metrics_model: Gives a PMax, Wbal and mFTP estimations from a given training...
In Bolshom/optimumtt: A colection of functions to help cyclists.
Description
Usage
Arguments
Value
Author(s)
References
Examples
View source: R/metrics_model.R
Description
From a mean maximum power file, containing the best power marks during a certain period, this function is able to calculate the Pmax, WBal and mFTP estimations.
Usage
1
metrics_model(mmp_file, wpk = F, weight)
Arguments
mmp_file
A vector containing the mean maximum power for each duration.
wpk
A boolean to indicate wheter the power information are in watts (wpk=F) or watts per kilo (wpk=T).
weight
The weight of the athlete in kilos.
Value
pmax
The maximum power estimation, in watts.
wbal
The anaerobic capacity estimation, in Joules.
mftp
The modeled functional threshold power estimation, in watts.
Author(s)
Natan Freitas Leite.
References
Power Profiling - R graph on Golden Cheetah, by "fabrylama".
Examples
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## The function is currently defined as
function (mmp_file, wpk = F, weight)
{
require(changepoint)
if (!wpk) {
mmp_file = mmp_file/weight
}
if (mmp_file[1] == 0) {
mm = mmp_file[-1]
}
else {
mm = mmp_file
}
i1 <- 15
i2 <- 90
i3 <- 120
i4 <- 300
i5 <- 600
i6 <- 3000
i7 <- 4000
i8 <- 40000
if (i7 > length(mm)) {
i7 <- (length(mm) - 1)
}
if (i8 > length(mm)) {
i8 <- length(mm)
}
paa = 15
etau = 1
ecp = 5
paa_dec = -2
ecp_del = -0.9
tau_del = -4.8
ecp_dec = -1
ecp_dec_del = -180
paa_pow = 1.05
paa_min = 5
etau_min = 0.5
paa_dec_max = -0.25
paa_dec_min = -3
ecp_dec_min = -5
etau_delta_max = 1e-04
paa_delta_max = 0.01
paa_dec_delta_max = 1e-04
ecp_del_delta_max = 1e-04
ecp_dec_delta_max = 1e-08
max_loops = 100
iteration = 0
repeat {
iteration <- iteration + 1
if (iteration > max_loops) {
break
}
etau_prev = etau
paa_prev = paa
paa_dec_prev = paa_dec
ecp_del_prev = ecp_del
ecp_dec_prev = ecp_dec
ecp = 0
avg_ecp = 0
count = 1
for (i in i5:i6) {
ecpn = (mm[i] - paa * exp(paa_dec * ((i/60)^(paa_pow))))/(1 -
exp(tau_del * i/60))/(1 - exp(ecp_del * i/60))/(1 +
ecp_dec * exp(ecp_dec_del/(i/60)))/(1 + etau/(i/60))
avg_ecp = ((count - 1) * avg_ecp + ecpn)/count
if (ecp < ecpn) {
ecp = ecpn
}
count <- count + 1
}
etau = etau_min
avg_etau = 0
count = 1
for (i in i3:i4) {
etaun = ((mm[i] - paa * exp(paa_dec * ((i/60)^(paa_pow))))/ecp/(1 -
exp(tau_del * i/60))/(1 - exp(ecp_del * i/60))/(1 +
ecp_dec * exp(ecp_dec_del/(i/60))) - 1) * (i/60)
avg_etau = ((count - 1) * avg_etau + etaun)/count
if (etau < etaun) {
etau = etaun
}
count <- count + 1
}
paa_dec = paa_dec_min
avg_paa_dec = 0
count = 1
for (i in i1:i2) {
paa_decn = log((mm[i] - ecp * (1 - exp(tau_del *
i/60)) * (1 - exp(ecp_del * i/60)) * (1 + ecp_dec *
exp(ecp_dec_del/(i/60))) * (1 + etau/(i/60)))/paa)/((i/60)^(paa_pow))
avg_paa_dec = ((count - 1) * avg_paa_dec + paa_decn)/count
if (is.na(paa_decn)) {
paa_decn <- paa_dec
}
else {
if ((paa_dec < paa_decn) && (paa_decn < paa_dec_max)) {
paa_dec = paa_decn
}
}
count <- count + 1
}
paa = paa_min
avg_paa = 0
count = 1
for (i in 1:8) {
paan = (mm[i] - ecp * (1 - exp(tau_del * i/60)) *
(1 - exp(ecp_del * i/60)) * (1 + ecp_dec * exp(ecp_dec_del/(i/60))) *
(1 + etau/(i/60)))/exp(paa_dec * ((i/60)^paa_pow))
avg_paa = ((count - 1) * avg_paa + paan)/count
if (paa < paan) {
paa = paan
}
count <- count + 1
}
if (avg_paa < 0.95 * paa) {
paa = avg_paa
}
ecp_dec = ecp_dec_min
avg_ecp_dec = 0
count = 1
for (i in seq(i7, i8, 120)) {
ecp_decn = ((mm[i] - paa * exp(paa_dec * ((i/60)^paa_pow)))/ecp/(1 -
exp(tau_del * i/60))/(1 - exp(ecp_del * i/60))/(1 +
etau/(i/60)) - 1)/exp(ecp_dec_del/(i/60))
avg_ecp_dec = ((count - 1) * avg_ecp_dec + ecp_decn)/count
if (ecp_decn > 0) {
ecp_decn = 0
}
if (ecp_dec < ecp_decn) {
ecp_dec = ecp_decn
}
count <- count + 1
}
if (!((abs(etau - etau_prev) > etau_delta_max) || (abs(paa -
paa_prev) > paa_delta_max) || (abs(paa_dec - paa_dec_prev) >
paa_dec_delta_max) || (abs(ecp_del - ecp_del_prev) >
ecp_del_delta_max) || (abs(ecp_dec - ecp_dec_prev) >
ecp_dec_delta_max))) {
break
}
}
pMax = paa * exp(paa_dec * ((1/60)^paa_pow)) + ecp * (1 -
exp(tau_del * (1/60))) * (1 - exp(ecp_del * (1/60))) *
(1 + ecp_dec * exp(ecp_dec_del/(1/60))) * (1 + etau/(1/60))
mmp60 = paa * exp(paa_dec * ((60)^paa_pow)) + ecp * (1 -
exp(tau_del * (60))) * (1 - exp(ecp_del * 60)) * (1 +
ecp_dec * exp(ecp_dec_del/60)) * (1 + etau/(60))
xemmp <- 1:620
yemmp <- 1:620
for (i in 1:620) {
t <- 5 * 10^(i/210) - 3
yemmp[i] <- paa * exp(paa_dec * (((t/60)^paa_pow))) +
ecp * (1 - exp(tau_del * (t/60))) * (1 - exp(ecp_del *
(t/60))) * (1 + ecp_dec * exp(ecp_dec_del/(t/60))) *
(1 + etau/((t/60)))
xemmp[i] <- t
}
myts = ts(yemmp, start = c(1), end = c(length(yemmp)), frequency = 1)
disc = cpt.mean(myts, penalty = "Manual", pen.value = "log(n)",
method = "PELT")
low_avg = which((disc@cpts > sum(xemmp < i5) + 1) & (disc@cpts <
sum(xemmp <= i7)))[1]
if (is.na(low_avg)) {
low_avg = length(disc@cpts) - 1
}
mftp = mean(yemmp[disc@cpts[low_avg]:sum(xemmp <= i7)]) *
weight
wbal = ecp * etau * 60 * weight
pmax = pMax * weight
return(c(pmax, wbal, mftp))
}
data(mmp_alldata)
metrics=metrics_model(mmp_alldata[[1]],F,65)
metrics
Bolshom/optimumtt documentation built on May 24, 2019, 8:56 a.m.
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Description Usage Arguments Value Author(s) References Examples
View source: R/metrics_model.R
Description
From a mean maximum power file, containing the best power marks during a certain period, this function is able to calculate the Pmax, WBal and mFTP estimations.
Usage
1 | metrics_model(mmp_file, wpk = F, weight)
|
Arguments
mmp_file |
A vector containing the mean maximum power for each duration. |
wpk |
A boolean to indicate wheter the power information are in watts (wpk=F) or watts per kilo (wpk=T). |
weight |
The weight of the athlete in kilos. |
Value
pmax |
The maximum power estimation, in watts. |
wbal |
The anaerobic capacity estimation, in Joules. |
mftp |
The modeled functional threshold power estimation, in watts. |
Author(s)
Natan Freitas Leite.
References
Power Profiling - R graph on Golden Cheetah, by "fabrylama".
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | ## The function is currently defined as
function (mmp_file, wpk = F, weight)
{
require(changepoint)
if (!wpk) {
mmp_file = mmp_file/weight
}
if (mmp_file[1] == 0) {
mm = mmp_file[-1]
}
else {
mm = mmp_file
}
i1 <- 15
i2 <- 90
i3 <- 120
i4 <- 300
i5 <- 600
i6 <- 3000
i7 <- 4000
i8 <- 40000
if (i7 > length(mm)) {
i7 <- (length(mm) - 1)
}
if (i8 > length(mm)) {
i8 <- length(mm)
}
paa = 15
etau = 1
ecp = 5
paa_dec = -2
ecp_del = -0.9
tau_del = -4.8
ecp_dec = -1
ecp_dec_del = -180
paa_pow = 1.05
paa_min = 5
etau_min = 0.5
paa_dec_max = -0.25
paa_dec_min = -3
ecp_dec_min = -5
etau_delta_max = 1e-04
paa_delta_max = 0.01
paa_dec_delta_max = 1e-04
ecp_del_delta_max = 1e-04
ecp_dec_delta_max = 1e-08
max_loops = 100
iteration = 0
repeat {
iteration <- iteration + 1
if (iteration > max_loops) {
break
}
etau_prev = etau
paa_prev = paa
paa_dec_prev = paa_dec
ecp_del_prev = ecp_del
ecp_dec_prev = ecp_dec
ecp = 0
avg_ecp = 0
count = 1
for (i in i5:i6) {
ecpn = (mm[i] - paa * exp(paa_dec * ((i/60)^(paa_pow))))/(1 -
exp(tau_del * i/60))/(1 - exp(ecp_del * i/60))/(1 +
ecp_dec * exp(ecp_dec_del/(i/60)))/(1 + etau/(i/60))
avg_ecp = ((count - 1) * avg_ecp + ecpn)/count
if (ecp < ecpn) {
ecp = ecpn
}
count <- count + 1
}
etau = etau_min
avg_etau = 0
count = 1
for (i in i3:i4) {
etaun = ((mm[i] - paa * exp(paa_dec * ((i/60)^(paa_pow))))/ecp/(1 -
exp(tau_del * i/60))/(1 - exp(ecp_del * i/60))/(1 +
ecp_dec * exp(ecp_dec_del/(i/60))) - 1) * (i/60)
avg_etau = ((count - 1) * avg_etau + etaun)/count
if (etau < etaun) {
etau = etaun
}
count <- count + 1
}
paa_dec = paa_dec_min
avg_paa_dec = 0
count = 1
for (i in i1:i2) {
paa_decn = log((mm[i] - ecp * (1 - exp(tau_del *
i/60)) * (1 - exp(ecp_del * i/60)) * (1 + ecp_dec *
exp(ecp_dec_del/(i/60))) * (1 + etau/(i/60)))/paa)/((i/60)^(paa_pow))
avg_paa_dec = ((count - 1) * avg_paa_dec + paa_decn)/count
if (is.na(paa_decn)) {
paa_decn <- paa_dec
}
else {
if ((paa_dec < paa_decn) && (paa_decn < paa_dec_max)) {
paa_dec = paa_decn
}
}
count <- count + 1
}
paa = paa_min
avg_paa = 0
count = 1
for (i in 1:8) {
paan = (mm[i] - ecp * (1 - exp(tau_del * i/60)) *
(1 - exp(ecp_del * i/60)) * (1 + ecp_dec * exp(ecp_dec_del/(i/60))) *
(1 + etau/(i/60)))/exp(paa_dec * ((i/60)^paa_pow))
avg_paa = ((count - 1) * avg_paa + paan)/count
if (paa < paan) {
paa = paan
}
count <- count + 1
}
if (avg_paa < 0.95 * paa) {
paa = avg_paa
}
ecp_dec = ecp_dec_min
avg_ecp_dec = 0
count = 1
for (i in seq(i7, i8, 120)) {
ecp_decn = ((mm[i] - paa * exp(paa_dec * ((i/60)^paa_pow)))/ecp/(1 -
exp(tau_del * i/60))/(1 - exp(ecp_del * i/60))/(1 +
etau/(i/60)) - 1)/exp(ecp_dec_del/(i/60))
avg_ecp_dec = ((count - 1) * avg_ecp_dec + ecp_decn)/count
if (ecp_decn > 0) {
ecp_decn = 0
}
if (ecp_dec < ecp_decn) {
ecp_dec = ecp_decn
}
count <- count + 1
}
if (!((abs(etau - etau_prev) > etau_delta_max) || (abs(paa -
paa_prev) > paa_delta_max) || (abs(paa_dec - paa_dec_prev) >
paa_dec_delta_max) || (abs(ecp_del - ecp_del_prev) >
ecp_del_delta_max) || (abs(ecp_dec - ecp_dec_prev) >
ecp_dec_delta_max))) {
break
}
}
pMax = paa * exp(paa_dec * ((1/60)^paa_pow)) + ecp * (1 -
exp(tau_del * (1/60))) * (1 - exp(ecp_del * (1/60))) *
(1 + ecp_dec * exp(ecp_dec_del/(1/60))) * (1 + etau/(1/60))
mmp60 = paa * exp(paa_dec * ((60)^paa_pow)) + ecp * (1 -
exp(tau_del * (60))) * (1 - exp(ecp_del * 60)) * (1 +
ecp_dec * exp(ecp_dec_del/60)) * (1 + etau/(60))
xemmp <- 1:620
yemmp <- 1:620
for (i in 1:620) {
t <- 5 * 10^(i/210) - 3
yemmp[i] <- paa * exp(paa_dec * (((t/60)^paa_pow))) +
ecp * (1 - exp(tau_del * (t/60))) * (1 - exp(ecp_del *
(t/60))) * (1 + ecp_dec * exp(ecp_dec_del/(t/60))) *
(1 + etau/((t/60)))
xemmp[i] <- t
}
myts = ts(yemmp, start = c(1), end = c(length(yemmp)), frequency = 1)
disc = cpt.mean(myts, penalty = "Manual", pen.value = "log(n)",
method = "PELT")
low_avg = which((disc@cpts > sum(xemmp < i5) + 1) & (disc@cpts <
sum(xemmp <= i7)))[1]
if (is.na(low_avg)) {
low_avg = length(disc@cpts) - 1
}
mftp = mean(yemmp[disc@cpts[low_avg]:sum(xemmp <= i7)]) *
weight
wbal = ecp * etau * 60 * weight
pmax = pMax * weight
return(c(pmax, wbal, mftp))
}
data(mmp_alldata)
metrics=metrics_model(mmp_alldata[[1]],F,65)
metrics
|
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