Description Usage Arguments Details Value References See Also Examples

This function estimates the linear mixed regression model when the dependent variable is interval censored. The estimation of the standard errors is fasciliated by a parametric bootstrap.

1 2 |

`formula` |
a two-sided linear formula object describing both the fixed-effects
and random-effects part of the model, with the response on the left of a ~ operator
and the terms, separated by + operators, on the right. Random-effects terms are
distinguished by vertical bars (|) separating expressions for design matrices from
grouping factors, as in |

`data` |
a data frame containing the variables of the model |

`classes` |
numeric vector of classes; |

`burnin` |
the number of burn-in iterations of the SEM-algorithm |

`samples` |
the number of additional iterations of the SEM-algorithm for parameter estimation |

`trafo` |
transformation of the dependent variable to fulfil the model assumptions "log" for Logarithmic transformation "bc" for Box-Cox transformation
default is |

`adjust` |
extends the number of iteration steps of the SEM-algorithm
for finding the optimal lambda of the Box-Cox transformation. The number of iterations
is extended in the following way: |

`bootstrap.se` |
if |

`b` |
number of bootstrap iterations for the estimation of the standard errors |

The model parameters are estimated using pseudo samples of the
interval censored dependent variable. The object `pseudo.y`

returns the
pseudo samples of each iteration step of the SEM-algorithm.

An object of class "sem" that provides parameter estimated for linear
regression models with interval censored dependent variable. Generic
functions such as, `print`

,
`plot`

, and `summary`

have methods that can be used
to obtain further information. See `semObject`

for descriptions
of components
of objects of class "sem".

Walter, P., Gross, M., Schmid, T. and Tzavidis, N. (2017). Estimation of Linear and Non-Linear Indicators using Interval Censored Income Data. FU-Berlin School of Business & Economics, Discussion Paper.

`lmer`

, `print.sem`

,
`plot.sem`

, `summary.sem`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## Not run:
# Load and prepare data
data <- Exam
classes <- c(1,1.5,2.5,3.5,4.5,5.5,6.5,7.7,8.5, Inf)
data$examsc.class<- cut(data$examsc, classes)
# Run model with random intercept and default settings
model1 <- semLme(formula = examsc.class ~ standLRT + schavg + (1|school),
data = data, classes = classes)
summary(model1)
# Run model with random intercept + random slope with default settings
model2 <- semLme(formula = examsc.class ~ standLRT + schavg +
(standLRT|school), data = data, classes = classes)
summary(model2)
## End(Not run)
``` |

smicd documentation built on May 2, 2019, 4:07 p.m.

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