| get_eigen_spline_matrix | R Documentation | 
Generate Ind x Time data.frame for each variable using get_ind_time_matrix and then concatenate all variables rowise. Resulting data.frame contrain Time as columns and Individuals and Variables as rows. Pairs of Individual and Timepoint without a measurement are left as NA. If ncore!=0 the function is parallelised, however the parallelisation overhead cost is high if not required.
get_eigen_spline_matrix(inputData, ind, time, ncores = 0)
| inputData | 
 | 
| ind | Vector of subject identifier (individual) corresponding to each measurement | 
| time | Vector of time corresponding to each measurement | 
| ncores | (int) Number of cores to use for parallelisation. Default 0 for no parallelisation. | 
data.frame of measurements for each IND x TIME + VAR. Rows are unique Individual IDs per variable, and columns unique measurement Time. Pairs of (IND,TIME+VAR) without a measurement are left as NA.
## Not run: 
## 6 measurements, 3 subjects, 3 unique time-points, 2 variables
inputData <- matrix(c(1,2,3,4,5,6, 7,8,9,10,11,12), ncol=2)
ind  <- c('ind_1','ind_1','ind_1','ind_2','ind_2','ind_3')
time <- c(0,5,10,0,10,5)
get_eigen_spline_matrix(inputData, ind, time, ncores=0)
#     0   5  10
# 1   1   2   3
# 2   4  NA   5
# 3  NA   6  NA
# 4   7   8   9
# 5  10  NA  11
# 6  NA  12  NA
## End(Not run)
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