Recipe function to quickly create factor loadings
Recipe function to quickly create factor loadings
prep.loadings(map, params = NULL, idvar, exo.names = character(0), intercept = FALSE)
map |
list giving how the latent variables map onto the observed variables |
params |
parameter numbers |
idvar |
names of the variables used to identify the factors |
exo.names |
names of the exogenous covariates |
intercept |
logical. Whether to include freely esimated intercepts |
The default pattern for 'idvar' is to fix the first factor loading
for each factor to one. The variable names listed in 'idvar' have
their factor loadings fixed to one. However, if the names of the
latent variables are used for 'idvar', then all the factor loadings
will be freely estimated and you should fix the factor variances
in the noise part of the model (e.g. prep.noise).
This function does not have the full set of features possible in
the dynr package. In particular, it does not have any regime-swtiching.
Covariates can be included with the exo.names argument, but
all covariate effects are freely estimated and the starting values
are all zero. Likewise, intercepts can be included with the intercept
logical argument, but all intercept terms are freely estimated with
zero as the starting value.
For complete functionality use prep.measurement.
Object of class 'dynrMeasurement'
#Single factor model with one latent variable fixing first loading
prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 2:4))
#Single factor model with one latent variable fixing the fourth loading
prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 1:3), idvar='y4')
#Single factor model with one latent variable freeing all loadings
prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 1:4), idvar='eta1')
#Single factor model with one latent variable fixing first loading
# and freely estimated intercept
prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 2:4),
intercept=TRUE)
#Single factor model with one latent variable fixing first loading
# and freely estimated covariate effects for u1 and u2
prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 2:4),
exo.names=paste0('u', 1:2))
# Two factor model with simple structure
prep.loadings(list(eta1=paste0('y', 1:4), eta2=paste0('y', 5:7)),
paste0("lambda_", c(2:4, 6:7)))
#Two factor model with repeated use of a free parameter
prep.loadings(list(eta1=paste0('y', 1:4), eta2=paste0('y', 5:8)),
paste0("lambda_", c(2:4, 6:7, 4)))
#Two factor model with a cross loading
prep.loadings(list(eta1=paste0('y', 1:4), eta2=c('y5', 'y2', 'y6')),
paste0("lambda_", c("21", "31", "41", "22", "62")))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.