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> require(glarma)
Loading required package: glarma
> ### Test glarmaSimModel
> data("Polio")
> y <- Polio[, 2]
> X <- as.matrix(Polio[, 3:8])
> glarmamod <- glarma(y, X, thetaLags = c(1,2,5), type = "Poi", method = "FS",
+                     residuals = "Pearson", maxit = 100, grad = 1e-6)
> summary(glarmamod)

Call: glarma(y = y, X = X, type = "Poi", method = "FS", residuals = "Pearson", 
    thetaLags = c(1, 2, 5), maxit = 100, grad = 1e-06)

Pearson Residuals:
    Min       1Q   Median       3Q      Max  
-2.0269  -0.9324  -0.1761   0.7719   4.4832  

GLARMA Coefficients:
        Estimate Std.Error z-ratio Pr(>|z|)    
theta_1  0.21846   0.04663   4.685  2.8e-06 ***
theta_2  0.12723   0.04732   2.689  0.00718 ** 
theta_5  0.08729   0.04226   2.066  0.03888 *  

Linear Model Coefficients:
              Estimate Std.Error z-ratio Pr(>|z|)    
Intcpt         0.12998   0.11160   1.165 0.244177    
Trend         -3.92837   2.14518  -1.831 0.067063 .  
CosAnnual     -0.09913   0.11757  -0.843 0.399142    
SinAnnual     -0.53084   0.13794  -3.848 0.000119 ***
CosSemiAnnual  0.21113   0.11084   1.905 0.056804 .  
SinSemiAnnual -0.39323   0.11561  -3.401 0.000671 ***

    Null deviance: 343.00  on 167  degrees of freedom
Residual deviance: 250.62  on 159  degrees of freedom
AIC: 536.7052 

Number of Fisher Scoring iterations: 21

LRT and Wald Test:
Alternative hypothesis: model is a GLARMA process
Null hypothesis: model is a GLM with the same regression structure
          Statistic  p-value    
LR Test       27.19 5.36e-06 ***
Wald Test     38.12 2.67e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> PolioModel <- glarmaSimModel(X, beta = coef.glarma(glarmamod, type = "beta"),
+                              phiLags = glarmamod$phiLags,
+                              phi = rep(0.1, length(glarmamod$phiLags)),
+                              thetaLags = glarmamod$thetaLags,
+                              theta = rep(0.1, length(glarmamod$thetaLags)),
+                              type = glarmamod$type,
+                              residType = glarmamod$residType)
> str(PolioModel)
List of 8
 $ X        : num [1:168, 1:6] 1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:6] "Intcpt" "Trend" "CosAnnual" "SinAnnual" ...
 $ beta     : Named num [1:6] 0.13 -3.9284 -0.0991 -0.5308 0.2111 ...
  ..- attr(*, "names")= chr [1:6] "Intcpt" "Trend" "CosAnnual" "SinAnnual" ...
 $ type     : chr "Poi"
 $ residType: chr "Pearson"
 $ phiLags  : num(0) 
 $ phi      : num(0) 
 $ thetaLags: num [1:3] 1 2 5
 $ theta    : num [1:3] 0.1 0.1 0.1
 - attr(*, "class")= chr "glarmaSimModel"
> coef.glarma(glarmamod, type = "ARMA")
  theta_1   theta_2   theta_5 
0.2184597 0.1272311 0.0872861 
> PolioModel <- extractGlarmaSimModel(glarmamod)
> str(PolioModel)
List of 8
 $ X        : num [1:168, 1:6] 1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:6] "Intcpt" "Trend" "CosAnnual" "SinAnnual" ...
 $ beta     : Named num [1:6] 0.13 -3.9284 -0.0991 -0.5308 0.2111 ...
  ..- attr(*, "names")= chr [1:6] "Intcpt" "Trend" "CosAnnual" "SinAnnual" ...
 $ type     : chr "Poi"
 $ residType: chr "Pearson"
 $ phiLags  : num(0) 
 $ phi      : num(0) 
 $ thetaLags: num [1:3] 1 2 5
 $ theta    : Named num [1:3] 0.2185 0.1272 0.0873
  ..- attr(*, "names")= chr [1:3] "theta_1" "theta_2" "theta_5"
 - attr(*, "class")= chr "glarmaSimModel"
> 
> proc.time()
   user  system elapsed 
   0.48    0.18    0.60