Estimate probabilities to be in each state
estimate_pt(data, NAafterTmax = FALSE)data.frame containing id, id of the trajectory, time, time at which a change occurs
and state, associated state.
if TRUE, return NA if t > Tmax otherwise return the state associated with Tmax (useful when individuals has different lengths)
A list of two elements:
t: vector of time
pt: a matrix with K (= number of states) rows and with length(t) columns containing the
probabilities to be in each state at each time.
Other Descriptive statistics:
boxplot.timeSpent(),
compute_duration(),
compute_number_jumps(),
compute_time_spent(),
hist.duration(),
hist.njump(),
plot.pt(),
plotData(),
statetable(),
summary_cfd()
# Simulate the Jukes-Cantor model of nucleotide replacement
K <- 4
PJK <- matrix(1 / 3, nrow = K, ncol = K) - diag(rep(1 / 3, K))
lambda_PJK <- c(1, 1, 1, 1)
d_JK <- generate_Markov(n = 10, K = K, P = PJK, lambda = lambda_PJK, Tmax = 10)
d_JK2 <- cut_data(d_JK, 10)
# estimate probabilities
estimate_pt(d_JK2)
#> $pt
#> 0 0.028 0.094 0.106 0.253 0.289 0.374 0.379 0.427 0.488 0.533 0.563 0.625
#> 1 1 0.9 0.8 0.7 0.8 0.7 0.7 0.7 0.7 0.6 0.5 0.6 0.5
#> 2 0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
#> 3 0 0.1 0.1 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.1 0.2
#> 4 0 0.0 0.1 0.2 0.2 0.3 0.2 0.2 0.3 0.4 0.4 0.3 0.3
#> 0.823 0.859 0.944 0.947 0.976 1.011 1.076 1.281 1.311 1.524 1.555 1.602 1.667
#> 1 0.4 0.4 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
#> 2 0.1 0.2 0.2 0.2 0.1 0.2 0.1 0.1 0.0 0.1 0.2 0.1 0.2
#> 3 0.2 0.1 0.2 0.2 0.3 0.3 0.3 0.2 0.3 0.2 0.2 0.2 0.2
#> 4 0.3 0.3 0.3 0.4 0.4 0.3 0.4 0.5 0.5 0.5 0.4 0.5 0.4
#> 1.921 1.958 2.115 2.146 2.218 2.226 2.307 2.354 2.638 2.675 2.715 2.748 2.805
#> 1 0.3 0.4 0.4 0.4 0.3 0.3 0.2 0.1 0.0 0.0 0.1 0.2 0.2
#> 2 0.1 0.1 0.1 0.1 0.1 0.2 0.3 0.3 0.3 0.4 0.3 0.3 0.2
#> 3 0.2 0.2 0.1 0.2 0.3 0.2 0.2 0.2 0.3 0.3 0.3 0.2 0.2
#> 4 0.4 0.3 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.4
#> 2.923 2.949 3.107 3.152 3.18 3.257 3.363 3.404 3.536 3.592 3.886 3.95 4.024
#> 1 0.3 0.2 0.2 0.3 0.3 0.4 0.5 0.4 0.4 0.4 0.3 0.3 0.3
#> 2 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.1 0.0 0.1 0.2 0.3 0.2
#> 3 0.2 0.2 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1
#> 4 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.3 0.4 0.3 0.3 0.3 0.4
#> 4.077 4.095 4.116 4.188 4.3 4.506 4.729 4.801 4.885 5.21 5.239 5.289 5.398
#> 1 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.2
#> 2 0.2 0.2 0.1 0.1 0.2 0.2 0.3 0.3 0.2 0.2 0.2 0.3 0.3
#> 3 0.1 0.1 0.1 0.2 0.1 0.2 0.2 0.3 0.4 0.3 0.4 0.3 0.4
#> 4 0.3 0.4 0.5 0.4 0.4 0.4 0.3 0.2 0.2 0.2 0.2 0.2 0.1
#> 5.496 5.791 5.8 5.816 6.021 6.109 6.139 6.224 6.226 6.332 6.35 6.383 6.498
#> 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.1 0.2 0.1 0.2
#> 2 0.4 0.3 0.3 0.3 0.2 0.2 0.3 0.4 0.5 0.4 0.3 0.4 0.4
#> 3 0.4 0.5 0.4 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3
#> 4 0.1 0.1 0.2 0.1 0.2 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.1
#> 6.707 6.897 6.969 6.978 7.087 7.108 7.199 7.268 7.28 7.36 7.437 7.48 7.605
#> 1 0.1 0.1 0.1 0.2 0.1 0.0 0.1 0.1 0.1 0.2 0.1 0.1 0.2
#> 2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.5 0.4 0.3
#> 3 0.3 0.2 0.1 0.1 0.1 0.2 0.2 0.3 0.2 0.1 0.1 0.2 0.2
#> 4 0.1 0.2 0.3 0.2 0.3 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.3
#> 7.644 7.76 7.912 7.93 7.997 8.032 8.328 8.419 8.504 8.528 8.608 8.634 8.635
#> 1 0.2 0.3 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.1
#> 2 0.4 0.4 0.4 0.3 0.4 0.5 0.5 0.6 0.7 0.6 0.6 0.6 0.7
#> 3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.2 0.1 0.1
#> 4 0.3 0.2 0.3 0.4 0.3 0.3 0.2 0.1 0.1 0.1 0.0 0.1 0.1
#> 8.792 8.898 9.014 9.03 9.195 9.298 9.307 9.317 9.32 9.354 9.422 9.437 9.489
#> 1 0.0 0.1 0.2 0.2 0.2 0.1 0.0 0.0 0.0 0.0 0.1 0.2 0.3
#> 2 0.8 0.8 0.7 0.6 0.7 0.8 0.8 0.7 0.6 0.5 0.5 0.5 0.4
#> 3 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#> 4 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.2 0.3 0.4 0.3 0.2 0.2
#> 9.686 9.857 10
#> 1 0.2 0.3 0.3
#> 2 0.4 0.4 0.4
#> 3 0.2 0.1 0.1
#> 4 0.2 0.2 0.2
#>
#> $t
#> [1] 0.00000000 0.02755240 0.09370509 0.10640850 0.25290377 0.28937582
#> [7] 0.37449061 0.37931910 0.42730239 0.48777133 0.53345456 0.56337430
#> [13] 0.62484774 0.82334431 0.85924055 0.94356750 0.94662363 0.97608685
#> [19] 1.01140755 1.07594812 1.28123824 1.31109399 1.52409244 1.55534139
#> [25] 1.60168136 1.66719715 1.92119632 1.95821066 2.11489079 2.14635918
#> [31] 2.21795628 2.22644841 2.30733092 2.35373320 2.63788043 2.67527691
#> [37] 2.71510565 2.74762224 2.80541116 2.92328515 2.94890936 3.10650492
#> [43] 3.15153045 3.17990375 3.25706108 3.36328579 3.40426873 3.53550653
#> [49] 3.59210510 3.88597177 3.95024470 4.02418914 4.07684936 4.09534683
#> [55] 4.11582266 4.18822801 4.30037150 4.50644960 4.72908831 4.80092198
#> [61] 4.88456999 5.20965145 5.23856971 5.28938896 5.39847150 5.49597440
#> [67] 5.79124782 5.80000751 5.81590326 6.02106221 6.10868350 6.13876469
#> [73] 6.22350061 6.22572965 6.33163800 6.34953384 6.38315936 6.49816604
#> [79] 6.70717214 6.89713464 6.96928660 6.97810277 7.08655702 7.10774498
#> [85] 7.19935150 7.26765439 7.28039476 7.36041711 7.43734252 7.48027194
#> [91] 7.60455723 7.64443079 7.75968137 7.91157307 7.92977601 7.99703962
#> [97] 8.03179696 8.32773497 8.41940166 8.50403122 8.52809897 8.60760633
#> [103] 8.63442683 8.63498440 8.79230153 8.89844938 9.01422385 9.03037631
#> [109] 9.19479834 9.29796501 9.30669196 9.31715795 9.31982081 9.35443685
#> [115] 9.42175231 9.43746113 9.48880374 9.68607408 9.85661948 10.00000000
#>
#> attr(,"class")
#> [1] "pt"