data=read.csv("D:/karpur.csv")
library(caret)
## Warning: package 'caret' was built under R version 4.4.2
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.4.2
## Loading required package: lattice
model_a<- lm(k.core~ .-Facies,data=data)
summary(model_a)
##
## Call:
## lm(formula = k.core ~ . - Facies, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5549.5 -755.5 -178.1 578.0 11260.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60762.728 16605.360 3.659 0.000269 ***
## depth -7.398 1.446 -5.115 3.92e-07 ***
## caliper -3955.952 1055.105 -3.749 0.000190 ***
## ind.deep -14.183 2.345 -6.048 2.24e-09 ***
## ind.med 17.300 2.509 6.896 1.08e-11 ***
## gamma -77.487 5.475 -14.153 < 2e-16 ***
## phi.N -1784.704 1301.772 -1.371 0.170763
## R.deep -26.007 6.974 -3.729 0.000206 ***
## R.med 63.525 9.841 6.455 1.86e-10 ***
## SP -8.784 3.460 -2.539 0.011313 *
## density.corr -523.060 5358.876 -0.098 0.922269
## density 8011.106 1120.554 7.149 1.96e-12 ***
## phi.core 18320.336 2380.161 7.697 4.07e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1442 on 806 degrees of freedom
## Multiple R-squared: 0.5903, Adjusted R-squared: 0.5842
## F-statistic: 96.77 on 12 and 806 DF, p-value: < 2.2e-16
k.predicted_a <-predict(model_a,data=data)
plot(k.predicted_a,data$k.core)
rmse_a<- RMSE(k.predicted_a,data$k.core )
rmse_a
## [1] 1430.118
model_b<-step(model_a , direction = "backward")
## Start: AIC=11926.91
## k.core ~ (depth + caliper + ind.deep + ind.med + gamma + phi.N +
## R.deep + R.med + SP + density.corr + density + phi.core +
## Facies) - Facies
##
## Df Sum of Sq RSS AIC
## - density.corr 1 19799 1675068713 11925
## - phi.N 1 3906205 1678955118 11927
## <none> 1675048914 11927
## - SP 1 13394190 1688443104 11931
## - R.deep 1 28897686 1703946599 11939
## - caliper 1 29214826 1704263740 11939
## - depth 1 54372650 1729421563 11951
## - ind.deep 1 76022788 1751071701 11961
## - R.med 1 86603706 1761652619 11966
## - ind.med 1 98823752 1773872666 11972
## - density 1 106221406 1781270319 11975
## - phi.core 1 123125117 1798174031 11983
## - gamma 1 416312526 2091361440 12107
##
## Step: AIC=11924.92
## k.core ~ depth + caliper + ind.deep + ind.med + gamma + phi.N +
## R.deep + R.med + SP + density + phi.core
##
## Df Sum of Sq RSS AIC
## <none> 1675068713 11925
## - phi.N 1 4564880 1679633593 11925
## - SP 1 13491079 1688559792 11930
## - R.deep 1 28896144 1703964857 11937
## - caliper 1 29253869 1704322581 11937
## - depth 1 54825159 1729893872 11949
## - ind.deep 1 77573926 1752642639 11960
## - R.med 1 86772220 1761840933 11964
## - ind.med 1 100740701 1775809413 11971
## - density 1 114209586 1789278299 11977
## - phi.core 1 124694278 1799762991 11982
## - gamma 1 417015194 2092083907 12105
summary(model_b)
##
## Call:
## lm(formula = k.core ~ depth + caliper + ind.deep + ind.med +
## gamma + phi.N + R.deep + R.med + SP + density + phi.core,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5545.3 -753.4 -177.1 576.8 11260.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60910.619 16525.937 3.686 0.000243 ***
## depth -7.409 1.442 -5.139 3.46e-07 ***
## caliper -3957.892 1054.270 -3.754 0.000186 ***
## ind.deep -14.146 2.314 -6.113 1.52e-09 ***
## ind.med 17.263 2.478 6.967 6.74e-12 ***
## gamma -77.461 5.465 -14.174 < 2e-16 ***
## phi.N -1825.771 1231.150 -1.483 0.138470
## R.deep -25.972 6.961 -3.731 0.000204 ***
## R.med 63.466 9.816 6.466 1.75e-10 ***
## SP -8.803 3.453 -2.549 0.010974 *
## density 7980.761 1075.902 7.418 3.02e-13 ***
## phi.core 18343.648 2366.693 7.751 2.75e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1441 on 807 degrees of freedom
## Multiple R-squared: 0.5903, Adjusted R-squared: 0.5847
## F-statistic: 105.7 on 11 and 807 DF, p-value: < 2.2e-16
k.predicted_b <-predict(model_b,data=data)
plot(k.predicted_b,data$k.core)
rmse_b<- RMSE(k.predicted_b,data$k.core )
rmse_b
## [1] 1430.126
model_c<- lm(k.core~ .,data=data)
summary(model_c)
##
## Call:
## lm(formula = k.core ~ ., data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5585.6 -568.9 49.2 476.5 8928.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.783e+04 1.760e+04 -3.853 0.000126 ***
## depth 8.544e+00 1.785e+00 4.786 2.02e-06 ***
## caliper 1.413e+03 1.019e+03 1.387 0.165789
## ind.deep -2.418e-01 2.354e+00 -0.103 0.918220
## ind.med 1.224e+00 2.585e+00 0.473 0.636062
## gamma -4.583e+01 6.010e+00 -7.626 6.88e-14 ***
## phi.N -2.010e+03 1.476e+03 -1.362 0.173540
## R.deep -2.344e+01 6.288e+00 -3.727 0.000207 ***
## R.med 5.643e+01 9.065e+00 6.225 7.76e-10 ***
## SP -7.125e+00 3.145e+00 -2.266 0.023736 *
## density.corr -2.567e+03 4.809e+03 -0.534 0.593602
## density 2.319e+03 1.173e+03 1.976 0.048458 *
## phi.core 1.921e+04 2.282e+03 8.418 < 2e-16 ***
## FaciesF10 8.921e+02 3.590e+02 2.485 0.013157 *
## FaciesF2 9.243e+02 5.818e+02 1.589 0.112514
## FaciesF3 4.393e+02 3.344e+02 1.313 0.189394
## FaciesF5 7.411e+02 3.428e+02 2.162 0.030908 *
## FaciesF7 -4.152e+01 5.742e+02 -0.072 0.942377
## FaciesF8 -1.179e+03 3.927e+02 -3.002 0.002770 **
## FaciesF9 -2.969e+03 4.298e+02 -6.908 1.00e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1262 on 799 degrees of freedom
## Multiple R-squared: 0.6889, Adjusted R-squared: 0.6815
## F-statistic: 93.12 on 19 and 799 DF, p-value: < 2.2e-16
k.predicted_c <-predict(model_c,data=data)
plot(k.predicted_c,data$k.core)
rmse_c<- RMSE(k.predicted_c,data$k.core )
rmse_c
## [1] 1246.201
model_d<-step(model_c , direction = "backward")
## Start: AIC=11715.43
## k.core ~ depth + caliper + ind.deep + ind.med + gamma + phi.N +
## R.deep + R.med + SP + density.corr + density + phi.core +
## Facies
##
## Df Sum of Sq RSS AIC
## - ind.deep 1 16793 1271937992 11713
## - ind.med 1 356746 1272277945 11714
## - density.corr 1 453661 1272374861 11714
## - phi.N 1 2953609 1274874809 11715
## - caliper 1 3063007 1274984206 11715
## <none> 1271921199 11715
## - density 1 6217927 1278139127 11717
## - SP 1 8171834 1280093033 11719
## - R.deep 1 22117394 1294038593 11728
## - depth 1 36466976 1308388176 11737
## - R.med 1 61690461 1333611660 11752
## - gamma 1 92579723 1364500923 11771
## - phi.core 1 112793101 1384714301 11783
## - Facies 7 403127714 1675048914 11927
##
## Step: AIC=11713.44
## k.core ~ depth + caliper + ind.med + gamma + phi.N + R.deep +
## R.med + SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - density.corr 1 437546 1272375538 11712
## - phi.N 1 2938766 1274876758 11713
## - caliper 1 3074396 1275012389 11713
## <none> 1271937992 11713
## - density 1 6228928 1278166920 11715
## - ind.med 1 6905855 1278843848 11716
## - SP 1 8191802 1280129794 11717
## - R.deep 1 22125695 1294063687 11726
## - depth 1 39139470 1311077462 11736
## - R.med 1 61773953 1333711946 11750
## - gamma 1 92865220 1364803212 11769
## - phi.core 1 112960440 1384898432 11781
## - Facies 7 479133709 1751071701 11961
##
## Step: AIC=11711.72
## k.core ~ depth + caliper + ind.med + gamma + phi.N + R.deep +
## R.med + SP + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - caliper 1 2980713 1275356252 11712
## <none> 1272375538 11712
## - phi.N 1 3279032 1275654571 11712
## - density 1 5792837 1278168375 11713
## - ind.med 1 6813959 1279189497 11714
## - SP 1 8391302 1280766840 11715
## - R.deep 1 22009402 1294384940 11724
## - depth 1 38705776 1311081314 11734
## - R.med 1 61436819 1333812357 11748
## - gamma 1 93974329 1366349868 11768
## - phi.core 1 115336515 1387712053 11781
## - Facies 7 480267100 1752642639 11960
##
## Step: AIC=11711.64
## k.core ~ depth + ind.med + gamma + phi.N + R.deep + R.med + SP +
## density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - phi.N 1 2534906 1277891157 11711
## <none> 1275356252 11712
## - density 1 7270311 1282626562 11714
## - SP 1 8733336 1284089587 11715
## - ind.med 1 12924050 1288280301 11718
## - R.deep 1 22449117 1297805369 11724
## - depth 1 51507476 1326863728 11742
## - R.med 1 60137982 1335494234 11747
## - phi.core 1 112564835 1387921086 11779
## - gamma 1 141535555 1416891807 11796
## - Facies 7 520094756 1795451008 11978
##
## Step: AIC=11711.26
## k.core ~ depth + ind.med + gamma + R.deep + R.med + SP + density +
## phi.core + Facies
##
## Df Sum of Sq RSS AIC
## <none> 1277891157 11711
## - density 1 5155969 1283047127 11713
## - SP 1 8515796 1286406953 11715
## - ind.med 1 10944937 1288836095 11716
## - R.deep 1 23273312 1301164469 11724
## - depth 1 49725248 1327616405 11740
## - R.med 1 59454645 1337345802 11746
## - phi.core 1 110154394 1388045551 11777
## - gamma 1 219059092 1496950249 11839
## - Facies 7 526383446 1804274603 11980
summary(model_d)
##
## Call:
## lm(formula = k.core ~ depth + ind.med + gamma + R.deep + R.med +
## SP + density + phi.core + Facies, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5608.3 -567.8 35.9 500.7 8989.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.322e+04 6.625e+03 -6.523 1.22e-10 ***
## depth 6.648e+00 1.189e+00 5.590 3.11e-08 ***
## ind.med 1.078e+00 4.111e-01 2.623 0.008894 **
## gamma -5.324e+01 4.537e+00 -11.733 < 2e-16 ***
## R.deep -2.395e+01 6.264e+00 -3.824 0.000141 ***
## R.med 5.515e+01 9.022e+00 6.112 1.53e-09 ***
## SP -7.214e+00 3.118e+00 -2.313 0.020960 *
## density 1.880e+03 1.044e+03 1.800 0.072240 .
## phi.core 1.817e+04 2.184e+03 8.320 3.77e-16 ***
## FaciesF10 8.266e+02 3.533e+02 2.340 0.019553 *
## FaciesF2 7.035e+02 5.567e+02 1.264 0.206697
## FaciesF3 4.100e+02 3.228e+02 1.270 0.204443
## FaciesF5 5.913e+02 3.211e+02 1.841 0.065924 .
## FaciesF7 -3.159e+02 5.402e+02 -0.585 0.558866
## FaciesF8 -1.455e+03 3.122e+02 -4.661 3.69e-06 ***
## FaciesF9 -3.017e+03 3.764e+02 -8.017 3.82e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1262 on 803 degrees of freedom
## Multiple R-squared: 0.6874, Adjusted R-squared: 0.6816
## F-statistic: 117.7 on 15 and 803 DF, p-value: < 2.2e-16
k.predicted_d <-predict(model_d,data=data)
plot(k.predicted_d,data$k.core)
rmse_d<- RMSE(k.predicted_d,data$k.core )
rmse_d
## [1] 1249.122
data$log10_k.core<-log10(data$k.core)
model_e<- lm(log10_k.core~.-k.core,data=data)
summary(model_e)
##
## Call:
## lm(formula = log10_k.core ~ . - k.core, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5804 -0.1138 0.0322 0.1529 0.7384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.3461877 4.6532000 -0.504 0.61425
## depth 0.0007425 0.0004718 1.574 0.11596
## caliper -0.4605945 0.2693103 -1.710 0.08760 .
## ind.deep -0.0007951 0.0006222 -1.278 0.20168
## ind.med 0.0007137 0.0006833 1.044 0.29659
## gamma -0.0091269 0.0015885 -5.746 1.30e-08 ***
## phi.N -1.7628155 0.3901024 -4.519 7.16e-06 ***
## R.deep -0.0025878 0.0016620 -1.557 0.11987
## R.med 0.0044073 0.0023960 1.839 0.06622 .
## SP -0.0016935 0.0008312 -2.037 0.04194 *
## density.corr 1.4462633 1.2712045 1.138 0.25558
## density 1.6148374 0.3100921 5.208 2.44e-07 ***
## phi.core 9.4863406 0.6032903 15.724 < 2e-16 ***
## FaciesF10 0.0786460 0.0948909 0.829 0.40746
## FaciesF2 -0.0184334 0.1537793 -0.120 0.90462
## FaciesF3 -0.0307548 0.0883957 -0.348 0.72799
## FaciesF5 0.1094193 0.0906034 1.208 0.22753
## FaciesF7 0.2811620 0.1517797 1.852 0.06433 .
## FaciesF8 -0.0976234 0.1038054 -0.940 0.34727
## FaciesF9 -0.3562116 0.1135966 -3.136 0.00178 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3335 on 799 degrees of freedom
## Multiple R-squared: 0.6806, Adjusted R-squared: 0.673
## F-statistic: 89.6 on 19 and 799 DF, p-value: < 2.2e-16
log_k.predicted_e <-predict(model_e,data=data)
k.predicted_e<-10^log_k.predicted_e
plot(k.predicted_e,data$k.core)
rmse_e<- RMSE(k.predicted_e,data$k.core )
rmse_e
## [1] 1333.017
model_f<-step(model_e, direction = "backward")
## Start: AIC=-1779.02
## log10_k.core ~ (depth + caliper + ind.deep + ind.med + gamma +
## phi.N + R.deep + R.med + SP + density.corr + density + phi.core +
## k.core + Facies) - k.core
##
## Df Sum of Sq RSS AIC
## - ind.med 1 0.1213 88.981 -1779.9
## - density.corr 1 0.1440 89.004 -1779.7
## - ind.deep 1 0.1816 89.042 -1779.3
## <none> 88.860 -1779.0
## - R.deep 1 0.2696 89.130 -1778.5
## - depth 1 0.2754 89.135 -1778.5
## - caliper 1 0.3253 89.185 -1778.0
## - R.med 1 0.3763 89.236 -1777.6
## - SP 1 0.4617 89.322 -1776.8
## - phi.N 1 2.2710 91.131 -1760.3
## - density 1 3.0160 91.876 -1753.7
## - gamma 1 3.6713 92.531 -1747.9
## - Facies 7 7.0758 95.936 -1730.3
## - phi.core 1 27.4982 116.358 -1560.2
##
## Step: AIC=-1779.9
## log10_k.core ~ depth + caliper + ind.deep + gamma + phi.N + R.deep +
## R.med + SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - density.corr 1 0.1931 89.174 -1780.1
## <none> 88.981 -1779.9
## - ind.deep 1 0.2179 89.199 -1779.9
## - R.deep 1 0.2447 89.226 -1779.7
## - caliper 1 0.2921 89.273 -1779.2
## - R.med 1 0.3397 89.321 -1778.8
## - SP 1 0.4101 89.391 -1778.1
## - depth 1 0.4622 89.444 -1777.7
## - phi.N 1 2.2035 91.185 -1761.9
## - density 1 3.0113 91.993 -1754.6
## - gamma 1 3.5761 92.557 -1749.6
## - Facies 7 9.1242 98.106 -1714.0
## - phi.core 1 27.4190 116.400 -1561.9
##
## Step: AIC=-1780.12
## log10_k.core ~ depth + caliper + ind.deep + gamma + phi.N + R.deep +
## R.med + SP + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - ind.deep 1 0.2180 89.392 -1780.1
## <none> 89.174 -1780.1
## - R.deep 1 0.2526 89.427 -1779.8
## - caliper 1 0.2676 89.442 -1779.7
## - R.med 1 0.3598 89.534 -1778.8
## - SP 1 0.3832 89.558 -1778.6
## - depth 1 0.5404 89.715 -1777.2
## - phi.N 1 2.0726 91.247 -1763.3
## - gamma 1 3.4838 92.658 -1750.7
## - density 1 3.6220 92.796 -1749.5
## - Facies 7 9.3567 98.531 -1712.4
## - phi.core 1 27.2273 116.402 -1563.9
##
## Step: AIC=-1780.12
## log10_k.core ~ depth + caliper + gamma + phi.N + R.deep + R.med +
## SP + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## <none> 89.392 -1780.1
## - R.deep 1 0.2869 89.679 -1779.5
## - depth 1 0.3332 89.726 -1779.1
## - SP 1 0.4296 89.822 -1778.2
## - R.med 1 0.5085 89.901 -1777.5
## - caliper 1 0.5746 89.967 -1776.9
## - phi.N 1 2.3337 91.726 -1761.0
## - gamma 1 3.8214 93.214 -1747.8
## - density 1 3.8626 93.255 -1747.5
## - Facies 7 9.2100 98.602 -1713.8
## - phi.core 1 27.0935 116.486 -1565.3
summary(model_f)
##
## Call:
## lm(formula = log10_k.core ~ depth + caliper + gamma + phi.N +
## R.deep + R.med + SP + density + phi.core + Facies, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.58182 -0.12001 0.03437 0.15230 0.70317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2671250 3.9316796 -0.322 0.74732
## depth 0.0006562 0.0003796 1.729 0.08420 .
## caliper -0.5608681 0.2470292 -2.270 0.02344 *
## gamma -0.0091497 0.0015626 -5.855 6.94e-09 ***
## phi.N -1.7463527 0.3816550 -4.576 5.50e-06 ***
## R.deep -0.0026554 0.0016551 -1.604 0.10903
## R.med 0.0049837 0.0023334 2.136 0.03300 *
## SP -0.0016140 0.0008221 -1.963 0.04996 *
## density 1.7602255 0.2990153 5.887 5.79e-09 ***
## phi.core 9.2753944 0.5949259 15.591 < 2e-16 ***
## FaciesF10 0.0896953 0.0945929 0.948 0.34330
## FaciesF2 0.0152576 0.1523676 0.100 0.92026
## FaciesF3 -0.0292379 0.0869197 -0.336 0.73667
## FaciesF5 0.1022238 0.0879087 1.163 0.24524
## FaciesF7 0.2794793 0.1462763 1.911 0.05641 .
## FaciesF8 -0.0932936 0.0927473 -1.006 0.31477
## FaciesF9 -0.3877078 0.1030388 -3.763 0.00018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3339 on 802 degrees of freedom
## Multiple R-squared: 0.6787, Adjusted R-squared: 0.6722
## F-statistic: 105.9 on 16 and 802 DF, p-value: < 2.2e-16
log_k.predicted_f <-predict(model_f,data=data)
k.predicted_f<-10^log_k.predicted_f
plot(k.predicted_f,data$k.core)
rmse_f<- RMSE(k.predicted_f,data$k.core )
rmse_f
## [1] 1330.932
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
set.seed(12345)
training<-sample_frac(data, .82)
testing<-anti_join(data,training)
## Joining with `by = join_by(depth, caliper, ind.deep, ind.med, gamma, phi.N,
## R.deep, R.med, SP, density.corr, density, phi.core, k.core, Facies,
## log10_k.core)`
model_g<- lm(log10_k.core~.-k.core,data=training)
summary(model_g)
##
## Call:
## lm(formula = log10_k.core ~ . - k.core, data = training)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.55478 -0.11698 0.02329 0.14872 0.80204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.6365007 5.3205904 -0.496 0.6204
## depth 0.0007139 0.0005446 1.311 0.1904
## caliper -0.4109392 0.3073498 -1.337 0.1817
## ind.deep -0.0006656 0.0007036 -0.946 0.3444
## ind.med 0.0006016 0.0007714 0.780 0.4358
## gamma -0.0084735 0.0018287 -4.634 4.34e-06 ***
## phi.N -1.7742527 0.4461085 -3.977 7.75e-05 ***
## R.deep -0.0023893 0.0019565 -1.221 0.2224
## R.med 0.0042790 0.0027986 1.529 0.1268
## SP -0.0021851 0.0009616 -2.272 0.0234 *
## density.corr 1.9845684 1.3939574 1.424 0.1550
## density 1.5690196 0.3506331 4.475 9.02e-06 ***
## phi.core 9.6978276 0.6843073 14.172 < 2e-16 ***
## FaciesF10 0.0824253 0.1078316 0.764 0.4449
## FaciesF2 -0.0017897 0.1656360 -0.011 0.9914
## FaciesF3 -0.0640161 0.1021388 -0.627 0.5310
## FaciesF5 0.1484119 0.1049353 1.414 0.1577
## FaciesF7 0.3359393 0.1651666 2.034 0.0424 *
## FaciesF8 -0.0783083 0.1201574 -0.652 0.5148
## FaciesF9 -0.3328250 0.1339958 -2.484 0.0132 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3435 on 652 degrees of freedom
## Multiple R-squared: 0.6706, Adjusted R-squared: 0.661
## F-statistic: 69.87 on 19 and 652 DF, p-value: < 2.2e-16
log_k.predicted_g <-predict(model_g,newdata=testing)
k.predicted_g<-10^log_k.predicted_g
plot(k.predicted_g,testing$k.core)
rmse_g<- RMSE(k.predicted_g,testing$k.core )
rmse_g
## [1] 1645.338
model_h<-step(model_g, direction = "backward")
## Start: AIC=-1416.42
## log10_k.core ~ (depth + caliper + ind.deep + ind.med + gamma +
## phi.N + R.deep + R.med + SP + density.corr + density + phi.core +
## k.core + Facies) - k.core
##
## Df Sum of Sq RSS AIC
## - ind.med 1 0.0718 77.007 -1417.8
## - ind.deep 1 0.1056 77.041 -1417.5
## - R.deep 1 0.1760 77.112 -1416.9
## - depth 1 0.2028 77.138 -1416.7
## - caliper 1 0.2109 77.146 -1416.6
## <none> 76.936 -1416.4
## - density.corr 1 0.2392 77.175 -1416.3
## - R.med 1 0.2758 77.211 -1416.0
## - SP 1 0.6093 77.545 -1413.1
## - phi.N 1 1.8665 78.802 -1402.3
## - density 1 2.3628 79.298 -1398.1
## - gamma 1 2.5336 79.469 -1396.7
## - Facies 7 6.1763 83.112 -1378.5
## - phi.core 1 23.6988 100.634 -1238.0
##
## Step: AIC=-1417.79
## log10_k.core ~ depth + caliper + ind.deep + gamma + phi.N + R.deep +
## R.med + SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - ind.deep 1 0.1210 77.128 -1418.7
## - R.deep 1 0.1601 77.167 -1418.4
## - caliper 1 0.1863 77.194 -1418.2
## <none> 77.007 -1417.8
## - R.med 1 0.2529 77.260 -1417.6
## - density.corr 1 0.2918 77.299 -1417.2
## - depth 1 0.3233 77.331 -1417.0
## - SP 1 0.5694 77.577 -1414.8
## - phi.N 1 1.8293 78.837 -1404.0
## - density 1 2.3684 79.376 -1399.4
## - gamma 1 2.4687 79.476 -1398.6
## - Facies 7 7.4395 84.447 -1369.8
## - phi.core 1 23.6373 100.645 -1239.9
##
## Step: AIC=-1418.74
## log10_k.core ~ depth + caliper + gamma + phi.N + R.deep + R.med +
## SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - R.deep 1 0.1820 77.310 -1419.2
## - depth 1 0.2094 77.338 -1418.9
## <none> 77.128 -1418.7
## - density.corr 1 0.2919 77.420 -1418.2
## - R.med 1 0.3458 77.474 -1417.7
## - caliper 1 0.3678 77.496 -1417.5
## - SP 1 0.6185 77.747 -1415.4
## - phi.N 1 2.0321 79.160 -1403.3
## - density 1 2.5162 79.644 -1399.2
## - gamma 1 2.6862 79.814 -1397.7
## - Facies 7 7.3691 84.497 -1371.4
## - phi.core 1 23.5998 100.728 -1241.3
##
## Step: AIC=-1419.15
## log10_k.core ~ depth + caliper + gamma + phi.N + R.med + SP +
## density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - depth 1 0.1470 77.457 -1419.9
## <none> 77.310 -1419.2
## - density.corr 1 0.2997 77.610 -1418.5
## - R.med 1 0.3342 77.645 -1418.2
## - caliper 1 0.3692 77.680 -1418.0
## - SP 1 0.5314 77.842 -1416.5
## - phi.N 1 2.1431 79.453 -1402.8
## - density 1 2.4020 79.712 -1400.6
## - gamma 1 2.5938 79.904 -1399.0
## - Facies 7 7.3252 84.636 -1372.3
## - phi.core 1 24.2519 101.562 -1237.8
##
## Step: AIC=-1419.88
## log10_k.core ~ caliper + gamma + phi.N + R.med + SP + density.corr +
## density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - R.med 1 0.1954 77.653 -1420.2
## <none> 77.457 -1419.9
## - density.corr 1 0.3571 77.814 -1418.8
## - SP 1 0.5280 77.985 -1417.3
## - caliper 1 1.3604 78.818 -1410.2
## - phi.N 1 1.9971 79.454 -1404.8
## - density 1 2.8427 80.300 -1397.7
## - gamma 1 4.0732 81.530 -1387.4
## - Facies 7 10.3135 87.771 -1349.9
## - phi.core 1 24.6275 102.085 -1236.3
##
## Step: AIC=-1420.18
## log10_k.core ~ caliper + gamma + phi.N + SP + density.corr +
## density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## <none> 77.653 -1420.2
## - density.corr 1 0.3665 78.019 -1419.0
## - SP 1 0.6846 78.337 -1416.3
## - caliper 1 1.5703 79.223 -1408.7
## - phi.N 1 1.9844 79.637 -1405.2
## - density 1 2.7400 80.393 -1398.9
## - gamma 1 4.5335 82.186 -1384.0
## - Facies 7 15.5604 93.213 -1311.5
## - phi.core 1 24.7056 102.358 -1236.6
summary(model_h)
##
## Call:
## lm(formula = log10_k.core ~ caliper + gamma + phi.N + SP + density.corr +
## density + phi.core + Facies, data = training)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.55484 -0.11842 0.02056 0.15786 0.79574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4455087 2.0252826 2.195 0.028512 *
## caliper -0.7557357 0.2073387 -3.645 0.000289 ***
## gamma -0.0096855 0.0015639 -6.193 1.04e-09 ***
## phi.N -1.7244543 0.4208591 -4.097 4.70e-05 ***
## SP -0.0022222 0.0009234 -2.407 0.016375 *
## density.corr 2.4053824 1.3660041 1.761 0.078722 .
## density 1.6184281 0.3361345 4.815 1.83e-06 ***
## phi.core 9.7385870 0.6735873 14.458 < 2e-16 ***
## FaciesF10 0.1096249 0.1061027 1.033 0.301892
## FaciesF2 0.0004190 0.1639821 0.003 0.997962
## FaciesF3 -0.0564001 0.0994990 -0.567 0.571016
## FaciesF5 0.2053784 0.0960792 2.138 0.032918 *
## FaciesF7 0.3461310 0.1571638 2.202 0.027987 *
## FaciesF8 -0.0235287 0.1022923 -0.230 0.818152
## FaciesF9 -0.2764159 0.0974532 -2.836 0.004703 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3438 on 657 degrees of freedom
## Multiple R-squared: 0.6676, Adjusted R-squared: 0.6605
## F-statistic: 94.23 on 14 and 657 DF, p-value: < 2.2e-16
log_k.predicted_h <-predict(model_h,newdata=testing)
k.predicted_h<-10^log_k.predicted_h
plot(k.predicted_h,testing$k.core)
rmse_h<- RMSE(k.predicted_h,testing$k.core )
rmse_h
## [1] 1697.524