doAll: Do all implemented analyses, write tables and figures.
In TRADER: Tree Ring Analysis of Disturbance Events in R
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
See Also
Examples
Description
The TRADER package provides only one way for disturbance reconstruction from tree-ring data. TRADER is a unique package bringing the first instrument for analysis of forest disturbance history in complementary ways. Final advantage of TRADER is the possibility of results comparison between individual studies. This is enabled by easy parameter changes in data processing, as well as by clearly arranged graphical and tabular outputs. We developed TRADER in open source R environment, to further support the on-going open-source software development for dendrochronological methods and data availability.
Usage
1
2
3
4
5
6
doAll(data, m1 = 10, m2 = 10, abs.threshold = NULL, boundary = NULL, buffer = 2,
criteriaNA = 0.2, criteria2NA = 0.5,
criteriaBA = 0.2, criteria2BA = 0.5, segmentBA = 0.5, segment2BA = 0.5,
criteriaS = 0.2, criteria2S = 0.5, segmentS = 0.5, segment2S = 0.5,
prefix = "all", gfun = mean, length = 2, notop = 10, notop2 = 10,
storedev = pdf, drawing=TRUE,...)
Arguments
data
A data.frame with series as columns and years as rows such as that produced by read.* function of dplR .
m1
Determines the number of years to be averaged (including target year) for period prior the potential releas.
m2
Determines the number of years to be averaged (including target year) for period prior the potential releas.
abs.threshold
Threshold of absolute-increase method.
boundary
Boundary line function of one argument, eg. boundary=function(x) {5.0067*exp(-0.664*x)}
buffer
Number of years determining how close to one another two releases can be.
criteriaNA
Threshold for detection of moderate release in NA method.
criteria2NA
Threshold for detection of major release in NA method.
criteriaBA
Threshold for detection of moderate release in BA method.
criteria2BA
Threshold for detection of major release in BA method.
criteriaS
Threshold for detection of moderate release in S method.
criteria2S
Threshold for detection of major release in S method.
segmentBA
Determines length of the segment on which prior growth will be divided in BA method.
segment2BA
Determines length of the segment on which first mm of prior growth will be divided in BA method.
segmentS
Determines length of the segment on which prior growth will be divided in S method.
segment2S
Determines length of the segment on which first mm of prior growth will be divided in S method.
prefix
Prefix of saved files.
gfun
Determines if M1 and M2 values are mean or median for selected period.
length
Determines how many years have to be given critera exceeded to be considered as release.
notop
Number of highest data points for fitting the boundary line.
notop2
Number of highest data points for fitting the boundary line in the segments for first mm.
storedev
Format for saving the graphical outputs, eg. pdf or jpeg.
drawing
If TRUE, graphical outputs for individual trees.
...
Parameters passed to plot function.
Details
For details look at methods that are evaluated: absoluteIncrease, noblabrams and splechtna.
Value
Write many tables and figures in the current directory.
Note
Check reference.
Author(s)
Pavel Fibich <pavel.fibich@prf.jcu.cz>, Jan Altman <altman.jan@gmail.com>, Tuomas Aakala <tuomas.aakala@helsinki.fi>, Jiri Dolezal <jiriddolezal@gmail.com>
References
Nowacki, G.J. & Abrams, M.D. 1997. Radial-growth averaging criteria for reconstructing disturbance histories from presettlement-origin oaks. Ecological Monographs, 67, 225-249.
Black, B.A. & Abrams, M.D. 2003. Use of boundary-line growth patterns as a basis for dendroecological release criteria. Ecological Applications, 13, 1733-1749.
Fraver, S. & White, A.S. 2005. Identifying growth releases in dendrochronological studies of forest disturbance. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 35, 1648-1656.
Splechtna, B.E., Gratzer, G. & Black, B.A. 2005. Disturbance history of a European old-growth mixed-species forest - A spatial dendro-ecological analysis. Journal of Vegetation Science, 16, 511-522.
See Also
absoluteIncreaseALL,
growthAveragingALL,
boundaryLineALL,
splechtnaALL
Examples
1
2
Example output
sh: 1: cannot create /dev/null: Permission denied
sh: 1: cannot create /dev/null: Permission denied
[1] "## Fraver & White analysis!"
[1] "Absolute threshold 1.2 m1 10 m2 10 Buffer 2 Length 5"
[1] "Total number of releases is 17"
inyears
1896 1899 1904 1935 1936 1938 1939 1944 1978
2 1 1 4 3 2 1 1 2
[1] "## Nowacki & Abrams analysis!"
[1] "Criteria 0.25 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 5"
[1] "Total number of releases >= 0.25 & < 0.5 is 13"
inyears
1888 1895 1899 1935 1936 1938 1978 1980 1986
1 1 1 1 2 3 1 2 1
[1] "Total number of releases >= 0.5 is 26"
inyears
1896 1899 1903 1927 1935 1936 1938 1939 1942 1947 1978 1984 1986 1988 1990 1993
3 1 1 1 4 4 2 1 1 1 2 1 1 1 1 1
[1] "## Black & Abrams analysis!"
[1] "Criteria 0.2 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 5 Segment 0.5 Segment2 0.5"
[1] "--Summary of y=a+bx fit."
Call:
lm(formula = tops ~ segments, data = boundaries)
Residuals:
1 2 3 4 5 6 7
-2.1637 1.7473 1.3473 -0.1650 -0.1749 -0.2988 -0.2922
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6631 1.0656 2.499 0.0545 .
segments -0.7266 0.5287 -1.374 0.2277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.399 on 5 degrees of freedom
Multiple R-squared: 0.2742, Adjusted R-squared: 0.129
F-statistic: 1.889 on 1 and 5 DF, p-value: 0.2277
[1] "--Summary of y=a+bx+cx^2 fit."
Call:
lm(formula = tops ~ segments + I(segments^2), data = boundaries)
Residuals:
1 2 3 4 5 6 7
-1.2627 1.7473 0.8067 -0.8858 -0.7155 -0.2988 0.6088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1766 1.5661 0.751 0.494
segments 1.7961 2.0898 0.859 0.439
I(segments^2) -0.7208 0.5796 -1.244 0.282
Residual standard error: 1.328 on 4 degrees of freedom
Multiple R-squared: 0.4765, Adjusted R-squared: 0.2148
F-statistic: 1.821 on 2 and 4 DF, p-value: 0.274
[1] "--Summary of y=ae^bx fit."
Formula: tops ~ a * exp(b * segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 2.4721 1.4860 1.664 0.157
b -0.3441 0.4231 -0.813 0.453
Residual standard error: 1.48 on 5 degrees of freedom
Number of iterations to convergence: 19
Achieved convergence tolerance: 7.85e-06
[1] "y=c+ae^bx nls error: step factor 0.000488281 reduced below 'minFactor' of 0.000976562"
[1] "y=c+dx+ae^bx nls error: singular gradient"
[1] "y=ae^bx+ce^dx nls error: Missing value or an infinity produced when evaluating the model"
[1] "--Summary of y=a+blog(x) fit."
Formula: tops ~ a + b * log(segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 1.5195 0.6389 2.378 0.0633 .
b -0.4241 0.7248 -0.585 0.5839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.588 on 5 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 8.688e-09
[1] "--Summary of y=a+bx+clog(x)+dxlog(x) fit."
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "The fitted boundary line summary!"
[1] "Logarithmic model y=a+bx+clog(x)+dxlog(x) was the best!"
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "Total number of releases >= 0.2 & < 0.5 is 18"
inyears
1888 1893 1895 1897 1899 1910 1934 1935 1938 1941 1944 1947 1982
1 2 1 1 2 1 1 2 3 1 1 1 1
[1] "Total number of releases >= 0.5 is 30"
inyears
1890 1893 1896 1899 1903 1935 1936 1938 1939 1940 1941 1942 1943 1944 1978 1980
1 1 3 2 2 5 1 3 2 1 2 1 1 2 2 1
[1] "## Splechtna analysis!"
[1] "Criteria 0.2 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 5 Segment 0.5 Segment2 0.5"
[1] "--Summary of y=a+bx fit."
Call:
lm(formula = tops ~ segments, data = boundaries)
Residuals:
1 2 3 4 5 6 7
-2.1637 1.7473 1.3473 -0.1650 -0.1749 -0.2988 -0.2922
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6631 1.0656 2.499 0.0545 .
segments -0.7266 0.5287 -1.374 0.2277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.399 on 5 degrees of freedom
Multiple R-squared: 0.2742, Adjusted R-squared: 0.129
F-statistic: 1.889 on 1 and 5 DF, p-value: 0.2277
[1] "--Summary of y=a+bx+cx^2 fit."
Call:
lm(formula = tops ~ segments + I(segments^2), data = boundaries)
Residuals:
1 2 3 4 5 6 7
-1.2627 1.7473 0.8067 -0.8858 -0.7155 -0.2988 0.6088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1766 1.5661 0.751 0.494
segments 1.7961 2.0898 0.859 0.439
I(segments^2) -0.7208 0.5796 -1.244 0.282
Residual standard error: 1.328 on 4 degrees of freedom
Multiple R-squared: 0.4765, Adjusted R-squared: 0.2148
F-statistic: 1.821 on 2 and 4 DF, p-value: 0.274
[1] "--Summary of y=ae^bx fit."
Formula: tops ~ a * exp(b * segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 2.4721 1.4860 1.664 0.157
b -0.3441 0.4231 -0.813 0.453
Residual standard error: 1.48 on 5 degrees of freedom
Number of iterations to convergence: 19
Achieved convergence tolerance: 7.85e-06
[1] "y=c+ae^bx nls error: step factor 0.000488281 reduced below 'minFactor' of 0.000976562"
[1] "y=c+dx+ae^bx nls error: singular gradient"
[1] "y=ae^bx+ce^dx nls error: Missing value or an infinity produced when evaluating the model"
[1] "--Summary of y=a+blog(x) fit."
Formula: tops ~ a + b * log(segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 1.5195 0.6389 2.378 0.0633 .
b -0.4241 0.7248 -0.585 0.5839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.588 on 5 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 8.688e-09
[1] "--Summary of y=a+bx+clog(x)+dxlog(x) fit."
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "The fitted boundary line summary!"
[1] "Logarithmic model y=a+bx+clog(x)+dxlog(x) was the best!"
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "Total number of releases >= 0.2 & < 0.5 is 23"
inyears
1896 1899 1927 1935 1936 1938 1939 1941 1942 1978 1993
1 2 1 5 3 5 1 1 1 2 1
[1] "Total number of releases >= 0.5 is 14"
inyears
1896 1899 1935 1936 1938 1939 1942 1947 1978
1 2 4 1 1 1 1 1 2
There were 50 or more warnings (use warnings() to see the first 50)
TRADER documentation built on May 2, 2019, 9:02 a.m.
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Description Usage Arguments Details Value Note Author(s) References See Also Examples
Description
The TRADER package provides only one way for disturbance reconstruction from tree-ring data. TRADER is a unique package bringing the first instrument for analysis of forest disturbance history in complementary ways. Final advantage of TRADER is the possibility of results comparison between individual studies. This is enabled by easy parameter changes in data processing, as well as by clearly arranged graphical and tabular outputs. We developed TRADER in open source R environment, to further support the on-going open-source software development for dendrochronological methods and data availability.
Usage
1 2 3 4 5 6 | doAll(data, m1 = 10, m2 = 10, abs.threshold = NULL, boundary = NULL, buffer = 2,
criteriaNA = 0.2, criteria2NA = 0.5,
criteriaBA = 0.2, criteria2BA = 0.5, segmentBA = 0.5, segment2BA = 0.5,
criteriaS = 0.2, criteria2S = 0.5, segmentS = 0.5, segment2S = 0.5,
prefix = "all", gfun = mean, length = 2, notop = 10, notop2 = 10,
storedev = pdf, drawing=TRUE,...)
|
Arguments
data |
A data.frame with series as columns and years as rows such as that produced by read.* function of dplR . |
m1 |
Determines the number of years to be averaged (including target year) for period prior the potential releas. |
m2 |
Determines the number of years to be averaged (including target year) for period prior the potential releas. |
abs.threshold |
Threshold of absolute-increase method. |
boundary |
Boundary line function of one argument, eg. |
buffer |
Number of years determining how close to one another two releases can be. |
criteriaNA |
Threshold for detection of moderate release in NA method. |
criteria2NA |
Threshold for detection of major release in NA method. |
criteriaBA |
Threshold for detection of moderate release in BA method. |
criteria2BA |
Threshold for detection of major release in BA method. |
criteriaS |
Threshold for detection of moderate release in S method. |
criteria2S |
Threshold for detection of major release in S method. |
segmentBA |
Determines length of the segment on which prior growth will be divided in BA method. |
segment2BA |
Determines length of the segment on which first mm of prior growth will be divided in BA method. |
segmentS |
Determines length of the segment on which prior growth will be divided in S method. |
segment2S |
Determines length of the segment on which first mm of prior growth will be divided in S method. |
prefix |
Prefix of saved files. |
gfun |
Determines if M1 and M2 values are mean or median for selected period. |
length |
Determines how many years have to be given critera exceeded to be considered as release. |
notop |
Number of highest data points for fitting the boundary line. |
notop2 |
Number of highest data points for fitting the boundary line in the segments for first mm. |
storedev |
Format for saving the graphical outputs, eg. pdf or jpeg. |
drawing |
If TRUE, graphical outputs for individual trees. |
... |
Parameters passed to plot function. |
Details
For details look at methods that are evaluated: absoluteIncrease, noblabrams and splechtna.
Value
Write many tables and figures in the current directory.
Note
Check reference.
Author(s)
Pavel Fibich <pavel.fibich@prf.jcu.cz>, Jan Altman <altman.jan@gmail.com>, Tuomas Aakala <tuomas.aakala@helsinki.fi>, Jiri Dolezal <jiriddolezal@gmail.com>
References
Nowacki, G.J. & Abrams, M.D. 1997. Radial-growth averaging criteria for reconstructing disturbance histories from presettlement-origin oaks. Ecological Monographs, 67, 225-249.
Black, B.A. & Abrams, M.D. 2003. Use of boundary-line growth patterns as a basis for dendroecological release criteria. Ecological Applications, 13, 1733-1749.
Fraver, S. & White, A.S. 2005. Identifying growth releases in dendrochronological studies of forest disturbance. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 35, 1648-1656.
Splechtna, B.E., Gratzer, G. & Black, B.A. 2005. Disturbance history of a European old-growth mixed-species forest - A spatial dendro-ecological analysis. Journal of Vegetation Science, 16, 511-522.
See Also
absoluteIncreaseALL,
growthAveragingALL,
boundaryLineALL,
splechtnaALL
Examples
1 2 |
Example output
sh: 1: cannot create /dev/null: Permission denied
sh: 1: cannot create /dev/null: Permission denied
[1] "## Fraver & White analysis!"
[1] "Absolute threshold 1.2 m1 10 m2 10 Buffer 2 Length 5"
[1] "Total number of releases is 17"
inyears
1896 1899 1904 1935 1936 1938 1939 1944 1978
2 1 1 4 3 2 1 1 2
[1] "## Nowacki & Abrams analysis!"
[1] "Criteria 0.25 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 5"
[1] "Total number of releases >= 0.25 & < 0.5 is 13"
inyears
1888 1895 1899 1935 1936 1938 1978 1980 1986
1 1 1 1 2 3 1 2 1
[1] "Total number of releases >= 0.5 is 26"
inyears
1896 1899 1903 1927 1935 1936 1938 1939 1942 1947 1978 1984 1986 1988 1990 1993
3 1 1 1 4 4 2 1 1 1 2 1 1 1 1 1
[1] "## Black & Abrams analysis!"
[1] "Criteria 0.2 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 5 Segment 0.5 Segment2 0.5"
[1] "--Summary of y=a+bx fit."
Call:
lm(formula = tops ~ segments, data = boundaries)
Residuals:
1 2 3 4 5 6 7
-2.1637 1.7473 1.3473 -0.1650 -0.1749 -0.2988 -0.2922
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6631 1.0656 2.499 0.0545 .
segments -0.7266 0.5287 -1.374 0.2277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.399 on 5 degrees of freedom
Multiple R-squared: 0.2742, Adjusted R-squared: 0.129
F-statistic: 1.889 on 1 and 5 DF, p-value: 0.2277
[1] "--Summary of y=a+bx+cx^2 fit."
Call:
lm(formula = tops ~ segments + I(segments^2), data = boundaries)
Residuals:
1 2 3 4 5 6 7
-1.2627 1.7473 0.8067 -0.8858 -0.7155 -0.2988 0.6088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1766 1.5661 0.751 0.494
segments 1.7961 2.0898 0.859 0.439
I(segments^2) -0.7208 0.5796 -1.244 0.282
Residual standard error: 1.328 on 4 degrees of freedom
Multiple R-squared: 0.4765, Adjusted R-squared: 0.2148
F-statistic: 1.821 on 2 and 4 DF, p-value: 0.274
[1] "--Summary of y=ae^bx fit."
Formula: tops ~ a * exp(b * segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 2.4721 1.4860 1.664 0.157
b -0.3441 0.4231 -0.813 0.453
Residual standard error: 1.48 on 5 degrees of freedom
Number of iterations to convergence: 19
Achieved convergence tolerance: 7.85e-06
[1] "y=c+ae^bx nls error: step factor 0.000488281 reduced below 'minFactor' of 0.000976562"
[1] "y=c+dx+ae^bx nls error: singular gradient"
[1] "y=ae^bx+ce^dx nls error: Missing value or an infinity produced when evaluating the model"
[1] "--Summary of y=a+blog(x) fit."
Formula: tops ~ a + b * log(segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 1.5195 0.6389 2.378 0.0633 .
b -0.4241 0.7248 -0.585 0.5839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.588 on 5 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 8.688e-09
[1] "--Summary of y=a+bx+clog(x)+dxlog(x) fit."
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "The fitted boundary line summary!"
[1] "Logarithmic model y=a+bx+clog(x)+dxlog(x) was the best!"
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "Total number of releases >= 0.2 & < 0.5 is 18"
inyears
1888 1893 1895 1897 1899 1910 1934 1935 1938 1941 1944 1947 1982
1 2 1 1 2 1 1 2 3 1 1 1 1
[1] "Total number of releases >= 0.5 is 30"
inyears
1890 1893 1896 1899 1903 1935 1936 1938 1939 1940 1941 1942 1943 1944 1978 1980
1 1 3 2 2 5 1 3 2 1 2 1 1 2 2 1
[1] "## Splechtna analysis!"
[1] "Criteria 0.2 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 5 Segment 0.5 Segment2 0.5"
[1] "--Summary of y=a+bx fit."
Call:
lm(formula = tops ~ segments, data = boundaries)
Residuals:
1 2 3 4 5 6 7
-2.1637 1.7473 1.3473 -0.1650 -0.1749 -0.2988 -0.2922
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6631 1.0656 2.499 0.0545 .
segments -0.7266 0.5287 -1.374 0.2277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.399 on 5 degrees of freedom
Multiple R-squared: 0.2742, Adjusted R-squared: 0.129
F-statistic: 1.889 on 1 and 5 DF, p-value: 0.2277
[1] "--Summary of y=a+bx+cx^2 fit."
Call:
lm(formula = tops ~ segments + I(segments^2), data = boundaries)
Residuals:
1 2 3 4 5 6 7
-1.2627 1.7473 0.8067 -0.8858 -0.7155 -0.2988 0.6088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1766 1.5661 0.751 0.494
segments 1.7961 2.0898 0.859 0.439
I(segments^2) -0.7208 0.5796 -1.244 0.282
Residual standard error: 1.328 on 4 degrees of freedom
Multiple R-squared: 0.4765, Adjusted R-squared: 0.2148
F-statistic: 1.821 on 2 and 4 DF, p-value: 0.274
[1] "--Summary of y=ae^bx fit."
Formula: tops ~ a * exp(b * segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 2.4721 1.4860 1.664 0.157
b -0.3441 0.4231 -0.813 0.453
Residual standard error: 1.48 on 5 degrees of freedom
Number of iterations to convergence: 19
Achieved convergence tolerance: 7.85e-06
[1] "y=c+ae^bx nls error: step factor 0.000488281 reduced below 'minFactor' of 0.000976562"
[1] "y=c+dx+ae^bx nls error: singular gradient"
[1] "y=ae^bx+ce^dx nls error: Missing value or an infinity produced when evaluating the model"
[1] "--Summary of y=a+blog(x) fit."
Formula: tops ~ a + b * log(segments)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 1.5195 0.6389 2.378 0.0633 .
b -0.4241 0.7248 -0.585 0.5839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.588 on 5 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 8.688e-09
[1] "--Summary of y=a+bx+clog(x)+dxlog(x) fit."
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "The fitted boundary line summary!"
[1] "Logarithmic model y=a+bx+clog(x)+dxlog(x) was the best!"
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
-0.007628 0.001627 0.189574 -0.418157 0.170181 0.193449 -0.129045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments -26.547 4.156 -6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple R-squared: 0.9782, Adjusted R-squared: 0.9564
F-statistic: 44.86 on 3 and 3 DF, p-value: 0.005431
[1] "Total number of releases >= 0.2 & < 0.5 is 23"
inyears
1896 1899 1927 1935 1936 1938 1939 1941 1942 1978 1993
1 2 1 5 3 5 1 1 1 2 1
[1] "Total number of releases >= 0.5 is 14"
inyears
1896 1899 1935 1936 1938 1939 1942 1947 1978
1 2 4 1 1 1 1 1 2
There were 50 or more warnings (use warnings() to see the first 50)
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