 1 0 0 0
#> [22,] 0 0 1 1 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 2 0 1 1 0 0 0 0
#> [24,] 0 1 0 0 0 1 1 0 0 0 0 0
#> [25,] 0 0 1 0 0 0 1 0 0 0 0 0
#> [26,] 1 1 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 1 0 0 0 0 0 0 0 0 1 0
#> [29,] 0 0 0 0 0 0 1 0 1 0 0 0
#> [30,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [31,] 0 1 1 0 0 0 0 0 0 0 0 0
#> [32,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 1 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0 0 0 1 0 0 0
#> [35,] 0 0 0 0 0 0 1 0 0 0 0 1
#> [36,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0 0 0 1 1 0 0
#> [38,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [39,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [42,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [43,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [44,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [46,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [48,] 0 1 0 0 0 0 0 0 1 0 0 0
#> [49,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [50,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [52,] 0 0 0 0 0 0 0 1 0 0 0 0
#> [53,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [54,] 0 0 0 0 1 0 0 0 0 0 0 0
#> [55,] 0 0 0 0 0 1 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [58,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [59,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [61,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0 0 1 0 0 0 0
#> [64,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [66,] 0 0 0 0 0 0 0 0 0 0 1 0
#> [67,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0 0 0 0 0 1 0
#> [69,] 0 0 1 0 0 0 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [76,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
#> [1,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 1 0 2 0 0 0 1 0 0 0 0
#> [5,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [7,] 1 0 0 0 1 0 0 0 1 0 0 0
#> [8,] 0 0 0 0 1 0 0 0 0 1 0 0
#> [9,] 0 2 0 0 0 0 0 1 0 1 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [12,] 0 0 1 1 0 0 0 1 0 0 0 0
#> [13,] 0 0 0 1 0 0 0 0 0 1 0 0
#> [14,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [15,] 0 0 1 1 0 0 0 0 1 0 0 0
#> [16,] 0 0 0 0 0 1 0 1 0 0 0 0
#> [17,] 0 0 0 0 0 0 1 0 0 0 0 0
#> [18,] 0 0 1 0 0 0 0 0 1 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 1 0 0 0
#> [20,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 1 0 0 0 0 0 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [24,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 1 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [30,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [31,] 0 0 0 0 0 0 0 1 0 0 0 0
#> [32,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [35,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [36,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [38,] 0 0 0 0 1 0 1 0 0 0 0 0
#> [39,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 0 0 0 0 1 0 0 0 0 0 0 0
#> [42,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [43,] 0 0 0 0 0 0 0 0 1 0 0 0
#> [44,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [46,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [48,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [49,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [50,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [52,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [53,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [54,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [55,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [58,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [59,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [61,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [64,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [66,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [67,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [69,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [76,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
#> [1,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 0 0 1 0 0 0 0 0 0 1 0 0
#> [14,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0 0 0 0 0 1 0
#> [16,] 0 0 0 0 0 1 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 0 0 0 0 0 0 1
#> [18,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 0 0 1 0 0 0 0 0 0 0
#> [22,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [25,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [30,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [31,] 0 0 0 0 0 0 0 0 0 0 1 0
#> [32,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [35,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [36,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [38,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [39,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [42,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [43,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [44,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [46,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [48,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [49,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [50,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [52,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [53,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [54,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [55,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [58,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [59,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [61,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [64,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [66,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [67,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [69,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [76,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
#> [1,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [6,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 1 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 1 0 0 0 1
#> [12,] 0 0 1 0 0 0 0 0 0 0 0 0
#> [13,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [14,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 0 1 0 0 0 0 0
#> [18,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [30,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [31,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [32,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [35,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [36,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [38,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [39,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [42,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [43,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [44,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [46,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [48,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [49,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [50,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [52,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [53,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [54,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [55,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [58,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [59,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [61,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [64,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [66,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [67,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [69,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [76,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73]
#> [1,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [14,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [18,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [30,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [31,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [32,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [35,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [36,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [38,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [39,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [42,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [43,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [44,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [46,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [48,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [49,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [50,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [52,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [53,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [54,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [55,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [58,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [59,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [61,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [64,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [66,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [67,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [69,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [76,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85]
#> [1,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 1 0 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [14,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [18,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [30,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [31,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [32,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [35,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [36,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [38,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [39,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [42,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [43,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [44,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [46,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [48,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [49,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [50,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [52,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [53,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [54,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [55,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [58,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [59,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [61,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [64,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [66,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [67,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [69,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [76,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [,86] [,87] [,88] [,89] [,90] [,91]
#> [1,] 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0
#> [13,] 0 0 0 0 0 0
#> [14,] 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 0
#> [18,] 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0
#> [21,] 0 0 0 0 0 0
#> [22,] 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0
#> [25,] 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0
#> [30,] 0 0 0 0 0 0
#> [31,] 0 0 0 0 0 0
#> [32,] 0 0 0 0 0 0
#> [33,] 0 0 0 0 0 0
#> [34,] 0 0 0 0 0 0
#> [35,] 0 0 0 0 0 0
#> [36,] 0 0 0 0 0 0
#> [37,] 0 0 0 0 0 0
#> [38,] 0 0 0 0 0 0
#> [39,] 0 0 0 0 0 0
#> [40,] 0 0 0 0 0 0
#> [41,] 0 0 0 0 0 0
#> [42,] 0 0 0 0 0 0
#> [43,] 0 0 0 0 0 0
#> [44,] 0 0 0 0 0 0
#> [45,] 0 0 0 0 0 0
#> [46,] 0 0 0 0 0 0
#> [47,] 0 0 0 0 0 0
#> [48,] 0 0 0 0 0 0
#> [49,] 0 0 0 0 0 0
#> [50,] 0 0 0 0 0 0
#> [51,] 0 0 0 0 0 0
#> [52,] 0 0 0 0 0 0
#> [53,] 0 0 0 0 0 0
#> [54,] 0 0 0 0 0 0
#> [55,] 0 0 0 0 0 0
#> [56,] 0 0 0 0 0 0
#> [57,] 0 0 0 0 0 0
#> [58,] 0 0 0 0 0 0
#> [59,] 0 0 0 0 0 0
#> [60,] 0 0 0 0 0 0
#> [61,] 0 0 0 0 0 0
#> [62,] 0 0 0 0 0 0
#> [63,] 0 0 0 0 0 0
#> [64,] 0 0 0 0 0 0
#> [65,] 0 0 0 0 0 0
#> [66,] 0 0 0 0 0 0
#> [67,] 0 0 0 0 0 0
#> [68,] 0 0 0 0 0 0
#> [69,] 0 0 0 0 0 0
#> [70,] 0 0 0 0 0 0
#> [71,] 0 0 0 0 0 0
#> [72,] 0 0 0 0 0 0
#> [73,] 0 0 0 0 0 0
#> [74,] 0 0 0 0 0 0
#> [75,] 0 0 0 0 0 0
#> [76,] 0 0 0 0 0 0
# Visualize midpoints of bins over u
print(binned_data$bins$midu)
#> [1] 23 53 83 113 143 173 203 233 263 293 323 353 383 413 443
#> [16] 473 503 533 563 593 623 653 683 713 743 773 803 833 863 893
#> [31] 923 953 983 1013 1043 1073 1103 1133 1163 1193 1223 1253 1283 1313 1343
#> [46] 1373 1403 1433 1463 1493 1523 1553 1583 1613 1643 1673 1703 1733 1763 1793
#> [61] 1823 1853 1883 1913 1943 1973 2003 2033 2063 2093 2123 2153 2183 2213 2243
#> [76] 2273
# Bin data over u = time at recurrence and s = time since recurrence, measured in day
# individual-level data required
# we provide two covariates: nodes (numerical) and rx (factor)
covs <- subset(reccolon2ts, select = c("nodes", "rx"))
binned_data <- prepare_data(
u = reccolon2ts$timer, s_out = reccolon2ts$timesr,
events = reccolon2ts$status, ds = 30, individual = TRUE, covs = covs
)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
# Visualize structure of binned data
print(str(binned_data$bindata))
#> List of 3
#> $ R: num [1:76, 1:91, 1:461] 0 0 0 0 0 0 0 0 0 0 ...
#> $ Y: num [1:76, 1:91, 1:461] 0 0 0 0 0 0 0 0 0 0 ...
#> $ Z: num [1:461, 1:3] 5 7 6 22 9 5 1 3 1 6 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "nodes" "rx_Lev" "rx_Lev+5FU"
#> NULL
# Alternatevely:
binned_data <- prepare_data(
data = reccolon2ts,
u = "timer", s_out = "timesr",
events = "status", ds = 30, individual = TRUE, covs = c("nodes", "rx")
)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
#> `s_in = NULL`. I will use `s_in = 0` for all observations.