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moving the application of the cosinor anlyses inside g.analyse.avday.…
…R to a seperate function to ease re-using it in g.part5 #708
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| applyCosinorAnalyses = function(ts, qcheck, midnightsi, shortEpoch, longEpoch) { | ||
| # qcheck - vector of length ts to indicate invalid values | ||
| ws2 = longEpoch | ||
| ws3 = shortEpoch | ||
| # Re-derive Xi but this time include entire time series | ||
| # Here, we ignore duplicated values (when clock moves backward due to DST) | ||
| handleDST = !duplicated(ts) | ||
| qcheck = qcheck[handleDST] | ||
| Xi = ts[handleDST, which(colnames(ts) %in% "timestamp" == FALSE)] | ||
| Nlong_epochs_day = (1440 * 60) / ws2 # this is 96 by default | ||
| dstgap = which(diff(midnightsi) != Nlong_epochs_day) | ||
| if (length(dstgap) > 0) { | ||
| # Time moved forward due to DST | ||
| gaplocation = ((midnightsi[dstgap[1]] * ws2) / ws3) + (2 * (3600/ws3)) | ||
| # Insert NA values | ||
| Xi = c(Xi[1:gaplocation], rep(NA, 3600/ws3), Xi[(gaplocation + 1):length(Xi)]) | ||
| qcheck = c(qcheck[1:gaplocation], rep(NA, 3600/ws3), qcheck[(gaplocation + 1):length(qcheck)]) | ||
| } | ||
|
|
||
| # Xi = log((Xi * 1000) + 1) # log transformed to be more robust against peaks in the data | ||
| # set non-wear to missing values, because for Cosinor fit | ||
| # it seems more logical to only fit with real data | ||
| # this comes at the price of not being able to extract F_pseudo | ||
| firstvalid = 1 | ||
| if (length(which(qcheck == 1)) > 0) { | ||
| is.na(Xi[which(qcheck == 1)]) = TRUE | ||
| # ignore invalid start of recording (if applicable) | ||
| # such that 24 hour blocks start from first valid value | ||
| firstvalid = which(qcheck == 0)[1] | ||
| if (is.na(firstvalid) == FALSE) { | ||
| if (firstvalid != 1) { | ||
| Xi = Xi[firstvalid:length(Xi)] | ||
| } | ||
| } | ||
| } | ||
| if (length(which(is.na(Xi) == FALSE)) > (1440 * (60/ws3))) { # Only attempt cosinor analyses if there is more than 24 hours of data | ||
| midnightsi_ws3 = (midnightsi - 1) * (ws2 / ws3) | ||
| timeOffsetHours = (midnightsi_ws3[which(midnightsi_ws3 >= firstvalid - 1)[1]] - (firstvalid - 1)) / (3600 / ws3) | ||
| if (ws3 < 60) { | ||
| # If epochsize < 1 minute then aggregate to 1 minute by taking maximum value | ||
| # but keep NA values | ||
| XTtime = rep(1:length(Xi), each = 60 / ws3) | ||
| XT = data.frame(Xi = Xi, time = XTtime[1:length(Xi)]) | ||
| custommean = function(x) { | ||
| y = NA | ||
| if (length(x) > 0) { | ||
| if (length(which(is.na(x) == FALSE) ) > 0) { | ||
| y = mean(x, na.rm = TRUE) | ||
| } | ||
| } | ||
| return(y) | ||
| } | ||
| XT = aggregate(x = XT, by = list(XT$time), FUN = custommean) | ||
| if (length(which(is.nan(XT$Xi) == TRUE)) > 0) { | ||
| is.na(XT$Xi[which(is.nan(XT$Xi) == TRUE)]) = TRUE | ||
| } | ||
| # experimental: clip all peaks above Xth percentile? | ||
| # Q9 = quantile(x = XT$Xi, probs = 0.75, na.rm = TRUE) | ||
| # XT$Xi[which(XT$Xi >= Q9)] = Q9 | ||
|
|
||
| # log transform of data in millig | ||
| notna = !is.na(XT$Xi) | ||
| XT$Xi[notna] = log((XT$Xi[notna]*1000) + 1) | ||
| Xi = XT$Xi | ||
| epochsize = 60 | ||
| } else { | ||
| epochsize = ws3 | ||
| } | ||
| cosinor_coef = cosinorAnalyses(Xi = Xi, epochsize = epochsize, timeOffsetHours = timeOffsetHours) | ||
| cosinor_coef$timeOffsetHours = timeOffsetHours | ||
| } else { | ||
| cosinor_coef = c() | ||
| } | ||
| return(cosinor_coef) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| \name{applyCosinorAnalyses} | ||
| \alias{applyCosinorAnalyses} | ||
| \title{ | ||
| Apply Cosinor Analyses to time series | ||
| } | ||
| \description{ | ||
| Wrapper function around \link{cosinorAnalyses} that first prepares the time series | ||
| before applying the cosinorAnlayses | ||
| } | ||
| \usage{ | ||
| applyCosinorAnalyses(ts, qcheck, midnightsi, shortEpoch, longEpoch) | ||
| } | ||
| \arguments{ | ||
| \item{ts}{ | ||
| Data.frame with timestamps and acceleration metric. | ||
| } | ||
| \item{qcheck}{ | ||
| Vector of equal length as number of rows in ts with value 1 for invalid | ||
| timestamps, 0 otherwise. | ||
| } | ||
| \item{midnightsi}{ | ||
| Indices for midnights in the time series | ||
| } | ||
| \item{shortEpoch}{ | ||
| First element of argument windowsizes, the resolution of the ts and qcheck values. | ||
| } | ||
| \item{longEpoch}{ | ||
| Second element of argument windowsizes, the resolution of the midnightsi | ||
| values in seconds. | ||
| } | ||
| } | ||
| \author{ | ||
| Vincent T van Hees <v.vanhees@accelting.com> | ||
| } |