Function to extrapolate missing years in MAgPIE objects.
Usage
time_interpolate(
dataset,
interpolated_year,
integrate_interpolated_years = FALSE,
extrapolation_type = "linear"
)Arguments
- dataset
An MAgPIE object
- interpolated_year
Vector of years, of which values are required. Can be in the formats 1999 or y1999.
- integrate_interpolated_years
FALSE returns only the dataset of the interpolated year, TRUE returns the whole dataset, including all years of data and the itnerpolated year
- extrapolation_type
Determines what happens if extrapolation is required, i.e. if a requested year lies outside the range of years in
dataset. Specify "linear" for a linear extrapolation. "constant" uses the value from dataset closest in time to the requested year.
Value
Uses linear extrapolation to estimate the values of the interpolated year, using the values of the two surrounding years. If the value is before or after the years in data, the two closest neighbours are used for extrapolation.
See also
Other TemporalOperations:
commonYears(),
convergence(),
lowpass()
Examples
p <- maxample("pop")
time_interpolate(p, "y2000", integrate = TRUE)
#> , , scenario = A2
#>
#> t
#> i y1995 y2000 y2005 y2015 y2025 y2035 y2045
#> AFR 552.6664 624.5532 696.44 889.18 1124.11 1389.33 1659.73
#> CPA 1280.6350 1355.0825 1429.53 1518.46 1592.09 1640.95 1671.94
#> EUR 554.4384 568.3992 582.36 593.76 605.27 614.58 618.97
#> FSU 276.3431 285.8616 295.38 302.62 308.59 313.30 315.72
#> LAM 451.9981 505.1440 558.29 646.02 733.13 812.69 880.98
#> MEA 277.7437 333.9619 390.18 489.22 596.13 698.33 790.61
#> NAM 292.1132 309.1016 326.09 353.25 382.53 409.44 431.12
#> PAO 133.7772 142.8886 152.00 155.27 157.35 158.81 159.70
#> PAS 383.2277 458.9788 534.73 604.94 668.49 723.13 767.30
#> SAS 1269.9243 1387.4722 1505.02 1796.76 2095.48 2369.60 2600.68
#> t
#> i y2055 y2065 y2075 y2085 y2095 y2105 y2115 y2125
#> AFR 1924.19 2172.30 2387.96 2560.32 2671.07 2708.86 2708.86 2708.86
#> CPA 1691.24 1719.25 1765.77 1832.31 1918.47 1965.05 1965.05 1965.05
#> EUR 619.37 618.74 622.03 630.52 642.20 648.98 648.98 648.98
#> FSU 317.36 319.61 322.30 327.08 332.39 334.66 334.66 334.66
#> LAM 939.44 989.54 1035.25 1079.39 1117.61 1134.64 1134.64 1134.64
#> MEA 871.83 942.81 1002.56 1052.19 1088.94 1103.31 1103.31 1103.31
#> NAM 448.70 465.22 481.39 494.88 505.93 511.41 511.41 511.41
#> PAO 160.45 160.95 161.47 163.53 166.31 167.49 167.49 167.49
#> PAS 798.68 819.21 834.31 844.38 843.52 839.53 839.53 839.53
#> SAS 2783.75 2920.70 3006.60 3040.10 3007.86 2972.39 2972.39 2972.39
#> t
#> i y2135 y2145
#> AFR 2708.86 2708.86
#> CPA 1965.05 1965.05
#> EUR 648.98 648.98
#> FSU 334.66 334.66
#> LAM 1134.64 1134.64
#> MEA 1103.31 1103.31
#> NAM 511.41 511.41
#> PAO 167.49 167.49
#> PAS 839.53 839.53
#> SAS 2972.39 2972.39
#>
#> , , scenario = B1
#>
#> t
#> i y1995 y2000 y2005 y2015 y2025 y2035 y2045
#> AFR 552.6664 637.2582 721.85 932.04 1118.33 1267.33 1383.24
#> CPA 1280.6350 1354.9475 1429.26 1499.74 1531.12 1518.73 1463.68
#> EUR 554.4384 570.8242 587.21 603.63 613.98 619.48 617.12
#> FSU 276.3431 286.5916 296.84 305.26 309.78 311.47 309.03
#> LAM 451.9981 502.3940 552.79 623.20 681.60 723.44 747.70
#> MEA 277.7437 338.3319 398.92 502.51 598.73 682.80 754.14
#> NAM 292.1132 308.5766 325.04 349.85 376.11 399.68 418.70
#> PAO 133.7772 143.4236 153.07 157.37 159.07 159.51 158.10
#> PAS 383.2277 456.9488 530.67 590.42 639.68 674.98 692.45
#> SAS 1269.9243 1372.7822 1475.64 1687.80 1870.96 1999.15 2072.68
#> t
#> i y2055 y2065 y2075 y2085 y2095 y2105 y2115 y2125
#> AFR 1469.16 1510.27 1505.16 1454.54 1361.24 1304.59 1304.59 1304.59
#> CPA 1370.97 1257.23 1139.25 1021.52 904.61 846.50 846.50 846.50
#> EUR 606.77 592.52 579.18 567.73 554.61 547.06 547.06 547.06
#> FSU 301.99 292.46 281.39 269.77 257.52 251.04 251.04 251.04
#> LAM 753.98 743.05 718.79 683.68 637.69 611.88 611.88 611.88
#> MEA 811.59 849.11 865.89 861.01 831.23 809.60 809.60 809.60
#> NAM 434.27 449.98 468.05 486.99 503.86 511.44 511.44 511.44
#> PAO 155.21 151.86 148.08 144.47 140.82 138.80 138.80 138.80
#> PAS 689.79 668.98 634.64 590.05 536.24 507.06 507.06 507.06
#> SAS 2090.96 2049.18 1953.77 1811.83 1629.07 1528.15 1528.15 1528.15
#> t
#> i y2135 y2145
#> AFR 1304.59 1304.59
#> CPA 846.50 846.50
#> EUR 547.06 547.06
#> FSU 251.04 251.04
#> LAM 611.88 611.88
#> MEA 809.60 809.60
#> NAM 511.44 511.44
#> PAO 138.80 138.80
#> PAS 507.06 507.06
#> SAS 1528.15 1528.15
#>
time_interpolate(p, c("y1980", "y2000"), integrate = TRUE, extrapolation_type = "constant")
#> , , scenario = A2
#>
#> t
#> i y1980 y1995 y2000 y2005 y2015 y2025 y2035
#> AFR 552.6664 552.6664 624.5532 696.44 889.18 1124.11 1389.33
#> CPA 1280.6350 1280.6350 1355.0825 1429.53 1518.46 1592.09 1640.95
#> EUR 554.4384 554.4384 568.3992 582.36 593.76 605.27 614.58
#> FSU 276.3431 276.3431 285.8616 295.38 302.62 308.59 313.30
#> LAM 451.9981 451.9981 505.1440 558.29 646.02 733.13 812.69
#> MEA 277.7437 277.7437 333.9619 390.18 489.22 596.13 698.33
#> NAM 292.1132 292.1132 309.1016 326.09 353.25 382.53 409.44
#> PAO 133.7772 133.7772 142.8886 152.00 155.27 157.35 158.81
#> PAS 383.2277 383.2277 458.9788 534.73 604.94 668.49 723.13
#> SAS 1269.9243 1269.9243 1387.4722 1505.02 1796.76 2095.48 2369.60
#> t
#> i y2045 y2055 y2065 y2075 y2085 y2095 y2105 y2115
#> AFR 1659.73 1924.19 2172.30 2387.96 2560.32 2671.07 2708.86 2708.86
#> CPA 1671.94 1691.24 1719.25 1765.77 1832.31 1918.47 1965.05 1965.05
#> EUR 618.97 619.37 618.74 622.03 630.52 642.20 648.98 648.98
#> FSU 315.72 317.36 319.61 322.30 327.08 332.39 334.66 334.66
#> LAM 880.98 939.44 989.54 1035.25 1079.39 1117.61 1134.64 1134.64
#> MEA 790.61 871.83 942.81 1002.56 1052.19 1088.94 1103.31 1103.31
#> NAM 431.12 448.70 465.22 481.39 494.88 505.93 511.41 511.41
#> PAO 159.70 160.45 160.95 161.47 163.53 166.31 167.49 167.49
#> PAS 767.30 798.68 819.21 834.31 844.38 843.52 839.53 839.53
#> SAS 2600.68 2783.75 2920.70 3006.60 3040.10 3007.86 2972.39 2972.39
#> t
#> i y2125 y2135 y2145
#> AFR 2708.86 2708.86 2708.86
#> CPA 1965.05 1965.05 1965.05
#> EUR 648.98 648.98 648.98
#> FSU 334.66 334.66 334.66
#> LAM 1134.64 1134.64 1134.64
#> MEA 1103.31 1103.31 1103.31
#> NAM 511.41 511.41 511.41
#> PAO 167.49 167.49 167.49
#> PAS 839.53 839.53 839.53
#> SAS 2972.39 2972.39 2972.39
#>
#> , , scenario = B1
#>
#> t
#> i y1980 y1995 y2000 y2005 y2015 y2025 y2035
#> AFR 552.6664 552.6664 637.2582 721.85 932.04 1118.33 1267.33
#> CPA 1280.6350 1280.6350 1354.9475 1429.26 1499.74 1531.12 1518.73
#> EUR 554.4384 554.4384 570.8242 587.21 603.63 613.98 619.48
#> FSU 276.3431 276.3431 286.5916 296.84 305.26 309.78 311.47
#> LAM 451.9981 451.9981 502.3940 552.79 623.20 681.60 723.44
#> MEA 277.7437 277.7437 338.3319 398.92 502.51 598.73 682.80
#> NAM 292.1132 292.1132 308.5766 325.04 349.85 376.11 399.68
#> PAO 133.7772 133.7772 143.4236 153.07 157.37 159.07 159.51
#> PAS 383.2277 383.2277 456.9488 530.67 590.42 639.68 674.98
#> SAS 1269.9243 1269.9243 1372.7822 1475.64 1687.80 1870.96 1999.15
#> t
#> i y2045 y2055 y2065 y2075 y2085 y2095 y2105 y2115
#> AFR 1383.24 1469.16 1510.27 1505.16 1454.54 1361.24 1304.59 1304.59
#> CPA 1463.68 1370.97 1257.23 1139.25 1021.52 904.61 846.50 846.50
#> EUR 617.12 606.77 592.52 579.18 567.73 554.61 547.06 547.06
#> FSU 309.03 301.99 292.46 281.39 269.77 257.52 251.04 251.04
#> LAM 747.70 753.98 743.05 718.79 683.68 637.69 611.88 611.88
#> MEA 754.14 811.59 849.11 865.89 861.01 831.23 809.60 809.60
#> NAM 418.70 434.27 449.98 468.05 486.99 503.86 511.44 511.44
#> PAO 158.10 155.21 151.86 148.08 144.47 140.82 138.80 138.80
#> PAS 692.45 689.79 668.98 634.64 590.05 536.24 507.06 507.06
#> SAS 2072.68 2090.96 2049.18 1953.77 1811.83 1629.07 1528.15 1528.15
#> t
#> i y2125 y2135 y2145
#> AFR 1304.59 1304.59 1304.59
#> CPA 846.50 846.50 846.50
#> EUR 547.06 547.06 547.06
#> FSU 251.04 251.04 251.04
#> LAM 611.88 611.88 611.88
#> MEA 809.60 809.60 809.60
#> NAM 511.44 511.44 511.44
#> PAO 138.80 138.80 138.80
#> PAS 507.06 507.06 507.06
#> SAS 1528.15 1528.15 1528.15
#>