* Segmented Analysis of Russian Casualties in Ukraine on Mon Jun 29 13:34:25 2026 - input data directory: ./results - results directory: ./results - transcript to: ./results/segmented-ukr-rus-casualties-transcript.txt Loading updated data: --------------------- * Updated data file: ./results/russian-casualties-in-ukraine-updated-2026-06-28.tsv - Found 125 rows x 3 columns: - Columns: DayNum, Date, Soldiers Doing 3-fold crossvalidated segmented fits: ------------------------------------------- - Segmented fit, testFold = 1 o Train data: 83 points o Test data: 42 points o Simple linear fit, no kinks: Call: lm(formula = Soldiers ~ DayNum, data = trainData) Residuals: Min 1Q Median 3Q Max -92132 -8198 3878 9973 46207 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.041e+05 2.255e+03 46.17 <2e-16 *** DayNum 9.981e+02 8.604e+00 116.00 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 18110 on 81 degrees of freedom Multiple R-squared: 0.994, Adjusted R-squared: 0.9939 F-statistic: 1.346e+04 on 1 and 81 DF, p-value: < 2.2e-16 o Davies test for need of kink: Davies' test for a change in the slope data: formula = Soldiers ~ DayNum , method = lm model = gaussian , link = identity segmented variable = DayNum 'best' at = 358.33, n.points = 10, p-value < 2.2e-16 alternative hypothesis: two.sided o Segmented fit: ***Regression Model with Segmented Relationship(s)*** Call: segmented.lm(obj = linearFit) Estimated Break-Point(s): Est. St.Err psi1.DayNum 381.53 15.625 Coefficients of the linear terms: Estimate Std. Error t value Pr(>|t|) (Intercept) 122569.14 1287.24 95.22 <2e-16 *** DayNum 698.58 19.21 36.36 <2e-16 *** U1.DayNum 485.93 20.92 23.23 NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5561 on 79 degrees of freedom Multiple R-Squared: 0.9994, Adjusted R-squared: 0.9994 Boot restarting based on 10 samples. Last fit: Convergence attained in 2 iterations (rel. change 7.0969e-11) Est. CI(95%).low CI(95%).up psi1.DayNum 381.53 350.43 412.631 $DayNum Est. St.Err. t value CI(95%).l CI(95%).u slope1 698.58 19.2110 36.363 660.34 736.82 slope2 1184.50 8.2736 143.170 1168.00 1201.00 - Segmented fit, testFold = 2 o Train data: 83 points o Test data: 42 points o Simple linear fit, no kinks: Call: lm(formula = Soldiers ~ DayNum, data = trainData) Residuals: Min 1Q Median 3Q Max -93617 -7815 3807 10070 42553 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.048e+05 2.352e+03 44.54 <2e-16 *** DayNum 9.886e+02 9.419e+00 104.95 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 18800 on 81 degrees of freedom Multiple R-squared: 0.9927, Adjusted R-squared: 0.9926 F-statistic: 1.102e+04 on 1 and 81 DF, p-value: < 2.2e-16 o Davies test for need of kink: Davies' test for a change in the slope data: formula = Soldiers ~ DayNum , method = lm model = gaussian , link = identity segmented variable = DayNum 'best' at = 358.33, n.points = 10, p-value < 2.2e-16 alternative hypothesis: two.sided o Segmented fit: ***Regression Model with Segmented Relationship(s)*** Call: segmented.lm(obj = linearFit) Estimated Break-Point(s): Est. St.Err psi1.DayNum 391.861 16.573 Coefficients of the linear terms: Estimate Std. Error t value Pr(>|t|) (Intercept) 122525.07 1378.97 88.85 <2e-16 *** DayNum 699.43 20.71 33.77 <2e-16 *** U1.DayNum 496.74 22.73 21.86 NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5998 on 79 degrees of freedom Multiple R-Squared: 0.9993, Adjusted R-squared: 0.9992 Boot restarting based on 6 samples. Last fit: Convergence attained in 2 iterations (rel. change 3.7462e-10) Est. CI(95%).low CI(95%).up psi1.DayNum 391.861 358.873 424.849 $DayNum Est. St.Err. t value CI(95%).l CI(95%).u slope1 699.43 20.7130 33.768 658.2 740.66 slope2 1196.20 9.3588 127.810 1177.5 1214.80 - Segmented fit, testFold = 3 o Train data: 84 points o Test data: 41 points o Simple linear fit, no kinks: Call: lm(formula = Soldiers ~ DayNum, data = trainData) Residuals: Min 1Q Median 3Q Max -96274 -7992 4130 10648 48586 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.040e+05 2.504e+03 41.53 <2e-16 *** DayNum 9.963e+02 9.112e+00 109.34 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 20220 on 82 degrees of freedom Multiple R-squared: 0.9932, Adjusted R-squared: 0.9931 F-statistic: 1.196e+04 on 1 and 82 DF, p-value: < 2.2e-16 o Davies test for need of kink: Davies' test for a change in the slope data: formula = Soldiers ~ DayNum , method = lm model = gaussian , link = identity segmented variable = DayNum 'best' at = 390, n.points = 10, p-value < 2.2e-16 alternative hypothesis: two.sided o Segmented fit: ***Regression Model with Segmented Relationship(s)*** Call: segmented.lm(obj = linearFit) Estimated Break-Point(s): Est. St.Err psi1.DayNum 386.076 15.959 Coefficients of the linear terms: Estimate Std. Error t value Pr(>|t|) (Intercept) 122457.02 1333.72 91.82 <2e-16 *** DayNum 700.73 20.17 34.74 <2e-16 *** U1.DayNum 481.46 21.48 22.41 NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5836 on 80 degrees of freedom Multiple R-Squared: 0.9994, Adjusted R-squared: 0.9994 Boot restarting based on 6 samples. Last fit: Convergence attained in 2 iterations (rel. change 2.2969e-10) Est. CI(95%).low CI(95%).up psi1.DayNum 386.076 354.316 417.836 $DayNum Est. St.Err. t value CI(95%).l CI(95%).u slope1 700.73 20.1720 34.738 660.58 740.87 slope2 1182.20 7.3944 159.880 1167.50 1196.90 - Segmented fit, testFold = NA o Train data: 125 points o Test data: 125 points o Simple linear fit, no kinks: Call: lm(formula = Soldiers ~ DayNum, data = trainData) Residuals: Min 1Q Median 3Q Max -95800 -8046 3947 10330 50444 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.043e+05 1.932e+03 53.97 <2e-16 *** DayNum 9.946e+02 7.362e+00 135.10 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 19010 on 123 degrees of freedom Multiple R-squared: 0.9933, Adjusted R-squared: 0.9933 F-statistic: 1.825e+04 on 1 and 123 DF, p-value: < 2.2e-16 o Davies test for need of kink: Davies' test for a change in the slope data: formula = Soldiers ~ DayNum , method = lm model = gaussian , link = identity segmented variable = DayNum 'best' at = 390, n.points = 10, p-value < 2.2e-16 alternative hypothesis: two.sided o Segmented fit: ***Regression Model with Segmented Relationship(s)*** Call: segmented.lm(obj = linearFit) Estimated Break-Point(s): Est. St.Err psi1.DayNum 386.067 13.046 Coefficients of the linear terms: Estimate Std. Error t value Pr(>|t|) (Intercept) 122516.84 1085.83 112.83 <2e-16 *** DayNum 699.58 16.31 42.89 <2e-16 *** U1.DayNum 487.14 17.63 27.63 NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5783 on 121 degrees of freedom Multiple R-Squared: 0.9994, Adjusted R-squared: 0.9994 Boot restarting based on 6 samples. Last fit: Convergence attained in 2 iterations (rel. change 8.6332e-11) Est. CI(95%).low CI(95%).up psi1.DayNum 386.067 360.24 411.894 $DayNum Est. St.Err. t value CI(95%).l CI(95%).u slope1 699.58 16.3120 42.888 667.28 731.87 slope2 1186.70 6.6961 177.230 1173.50 1200.00 * Crossvalidation and final whole-dataset fit results: TestFold Kink sd.Kink. Slope1 sd.Slope1. Slope2 sd.Slope2. lm.Adj.R2 1 1 381.530 15.625 698.58 19.211 1184.5 8.274 0.994 2 2 391.861 16.573 699.43 20.713 1196.2 9.359 0.993 3 3 386.076 15.959 700.73 20.172 1182.2 7.394 0.993 4 NA 386.067 13.046 699.58 16.312 1186.7 6.696 0.993 Adj.R2 lm.RMSE RMSE 1 0.999 20700.00 6204.391 2 0.999 19576.05 5533.839 3 0.999 16346.54 5769.339 4 0.999 18859.75 5689.933 * Segmented Analysis of Russian Casualties in Ukraine completed Mon Jun 29 13:34:26 2026 (0.4 sec elapsed).