Spline_QMER.R 9.01 KB
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library(scam)

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`vcov.scam` <- function (object, freq = FALSE, dispersion = NULL,
                         parametrized = TRUE, ...)  {
    if (freq) {
        vc <- if (parametrized) {
            object$Ve.t
        } else {
            object$Ve
        }
    } else {
        vc <- if (parametrized) {
            object$Vp.t
        } else {
            object$Vp
        }
    }
    if (!is.null(dispersion)) {
        vc <- dispersion * vc/object$sig2
    }
    name <- names(object$edf)
    dimnames(vc) <- list(name, name)
    vc
}

`coef.scam` <- function(object, parametrized = TRUE, ...) {
    coefs <- if (parametrized) {
        object$coefficients.t
    } else {
        object$coefficients
    }
    coefs
}

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fit_scam_spline <- function(x, y, k = NA, bs = "mpi", label = NULL, optimizer = "bfgs") {
  if (!is.null(label)) { print(label) }
  if (is.na(k)) k <- round(length(x) * 3 / 4)
  if (k > 20) k <- 20

  spline <- NULL
  try(
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    spline <- R.utils::withTimeout(scam(y ~ s(as.numeric(x), k = k, bs = bs), optimizer = optimizer), timeout=10)
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  )
  if (is.null(spline)) {
    print("decreasing k")
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    try(
      spline <- R.utils::withTimeout(scam(y ~ s(as.numeric(x), k = k - 1, bs = bs)), timeout=10)
    )
    if (is.null(spline)) {
    print("2. time decreasing k")
    try(
      spline <- R.utils::withTimeout(scam(y ~ s(as.numeric(x), k = k - 2, bs = bs)), timeout=10)
    )
  }
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  }
  return(spline)
}

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fit_scam_spline_weights <- function(x, y, w, k = NA, bs = "mpi", label = NULL, optimizer = "bfgs") {
  w <- 1 / (w^2)
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  if (!is.null(label)) { print(label) }
  if (is.na(k)) k <- round(length(x) * 3 / 4)
  if (k > 20) k <- 20

  spline <- NULL
  try(
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    spline <- R.utils::withTimeout(scam(y ~ s(as.numeric(x), k = k, bs = bs), optimizer = optimizer, weights = w), timeout=0.3)
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  )
  if (is.null(spline)) {
    print("decreasing k")
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    try(
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      spline <- R.utils::withTimeout(scam(y ~ s(as.numeric(x), k = k - 1, bs = bs), optimizer = optimizer, weights = w), timeout=0.3)
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    )
    if (is.null(spline)) {
    print("2. time decreasing k")
    try(
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      spline <- R.utils::withTimeout(scam(y ~ s(as.numeric(x), k = k - 2, bs = bs), optimizer = optimizer, weights = w), timeout=0.3)
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    )
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    if (is.null(spline)) {
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      spline <- scam(y ~ s(as.numeric(x), k = 8, bs = bs), optimizer = optimizer)
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    }
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  }
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  }
  return(spline)
}

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predict_scam_spline <- function(spline, x_, deriv = NULL, se = FALSE)
{

  if (!is.null(deriv)) {
    predicts <- predict.scam(spline, newdata = x_, se.fit = FALSE)
    for (i in 1:deriv) {
      predicts <- c(0, diff(predicts))
    }
  } else {
    predicts <- predict.scam(spline, newdata = x_, se.fit = se)
    if (se) {
      predicts <- predicts$se.fit
    } else {
      predicts <- predicts
    }

  }

  return(predicts)
}

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predict_scam_spline_posteriors <- function(spline, x_)
{
  # Design matrix
  lp <- predict.scam(spline, newdata = x_, type = "lpmatrix")

  # Estimated coefficients
  coef <- coef.scam(spline)
  # install.packages("gratia")
  vc <- vcov.scam(spline)
  if (!all( eigen(vc)$values >0 )) {
    predicts <- predict.scam(spline, newdata = x_, se.fit = FALSE)
    return(list(predicts))
  }

  # Sample from the distrubitions of the coefficients
  set.seed(35)
  sim <- MASS::mvrnorm(1000, mu = c(coef), Sigma = unname(vc))

  # For each realisation, obtain the "fitted" curve
  predicts_ <- lp %*% t(sim)
  predicts <- lapply(seq_len(ncol(predicts_)), function(i) predicts_[,i])

  predicts_r <- sapply (predicts, function (x) {length (x) <- nrow(predicts_); return (x)})
  predicts_rr <- lapply(seq_len(nrow(predicts_r)), function(i) predicts_r[i,])

  return(predicts_rr)
}

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# Finds start/stop of growth phase, final value and key percentiles
find_start_stop_growth_phase <- function(df, text = NA, threshold_start = 1 / 4, threshold_stop = 1 / 4, delta_days = 40, final_height_agg = 24) {
  if (!is.na(text)) print(text)
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  # Extract maximum growth phase
  max_growth <- max(df$predict_deriv)
  if (max_growth == 0) {
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    #print("Spline has zero growth!")
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    return(tibble(predict_start_growth = NA,
                  predict_start_growth_se = NA,
                  predict_stop_growth = NA,
                  predict_stop_growth_se = NA,
                  predict_final_value = NA,
                  predict_final_value_se = NA,
                  predict_p15 = NA,
                  predict_p15_se = NA,
                  predict_p95 = NA,
                  predict_p95_se = NA))
  }

  timepoint_max_growth <- (df %>% slice(which.max(predict_deriv)))$timestamp
  timepoint_max_growth <- ifelse(timepoint_max_growth < max(df$timestamp), timepoint_max_growth, min(df$timestamp))

  # Filter out datapoints which have growth rate higher than threshold * max, which corresponds to main growth phase
  df_ <- df %>%
    filter(predict_deriv > max_growth * threshold_start)
  # Earlieast value that is left indicates start of growth
  start <- (df_ %>% slice(1))$timestamp
  # First value after start that is at least 40 days later, after max. growth peak, and has growth rate smaller than threshold * max is stop of growth phase
  df__ <- df %>% filter(timestamp >= timepoint_max_growth &
                          timestamp > start + days(delta_days) &
                          predict_deriv <= (max_growth * threshold_stop))
  if (nrow(df__) > 0) {
    stop <- (df__ %>% slice(1))$timestamp
  }  else {
    stop <- max(df$timestamp)
  }

  # SEs
  start_se <- (df %>% filter(timestamp == start))$predict_se[[1]]
  stop_se <- (df %>% filter(timestamp == stop))$predict_se[[1]]

  # Correct start if out of measurement period
  start <- if_else(yday(start) != yday(min(df$timestamp)), start, as.POSIXct(NA))

  # Final value is median of measurement values after stop of growth phase
  final_value <- (df %>%
    filter(timestamp >= stop) %>%
    top_n(final_height_agg, predict) %>%
    summarize(final_height = median(predict, na.rm = T)))$final_height
  final_value_se <- (df %>%
     filter(timestamp >= stop) %>%
     top_n(final_height_agg, predict) %>%
     summarize(final_height = median(predict_se, na.rm = T)))$final_height

  # Timepoint where 15 and 95 percentile are reached are physiologically interesting (Kronenberg et al 2020)
  p15_final_value <- (df %>%
    filter(predict > final_value * 0.15) %>%
    slice(1))$timestamp
  if (length(p15_final_value) == 0) p15_final_value <- as.POSIXct(NA)
  p15_final_value <- if_else(yday(p15_final_value) != yday(min(df$timestamp)), p15_final_value, as.POSIXct(NA))
  p15_final_value <- if_else(length(p15_final_value) != 1, as.POSIXct(NA), p15_final_value)
  p15_final_value_se <- (df %>%
    filter(predict > final_value * 0.15) %>%
    slice(1))$predict_se
  if (length(p15_final_value_se) > 0) {
    p15_final_value_se <- p15_final_value_se[[1]]
  } else {
    p15_final_value_se <- NA
  }

  p95_final_value <- (df %>%
    filter(predict < final_value * 0.95) %>%
    slice(n()))$timestamp
  if (length(p95_final_value) == 0) p95_final_value <- as.POSIXct(NA)
  p95_final_value <- if_else(yday(p95_final_value) != yday(max(df$timestamp)), p95_final_value, as.POSIXct(NA))
  p95_final_value <- if_else(length(p95_final_value) != 1, as.POSIXct(NA), p95_final_value)
  p95_final_value_se <- (df %>%
    filter(predict < final_value * 0.95) %>%
    slice(n()))$predict_se
  if (length(p95_final_value_se) > 0) {
    p95_final_value_se <- p95_final_value_se[[1]]
  } else {
    p95_final_value_se <- NA
  }

  return(tibble(predict_start_growth = start,
                predict_start_growth_se = start_se,
                predict_stop_growth = stop,
                predict_stop_growth_se = stop_se,
                predict_final_value = final_value,
                predict_final_value_se = final_value_se,
                predict_p15 = p15_final_value,
                predict_p15_se = p15_final_value_se,
                predict_p95 = p95_final_value,
                predict_p95_se = p95_final_value_se))
}
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find_start_stop_growth_phase_posterior <- function(df, text = NA, threshold_start = 1 / 4, threshold_stop = 1 / 4, delta_days = 40, final_height_agg = 10) {

  f <- function(pred) {return(c(0, diff(pred)))}
  predict <- find_start_stop_growth_phase(df, text, threshold_start, threshold_stop, delta_days, final_height_agg)

  df_posterior_1 <- sapply (df$predict_posteriors, function (x) {length (x) <- length(df$predict_posteriors[[1]]); return (x)})
  df_posterior_1 <- lapply(seq_len(nrow(df_posterior_1)), function(i) df_posterior_1[i,])

  df_posterior_2 <- lapply(df_posterior_1, f)

  df_posterior <- mapply(function(X, Y) {tibble(predict = X, predict_deriv=Y, timestamp = df$timestamp, predict_se = df$predict_se)}, X=df_posterior_1, Y=df_posterior_2, SIMPLIFY = F)
  predict_posterior <- lapply(df_posterior, find_start_stop_growth_phase, NA, threshold_start, threshold_stop, delta_days, final_height_agg)
  df_predict_posterior <- bind_rows(predict_posterior)
  df_predict_posterior_se <- df_predict_posterior %>% dplyr::select(-ends_with("se")) %>%
    summarise_each(funs(mean,sd,sepost=sd(.)/sqrt(n()))) %>%
    dplyr::select(ends_with("sepost"))

  predict <- bind_cols(predict, df_predict_posterior_se)
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  print(text)
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  return(predict)
}