Fits the sigmoidal and double-sigmoidal models to the data and then categorizes the data according to which model fits best.
Usage
fitAndCategorize(
dataInput,
dataInputName = NA,
n_runs_min_sm = 20,
n_runs_max_sm = 500,
n_runs_min_dsm = 20,
n_runs_max_dsm = 500,
showDetails = FALSE,
startList_sm = list(maximum = 1, slopeParam = 1, midPoint = 0.33),
lowerBounds_sm = c(maximum = 0.3, slopeParam = 0.01, midPoint = -0.52),
upperBounds_sm = c(maximum = 1.5, slopeParam = 180, midPoint = 1.15),
min_Factor_sm = 1/2^20,
n_iterations_sm = 1000,
startList_dsm = list(finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1,
midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam = 0.29),
lowerBounds_dsm = c(finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param =
0.01, midPoint1Param = -0.52, slope2Param = 0.01, midPointDistanceParam = 0.04),
upperBounds_dsm = c(finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180,
midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63),
min_Factor_dsm = 1/2^20,
n_iterations_dsm = 1000,
threshold_intensity_range = 0.1,
threshold_minimum_for_intensity_maximum = 0.3,
threshold_bonus_sigmoidal_AIC = 0,
threshold_sm_tmax_IntensityRatio = 0.85,
threshold_dsm_tmax_IntensityRatio = 0.75,
threshold_AIC = -10,
threshold_t0_max_int = 1e+10,
stepSize = 1e-05,
startList_sm_h0 = list(maximum = 1, slopeParam = 1, midPoint = 0.33, h0 = 0),
lowerBounds_sm_h0 = c(maximum = 0.3, slopeParam = 0.01, midPoint = -0.52, h0 = -0.1),
upperBounds_sm_h0 = c(maximum = 1.5, slopeParam = 180, midPoint = 1.15, h0 = 0.3),
startList_dsm_h0 = list(finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1,
midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam = 0.29, h0 = 0),
lowerBounds_dsm_h0 = c(finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param =
0.01, midPoint1Param = -0.52, slope2Param = 0.01, midPointDistanceParam = 0.04, h0 =
-0.1),
upperBounds_dsm_h0 = c(finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param =
180, midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63, h0 =
0.3),
use_h0 = FALSE,
...
)Arguments
- dataInput
Unnormalized input data that will be fitted transferred into related functions
- dataInputName
Name of data set (Default is 'NA').
- n_runs_min_sm
This number indicates the lower limit of the successful fitting attempts for sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_sm). Default is 20
- n_runs_max_sm
This number indicates the upper limit of the fitting attempts for sigmoidal model. Default is 500
- n_runs_min_dsm
This number indicates the lower limit of the successful fitting attempts for double sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_dsm). Default is 20
- n_runs_max_dsm
This number indicates the upper limit of the fitting attempts for sigmoidal model for double sigmoidal model. Default is 500
- showDetails
Logical if TRUE prints details of intermediate steps of individual fits (Default is FALSE).
- startList_sm
The initial set of parameters vector that sigmoidal fit algorithm tries for the first fit attempt for the relevant parameters. The vector composes of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1, slopeParam = 1 and, midPoint = 0.33. The numbers are in normalized time intensity scale.
- lowerBounds_sm
The lower bounds for the randomly generated start parameters for the sigmoidal fit. The vector composes of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 0.3, slopeParam = 0.01, and midPoint = -0.52. The numbers are in normalized time intensity scale.
- upperBounds_sm
The upper bounds for the randomly generated start parameters for the sigmoidal fit. The vector composes of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1.5, slopeParam = 180, midPoint = 1.15. The numbers are in normalized time intensity scale.
- min_Factor_sm
Defines Defines the minimum step size used by the sigmoidal fit algorithm. Default is 1/2^20.
- n_iterations_sm
Defines maximum number of iterations used by the sigmoidal fit algorithm. Default is 1000
- startList_dsm
The initial set of parameters vector that double sigmoidal fit algorithm tries for the first fit attempt for the relevant parameters. The vector composes of six elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', and 'midPointDistanceParam'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1, midPoint1Param = 0.33, slope2Param = 1, and midPointDistanceParam=0.29. The numbers are in normalized time intensity scale.
- lowerBounds_dsm
The lower bounds for the randomly generated start parameters for double sigmoidal fit. The vector composes of six elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', and 'midPointDistanceParam'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = .01, midPoint1Param = -0.52, slope2Param = .01, and midPointDistanceParam = 0.04. The numbers are in normalized time intensity scale.
- upperBounds_dsm
The upper bounds for the randomly generated start parameters for double sigmoidal fit. The vector composes of six elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', and 'midPointDistanceParam'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180, midPoint1Param = 1.15, slope2Param = 180, and midPointDistanceParam = 0.63. The numbers are in normalized time intensity scale.
- min_Factor_dsm
Defines the minimum step size used by the double sigmoidal fit algorithm. Default is 1/2^20.
- n_iterations_dsm
Define maximum number of iterations used by the double sigmoidal fit algorithm. Default is 1000
- threshold_intensity_range
Minimum for intensity range, i.e. it is the lower limit for the allowed difference between the maximum and minimum of the intensities (Default is 0.1, and the values are based on actual, not the rescaled data.).
- threshold_minimum_for_intensity_maximum
Minimum allowed value for intensity maximum. (Default is 0.3, and the values are based on actual, not the rescaled data.).
- threshold_bonus_sigmoidal_AIC
Bonus AIC points for sigmoidal fit. Negative values help the sigmoidal model to win. Only helps in competition between sigmoidal and double sigmoidal fit at decision step "9", i.e. if none of the models fail in any of the tests and stay as a candidate until the last step (Default is 0).
- threshold_sm_tmax_IntensityRatio
The threshold for the minimum intensity ratio between the last observed time points intensity and theoretical maximum intensity of the sigmoidal curve. If the value is below the threshold, then the data can not be represented with the sigmoidal model. (Default is 0.85)
- threshold_dsm_tmax_IntensityRatio
The threshold for the minimum intensity ratio between the last observed time points intensity and maximum intensity of the double sigmoidal curve. If the value is above the threshold, then the data can not be represented with the double sigmoidal model. (Default is 0.75)
- threshold_AIC
Maximum AIC values in order to have a meaningful fit (Default is -10).
- threshold_t0_max_int
Maximum allowed intensity of the fitted curve at time is equal to zero (t=0). (Default is 1E10, and the values are based on actual, not the rescaled data.).
- stepSize
Step size used by the fitting algorithm. Smaller numbers gave more accurate results than larger numbers, and larger numbers gave the results faster than small numbers. The default value is 0.00001.
- startList_sm_h0
The initial set of parameters vector that sigmoidal fit algorithm tries for the first fit attempt for the relevant parameters. The vector composes of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1, slopeParam = 1 and, midPoint = 0.33, h0 = 0. The numbers are in normalized time intensity scale.
- lowerBounds_sm_h0
The lower bounds for the randomly generated start parameters for the sigmoidal fit. The vector composes of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 0.3, slopeParam = 0.01, midPoint = -0.52, and h0 = -0.1. The numbers are in normalized time intensity scale.
- upperBounds_sm_h0
The upper bounds for the randomly generated start parameters for the sigmoidal fit. The vector composes of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1.5, slopeParam = 180, midPoint = 1.15, and h0 = 0.3. The numbers are in normalized time intensity scale.
- startList_dsm_h0
The initial set of parameters vector that double sigmoidal fit algorithm tries for the first fit attempt for the relevant parameters. The vector composes of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1, midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam=0.29, h0 = 0. The numbers are in normalized time intensity scale.
- lowerBounds_dsm_h0
The lower bounds for the randomly generated start parameters for double sigmoidal fit. The vector composes of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = .01, midPoint1Param = -0.52, slope2Param = .01, midPointDistanceParam = 0.04, and h0 = -0.1. The numbers are in normalized time intensity scale.
- upperBounds_dsm_h0
The upper bounds for the randomly generated start parameters for double sigmoidal fit. The vector composes of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180, midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63, and h0 = 0.3. The numbers are in normalized time intensity scale.
- use_h0
Boolean which decides whether to fix the lower asymptote of h0 at 0 (FALSE, default) or to freely estimate h0 (TRUE)
- ...
All other arguments that model functions ("sigmoidalFitFunction" and, "doublesigmoidalFitFunction") may need.
Examples
# Example 1
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
fitObj <- sicegar::fitAndCategorize(dataInput = dataInput)