Package 'trajeR' reference manual

Title:	Group Based Modeling Trajectory
Description:	Estimation of group-based trajectory models, including finite mixture models for longitudinal data, supporting censored normal, zero-inflated Poisson, logit, and beta distributions, using expectation-maximization and quasi-Newton methods, with tools for model selection, diagnostics, and visualization of latent trajectory groups, <doi:10.4159/9780674041318>, Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), <https://orbilu.uni.lu/>, thesis.
Authors:	Cédric Noel [aut, cre], Jang Schiltz [aut]
Maintainer:	Cédric Noel <[email protected]>
License:	GPL (>= 2)
Version:	0.11.1
Built:	2026-06-03 09:36:38 UTC
Source:	https://github.com/gitedric/trajer

Adequacy of the model

Description

Calculate the summary of the five methods : assignment proportion, average posterior probability, confidence interval, odds of Correct Classification.

Usage

adequacy(sol, Y, A, nb = 10000, alpha = 0.98)
adequacy(sol, Y, A, nb = 10000, alpha = 0.98)

Arguments

sol

Trajectory's object. An object of type Trajectory.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

nb

Integer. The numbers of repetitions in the bootstrap method.

alpha

Real. The degree of confidence of the interval.

Value

A table of reals. A table with 5 rows: the estimate probabilities, the two bounds of the confidence interval, the proportion of assignment, the Average Posterior Probability and the Odds of Correct Classification.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
adequacy(sol, Y = data[, 2:6], A = data[, 7:11])
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
adequacy(sol, Y = data[, 2:6], A = data[, 7:11])

Average Posterior Probability

Description

Calculate the Average Posterior Probability. Average Posterior Probability (AvePP) is the average posterior probability of membership for each group for those individuals that were assigned to.

Usage

AvePP(sol, Y, A, X = NULL)
AvePP(sol, Y, A, X = NULL)

Arguments

sol

Trajectory's object. An object of type Trajectory.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modifies the probability of belong to group. By default its value is a one column matrix with value 1.

Value

A vector of reals. The average posterior probability.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
AvePP(sol, Y = data[, 2:6], A = data[, 7:11])
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
AvePP(sol, Y = data[, 2:6], A = data[, 7:11])

Compute Confidence Intervals for Predicted Trajectories

Usage

confidenceInt(Obj, newtime, alpha = 0.05)
confidenceInt(Obj, newtime, alpha = 0.05)

Arguments

Obj

An object containing model parameters, including:

degre: Vector of polynomial degrees for each group.
varcov: Variance-covariance matrix of the model coefficients.
groups: Number of groups in the model.
Model: Character string specifying the model type ("CNORM" or "LOGIT").
beta: Vector of model coefficients.

newtime

Numeric vector of time points at which to compute predictions and confidence intervals.

alpha

Numeric value specifying the significance level for the confidence intervals (default is 0.05, corresponding to 95

A list of class "Trajectory.predict" containing:

newtime: The input time points.
Ypred: Matrix of predicted values for each group at each time point.
SEYpred: Matrix of standard errors for the predicted values.
IC.inf: Matrix of lower bounds of the confidence intervals.
IC.max: Matrix of upper bounds of the confidence intervals.
groups: Number of groups from the input object.

Calculates predicted values and confidence intervals for a given model object at specified time points, based on model. The function computes standard errors and confidence bounds for the predictions.

# Example with a hypothetical model object model <- list( degre = c(2, 2), varcov = diag(4), groups = 2, Model = "CNORM", beta = c(1, 0.5, 0.2, 2, 0.3, 0.1) ) newtime <- seq(0, 1, by = 0.1) result <- confidenceInt(model, newtime, alpha = 0.05) print(result)

Confidence interval

Description

Calculate the confidence interval of the probabilities with bootstrap method. We have to specify the number of the repetitions of bootstrap and the degree of confidence.

Usage

ConfIntT(sol, Y, A, nb = 10000, alpha = 0.98)
ConfIntT(sol, Y, A, nb = 10000, alpha = 0.98)

Arguments

sol

Trajectory's object. An object of type Trajectory.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

nb

An integer. The number of repetitions in the bootstrap method.

alpha

A number. The degree of confidence of the interval.

Value

A vector of reals. The two bounds of the confidence interval given a degree of confidence.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
ConfIntT(sol, Y = data[, 2:6], A = data[, 7:11])
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
ConfIntT(sol, Y = data[, 2:6], A = data[, 7:11])

Simulated CNORM Dataset (data01)

Description

The data01 dataset is a simulated dataset included in the package. It contains 500 trajectories with 10 time-points each, simulating a 3-group solution. The parameters for the groups are summarized in the table below:

Parameter	Group 1	Group 2	Group 3
$\beta_{k0}$	2.797	7.000	19.545
$\beta_{k1}$	8.809	-0.297	-0.407
$\beta_{k2}$	-3.201	0.463	0.026
$\beta_{k3}$	0.000	-0.021	0.000
$\pi_k$	0.32	0.54	0.14
$\sigma_k$	4.000	4.000	4.000

Usage

dataNORM01
dataNORM01

Format

A data frame with 500 rows and 43 columns:

Columns 2:11: Matrix of real numbers (variable of interest, Y).
Columns 12:21: Matrix of time points (1 to 10, A).
Columns 22:31: Matrix of binary values (0 or 1, time-dependent covariate W).
Columns 32:41: Matrix of real numbers (additional time-dependent covariate W).
Columns 42:43: Matrix of binary values (0 or 1, covariate X).

Details

The dataset includes the following variables:

Y (columns 2:11): Matrix of real numbers representing the variable of interest.
A (columns 12:21): Matrix of time points (1 to 10).
W (columns 22:31): Matrix with values 0 and 1, indicating the presence or absence of a characteristic.
W (columns 32:41): Matrix of real numbers (additional time-dependent covariate).
X (columns 42:43): Matrix with values 0 and 1, influencing group membership probability.

Source

Simulated data for illustrative purposes.

Differential

Description

Differential

Usage

diffaitbeta(betak, i, t, A, TCOV, fct, diffct)
diffaitbeta(betak, i, t, A, TCOV, fct, diffct)

Arguments

betak

Vector of integer.

i

Integer.

t

Real.

A

Matrix of real.

TCOV

Matrix of real.

fct

Function.

diffct

Function.

Value

real. Compute the value of the differential function fct for individual i, time t and group k.

Function fait

Description

Function fait

Usage

fait(betak, i, t, A, TCOV, fct, diffct)
fait(betak, i, t, A, TCOV, fct, diffct)

Arguments

betak

Vector of integer.

i

Integer.

t

Real.

A

Matrix of real.

TCOV

Matrix of real.

fct

Function.

diffct

Function.

Value

real. Compute the value of the function fct for individual i, time t and group k.

Membership's probabilities

Description

GroupProb calculate the membership probability of each value of the data.

Usage

GroupProb(Obj, Y, A, TCOV = NULL, X = NULL)
GroupProb(Obj, Y, A, TCOV = NULL, X = NULL)

Arguments

Obj

Trajectory's object. A trajectory object that is return by trajeR function.

Y

Matrix. A real matrix. The data.

A

Matrix. A real matrix. The time variable.

TCOV

Matrix. A real matrix. Optional, by default the value is NULL. It contained the time dependent covariate.

X

Matrix. A real matrix. Optional, by default the value is NULL. It contained a covariate that modify the probability membership.

Value

a real matrix. For each individual i in the data, this matrix contained the membership probability of each group.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
GroupProb(sol, Y = data[, 2:6], A = data[, 7:11])
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
GroupProb(sol, Y = data[, 2:6], A = data[, 7:11])

Profiles of each group

Description

GroupProfiles calculate the profile of a group regarding covariate. It is a cross tabulation of individual level trajectory group assignments with individual level characteristic that might be associated with trajectory group membership.

Usage

GroupProfiles(sol, Y, A, X)
GroupProfiles(sol, Y, A, X)

Arguments

sol

Trajectory's object. A object of type trajectory.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

Value

A table of real.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11], Risk = data[, 12],
  degre = c(2, 2), Model = "CNORM", Method = "L"
)
GroupProfiles(sol, Y = data[, 2:6], A = data[, 7:11], X = data[, 12])
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11], Risk = data[, 12],
  degre = c(2, 2), Model = "CNORM", Method = "L"
)
GroupProfiles(sol, Y = data[, 2:6], A = data[, 7:11], X = data[, 12])

Odds of Correct Classification

Description

Calculate Odds of Correct Classification. The Odds of Correct Classification for group k (OCCj) is the ratio between the odds of a correct classification into group j on the basis of the posterior probability rule and the odds of correct assignment based on random assignments with the probability of assignment to group j is the probability estimate by the model.

Usage

OCC(sol, Y, A)
OCC(sol, Y, A)

Arguments

sol

Trajectory's object. An object of type Trajectory.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Value

A vector of reals. The Odds of Correct Classification.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
OCC(sol, Y = data[, 2:6], A = data[, 7:11])
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
OCC(sol, Y = data[, 2:6], A = data[, 7:11])

plot trajectory

Description

plot trajectory

Usage

plotrajeR(Obj, ...)
plotrajeR(Obj, ...)

Arguments

Obj

an object of class "Trajectory".

...

optional parameters

Value

a graphic.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
plotrajeR(sol)

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
plotrajeR(sol)

plot BETA trajectory

Description

plot BETA trajectory

Usage

## S3 method for class 'Trajectory.BETA'
plotrajeR(
  Obj,
  plotcov = NULL,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  TCOV = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)
## S3 method for class 'Trajectory.BETA'
plotrajeR(
  Obj,
  plotcov = NULL,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  TCOV = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)

Arguments

Obj

an object of class "Trajectory.LOGIT".

plotcov

an optional vector or matrix with the same length as the time period. Default value is NULL.

col

an optional vector. The vector of colors. It must contain a color for each trajectory and each points of groups. Its length is the double of the number of group. Default value is a grayscale.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Risk

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

mean

an optional logical. Indicate if the mean of ech group and time value must be draw.

alpha

on optional real. Indicate the alpha channel of the points color.

...

optional parameters

Value

a graphic.

plot CNORM trajectory

Description

plot CNORM trajectory

Usage

## S3 method for class 'Trajectory.CNORM'
plotrajeR(
  Obj,
  plotcov = NULL,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)
## S3 method for class 'Trajectory.CNORM'
plotrajeR(
  Obj,
  plotcov = NULL,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)

Arguments

Obj

an object of class "Trajectory.CNORM".

plotcov

an optional vector or matrix with the same length as the time period. Default value is NULL.

col

an optional vector. The vector of colors. It must contain a color for each trajectory and each points of groups. Its length is the double of the number of group. Default value is a grayscale.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Risk

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

mean

an optional logical. Indicate if the mean of ech group and time value must be draw.

alpha

on optional real. Indicate the alpha channel of the points color.

...

optional parameters

Value

a graphic.

plot LOGIT trajectory

Description

plot LOGIT trajectory

Usage

## S3 method for class 'Trajectory.LOGIT'
plotrajeR(
  Obj,
  plotcov = NULL,
  dec = 0,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)
## S3 method for class 'Trajectory.LOGIT'
plotrajeR(
  Obj,
  plotcov = NULL,
  dec = 0,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)

Arguments

Obj

an object of class "Trajectory.LOGIT".

plotcov

an optional vector or matrix with the same length as the time period. Default value is NULL.

dec

an optional real. It precise the shift to draw the data points.

col

an optional vector. The vector of colors. It must contain a color for each trajectory and each points of groups. Its length is the double of the number of group. Default value is a grayscale.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Risk

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

mean

an optional logical. Indicate if the mean of ech group and time value must be draw.

alpha

on optional real. Indicate the alpha channel of the points color.

...

optional parameters

Value

a graphic.

plot Non Linear trajectory

Description

plot Non Linear trajectory

Usage

## S3 method for class 'Trajectory.NL'
plotrajeR(
  Obj,
  plotcov = NULL,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  mean = FALSE,
  alpha = 1,
  TCOV = NULL,
  ...
)
## S3 method for class 'Trajectory.NL'
plotrajeR(
  Obj,
  plotcov = NULL,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  mean = FALSE,
  alpha = 1,
  TCOV = NULL,
  ...
)

Arguments

Obj

an object of class "Trajectory.LOGIT".

plotcov

an optional vector or matrix with the same length as the time period. Default value is NULL.

col

an optional vector. The vector of colors. It must contain a color for each trajectory and each points of groups. Its length is the double of the number of group. Default value is a grayscale.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Risk

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

mean

an optional logical. Indicate if the mean of ech group and time value must be draw.

alpha

on optional real. Indicate the alpha channel of the points color.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

...

optional parameters

Value

a graphic.

plot POIS trajectory

Description

plot POIS trajectory

Usage

## S3 method for class 'Trajectory.POIS'
plotrajeR(
  Obj,
  plotcov = NULL,
  dec = 0,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  TCOV = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)
## S3 method for class 'Trajectory.POIS'
plotrajeR(
  Obj,
  plotcov = NULL,
  dec = 0,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  TCOV = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)

Arguments

Obj

an object of class "Trajectory.POIS".

plotcov

an optional vector or matrix with the same length as the time period. Default value is NULL.

dec

an optional real. It precise the shift to draw the data points.

col

an optional vector. The vector of colors. It must contain a color for each trajectory and each points of groups. Its length is the double of the number of group. Default value is a grayscale.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Risk

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

mean

an optional logical. Indicate if the mean of ech group and time value must be draw.

alpha

on optional real. Indicate the alpha channel of the points color.

...

optional parameters

Value

a graphic.

plot ZIP trajectory

Description

plot ZIP trajectory

Usage

## S3 method for class 'Trajectory.ZIP'
plotrajeR(
  Obj,
  plotcov = NULL,
  dec = 0,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  TCOV = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)
## S3 method for class 'Trajectory.ZIP'
plotrajeR(
  Obj,
  plotcov = NULL,
  dec = 0,
  col = "black",
  Y = NULL,
  A = NULL,
  Risk = NULL,
  TCOV = NULL,
  mean = FALSE,
  alpha = 1,
  ...
)

Arguments

Obj

an object of class "Trajectory.LOGIT".

plotcov

an optional vector or matrix with the same length as the time period. Default value is NULL.

dec

an optional real. It precise the shift to draw the data points.

col

an optional vector. The vector of colors. It must contain a color for each trajectory and each points of groups. Its length is the double of the number of group. Default value is a grayscale.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Risk

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

mean

an optional logical. Indicate if the mean of ech group and time value must be draw.

alpha

on optional real. Indicate the alpha channel of the points color.

...

optional parameters

Value

a graphic.

Print BETA

Description

Print method for an object of class "Trajectory.BETA".

Usage

## S3 method for class 'Trajectory.BETA'
print(x, ...)
## S3 method for class 'Trajectory.BETA'
print(x, ...)

Arguments

x

Trajectory's object. An object of class "Trajectory.BETA".

...

optional parameters

Value

The print of Obj.

Examples

data <- read.csv(system.file("extdata", "BETA2gr.csv", package = "trajeR"))
data <- as.matrix(data)
data[, 2:6] <- data[, 2:6] * (nrow(data[, 2:6]) - 1 + 0.5) / nrow(data[, 2:6])
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11], itermax = 50,
  degre = c(2, 2), degre.phi = c(1, 1), Model = "BETA", Method = "L"
)
sol
data <- read.csv(system.file("extdata", "BETA2gr.csv", package = "trajeR"))
data <- as.matrix(data)
data[, 2:6] <- data[, 2:6] * (nrow(data[, 2:6]) - 1 + 0.5) / nrow(data[, 2:6])
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11], itermax = 50,
  degre = c(2, 2), degre.phi = c(1, 1), Model = "BETA", Method = "L"
)
sol

Print CNORM

Description

Print method for an object of class "Trajectory.CNORM".

Usage

## S3 method for class 'Trajectory.CNORM'
print(x, ...)
## S3 method for class 'Trajectory.CNORM'
print(x, ...)

Arguments

x

Trajectory's object. An object of class "Trajectory.CNORM".

...

optional parameters

Value

The print of Obj.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
sol
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
sol

Print LOGIT

Description

Print method for an object of class "Trajectory.LOGIT".

Usage

## S3 method for class 'Trajectory.LOGIT'
print(x, ...)
## S3 method for class 'Trajectory.LOGIT'
print(x, ...)

Arguments

x

Trajectory's object. . An object of class "Trajectory.LOGIT".

...

optional parameters

Value

The print of Obj.

Examples

data <- read.csv(system.file("extdata", "LOGIT2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(1, 2), Model = "LOGIT", Method = "L")
sol
data <- read.csv(system.file("extdata", "LOGIT2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(1, 2), Model = "LOGIT", Method = "L")
sol

print NL trajectory

Description

Print method for an object of class "Trajectory.NL".

Usage

## S3 method for class 'Trajectory.NL'
print(x, ...)
## S3 method for class 'Trajectory.NL'
print(x, ...)

Arguments

x

Trajectory's object. . An object of class "Trajectory.NL".

...

optional parameters

Value

The print of Obj.

Print POIS

Description

Print method for an object of class "Trajectory.POIS".

Usage

## S3 method for class 'Trajectory.POIS'
print(x, ...)
## S3 method for class 'Trajectory.POIS'
print(x, ...)

Arguments

x

Trajectory's object. . An object of class "Trajectory.POIS".

...

optional parameters

Value

The print of Obj.

Examples

data <- read.csv(system.file("extdata", "POIS2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11],
  degre = c(2, 2), Model = "POIS", Method = "L", hessian = FALSE
)
sol
data <- read.csv(system.file("extdata", "POIS2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11],
  degre = c(2, 2), Model = "POIS", Method = "L", hessian = FALSE
)
sol

Print ZIP

Description

Print method for an object of class "Trajectory.ZIP".

Usage

## S3 method for class 'Trajectory.ZIP'
print(x, ...)
## S3 method for class 'Trajectory.ZIP'
print(x, ...)

Arguments

x

Trajectory's object. An object of class "Trajectory.ZIP".

...

optional parameters

Value

The print of Obj.

Examples

data <- read.csv(system.file("extdata", "ZIP2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11],
  degre = c(1, 2), degre.nu = c(1, 1), Model = "ZIP", Method = "L"
)
sol
data <- read.csv(system.file("extdata", "ZIP2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(
  Y = data[, 2:6], A = data[, 7:11],
  degre = c(1, 2), degre.nu = c(1, 1), Model = "ZIP", Method = "L"
)
sol

Assignment proportion

Description

Calculate the proportion of individuals in a given group. That is the ratio of the number of individuals in one group and all the individuals.

Usage

propAssign(sol, Y, A)
propAssign(sol, Y, A)

Arguments

sol

Trajectory's object. An object of type Trajectory.

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Value

A vector of real. The proportion.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
propAssign(sol, Y = data[, 2:6], A = data[, 7:11])
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
propAssign(sol, Y = data[, 2:6], A = data[, 7:11])

Fitting longitudinal mixture models

Description

trajeR is used to fit longitudinal mixture models. It used 3 types of mixture models : LOGIT, ZIP and censored Normal.

Usage

trajeR(
  Y,
  A,
  Risk = NULL,
  TCOV = NULL,
  degre = NULL,
  degre.nu = 0,
  degre.phi = 0,
  Model,
  Method = "L",
  ssigma = FALSE,
  ymax = max(Y, na.rm = TRUE) + 1,
  ymin = min(Y, na.rm = TRUE) - 1,
  hessian = TRUE,
  itermax = 100,
  paraminit = NULL,
  ProbIRLS = TRUE,
  refgr = 1,
  fct = NULL,
  diffct = NULL,
  nbvar = NULL,
  ng.nl = NULL,
  nls.lmiter = 50
)
trajeR(
  Y,
  A,
  Risk = NULL,
  TCOV = NULL,
  degre = NULL,
  degre.nu = 0,
  degre.phi = 0,
  Model,
  Method = "L",
  ssigma = FALSE,
  ymax = max(Y, na.rm = TRUE) + 1,
  ymin = min(Y, na.rm = TRUE) - 1,
  hessian = TRUE,
  itermax = 100,
  paraminit = NULL,
  ProbIRLS = TRUE,
  refgr = 1,
  fct = NULL,
  diffct = NULL,
  nbvar = NULL,
  ng.nl = NULL,
  nls.lmiter = 50
)

Arguments

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

Risk

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

degre

Vector of integer. The degree of every polynomial function.

degre.nu

Vector of integer. The degree of all Poisson part for a ZIP model.

degre.phi

Vector of integer. The degree of beta parameter for a BETA model.

Model

String. The model used. The value are LOGIT for a Logit Mixture model, CNORM for a Censored Normal Mixture Model or ZIP for Zero Inflated Poisson Mixture model.

Method

String. Determine the method used for find the parameters of the model. The value are L for the Maximum Likelihood Estimation, EM for Expectation Maximization method with quasi newton method inside, EMIWRLS for Expectation Maximization method with Iterative Weighted Least Square.

ssigma

Logical. By default its value is FALSE. For the CNORM model, indicate if we want the same sigma for all normal density function.

ymax

Real. For the CNORM model, indicate the maximum value of the data. It concern only the model with censored data. By default its value is the maximum value of the data plus 1.

ymin

Real. For the CNORM model, indicate the minimum value of the data. It concern only the model with censored data. By default its value is the maximum value of the data minus 1.

hessian

Logical. Indicate if we want calculate the hessian matrix. Default is FALSE. If the method use is Likelihood, the hessian is calculated by inverting the Information's Fisher Matrix. To avoid numerically singular matrix we find the pseudo inverse matrix by using the ginv function int he package MASS. If the method is EM or EMIWRLS, the hessian is calculated by using Louis method.

itermax

Integer. Indicate the maximal number of iteration for optim function or for the EM algorithm.

paraminit

Vector. The vector of initial parameters. By default trajeR calculate the initial value based of the range or the standard deviation.

ProbIRLS

Logical. Indicate the method to sue in the search of predictor's probability. If TRUE (by default) we use IRLS method and if FALSE we use optimization method.

refgr

Integer. The number of reference group. By default is 1.

fct

Function. The definition of the function f in the definition in nonlinear model.

diffct

Function. The differential of the function f in the nonlinear model.

nbvar

Integer. The number of variable in the nonlinear model.

ng.nl

Integer. The number of group for a non linear model.

nls.lmiter

Integer. In the case of non linear model, the maximum number of iterations allowed.

Details

Models for trajeR is, by default, a polynomial regression of the time value parameters for each groups. The number fo group is controlled by the integer ng. We can specify the degre of the polynomial shape for each groups by the vector degre.

Value

return an object of class "Trajectory.LOGIT". The generic accessor functions beta, delta, theta, sd, tab, Likelihood, ng, model and method extract various useful features of the value returned by trajeR.

An object of class "Trajectory.LOGIT" is a list containing at least the following components:

beta: a vector of the parameters beta.
delta: a vector of the parameter delta. Only if we use time covariate.
theta: a vector with the parameter theta if there exist a covariate X that modify the probability or the probability of group membership.
sd: a vector of the standard deviation of the parameters.
tab: a matrix with all the parameters and standard deviation.
Likelihood: a real with the Likelihood obtained by the parameters.
ng: a integer with the number of group.
model: a string with the model used.
method: a string with the method used.

Examples


data("dataNORM01")
solL <- trajeR(dataNORM01[, 1:5], dataNORM01[, 6:10],
  ng = 3, degre = c(2, 2, 2),
  Model = "CNORM", Method = "L", ssigma = FALSE,
  hessian = TRUE
)

data("dataNORM01")
solL <- trajeR(dataNORM01[, 1:5], dataNORM01[, 6:10],
  ng = 3, degre = c(2, 2, 2),
  Model = "CNORM", Method = "L", ssigma = FALSE,
  hessian = TRUE
)

Internal function to fit Beta regression

Description

Internal function to fit Beta regression

Usage

trajeR.BETA(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nphi,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  phi,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)
trajeR.BETA(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nphi,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  phi,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)

Arguments

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

ng

Integer. The number of groups.

nx

Integer. The number of covariates.

n

Integer. Number of individuals.

nbeta

Vector of integers. Number of beta parameters for each group.

nphi

Vector of integers. Number of phi parameters for each group.

nw

Integer. Number of time dependent covariate.

ntheta

Vector of integers. Number of theta parameters for each group.

period

Integer.

degre

Vector of integer. The degree of every polynomial function.

theta

Vector of real. The parameter for calculated the group membership probability.

beta

Vector of real. The beta parameter.

phi

Vector of real. The phi parameter.

delta

Vector of real. The delta parameter.

pi

Vector of real. The group membership probability.

Method

hessian

itermax

Integer. Indicate the maximal number of iteration for optim function or for the EM algorithm.

paraminit

Vector. The vector of initial parameters. By default trajeR calculate the initial value based of the range or the standard deviation.

EMIRLS

Boolean. True if we use EMIRLS method.

refgr

Integer. The number of reference group. By default is 1.

Value

return a object of class Trajectory.NL

beta - vector of the parameter beta.
sigma - vector of the parameters sigma.
delta - vector of the parameter delta. Only if we use time covariate.
theta - vector with the parameter theta if there exist a covariate X that modify the probability or the probability of group membership.
sd - vector of the standard deviation of the parameters.
tab - a matrix with all the parameters and standard deviation.
Model - a string with the model used.
groups - a integer with the number of group.
Names - strings with the name of the parameters.
Method - a string with the method used.
Size - a integer with the number of individuals.
Likelihood - a real with the Likelihood obtained by the parameters.
Time - a vector with the first row of time values.
degre - a vector with the degree of the polynomial shape.

Internal function to fit CNORM Model

Description

Internal function to fit CNORM Model

Usage

trajeR.CNORM(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  sigma,
  delta,
  pi,
  Method,
  ssigma,
  ymax,
  ymin,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)
trajeR.CNORM(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  sigma,
  delta,
  pi,
  Method,
  ssigma,
  ymax,
  ymin,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)

Arguments

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

ng

Integer. The number of groups.

nx

Integer. The number of covariates.

n

Integer. Number of individuals.

nbeta

Vector of integers. Number of beta parameters for each group.

nw

Integer. Number of time dependent covariate.

ntheta

Vector of integers. Number of theta parameters for each group.

period

Integer.

degre

Vector of integer. The degree of every polynomial function.

theta

Vector of real. The parameter for calculated the group membership probability.

beta

Vector of real. The beta parameter.

sigma

Vector of real. The sigma parameter.

delta

Vector of real. The delta parameter.

pi

Vector of real. The group membership probability.

Method

ssigma

Logical. By default its value is FALSE. For the CNORM model, indicate if we want the same sigma for all normal density function.

ymax

Real. For the CNORM model, indicate the maximum value of the data. It concern only the model with censored data. By default its value is the maximum value of the data plus 1.

ymin

Real. For the CNORM model, indicate the minimum value of the data. It concern only the model with censored data. By default its value is the maximum value of the data minus 1.

hessian

itermax

Integer. Indicate the maximal number of iteration for optim function or for the EM algorithm.

paraminit

Vector. The vector of initial parameters. By default trajeR calculate the initial value based of the range or the standard deviation.

EMIRLS

Boolean. True if we use EMIRLS method.

refgr

Integer. The number of reference group. By default is 1.

Value

return a object of class Trajectory.CNORM

beta - vector of the parameter beta.
sigma - vector of the parameters sigma.
delta - vector of the parameter delta. Only if we use time covariate.
theta - vector with the parameter theta if there exist a covariate X that modify the probability or the probability of group membership.
sd - vector of the standard deviation of the parameters.
tab - a matrix with all the parameters and standard deviation.
Model - a string with the model used.
groups - a integer with the number of group.
Names - strings with the name of the parameters.
Method - a string with the method used.
Size - a integer with the number of individuals.
Likelihood - a real with the Likelihood obtained by the parameters.
Time - a vector with the first row of time values.
degre - a vector with the degree of the polynomial shape.
min - a real with the minimum value for censored data.
max - a real with the maximum value for censored data.

Internal function to fit LOGIT Model

Description

Internal function to fit LOGIT Model

Usage

trajeR.LOGIT(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)
trajeR.LOGIT(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)

Arguments

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

ng

Integer. The number of groups.

nx

Integer. The number of covariates.

n

Integer. Number of individuals.

nbeta

Vector of integers. Number of beta parameters for each group.

nw

Integer. Number of time dependent covariate.

ntheta

Vector of integers. Number of theta parameters for each group.

period

Integer.

degre

Vector of integer. The degree of every polynomial function.

theta

Vector of real. The parameter for calculated the group membership probability.

beta

Vector of real. The beta parameter.

delta

Vector of real. The delta parameter.

pi

Vector of real. The group membership probability.

Method

hessian

itermax

Integer. Indicate the maximal number of iteration for optim function or for the EM algorithm.

paraminit

Vector. The vector of initial parameters. By default trajeR calculate the initial value based of the range or the standard deviation.

EMIRLS

Boolean. True if we use EMIRLS method.

refgr

Integer. The number of reference group. By default is 1.

Value

return a object of class Trajectory.LOGIT

beta - vector of the parameter beta.
delta - vector of the parameter delta. Only if we use time covariate.
theta - vector with the parameter theta if there exist a covariate X that modify the probability or the probability of group membership.
sd - vector of the standard deviation of the parameters.
tab - a matrix with all the parameters and standard deviation.
Model - a string with the model used.
groups - a integer with the number of group.
Names - strings with the name of the parameters.
Method - a string with the method used.
Size - a integer with the number of individuals.
Likelihood - a real with the Likelihood obtained by the parameters.
Time - a vector with the first row of time values.
degre - a vector with the degree of the polynomial shape.

Internal function to fit Non Linear Model

Description

Internal function to fit Non Linear Model

Usage

trajeR.NL(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  sigma,
  pi,
  Method,
  ssigma,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr,
  fct,
  diffct,
  nls.lmiter
)
trajeR.NL(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  sigma,
  pi,
  Method,
  ssigma,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr,
  fct,
  diffct,
  nls.lmiter
)

Arguments

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

ng

Integer. The number of groups.

nx

Integer. The number of covariates.

n

Integer. Number of individuals.

nbeta

Vector of integers. Number of beta parameters for each group.

nw

Integer. Number of time dependent covariate.

ntheta

Vector of integers. Number of theta parameters for each group.

period

Integer.

degre

Vector of integer. The degree of every polynomial function.

theta

Vector of real. The parameter for calculated the group membership probability.

beta

Vector of real. The beta parameter.

sigma

Vector of real. The sigma parameter.

pi

Vector of real. The group membership probability.

Method

ssigma

Logical. By default its value is FALSE. For the CNORM model, indicate if we want the same sigma for all normal density function.

hessian

itermax

Integer. Indicate the maximal number of iteration for optim function or for the EM algorithm.

paraminit

Vector. The vector of initial parameters. By default trajeR calculate the initial value based of the range or the standard deviation.

EMIRLS

Boolean. True if we use EMIRLS method.

refgr

Integer. The number of reference group. By default is 1.

fct

Function. The definition of the function f in the definition in nonlinear model.

diffct

Function. The differential of the function f in the nonlinear model.

nls.lmiter

Integer. In the case of non linear model, the maximum number of iterations allowed.

Value

return a object of class Trajectory.NL

beta - vector of the parameter beta.
sigma - vector of the parameters sigma.
delta - vector of the parameter delta. Only if we use time covariate.
theta - vector with the parameter theta if there exist a covariate X that modify the probability or the probability of group membership.
sd - vector of the standard deviation of the parameters.
tab - a matrix with all the parameters and standard deviation.
Model - a string with the model used.
groups - a integer with the number of group.
Names - strings with the name of the parameters.
Method - a string with the method used.
Size - a integer with the number of individuals.
Likelihood - a real with the Likelihood obtained by the parameters.
Time - a vector with the first row of time values.
degre - a vector with the degree of the polynomial shape.
fct - the definition of the function used int this model.

Internal function to fit poisson Model

Description

Internal function to fit poisson Model

Usage

trajeR.POIS(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)
trajeR.POIS(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  theta,
  beta,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)

Arguments

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

ng

Integer. The number of groups.

nx

Integer. The number of covariates.

n

Integer. Number of individuals.

nbeta

Vector of integers. Number of beta parameters for each group.

nw

Integer. Number of time dependent covariate.

ntheta

Vector of integers. Number of theta parameters for each group.

period

Integer.

degre

Vector of integer. The degree of every polynomial function.

theta

Vector of real. The parameter for calculated the group membership probability.

beta

Vector of real. The beta parameter.

delta

Vector of real. The delta parameter.

pi

Vector of real. The group membership probability.

Method

hessian

itermax

Integer. Indicate the maximal number of iteration for optim function or for the EM algorithm.

paraminit

Vector. The vector of initial parameters. By default trajeR calculate the initial value based of the range or the standard deviation.

EMIRLS

Boolean. True if we use EMIRLS method.

refgr

Integer. The number of reference group. By default is 1.

Value

return a object of class Trajectory.Pois

beta - vector of the parameter beta.
delta - vector of the parameter delta. Only if we use time covariate.
theta - vector with the parameter theta if there exist a covariate X that modify the probability or the probability of group membership.
sd - vector of the standard deviation of the parameters.
tab - a matrix with all the parameters and standard deviation.
Model - a string with the model used.
groups - a integer with the number of group.
Names - strings with the name of the parameters.
Method - a string with the method used.
Size - a integer with the number of individuals.
Likelihood - a real with the Likelihood obtained by the parameters.
Time - a vector with the first row of time values.
degre - a vector with the degree of the polynomial shape for the Poisson part.

Internal function to fit ZIP Model

Description

Internal function to fit ZIP Model

Usage

trajeR.ZIP(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  degre.nu,
  theta,
  beta,
  nu,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)
trajeR.ZIP(
  Y,
  A,
  X,
  TCOV,
  ng,
  nx,
  n,
  nbeta,
  nw,
  ntheta,
  period,
  degre,
  degre.nu,
  theta,
  beta,
  nu,
  delta,
  pi,
  Method,
  hessian,
  itermax,
  paraminit,
  EMIRLS,
  refgr
)

Arguments

Y

Matrix. A matrix containing the variables in the model.

A

Matrix. A matrix containing the time variable data.

X

Matrix. An optional matrix that modify the probability of belong to group. By default its value is a matrix with one column with value 1.

TCOV

Matrix. An optional matrix containing the time covariate that influence the trajectory themselves. By default its value is NULL.

ng

Integer. The number of groups.

nx

Integer. The number of covariates.

n

Integer. Number of individuals.

nbeta

Vector of integers. Number of beta parameters for each group.

nw

Integer. Number of time dependent covariate.

ntheta

Vector of integers. Number of theta parameters for each group.

period

Integer.

degre

Vector of integer. The degree of every polynomial function.

degre.nu

Vector of integer. The degree of all Poisson part for a ZIP model.

theta

Vector of real. The parameter for calculated the group membership probability.

beta

Vector of real. The beta parameter.

nu

Vector of real. The nu parameter.

delta

Vector of real. The delta parameter.

pi

Vector of real. The group membership probability.

Method

hessian

itermax

Integer. Indicate the maximal number of iteration for optim function or for the EM algorithm.

paraminit

Vector. The vector of initial parameters. By default trajeR calculate the initial value based of the range or the standard deviation.

EMIRLS

Boolean. True if we use EMIRLS method.

refgr

Integer. The number of reference group. By default is 1.

Value

return a object of class Trajectory.ZIP

beta - vector of the parameter beta.
delta - vector of the parameter delta. Only if we use time covariate.
theta - vector with the parameter theta if there exist a covariate X that modify the probability or the probability of group membership.
nu - vector of the parameters nu.
sd - vector of the standard deviation of the parameters.
tab - a matrix with all the parameters and standard deviation.
Model - a string with the model used.
groups - a integer with the number of group.
Names - strings with the name of the parameters.
Method - a string with the method used.
Size - a integer with the number of individuals.
Likelihood - a real with the Likelihood obtained by the parameters.
Time - a vector with the first row of time values.
degre - a vector with the degree of the polynomial shape for the Poisson part.
degre.nu - a vector with the degree of the polynomial shape for the exceeded zero state.

AIC function to an trajectory object

Description

Calculate the AIC value to an trajectory object.

Usage

trajeRAIC(sol)
trajeRAIC(sol)

Arguments

sol

Trajectory's object. An object of type trajectory.

Value

A real.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
trajeRAIC(sol)
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
trajeRAIC(sol)

BIC function to an trajectory object

Description

Calculate the BIC value to an trajectory object.

Usage

trajeRBIC(sol)
trajeRBIC(sol)

Arguments

sol

Trajectory's object. An object of type trajectory.

Value

A real.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
trajeRBIC(sol)
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
sol <- trajeR(Y = data[, 2:6], A = data[, 7:11], degre = c(2, 2), Model = "CNORM", Method = "EM")
trajeRBIC(sol)

SH function to an trajectory object

Description

Calculate the Slope Heuristic value to a list of trajectory objects.

Usage

trajeRSH(l)
trajeRSH(l)

Arguments

l

List. A list of objects of type trajectory.

Value

A vector of real.

Examples

data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
degre <- list(c(2, 2), c(1, 1), c(1, 2), c(2, 1), c(0, 0),
c(0, 1), c(1, 0), c(0, 0), c(0, 2), c(2, 0))
sol <- list()
for (i in 1:10) {
  sol[[i]] <- trajeR(
    Y = data[, 2:6], A = data[, 7:11],
    degre = degre[[i]], Model = "CNORM", Method = "EM"
  )
}
trajeRSH(sol)
data <- read.csv(system.file("extdata", "CNORM2gr.csv", package = "trajeR"))
data <- as.matrix(data)
degre <- list(c(2, 2), c(1, 1), c(1, 2), c(2, 1), c(0, 0),
c(0, 1), c(1, 0), c(0, 0), c(0, 2), c(2, 0))
sol <- list()
for (i in 1:10) {
  sol[[i]] <- trajeR(
    Y = data[, 2:6], A = data[, 7:11],
    degre = degre[[i]], Model = "CNORM", Method = "EM"
  )
}
trajeRSH(sol)

Package 'trajeR'

Help Index

Adequacy of the model

Description

Usage

Arguments

Value

Examples

Average Posterior Probability

Description

Usage

Arguments

Value

Examples

Compute Confidence Intervals for Predicted Trajectories

Usage

Arguments

Confidence interval

Description

Usage

Arguments

Value

Examples

Simulated CNORM Dataset (data01)

Description

Usage

Format

Details

Source

Differential

Description

Usage

Arguments

Value

Function fait

Description

Usage

Arguments

Value

Membership's probabilities

Description

Usage

Arguments

Value

Examples

Profiles of each group

Description

Usage

Arguments

Value

Examples

Odds of Correct Classification

Description

Usage

Arguments

Value

Examples

plot trajectory

Description

Usage

Arguments

Value

Examples

plot BETA trajectory

Description

Usage

Arguments

Value

plot CNORM trajectory

Description

Usage

Arguments

Value

plot LOGIT trajectory

Description

Usage

Arguments

Value

plot Non Linear trajectory

Description