Executing README examples.

Author: SebKrantz

README.md

@@ -48,23 +48,136 @@ install.packages("dfms")
 install.packages('dfms', repos = c('https://sebkrantz.r-universe.dev', 'https://cloud.r-project.org'))
 
 ```
+
 ### Usage Example 
+
+
 ```r
 library(dfms)
 
 # Fit DFM with 6 factors and 3 lags in the transition equation
 mod = DFM(diff(BM14_M), r = 6, p = 3) 
+```
 
+```
+## Converged after 32 iterations.
+```
+
+```r
 # 'dfm' methods
 summary(mod)
+```
+
+```
+## Mixed Frequency Dynamic Factor Model
+## n = 92, nm = 92, nq = 0, T = 356, r = 6, p = 3
+## %NA = 25.8366, %NAm = NA
+## 
+## Call:  DFM(X = diff(BM14_M), r = 6, p = 3)
+## 
+## Summary Statistics of Factors [F]
+##       N     Mean   Median      SD       Min      Max
+## f1  356  -0.1189   0.4409  4.0228  -22.9164   7.8513
+## f2  356  -0.4615  -0.3476  2.9201   -9.0973  10.7003
+## f3  356  -0.0173   0.0377  2.2719   -8.5067   7.3009
+## f4  356   -0.007  -0.1338  1.9378   -9.5052   9.3673
+## f5  356    0.237   0.1091  2.0857   -8.7252   9.6715
+## f6  356  -0.8361   -0.304  3.1406  -11.6611  15.4897
+## 
+## Factor Transition Matrix [A]
+##       L1.f1    L1.f2     L1.f3    L1.f4    L1.f5    L1.f6    L2.f1    L2.f2
+## f1  0.53029 -0.53009  0.367302  0.04607 -0.06351  0.10310  0.02457  0.11673
+## f2 -0.28380  0.07421 -0.032292  0.29741 -0.10094  0.21989  0.09958 -0.09149
+## f3  0.17607  0.12979  0.378798 -0.06662 -0.12236  0.06685 -0.08068  0.09101
+## f4  0.02711  0.08936  0.004643  0.37159  0.12100 -0.02763  0.01234 -0.05147
+## f5 -0.26227 -0.03469 -0.046294  0.12712  0.26847  0.03141  0.06400  0.01971
+## f6  0.08251  0.17619 -0.013374 -0.08731 -0.03875  0.27812 -0.01662  0.04877
+##       L2.f3    L2.f4     L2.f5    L2.f6    L3.f1    L3.f2    L3.f3    L3.f4
+## f1 -0.12638  0.23135  0.117184  0.21941  0.18478  0.02259 -0.03719 -0.07236
+## f2  0.06708 -0.09768 -0.043057  0.08489  0.21107  0.16261  0.03057  0.04835
+## f3 -0.22232  0.09799 -0.060666 -0.18028 -0.02773  0.01798  0.10143 -0.12420
+## f4  0.02195  0.01266  0.050912  0.05144 -0.05601  0.04665  0.05710 -0.11412
+## f5  0.04806 -0.03965 -0.009952 -0.18471  0.08332 -0.04640 -0.02047  0.02458
+## f6  0.02279  0.01163 -0.100859  0.07152  0.00792  0.06071  0.11381  0.02520
+##       L3.f5    L3.f6
+## f1 -0.03026 -0.12606
+## f2  0.12249  0.13357
+## f3  0.04207 -0.07011
+## f4 -0.05680 -0.01609
+## f5  0.16397  0.07820
+## f6 -0.17897  0.30328
+## 
+## Factor Covariance Matrix [cov(F)]
+##           f1        f2        f3        f4        f5        f6
+## f1  16.1832   -0.4329    0.2483   -0.8224*  -1.7708*   0.7702 
+## f2  -0.4329    8.5272    0.0051    0.2954   -0.2114    4.2080*
+## f3   0.2483    0.0051    5.1614   -0.1851   -0.3979    0.2979 
+## f4  -0.8224*   0.2954   -0.1851    3.7550    0.4344*   0.2211 
+## f5  -1.7708*  -0.2114   -0.3979    0.4344*   4.3503   -1.9785*
+## f6   0.7702    4.2080*   0.2979    0.2211   -1.9785*   9.8634 
+## 
+## Factor Transition Error Covariance Matrix [Q]
+##         u1      u2      u3      u4      u5      u6
+## u1  7.2142  0.1151 -0.8208 -0.4379  0.4110 -0.1206
+## u2  0.1151  4.8724  0.1076 -0.1438  0.1418  0.1759
+## u3 -0.8208  0.1076  4.0584 -0.0788  0.0163  0.0038
+## u4 -0.4379 -0.1438 -0.0788  3.0003  0.2562  0.0243
+## u5  0.4110  0.1418  0.0163  0.2562  2.8410 -0.1031
+## u6 -0.1206  0.1759  0.0038  0.0243 -0.1031  2.9284
+## 
+## Summary of Residual AR(1) Serial Correlations
+##    N     Mean   Median      SD      Min     Max
+##   92  -0.0644  -0.1024  0.2702  -0.5113  0.6674
+## 
+## Summary of Individual R-Squared's
+##    N    Mean  Median      SD     Min     Max
+##   92  0.4556  0.4069  0.3041  0.0112  0.9989
+```
+
+```r
 plot(mod)
-as.data.frame(mod)
+```
 
+<div class="figure">
+<img src="misc/figure/unnamed-chunk-1-1.png" alt="plot of chunk unnamed-chunk-1" width="100%" />
+</div>
+
+```r
+as.data.frame(mod) |> head()
+```
+
+```
+##   Method Factor Time      Value
+## 1    PCA     f1    1  0.8445713
+## 2    PCA     f1    2  0.5259228
+## 3    PCA     f1    3 -1.2107116
+## 4    PCA     f1    4 -1.5399532
+## 5    PCA     f1    5 -0.4631786
+## 6    PCA     f1    6  0.2399304
+```
+
+```r
 # Forecasting 20 periods ahead
 fc = predict(mod, h = 20)
 
 # 'dfm_forecast' methods
-print(fc)
 plot(fc)
-as.data.frame(fc)
+```
+
+<div class="figure">
+<img src="misc/figure/unnamed-chunk-1-2.png" alt="plot of chunk unnamed-chunk-1" width="100%" />
+</div>
+
+```r
+as.data.frame(fc) |> head()
+```
+
+```
+##   Variable Time Forecast      Value
+## 1       f1    1    FALSE   4.179331
+## 2       f1    2    FALSE  -1.368577
+## 3       f1    3    FALSE -12.845157
+## 4       f1    4    FALSE -14.562265
+## 5       f1    5    FALSE  -7.791254
+## 6       f1    6    FALSE  -1.254970
 ```