Calculate trend correlations based on latent factor loadings for mvgam models

This function uses factor loadings from a fitted dynamic factor mvgam model to calculate temporal correlations among series' trends

Usage

lv_correlations(object)

Arguments

object: list object of class mvgam that used latent factors, either with use_lv = TRUE or by supplying a trend_map. See mvgam() for details and for an example

Value

A list object containing the mean posterior correlations and the full array of posterior correlations

Details

Although this function will still work, it is now recommended to use residual_cor() to obtain residual correlation information in a more user-friendly format that allows for a deeper investigation of relationships among the time series.

Examples

# \donttest{
# Fit a model that uses two AR(1) dynamic factors to model
# the temporal dynamics of the four rodent species in the portal_data
mod <- mvgam(captures ~ series,
            trend_model = AR(),
            use_lv = TRUE,
            n_lv = 2,
            data = portal_data,
            chains = 2,
            silent = 2)
#> Warning in '/tmp/RtmpnL3qIL/model_fd0fdfeaee023a8e623b5071c6b9ec02.stan', line 20, column 31: Found
#>     int division:
#>       n_lv * (n_lv - 1) / 2
#>     Values will be rounded towards zero. If rounding is not desired you can
#>     write
#>     the division as
#>       n_lv * (n_lv - 1) / 2.0
#>     If rounding is intended please use the integer division operator %/%.
#> Warning in '/tmp/RtmpnL3qIL/model-241c273eb8a0.stan', line 20, column 33: Found
#>     int division:
#>       n_lv * (n_lv - 1) / 2
#>     Values will be rounded towards zero. If rounding is not desired you can
#>     write
#>     the division as
#>       n_lv * (n_lv - 1) / 2.0
#>     If rounding is intended please use the integer division operator %/%.

# Plot the two dynamic factors
plot(mod, type = 'factors')

#> # A tibble: 2 × 2
#>   Factor   Contribution
#>   <chr>           <dbl>
#> 1 Factor 1        0.532
#> 2 Factor 2        0.468

# Calculate correlations among the series using lv_correlations()
lvcors <- lv_correlations(mod)
names(lvcors)
#> [1] "mean_correlations"      "posterior_correlations"
lapply(lvcors, class)
#> $mean_correlations
#> [1] "matrix" "array" 
#> 
#> $posterior_correlations
#> [1] "list"
#> 

# The above works, but it is now recommended to use the more
# flexible and informative residual_cor() function to
# calculate and work with these correlations
lvcors <- residual_cor(mod)
names(lvcors)
#>  [1] "cor"        "cor_lower"  "cor_upper"  "sig_cor"    "cov"       
#>  [6] "prec"       "prec_lower" "prec_upper" "sig_prec"   "trace"     
lvcors$cor
#>            DM          DO         PB           PP
#> DM  1.0000000 0.538926931 -0.5026149 -0.813423512
#> DO  0.5389269 1.000000000  0.4153731  0.005680965
#> PB -0.5026149 0.415373088  1.0000000  0.895825721
#> PP -0.8134235 0.005680965  0.8958257  1.000000000

# For those correlations whose credible intervals did not include
# zero, plot them as a correlation matrix (all other correlations
# are shown as zero on this plot)
plot(lvcors, cluster = TRUE)


# \dontshow{
# For R CMD check: make sure any open connections are closed afterward
 closeAllConnections()
# }
# }