Fit double logistic model to NDVI time series given parameters estimated with model_params.
model_ndvi(DT, observed = TRUE)data.table of model parameters (output from model_params).
boolean indicating if a full year of fitted values should be returned (observed = FALSE) or if only observed values will be fit (observed = TRUE)
Model parameter data.table appended with 'fitted' column of double logistic model of NDVI for a full year. Calculated at the daily scale with the following formula from Bischoff et al. (2012).
$$fitted = \frac{1}{1 + \exp{\frac{xmidS - t}{scalS}}} - \frac{1}{1 + \exp{\frac{xmidA - t}{scalA}}}$$
(See the "Getting started with irg vignette" for a better formatted formula.)
Other model:
model_params(),
model_start()
# Load data.table
library(data.table)
# Read in example data
ndvi <- fread(system.file("extdata", "sampled-ndvi-MODIS-MOD13Q1.csv", package = "irg"))
# Filter and scale NDVI time series
filter_ndvi(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500 0.8735000
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400 0.8632175
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875 0.8707500
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750 0.8635525
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150 0.8476000
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875 0.8707500
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750 0.8635525
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150 0.8476000
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400 0.8632175
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325 0.8632000
scale_doy(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500 0.8735000
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400 0.8632175
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875 0.8707500
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750 0.8635525
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150 0.8476000
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875 0.8707500
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750 0.8635525
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150 0.8476000
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400 0.8632175
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325 0.8632000
#> t
#> 1: 0.027397260
#> 2: 0.005479452
#> 3: 0.027397260
#> 4: 0.010958904
#> 5: 0.005479452
#> ---
#> 801: 0.994520548
#> 802: 0.994520548
#> 803: 0.994520548
#> 804: 0.994520548
#> 805: 0.989041096
scale_ndvi(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500 0.8735000
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400 0.8632175
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875 0.8707500
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750 0.8635525
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150 0.8476000
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875 0.8707500
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750 0.8635525
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150 0.8476000
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400 0.8632175
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325 0.8632000
#> t scaled
#> 1: 0.027397260 0
#> 2: 0.005479452 0
#> 3: 0.027397260 0
#> 4: 0.010958904 0
#> 5: 0.005479452 0
#> ---
#> 801: 0.994520548 0
#> 802: 0.994520548 0
#> 803: 0.994520548 0
#> 804: 0.994520548 0
#> 805: 0.989041096 0
# Guess starting parameters for xmidS and xmidA
model_start(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.0791 2 61 2016 NA 0.3076500 NA 0.8735000
#> 2: 1 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> 3: 2 0.0707 2 61 2016 NA 0.2649875 NA 0.8707500
#> 4: 3 0.0456 2 61 2016 NA 0.2301750 NA 0.8635525
#> 5: 5 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> ---
#> 801: 1 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 802: 2 0.8740 1 225 2015 0.8740 0.2649875 0.8732 0.8707500
#> 803: 3 0.8643 1 225 2015 0.8643 0.2301750 0.8643 0.8635525
#> 804: 5 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 805: 6 0.8729 1 225 2015 0.8729 0.3149325 0.8688 0.8632000
#> t scaled xmidS_start xmidA_start
#> 1: 0.1643836 NA 0.3452055 0.7095890
#> 2: 0.1643836 NA 0.3452055 0.7095890
#> 3: 0.1643836 NA 0.3452055 0.7095890
#> 4: 0.1643836 NA 0.3452055 0.7095890
#> 5: 0.1643836 NA 0.3452055 0.7095890
#> ---
#> 801: 0.6136986 1 0.4000000 0.7123288
#> 802: 0.6136986 1 0.3808219 0.7123288
#> 803: 0.6136986 1 0.3808219 0.7808219
#> 804: 0.6136986 1 0.4000000 0.7123288
#> 805: 0.6136986 1 0.3808219 0.7808219
## Two options: fit to full year or observed data
# Option 1 - returns = 'models'
# Double logistic model parameters
# given global starting parameters for scalS, scalA
# and output of model_start for xmidS, xmidA
mods <- model_params(
ndvi,
returns = 'models',
xmidS = 'xmidS_start',
xmidA = 'xmidA_start',
scalS = 0.05,
scalA = 0.01
)
# Fit to the whole year (requires assignment)
fit <- model_ndvi(mods, observed = FALSE)
# Option 2 - returns = 'columns'
model_params(
ndvi,
returns = 'columns',
xmidS = 'xmidS_start',
xmidA = 'xmidA_start',
scalS = 0.05,
scalA = 0.01
)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.0791 2 61 2016 NA 0.3076500 NA 0.8735000
#> 2: 1 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> 3: 2 0.0707 2 61 2016 NA 0.2649875 NA 0.8707500
#> 4: 3 0.0456 2 61 2016 NA 0.2301750 NA 0.8635525
#> 5: 5 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> ---
#> 801: 1 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 802: 2 0.8740 1 225 2015 0.8740 0.2649875 0.8732 0.8707500
#> 803: 3 0.8643 1 225 2015 0.8643 0.2301750 0.8643 0.8635525
#> 804: 5 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 805: 6 0.8729 1 225 2015 0.8729 0.3149325 0.8688 0.8632000
#> t scaled xmidS_start xmidA_start xmidS xmidA scalS
#> 1: 0.1643836 NA 0.3452055 0.7095890 0.3373884 0.7165697 0.022648297
#> 2: 0.1643836 NA 0.3452055 0.7095890 0.3561967 0.7285859 0.031276884
#> 3: 0.1643836 NA 0.3452055 0.7095890 0.3411214 0.7208200 0.034678822
#> 4: 0.1643836 NA 0.3452055 0.7095890 0.3456795 0.7250997 0.033220445
#> 5: 0.1643836 NA 0.3452055 0.7095890 0.3561967 0.7285859 0.031276884
#> ---
#> 801: 0.6136986 1 0.4000000 0.7123288 0.3904855 0.7400015 0.009961818
#> 802: 0.6136986 1 0.3808219 0.7123288 0.3823661 0.7475907 0.009083805
#> 803: 0.6136986 1 0.3808219 0.7808219 0.3787929 0.7611913 0.025539395
#> 804: 0.6136986 1 0.4000000 0.7123288 0.3904855 0.7400015 0.009961818
#> 805: 0.6136986 1 0.3808219 0.7808219 0.3859433 0.7492424 0.013003034
#> scalA
#> 1: 0.03686743
#> 2: 0.04512882
#> 3: 0.03669032
#> 4: 0.04583330
#> 5: 0.04512882
#> ---
#> 801: 0.03408254
#> 802: 0.03335550
#> 803: 0.03799136
#> 804: 0.03408254
#> 805: 0.03646694
# Fit double logistic curve to NDVI time series for the observed days
model_ndvi(ndvi, observed = TRUE)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.0791 2 61 2016 NA 0.3076500 NA 0.8735000
#> 2: 1 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> 3: 2 0.0707 2 61 2016 NA 0.2649875 NA 0.8707500
#> 4: 3 0.0456 2 61 2016 NA 0.2301750 NA 0.8635525
#> 5: 5 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> ---
#> 801: 1 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 802: 2 0.8740 1 225 2015 0.8740 0.2649875 0.8732 0.8707500
#> 803: 3 0.8643 1 225 2015 0.8643 0.2301750 0.8643 0.8635525
#> 804: 5 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 805: 6 0.8729 1 225 2015 0.8729 0.3149325 0.8688 0.8632000
#> t scaled xmidS_start xmidA_start xmidS xmidA scalS
#> 1: 0.1643836 NA 0.3452055 0.7095890 0.3373884 0.7165697 0.022648297
#> 2: 0.1643836 NA 0.3452055 0.7095890 0.3561967 0.7285859 0.031276884
#> 3: 0.1643836 NA 0.3452055 0.7095890 0.3411214 0.7208200 0.034678822
#> 4: 0.1643836 NA 0.3452055 0.7095890 0.3456795 0.7250997 0.033220445
#> 5: 0.1643836 NA 0.3452055 0.7095890 0.3561967 0.7285859 0.031276884
#> ---
#> 801: 0.6136986 1 0.4000000 0.7123288 0.3904855 0.7400015 0.009961818
#> 802: 0.6136986 1 0.3808219 0.7123288 0.3823661 0.7475907 0.009083805
#> 803: 0.6136986 1 0.3808219 0.7808219 0.3787929 0.7611913 0.025539395
#> 804: 0.6136986 1 0.4000000 0.7123288 0.3904855 0.7400015 0.009961818
#> 805: 0.6136986 1 0.3808219 0.7808219 0.3859433 0.7492424 0.013003034
#> scalA fitted
#> 1: 0.03686743 0.0004808814
#> 2: 0.04512882 0.0021621933
#> 3: 0.03669032 0.0060811435
#> 4: 0.04583330 0.0042418180
#> 5: 0.04512882 0.0021621933
#> ---
#> 801: 0.03408254 0.9760090507
#> 802: 0.03335550 0.9822610413
#> 803: 0.03799136 0.9797106603
#> 804: 0.03408254 0.9760090507
#> 805: 0.03646694 0.9762675518