R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
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> library(tsDyn)
> suppressWarnings(RNGversion("3.5.3"))
> 
> ### linear
> grid <-  expand.grid(include = c( "const", "trend","none", "both"),
+                      lag = 1:3)
> all_lin <- mapply(linear, include = as.character(grid$include),  m = grid$lag, MoreArgs = list(x = lh),
+                   SIMPLIFY = FALSE)
> names(all_lin) <-  paste(grid$include, "l", grid$lag, sep="_")
> lapply(all_lin, coef)
$const_l_1
    const     phi.1 
0.9998652 0.5859870 

$trend_l_1
      trend       phi.1 
0.008879993 0.895448654 

$none_l_1
    phi.1 
0.9836385 

$both_l_1
      const       trend       phi.1 
0.960001423 0.007328862 0.529055888 

$const_l_2
     const      phi.1      phi.2 
 1.2281886  0.7110028 -0.2217373 

$trend_l_2
       trend        phi.1        phi.2 
 0.009393203  0.896697060 -0.004950298 

$none_l_2
     phi.1      phi.2 
0.95299035 0.03115904 

$both_l_2
       const        trend        phi.1        phi.2 
 1.217407570  0.009017984  0.659082417 -0.254184319 

$const_l_3
      const       phi.1       phi.2       phi.3 
 1.53752119  0.65782378 -0.06581322 -0.23483547 

$trend_l_3
      trend       phi.1       phi.2       phi.3 
 0.00960834  0.89440447 -0.06374027  0.06254620 

$none_l_3
      phi.1       phi.2       phi.3 
 0.94986030 -0.05445084  0.09133812 

$both_l_3
      const       trend       phi.1       phi.2       phi.3 
 1.58894725  0.01110629  0.58395447 -0.07693093 -0.27902573 

> 
> ## compare with ar?
> ar_1_noMean <- ar(lh, order.max =1, demean = FALSE, method = "ols")
> ar_1_Mean <- ar(lh, order.max =1, demean = TRUE, method = "ols")
> 
> ar_2_noMean <- ar(lh, aic = FALSE, order.max =2, demean = FALSE, method = "ols")
> ar_2_Mean <- ar(lh, aic = FALSE, order.max =2, demean = TRUE, method = "ols")
> 
> ## compare coefs
> all.equal(coef(all_lin[["const_l_1"]])[2], ar_1_Mean$ar[,,1], check.attributes = FALSE)
[1] TRUE
> all.equal(coef(all_lin[["none_l_1"]]), ar_1_noMean$ar[,,1], check.attributes = FALSE)
[1] TRUE
> 
> all.equal(coef(all_lin[["const_l_2"]])[-1], ar_2_Mean$ar[,,1], check.attributes = FALSE)
[1] TRUE
> all.equal(coef(all_lin[["none_l_2"]]), ar_2_noMean$ar[,,1], check.attributes = FALSE)
[1] TRUE
> 
> ## compare means: intercept in ar is (1-phi)(diff means), ch Hamilton 3.4.35
> ## diff means is difference between estimated mean, and c/(1- phi)
> linear_1_Mean <-  all_lin[["const_l_1"]] 
> comp_means <- function(ar, linear) {
+   ar_empimean <- ar$x.mean  
+   ar_int <- ar$x.intercept  
+   lin_coefs <- coef(linear)
+   lin_LR_mean <-  ar_mean(linear)
+   diff_means <- lin_LR_mean - ar_empimean
+   res <- c(ar_int = ar_int, diff = (1- sum(lin_coefs[-1])) * diff_means)
+   list(res, check = all.equal(res[1], res[2], check.attributes = FALSE))
+ }
> comp_means(ar = ar_1_Mean, linear = linear_1_Mean)$check
[1] TRUE
> comp_means(ar = ar_2_Mean, linear = all_lin[["const_l_2"]] )$check
[1] TRUE
> 
> 
> ## predict?
> all_lin_const <- all_lin[grep("const", names(all_lin))]
> all_lin_pred <- lapply(all_lin_const, predict, n.ahead = 5)
> 
> ar_1_Mean_pred <- predict(ar_1_Mean, n.ahead = 5)$pred
> 
> all.equal(ar_1_Mean_pred, all_lin_pred[["const_l_1"]], check.attributes = TRUE)
[1] TRUE
> 
> ## ar_mean
> all_lin_noTrend <- all_lin[grep("const|none", names(all_lin))]
> sapply(all_lin_noTrend, ar_mean)
const_l_1.ar_mean  none_l_1.ar_mean const_l_2.ar_mean  none_l_2.ar_mean 
         2.415057          0.000000          2.404750          0.000000 
const_l_3.ar_mean  none_l_3.ar_mean 
         2.391820          0.000000 
> 
> 
> ## charac root
> do.call("rbind", lapply(all_lin_noTrend, charac_root))
            root value_all
const_l_1      1  1.706523
none_l_1       1  1.016634
const_l_2.1    1  2.123638
const_l_2.2    2  2.123638
none_l_2.1     1  1.015604
none_l_2.2     2 31.600320
const_l_3.1    1  1.360258
const_l_3.2    2  2.301410
const_l_3.3    3  1.360258
none_l_3.1     1  1.011858
none_l_3.2     2  3.289381
none_l_3.3     3  3.289381
> 
> proc.time()
   user  system elapsed 
   2.06    0.29    2.35