Saving and loading
ExeterUQ_mogp emulators are not standard objects. They are lists with
2 elements: the first of which is a python mogp_emulator object, and
the second is a list of statistical elements of the fit such as prior
choices, mean function choices, elements useful for diagnostics and
other things we would like for transparent inference.
The usual save() and load() functions will seem to work, but will
only save a pointer for the python object (which once removed/reloaded
won’t work). Here is a MWE
mogp_dir <- "~/Dropbox/BayesExeter/mogp_emulator"
setwd('..')
source('BuildEmulator/BuildEmulator.R')
load("ConvectionModelExample.Rdata")
TestEm <- BuildNewEmulators(tData, HowManyEmulators = 2, meanFun="fitted")
## [1] "Max reduction is 0.141510566268957 using A_EPSILON"
## [1] "Max reduction is 0.0719279431890675 using A_U"
## [1] "Max reduction is 0.0593934956606317 using A_T"
## [1] "Max reduction is 0.0841015596079704 using A_EPSILON"
## [1] "Max reduction is 0.0215815680504046 using A_EPSILON"
## [1] "Max reduction is 0.0221974794878812 using A_EPSILON"
## [1] "Noise fitted, stopping algorithm"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_zav.400.600.theta_5_6 ~ A_EPSILON +
## I(A_EPSILON^2) + I(A_EPSILON^3) + I(A_EPSILON^4) + A_U +
## A_T + I(A_U * A_EPSILON) + I(A_T * A_EPSILON) + I(A_T * A_U),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15059 -0.06624 0.01615 0.05931 0.15216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 306.35982 0.03461 8852.437 < 2e-16 ***
## A_EPSILON -0.31275 0.08180 -3.824 0.00106 **
## I(A_EPSILON^2) -0.19500 0.21788 -0.895 0.38145
## I(A_EPSILON^3) -0.43915 0.12490 -3.516 0.00217 **
## I(A_EPSILON^4) 0.83401 0.24582 3.393 0.00289 **
## A_U 0.30423 0.03234 9.409 8.73e-09 ***
## A_T 0.33990 0.03172 10.715 9.78e-10 ***
## I(A_U * A_EPSILON) -0.08042 0.05404 -1.488 0.15233
## I(A_T * A_EPSILON) -0.08069 0.06159 -1.310 0.20500
## I(A_T * A_U) -0.03824 0.06560 -0.583 0.56648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09867 on 20 degrees of freedom
## Multiple R-squared: 0.9731, Adjusted R-squared: 0.961
## F-statistic: 80.31 on 9 and 20 DF, p-value: 1.004e-13
##
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_zav.400.600.theta_5_6 ~ A_EPSILON +
## I(A_EPSILON^2) + I(A_EPSILON^3) + I(A_EPSILON^4) + A_U +
## A_T + I(A_U * A_EPSILON) + I(A_T * A_EPSILON) + I(A_T * A_U),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15059 -0.06624 0.01615 0.05931 0.15216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 306.35982 0.03461 8852.437 < 2e-16 ***
## A_EPSILON -0.31275 0.08180 -3.824 0.00106 **
## I(A_EPSILON^2) -0.19500 0.21788 -0.895 0.38145
## I(A_EPSILON^3) -0.43915 0.12490 -3.516 0.00217 **
## I(A_EPSILON^4) 0.83401 0.24582 3.393 0.00289 **
## A_U 0.30423 0.03234 9.409 8.73e-09 ***
## A_T 0.33990 0.03172 10.715 9.78e-10 ***
## I(A_U * A_EPSILON) -0.08042 0.05404 -1.488 0.15233
## I(A_T * A_EPSILON) -0.08069 0.06159 -1.310 0.20500
## I(A_T * A_U) -0.03824 0.06560 -0.583 0.56648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09867 on 20 degrees of freedom
## Multiple R-squared: 0.9731, Adjusted R-squared: 0.961
## F-statistic: 80.31 on 9 and 20 DF, p-value: 1.004e-13
##
## [1] "1 I(A_T * A_U)"
## [1] "2 I(A_T * A_EPSILON)"
## [1] "3 I(A_U * A_EPSILON)"
## [1] "4 I(A_EPSILON^4)"
## [1] "5 I(A_EPSILON^3)"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_zav.400.600.theta_5_6 ~ A_EPSILON +
## I(A_EPSILON^2) + A_U + A_T, data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.31141 -0.07230 0.00132 0.06857 0.49418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 306.28286 0.04193 7304.864 < 2e-16 ***
## A_EPSILON -0.57626 0.04796 -12.015 7.00e-12 ***
## I(A_EPSILON^2) 0.53877 0.09315 5.784 4.98e-06 ***
## A_U 0.32558 0.04794 6.792 4.06e-07 ***
## A_T 0.34208 0.04790 7.142 1.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.152 on 25 degrees of freedom
## Multiple R-squared: 0.9202, Adjusted R-squared: 0.9074
## F-statistic: 72.06 on 4 and 25 DF, p-value: 2.36e-13
##
## [1] "Max reduction is 0.0516838339369975 using A_EPSILON"
## [1] "Max reduction is 0.0181512292708917 using A_T"
## [1] "Max reduction is 0.00908627733663028 using A_U"
## [1] "Noise fitted, stopping algorithm"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_Ay.theta_5_6 ~ A_EPSILON + A_T +
## A_U + I(A_T * A_EPSILON) + I(A_U * A_EPSILON) + I(A_U * A_T),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.122972 -0.017118 0.003162 0.026879 0.108563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.466705 0.009108 -51.243 < 2e-16 ***
## A_EPSILON 0.173565 0.015924 10.900 1.47e-10 ***
## A_T -0.078502 0.015793 -4.971 5.01e-05 ***
## A_U -0.050460 0.015915 -3.171 0.00427 **
## I(A_T * A_EPSILON) 0.066547 0.030606 2.174 0.04022 *
## I(A_U * A_EPSILON) -0.025424 0.025766 -0.987 0.33405
## I(A_U * A_T) -0.035683 0.031953 -1.117 0.27564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0496 on 23 degrees of freedom
## Multiple R-squared: 0.8819, Adjusted R-squared: 0.8511
## F-statistic: 28.62 on 6 and 23 DF, p-value: 1.439e-09
##
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_Ay.theta_5_6 ~ A_EPSILON + A_T +
## A_U + I(A_T * A_EPSILON) + I(A_U * A_EPSILON) + I(A_U * A_T),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.122972 -0.017118 0.003162 0.026879 0.108563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.466705 0.009108 -51.243 < 2e-16 ***
## A_EPSILON 0.173565 0.015924 10.900 1.47e-10 ***
## A_T -0.078502 0.015793 -4.971 5.01e-05 ***
## A_U -0.050460 0.015915 -3.171 0.00427 **
## I(A_T * A_EPSILON) 0.066547 0.030606 2.174 0.04022 *
## I(A_U * A_EPSILON) -0.025424 0.025766 -0.987 0.33405
## I(A_U * A_T) -0.035683 0.031953 -1.117 0.27564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0496 on 23 degrees of freedom
## Multiple R-squared: 0.8819, Adjusted R-squared: 0.8511
## F-statistic: 28.62 on 6 and 23 DF, p-value: 1.439e-09
##
## [1] "1 I(A_U * A_EPSILON)"
## [1] "2 I(A_U * A_T)"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_Ay.theta_5_6 ~ A_EPSILON + A_T +
## A_U + I(A_T * A_EPSILON), data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.125503 -0.019057 0.002386 0.026174 0.097011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.466250 0.009099 -51.240 < 2e-16 ***
## A_EPSILON 0.175425 0.015790 11.110 3.68e-11 ***
## A_T -0.076189 0.015710 -4.850 5.50e-05 ***
## A_U -0.052910 0.015894 -3.329 0.0027 **
## I(A_T * A_EPSILON) 0.064706 0.030065 2.152 0.0412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04981 on 25 degrees of freedom
## Multiple R-squared: 0.8705, Adjusted R-squared: 0.8498
## F-statistic: 42.01 on 4 and 25 DF, p-value: 9.526e-11
print(TestEm[[1]])
## Multi-Output Gaussian Process with:
## 2 emulators
## 30 training examples
## 3 input variables
save(TestEm, file="TestEm.RData")
rm(TestEm)
load("TestEm.RData")
print(TestEm[[1]])
## <pointer: 0x0>
The mogp part has gone on reload (it was never saved). To overcome this
we use the python functions py_save_object and py_load_object to
save out the mogp part. This means that our saving and loading of an
mogp emulator actually saves an RData file and a python object
separately. This is all handled automatically within our package using
the functions saveExUQmogp and loadExUQmogp First we build an
emulator again
TestEm <- BuildNewEmulators(tData, HowManyEmulators = 2, meanFun="fitted")
## [1] "Max reduction is 0.141510566268957 using A_EPSILON"
## [1] "Max reduction is 0.0719279431890675 using A_U"
## [1] "Max reduction is 0.0593934956606317 using A_T"
## [1] "Max reduction is 0.0841015596079704 using A_EPSILON"
## [1] "Max reduction is 0.0215815680504046 using A_EPSILON"
## [1] "Max reduction is 0.0221974794878812 using A_EPSILON"
## [1] "Noise fitted, stopping algorithm"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_zav.400.600.theta_5_6 ~ A_EPSILON +
## I(A_EPSILON^2) + I(A_EPSILON^3) + I(A_EPSILON^4) + A_U +
## A_T + I(A_U * A_EPSILON) + I(A_T * A_EPSILON) + I(A_T * A_U),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15059 -0.06624 0.01615 0.05931 0.15216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 306.35982 0.03461 8852.437 < 2e-16 ***
## A_EPSILON -0.31275 0.08180 -3.824 0.00106 **
## I(A_EPSILON^2) -0.19500 0.21788 -0.895 0.38145
## I(A_EPSILON^3) -0.43915 0.12490 -3.516 0.00217 **
## I(A_EPSILON^4) 0.83401 0.24582 3.393 0.00289 **
## A_U 0.30423 0.03234 9.409 8.73e-09 ***
## A_T 0.33990 0.03172 10.715 9.78e-10 ***
## I(A_U * A_EPSILON) -0.08042 0.05404 -1.488 0.15233
## I(A_T * A_EPSILON) -0.08069 0.06159 -1.310 0.20500
## I(A_T * A_U) -0.03824 0.06560 -0.583 0.56648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09867 on 20 degrees of freedom
## Multiple R-squared: 0.9731, Adjusted R-squared: 0.961
## F-statistic: 80.31 on 9 and 20 DF, p-value: 1.004e-13
##
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_zav.400.600.theta_5_6 ~ A_EPSILON +
## I(A_EPSILON^2) + I(A_EPSILON^3) + I(A_EPSILON^4) + A_U +
## A_T + I(A_U * A_EPSILON) + I(A_T * A_EPSILON) + I(A_T * A_U),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15059 -0.06624 0.01615 0.05931 0.15216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 306.35982 0.03461 8852.437 < 2e-16 ***
## A_EPSILON -0.31275 0.08180 -3.824 0.00106 **
## I(A_EPSILON^2) -0.19500 0.21788 -0.895 0.38145
## I(A_EPSILON^3) -0.43915 0.12490 -3.516 0.00217 **
## I(A_EPSILON^4) 0.83401 0.24582 3.393 0.00289 **
## A_U 0.30423 0.03234 9.409 8.73e-09 ***
## A_T 0.33990 0.03172 10.715 9.78e-10 ***
## I(A_U * A_EPSILON) -0.08042 0.05404 -1.488 0.15233
## I(A_T * A_EPSILON) -0.08069 0.06159 -1.310 0.20500
## I(A_T * A_U) -0.03824 0.06560 -0.583 0.56648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09867 on 20 degrees of freedom
## Multiple R-squared: 0.9731, Adjusted R-squared: 0.961
## F-statistic: 80.31 on 9 and 20 DF, p-value: 1.004e-13
##
## [1] "1 I(A_T * A_U)"
## [1] "2 I(A_T * A_EPSILON)"
## [1] "3 I(A_U * A_EPSILON)"
## [1] "4 I(A_EPSILON^4)"
## [1] "5 I(A_EPSILON^3)"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_zav.400.600.theta_5_6 ~ A_EPSILON +
## I(A_EPSILON^2) + A_U + A_T, data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.31141 -0.07230 0.00132 0.06857 0.49418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 306.28286 0.04193 7304.864 < 2e-16 ***
## A_EPSILON -0.57626 0.04796 -12.015 7.00e-12 ***
## I(A_EPSILON^2) 0.53877 0.09315 5.784 4.98e-06 ***
## A_U 0.32558 0.04794 6.792 4.06e-07 ***
## A_T 0.34208 0.04790 7.142 1.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.152 on 25 degrees of freedom
## Multiple R-squared: 0.9202, Adjusted R-squared: 0.9074
## F-statistic: 72.06 on 4 and 25 DF, p-value: 2.36e-13
##
## [1] "Max reduction is 0.0516838339369975 using A_EPSILON"
## [1] "Max reduction is 0.0181512292708917 using A_T"
## [1] "Max reduction is 0.00908627733663028 using A_U"
## [1] "Noise fitted, stopping algorithm"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_Ay.theta_5_6 ~ A_EPSILON + A_T +
## A_U + I(A_T * A_EPSILON) + I(A_U * A_EPSILON) + I(A_U * A_T),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.122972 -0.017118 0.003162 0.026879 0.108563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.466705 0.009108 -51.243 < 2e-16 ***
## A_EPSILON 0.173565 0.015924 10.900 1.47e-10 ***
## A_T -0.078502 0.015793 -4.971 5.01e-05 ***
## A_U -0.050460 0.015915 -3.171 0.00427 **
## I(A_T * A_EPSILON) 0.066547 0.030606 2.174 0.04022 *
## I(A_U * A_EPSILON) -0.025424 0.025766 -0.987 0.33405
## I(A_U * A_T) -0.035683 0.031953 -1.117 0.27564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0496 on 23 degrees of freedom
## Multiple R-squared: 0.8819, Adjusted R-squared: 0.8511
## F-statistic: 28.62 on 6 and 23 DF, p-value: 1.439e-09
##
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_Ay.theta_5_6 ~ A_EPSILON + A_T +
## A_U + I(A_T * A_EPSILON) + I(A_U * A_EPSILON) + I(A_U * A_T),
## data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.122972 -0.017118 0.003162 0.026879 0.108563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.466705 0.009108 -51.243 < 2e-16 ***
## A_EPSILON 0.173565 0.015924 10.900 1.47e-10 ***
## A_T -0.078502 0.015793 -4.971 5.01e-05 ***
## A_U -0.050460 0.015915 -3.171 0.00427 **
## I(A_T * A_EPSILON) 0.066547 0.030606 2.174 0.04022 *
## I(A_U * A_EPSILON) -0.025424 0.025766 -0.987 0.33405
## I(A_U * A_T) -0.035683 0.031953 -1.117 0.27564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0496 on 23 degrees of freedom
## Multiple R-squared: 0.8819, Adjusted R-squared: 0.8511
## F-statistic: 28.62 on 6 and 23 DF, p-value: 1.439e-09
##
## [1] "1 I(A_U * A_EPSILON)"
## [1] "2 I(A_U * A_T)"
##
## Call:
## lm(formula = WAVE1_AYOTTE_24SC_Ay.theta_5_6 ~ A_EPSILON + A_T +
## A_U + I(A_T * A_EPSILON), data = tData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.125503 -0.019057 0.002386 0.026174 0.097011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.466250 0.009099 -51.240 < 2e-16 ***
## A_EPSILON 0.175425 0.015790 11.110 3.68e-11 ***
## A_T -0.076189 0.015710 -4.850 5.50e-05 ***
## A_U -0.052910 0.015894 -3.329 0.0027 **
## I(A_T * A_EPSILON) 0.064706 0.030065 2.152 0.0412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04981 on 25 degrees of freedom
## Multiple R-squared: 0.8705, Adjusted R-squared: 0.8498
## F-statistic: 42.01 on 4 and 25 DF, p-value: 9.526e-11
Now to save we call
save_ExUQmogp(TestEm, filename = "SavedEmulator")
It is important that the filename here has no extensions. Within your
directory, this function has now saved 2 files: SavedEmulator.RData is
the Rlist and SavedEmulator_mogp is the Python object.
To load an emulator, both the R and Python objects have to exist in the
directory (so you would be loading an emulator that was saved using
save_ExUQmogp). Removing TestEm to check the call we have
rm(TestEm)
TestEm <- load_ExUQmogp("SavedEmulator")
Checking successful loading:
newDesign <- 2*randomLHS(100,3)-1
preds <- TestEm$mogp$predict(newDesign, deriv=FALSE)
preds$mean
## [,1] [,2] [,3] [,4] [,5]
## [1,] 306.0219210 305.6973448 306.144318 307.0975281 306.9691922
## [2,] -0.4211346 -0.2650799 -0.367697 -0.5929042 -0.5568523
## [,6] [,7] [,8] [,9] [,10]
## [1,] 306.4532875 306.0202856 306.0838060 306.622345 306.6490272
## [2,] -0.4266806 -0.3056318 -0.3671993 -0.570047 -0.4978625
## [,11] [,12] [,13] [,14] [,15]
## [1,] 305.6230907 306.705455 305.6879727 307.1867569 306.0159737
## [2,] -0.3376158 -0.586262 -0.3585313 -0.7057138 -0.3890823
## [,16] [,17] [,18] [,19] [,20] [,21]
## [1,] 306.2738743 307.2678981 306.865798 306.371496 306.2564627 305.5319005
## [2,] -0.3160374 -0.6698086 -0.550335 -0.426089 -0.3328492 -0.2768971
## [,22] [,23] [,24] [,25] [,26] [,27]
## [1,] 306.181522 305.9641676 307.500365 306.0692302 306.2758465 306.3419297
## [2,] -0.428255 -0.3156126 -0.740213 -0.2843562 -0.4300289 -0.3501341
## [,28] [,29] [,30] [,31] [,32]
## [1,] 307.5079830 306.3665314 306.7579515 306.063023 306.3223994
## [2,] -0.6627713 -0.4396135 -0.5207699 -0.363739 -0.4159208
## [,33] [,34] [,35] [,36] [,37]
## [1,] 307.2215527 306.4134038 306.4237424 306.1211470 306.3717343
## [2,] -0.5843013 -0.3825297 -0.5000733 -0.3124263 -0.4045056
## [,38] [,39] [,40] [,41] [,42]
## [1,] 306.4958289 306.3426264 305.8842076 306.9088159 306.2090776
## [2,] -0.4911815 -0.4382054 -0.3229232 -0.6209635 -0.4545825
## [,43] [,44] [,45] [,46] [,47]
## [1,] 306.772238 305.5587501 306.2015189 306.2637945 306.6474336
## [2,] -0.611154 -0.3006546 -0.4098229 -0.3960148 -0.5600961
## [,48] [,49] [,50] [,51] [,52]
## [1,] 306.3807224 305.7335969 306.9181139 306.9030389 306.5360954
## [2,] -0.4576071 -0.3559677 -0.6199427 -0.5368094 -0.5198106
## [,53] [,54] [,55] [,56] [,57]
## [1,] 306.1075841 306.1644704 306.3678451 306.2562300 306.4275328
## [2,] -0.3967853 -0.4348353 -0.3354886 -0.4467822 -0.4722101
## [,58] [,59] [,60] [,61] [,62]
## [1,] 305.7236535 306.6222794 306.2840862 306.1085230 306.4205846
## [2,] -0.3650616 -0.5390255 -0.3439853 -0.3949218 -0.4543767
## [,63] [,64] [,65] [,66] [,67]
## [1,] 306.1708736 306.3013217 306.6269890 306.1652252 307.1543438
## [2,] -0.3480482 -0.4508516 -0.5426265 -0.3457983 -0.6266626
## [,68] [,69] [,70] [,71] [,72]
## [1,] 306.2890326 307.1977145 306.5703311 306.7116652 306.7436590
## [2,] -0.2781456 -0.6620267 -0.4098238 -0.5187069 -0.5675807
## [,73] [,74] [,75] [,76] [,77]
## [1,] 307.1136562 306.6425476 307.5166046 306.810018 306.3892351
## [2,] -0.5894745 -0.4921519 -0.6906846 -0.608987 -0.4602103
## [,78] [,79] [,80] [,81] [,82]
## [1,] 306.1848345 306.3061682 307.3602148 306.8057425 306.0801601
## [2,] -0.4103142 -0.4113795 -0.7373195 -0.6016241 -0.3136388
## [,83] [,84] [,85] [,86] [,87]
## [1,] 307.5806088 306.4744420 306.1908663 306.9205447 306.5459928
## [2,] -0.7210212 -0.5091084 -0.4284909 -0.6349606 -0.4854509
## [,88] [,89] [,90] [,91] [,92]
## [1,] 305.4927634 306.4127937 306.1494338 306.6147497 306.1759965
## [2,] -0.2819085 -0.3731011 -0.3669052 -0.5148394 -0.4108669
## [,93] [,94] [,95] [,96] [,97]
## [1,] 306.460895 306.1331615 307.2662352 307.1437253 306.2739008
## [2,] -0.469893 -0.4234563 -0.6167827 -0.7004072 -0.4690272
## [,98] [,99] [,100]
## [1,] 306.3113668 306.5578412 306.5973617
## [2,] -0.4799531 -0.4899127 -0.5066341