CRAN Package Check Results for Package assignR

Last updated on 2020-05-29 17:48:59 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2.0 11.49 1025.91 1037.40 ERROR
r-devel-linux-x86_64-debian-gcc 1.2.0 9.98 813.88 823.86 ERROR
r-devel-linux-x86_64-fedora-clang 1.2.0 1073.29 NOTE
r-devel-linux-x86_64-fedora-gcc 1.2.0 1073.06 NOTE
r-devel-windows-ix86+x86_64 1.2.0 34.00 997.00 1031.00 OK
r-patched-linux-x86_64 1.2.0 12.56 780.82 793.38 OK
r-patched-solaris-x86 1.2.0 1359.20 NOTE
r-release-linux-x86_64 1.2.0 13.22 780.68 793.90 OK
r-release-osx-x86_64 1.2.0 NOTE
r-release-windows-ix86+x86_64 1.2.0 28.00 876.00 904.00 OK
r-oldrel-osx-x86_64 1.2.0 NOTE
r-oldrel-windows-ix86+x86_64 1.2.0 26.00 758.00 784.00 OK

Check Details

Version: 1.2.0
Check: tests
Result: ERROR
     Running 'testthat.R' [10m/11m]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(assignR)
     >
     > test_check("assignR")
    
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     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.575 -3.848 0.273 4.207 23.026
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.94258 1.30067 -58.39 <2e-16 ***
     isoscape.iso[, 1] 0.39367 0.02002 19.67 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.844 on 208 degrees of freedom
     Multiple R-squared: 0.6503, Adjusted R-squared: 0.6487
     F-statistic: 386.8 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
     NO isoscape values found at the following locations:
     -39.8194445, 38.5611111
     -99.09, 19.2
     -99.09, 19.2
     -99.09, 19.2
     -101.3, 20.72
     -101.37, 20.73
     -99.5, 17.55
     -99.5, 17.55
     -99.5, 18.9
     -100.07, 19.09
     -100.07, 19.09
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15289, 20.0092
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.6, 19.47
     -98.98, 18.7
     -99.06, 18.8
     -99.08, 18.86
     -99.25, 18.75
     -97.77, 17.8
     -97.77, 17.8
     -101.14604, 20.01137
     -101.15265, 20.0074
     -101.14527, 19.99508
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -0.3, 39.3833
     -0.3, 39.3833
     -0.3, 39.3833
     -0.3, 39.3833
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.867, 54.4
     -2.1986, 55.3175
     -2.3042, 54.7081
     -2.1886, 54.2269
     0.8419, 52.6147
     0.8419, 52.6147
     -4.1678, 56.8581
     -4.1678, 56.8581
     -4.1678, 56.8581
     -4.1678, 56.8581
     28.2333, 45.1833
     13.0833, 44.0333
     13.0833, 44.0333
     27.7333, 52.0667
     27.7333, 52.0667
     27.7333, 52.0667
     27.7333, 52.0667
     16.7639, 52.1347
     16.7639, 52.1347
     16.7639, 52.1347
     26.5056, 58.595
     26.5056, 58.595
     26.5056, 58.595
     26.4278, 58.4611
     26.4278, 58.4611
     26.4278, 58.4611
     -0.378, 46.361
     -0.378, 46.361
     -0.378, 46.361
     -0.248, 46.401
     -0.244, 46.088
     -4.037, 48.578
     -4.037, 48.578
     -4.037, 48.578
     -2.578, 47.519
     1.462, 48.297
     1.578, 48.192
     1.385, 48.337
     1.276, 48.249
     1.276, 48.249
     3.711, 49.825
     3.711, 49.825
     3.711, 49.825
     3.711, 49.825
     3.711, 49.825
     42.817, 62.017
     42.817, 62.017
     42.817, 62.017
     42.817, 62.017
     42.817, 62.017
     55.767, 57.267
     55.767, 57.267
     55.767, 57.267
     55.767, 57.267
     55.767, 57.267
     38.783, 55
     38.783, 55
     38.783, 55
     38.783, 55
     38.783, 55
     19.117, 48.567
     19.117, 48.567
     19.117, 48.567
     19.117, 48.567
     -1.75, 53.783
     -2.35, 55
     -2.95, 54.333
     1.933, 42.433
     24.2525, 65.8506
     24.2333, 65.8167
     24.2839, 65.8522
     24.2833, 65.8681
     30.85, 59.0333
     30.4, 60.15
     30.4, 60.15
     30.4, 60.15
     30.4, 60.15
     27.25, 69.5
     27.25, 69.5
     27.25, 69.5
     27.25, 69.5
     27.25, 69.5
     25.3333, 63.2667
     26.1, 63.2
     26.1, 63.2
     25.6, 63.05
     26.1, 63.2
     27.5833, 66.5
     27.3333, 66.75
     27.3333, 66.75
     27.3333, 66.75
     27.3333, 66.75
     29.2833, 64.9667
     29, 64.8333
     29, 64.6833
     29.5, 65.2167
     29.5333, 64.9167
     29, 64.6833
     14.1128, 49.2639
     14.1583, 49.2939
     14.1528, 49.2989
     14.2, 49.2528
     22.7667, 62.0333
     22.7667, 62.0333
     22.95, 62.15
     22.95, 62.3167
     22.95, 62.15
     23.3333, 61.9167
     6.0178, 52.0939
     6.1, 52.2308
     6.0719, 52.2308
     6.115, 52.29
     6.0619, 52.1608
     14.1356, 49.2778
     14.1292, 49.2764
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -98.954 -18.048 -2.931 15.518 101.439
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -9.6942 1.3001 -7.456 1.29e-13 ***
     isoscape.iso[, 1] 0.8860 0.0175 50.631 < 2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 25.79 on 2138 degrees of freedom
     Multiple R-squared: 0.5452, Adjusted R-squared: 0.545
     F-statistic: 2563 on 1 and 2138 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
     -- 1. Failure: pdRaster can correctly calculate posterior probabilities of origi
     `pdRaster(r, un, outDir = "temp")` produced warnings.
    
    
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     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
     Killed
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.2.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [8m/12m]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(assignR)
     >
     > test_check("assignR")
    
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     |========================================================= | 82%
     |
     |=========================================================== | 84%
     |
     |============================================================ | 86%
     |
     |============================================================== | 88%
     |
     |=============================================================== | 90%
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     |================================================================ | 92%
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     |=================================================================== | 96%
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     |===================================================================== | 98%
     |
     |======================================================================| 100%
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.575 -3.848 0.273 4.207 23.026
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.94258 1.30067 -58.39 <2e-16 ***
     isoscape.iso[, 1] 0.39367 0.02002 19.67 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.844 on 208 degrees of freedom
     Multiple R-squared: 0.6503, Adjusted R-squared: 0.6487
     F-statistic: 386.8 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
     NO isoscape values found at the following locations:
     -39.8194445, 38.5611111
     -99.09, 19.2
     -99.09, 19.2
     -99.09, 19.2
     -101.3, 20.72
     -101.37, 20.73
     -99.5, 17.55
     -99.5, 17.55
     -99.5, 18.9
     -100.07, 19.09
     -100.07, 19.09
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15289, 20.0092
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.6, 19.47
     -98.98, 18.7
     -99.06, 18.8
     -99.08, 18.86
     -99.25, 18.75
     -97.77, 17.8
     -97.77, 17.8
     -101.14604, 20.01137
     -101.15265, 20.0074
     -101.14527, 19.99508
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -101.15265, 20.0074
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -6.0833, 53.1833
     -0.3, 39.3833
     -0.3, 39.3833
     -0.3, 39.3833
     -0.3, 39.3833
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.6917, 54.5717
     -0.867, 54.4
     -2.1986, 55.3175
     -2.3042, 54.7081
     -2.1886, 54.2269
     0.8419, 52.6147
     0.8419, 52.6147
     -4.1678, 56.8581
     -4.1678, 56.8581
     -4.1678, 56.8581
     -4.1678, 56.8581
     28.2333, 45.1833
     13.0833, 44.0333
     13.0833, 44.0333
     27.7333, 52.0667
     27.7333, 52.0667
     27.7333, 52.0667
     27.7333, 52.0667
     16.7639, 52.1347
     16.7639, 52.1347
     16.7639, 52.1347
     26.5056, 58.595
     26.5056, 58.595
     26.5056, 58.595
     26.4278, 58.4611
     26.4278, 58.4611
     26.4278, 58.4611
     -0.378, 46.361
     -0.378, 46.361
     -0.378, 46.361
     -0.248, 46.401
     -0.244, 46.088
     -4.037, 48.578
     -4.037, 48.578
     -4.037, 48.578
     -2.578, 47.519
     1.462, 48.297
     1.578, 48.192
     1.385, 48.337
     1.276, 48.249
     1.276, 48.249
     3.711, 49.825
     3.711, 49.825
     3.711, 49.825
     3.711, 49.825
     3.711, 49.825
     42.817, 62.017
     42.817, 62.017
     42.817, 62.017
     42.817, 62.017
     42.817, 62.017
     55.767, 57.267
     55.767, 57.267
     55.767, 57.267
     55.767, 57.267
     55.767, 57.267
     38.783, 55
     38.783, 55
     38.783, 55
     38.783, 55
     38.783, 55
     19.117, 48.567
     19.117, 48.567
     19.117, 48.567
     19.117, 48.567
     -1.75, 53.783
     -2.35, 55
     -2.95, 54.333
     1.933, 42.433
     24.2525, 65.8506
     24.2333, 65.8167
     24.2839, 65.8522
     24.2833, 65.8681
     30.85, 59.0333
     30.4, 60.15
     30.4, 60.15
     30.4, 60.15
     30.4, 60.15
     27.25, 69.5
     27.25, 69.5
     27.25, 69.5
     27.25, 69.5
     27.25, 69.5
     25.3333, 63.2667
     26.1, 63.2
     26.1, 63.2
     25.6, 63.05
     26.1, 63.2
     27.5833, 66.5
     27.3333, 66.75
     27.3333, 66.75
     27.3333, 66.75
     27.3333, 66.75
     29.2833, 64.9667
     29, 64.8333
     29, 64.6833
     29.5, 65.2167
     29.5333, 64.9167
     29, 64.6833
     14.1128, 49.2639
     14.1583, 49.2939
     14.1528, 49.2989
     14.2, 49.2528
     22.7667, 62.0333
     22.7667, 62.0333
     22.95, 62.15
     22.95, 62.3167
     22.95, 62.15
     23.3333, 61.9167
     6.0178, 52.0939
     6.1, 52.2308
     6.0719, 52.2308
     6.115, 52.29
     6.0619, 52.1608
     14.1356, 49.2778
     14.1292, 49.2764
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -98.954 -18.048 -2.931 15.518 101.439
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -9.6942 1.3001 -7.456 1.29e-13 ***
     isoscape.iso[, 1] 0.8860 0.0175 50.631 < 2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 25.79 on 2138 degrees of freedom
     Multiple R-squared: 0.5452, Adjusted R-squared: 0.545
     F-statistic: 2563 on 1 and 2138 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
     ── 1. Failure: pdRaster can correctly calculate posterior probabilities of origi
     `pdRaster(r, un, outDir = "temp")` produced warnings.
    
    
     |
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     |======================================================================| 100%
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
    
    
     ---------------------------------------
     ------------------------------------------
     rescale function uses linear regression model,
     the summary of this model is:
     -------------------------------------------
     --------------------------------------
    
     Call:
     lm(formula = tissue.iso ~ isoscape.iso[, 1])
    
     Residuals:
     Min 1Q Median 3Q Max
     -38.625 -3.897 0.313 4.261 22.994
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) -75.93649 1.29661 -58.57 <2e-16 ***
     isoscape.iso[, 1] 0.39330 0.01993 19.74 <2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Residual standard error: 7.826 on 208 degrees of freedom
     Multiple R-squared: 0.6519, Adjusted R-squared: 0.6503
     F-statistic: 389.6 on 1 and 208 DF, p-value: < 2.2e-16
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 87 | SKIPPED: 0 | WARNINGS: 3851 | FAILED: 1 ]
     1. Failure: pdRaster can correctly calculate posterior probabilities of origin
     for a sample based on its isotope ratio (@test_pdRaster.R#30)
    
     Error: testthat unit tests failed
     In addition: Warning message:
     In extract_lang(f = comp_lang, y = quote(if (.isMethodsDispatchOn() && :
     devtools is incompatible with the current version of R. `load_all()` may function incorrectly.
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.2.0
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘rgdal’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64