You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@systemml.apache.org by de...@apache.org on 2017/05/19 21:03:47 UTC
incubator-systemml git commit: [SYSTEMML-1455] Change the term
PLAIN_R2 to R2
Repository: incubator-systemml
Updated Branches:
refs/heads/master 1ebe2e178 -> a6428f7d8
[SYSTEMML-1455] Change the term PLAIN_R2 to R2
Closes #500.
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/a6428f7d
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/a6428f7d
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/a6428f7d
Branch: refs/heads/master
Commit: a6428f7d88b39792ccb5440b5bc3bedff4df21de
Parents: 1ebe2e1
Author: krishnakalyan3 <kr...@gmail.com>
Authored: Fri May 19 14:01:07 2017 -0700
Committer: Deron Eriksson <de...@us.ibm.com>
Committed: Fri May 19 14:01:07 2017 -0700
----------------------------------------------------------------------
docs/algorithms-regression.md | 16 ++++++++--------
docs/hadoop-batch-mode.md | 10 +++++-----
docs/standalone-guide.md | 10 +++++-----
scripts/algorithms/GLM-predict.dml | 12 ++++++------
scripts/algorithms/LinearRegCG.dml | 18 +++++++++---------
scripts/algorithms/LinearRegDS.dml | 18 +++++++++---------
scripts/algorithms/StepLinearRegDS.dml | 18 +++++++++---------
7 files changed, 51 insertions(+), 51 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/a6428f7d/docs/algorithms-regression.md
----------------------------------------------------------------------
diff --git a/docs/algorithms-regression.md b/docs/algorithms-regression.md
index 494693c..22c6959 100644
--- a/docs/algorithms-regression.md
+++ b/docs/algorithms-regression.md
@@ -346,11 +346,11 @@ pair per each line.
| AVG\_RES\_Y | Average of the residual $Y - \mathop{\mathrm{pred}}(Y \mid X)$, i.e. residual bias
| STDEV\_RES\_Y | Standard Deviation of the residual $Y - \mathop{\mathrm{pred}}(Y \mid X)$
| DISPERSION | GLM-style dispersion, i.e. residual sum of squares / \#deg. fr.
-| PLAIN\_R2 | Plain $R^2$ of residual with bias included vs. total average
+| R2 | $R^2$ of residual with bias included vs. total average
| ADJUSTED\_R2 | Adjusted $R^2$ of residual with bias included vs. total average
-| PLAIN\_R2\_NOBIAS | Plain $R^2$ of residual with bias subtracted vs. total average
+| R2\_NOBIAS | Plain $R^2$ of residual with bias subtracted vs. total average
| ADJUSTED\_R2\_NOBIAS | Adjusted $R^2$ of residual with bias subtracted vs. total average
-| PLAIN\_R2\_VS\_0 | * Plain $R^2$ of residual with bias included vs. zero constant
+| R2\_VS\_0 | * $R^2$ of residual with bias included vs. zero constant
| ADJUSTED\_R2\_VS\_0 | * Adjusted $R^2$ of residual with bias included vs. zero constant
\* The last two statistics are only printed if there is no intercept (`icpt=0`)
@@ -471,7 +471,7 @@ $n\,{-}\,m\,{-}\,1$ is positive and the regularization constant
$\lambda$ is negligible or zero. The formulas for $\sigma$ and
$R^2$ are:
-$$R^2_{\textrm{plain}} = 1 - \frac{\mathrm{RSS}}{\mathrm{TSS}},\quad
+$$R^2 = 1 - \frac{\mathrm{RSS}}{\mathrm{TSS}},\quad
\sigma \,=\, \sqrt{\frac{\mathrm{RSS}}{n - m - 1}},\quad
R^2_{\textrm{adj.}} = 1 - \frac{\sigma^2 (n-1)}{\mathrm{TSS}}$$
@@ -1881,9 +1881,9 @@ statistic;
| AVG\_RES\_Y | + | | $Y$-column residual average of $Y - pred. mean(Y\\|X)$ |
| STDEV\_RES\_Y | + | | $Y$-column residual st. dev. of $Y - pred. mean(Y\\|X)$ |
| PRED\_STDEV\_RES | + | + | Model-predicted $Y$-column residual st. deviation|
-| PLAIN\_R2 | + | | Plain $R^2$ of $Y$-column residual with bias included |
+| R2 | + | | $R^2$ of $Y$-column residual with bias included |
| ADJUSTED\_R2 | + | | Adjusted $R^2$ of $Y$-column residual w. bias included |
-| PLAIN\_R2\_NOBIAS | + | | Plain $R^2$ of $Y$-column residual, bias subtracted |
+| R2\_NOBIAS | + | | $R^2$ of $Y$-column residual, bias subtracted |
| ADJUSTED\_R2\_NOBIAS | + | | Adjusted $R^2$ of $Y$-column residual, bias subtracted |
* * *
@@ -2114,7 +2114,7 @@ $m$ with the intercept or $m+1$ without the intercept.
| Statistic | Formula |
| --------------------- | ------------- |
-| $\texttt{PLAIN_R2}_j$ | $$ \displaystyle 1 - \frac{\sum\limits_{i=1}^n \,(y_{i,j} - \mu_{i,j})^2}{\sum\limits_{i=1}^n \Big(y_{i,j} - \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n y_{i',j} \Big)^{2}} $$
+| $\texttt{R2}_j$ | $$ \displaystyle 1 - \frac{\sum\limits_{i=1}^n \,(y_{i,j} - \mu_{i,j})^2}{\sum\limits_{i=1}^n \Big(y_{i,j} - \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n y_{i',j} \Big)^{2}} $$
| $\texttt{ADJUSTED_R2}_j$ | $$ \displaystyle 1 - {\textstyle\frac{N_{\mathstrut} - 1}{N^{\mathstrut} - m}} \, \frac{\sum\limits_{i=1}^n \,(y_{i,j} - \mu_{i,j})^2}{\sum\limits_{i=1}^n \Big(y_{i,j} - \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n y_{i',j} \Big)^{2}} $$
@@ -2125,7 +2125,7 @@ $m$ with the intercept or $m+1$ without the intercept.
| Statistic | Formula |
| --------------------- | ------------- |
-| $\texttt{PLAIN_R2_NOBIAS}_j$ | $$ \displaystyle 1 - \frac{\sum\limits_{i=1}^n \Big(y_{i,j} \,{-}\, \mu_{i,j} \,{-}\, \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n (y_{i',j} \,{-}\, \mu_{i',j}) \Big)^{2}}{\sum\limits_{i=1}^n \Big(y_{i,j} - \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n y_{i',j} \Big)^{2}} $$
+| $\texttt{R2_NOBIAS}_j$ | $$ \displaystyle 1 - \frac{\sum\limits_{i=1}^n \Big(y_{i,j} \,{-}\, \mu_{i,j} \,{-}\, \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n (y_{i',j} \,{-}\, \mu_{i',j}) \Big)^{2}}{\sum\limits_{i=1}^n \Big(y_{i,j} - \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n y_{i',j} \Big)^{2}} $$
| $\texttt{ADJUSTED_R2_NOBIAS}_j$ | $$ \displaystyle 1 - {\textstyle\frac{N_{\mathstrut} - 1}{N^{\mathstrut} - m'}} \, \frac{\sum\limits_{i=1}^n \Big(y_{i,j} \,{-}\, \mu_{i,j} \,{-}\, \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n (y_{i',j} \,{-}\, \mu_{i',j}) \Big)^{2}}{\sum\limits_{i=1}^n \Big(y_{i,j} - \frac{N_{i\mathstrut}}{N^{\mathstrut}} \sum\limits_{i'=1}^n y_{i',j} \Big)^{2}} $$
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/a6428f7d/docs/hadoop-batch-mode.md
----------------------------------------------------------------------
diff --git a/docs/hadoop-batch-mode.md b/docs/hadoop-batch-mode.md
index 3af7c0c..37df064 100644
--- a/docs/hadoop-batch-mode.md
+++ b/docs/hadoop-batch-mode.md
@@ -760,11 +760,11 @@ Let's go ahead and run the SystemML example from the GitHub README.
AVG_RES_Y,1.5905895170230406E-10
STDEV_RES_Y,2.0668015575844624E-8
DISPERSION,4.262683023432828E-16
- PLAIN_R2,1.0
+ R2,1.0
ADJUSTED_R2,1.0
- PLAIN_R2_NOBIAS,1.0
+ R2_NOBIAS,1.0
ADJUSTED_R2_NOBIAS,1.0
- PLAIN_R2_VS_0,1.0
+ R2_VS_0,1.0
ADJUSTED_R2_VS_0,1.0
Writing the output matrix...
END LINEAR REGRESSION SCRIPT
@@ -795,9 +795,9 @@ Let's go ahead and run the SystemML example from the GitHub README.
AVG_RES_Y,1,,2.5577864570734575E-10
STDEV_RES_Y,1,,2.390848397359923E-8
PRED_STDEV_RES,1,TRUE,1.0
- PLAIN_R2,1,,1.0
+ R2,1,,1.0
ADJUSTED_R2,1,,1.0
- PLAIN_R2_NOBIAS,1,,1.0
+ R2_NOBIAS,1,,1.0
ADJUSTED_R2_NOBIAS,1,,1.0
15/11/17 15:51:17 INFO api.DMLScript: SystemML Statistics:
Total execution time: 0.269 sec.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/a6428f7d/docs/standalone-guide.md
----------------------------------------------------------------------
diff --git a/docs/standalone-guide.md b/docs/standalone-guide.md
index 586e56e..a2a95d4 100644
--- a/docs/standalone-guide.md
+++ b/docs/standalone-guide.md
@@ -527,11 +527,11 @@ The LinearRegDS.dml script generates statistics to standard output similar to th
AVG_RES_Y,-3.3127468704080085E-10
STDEV_RES_Y,1.7231785003947183E-8
DISPERSION,2.963950542926297E-16
- PLAIN_R2,1.0
+ R2,1.0
ADJUSTED_R2,1.0
- PLAIN_R2_NOBIAS,1.0
+ R2_NOBIAS,1.0
ADJUSTED_R2_NOBIAS,1.0
- PLAIN_R2_VS_0,1.0
+ R2_VS_0,1.0
ADJUSTED_R2_VS_0,1.0
Writing the output matrix...
END LINEAR REGRESSION SCRIPT
@@ -572,9 +572,9 @@ This generates statistics similar to the following to standard output.
AVG_RES_Y,1,,-4.1450397073455047E-10
STDEV_RES_Y,1,,2.0519206226041048E-8
PRED_STDEV_RES,1,TRUE,1.0
- PLAIN_R2,1,,1.0
+ R2,1,,1.0
ADJUSTED_R2,1,,1.0
- PLAIN_R2_NOBIAS,1,,1.0
+ R2_NOBIAS,1,,1.0
ADJUSTED_R2_NOBIAS,1,,1.0
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/a6428f7d/scripts/algorithms/GLM-predict.dml
----------------------------------------------------------------------
diff --git a/scripts/algorithms/GLM-predict.dml b/scripts/algorithms/GLM-predict.dml
index e24b16f..251d85a 100644
--- a/scripts/algorithms/GLM-predict.dml
+++ b/scripts/algorithms/GLM-predict.dml
@@ -75,9 +75,9 @@
# AVG_RES_Y + Average of column residual, i.e. of Y - mean(Y|X)
# STDEV_RES_Y + St.Dev. of column residual, i.e. of Y - mean(Y|X)
# PRED_STDEV_RES + + Model-predicted St.Dev. of column residual
-# PLAIN_R2 + Plain R^2 of Y column residual with bias included
+# R2 + R^2 of Y column residual with bias included
# ADJUSTED_R2 + Adjusted R^2 of Y column residual with bias included
-# PLAIN_R2_NOBIAS + Plain R^2 of Y column residual with bias subtracted
+# R2_NOBIAS + R^2 of Y column residual with bias subtracted
# ADJUSTED_R2_NOBIAS + Adjusted R^2 of Y column residual with bias subtracted
# ---------------------------------------------------------------------------------------------
#
@@ -284,9 +284,9 @@ if (fileY != " ")
} else {
var_res_Y = matrix (0.0, rows = 1, cols = ncol (Y)) / 0.0;
}
- plain_R2_nobias = 1 - ss_avg_res_Y / ss_avg_tot_Y;
+ R2_nobias = 1 - ss_avg_res_Y / ss_avg_tot_Y;
adjust_R2_nobias = 1 - var_res_Y / var_tot_Y;
- plain_R2 = 1 - ss_res_Y / ss_avg_tot_Y;
+ R2 = 1 - ss_res_Y / ss_avg_tot_Y;
if (df_ss_res_Y > 0) {
adjust_R2 = 1 - (ss_res_Y / df_ss_res_Y) / var_tot_Y;
} else {
@@ -320,9 +320,9 @@ if (fileY != " ")
str = append (str, "AVG_RES_Y," + i + ",," + as.scalar (avg_res_Y [1, i]));
str = append (str, "STDEV_RES_Y," + i + ",," + as.scalar (sqrt (var_res_Y [1, i])));
str = append (str, "PRED_STDEV_RES," + i + ",TRUE," + as.scalar (sqrt (predicted_avg_var_res_Y [1, i])));
- str = append (str, "PLAIN_R2," + i + ",," + as.scalar (plain_R2 [1, i]));
+ str = append (str, "R2," + i + ",," + as.scalar (R2 [1, i]));
str = append (str, "ADJUSTED_R2," + i + ",," + as.scalar (adjust_R2 [1, i]));
- str = append (str, "PLAIN_R2_NOBIAS," + i + ",," + as.scalar (plain_R2_nobias [1, i]));
+ str = append (str, "R2_NOBIAS," + i + ",," + as.scalar (R2_nobias [1, i]));
str = append (str, "ADJUSTED_R2_NOBIAS," + i + ",," + as.scalar (adjust_R2_nobias [1, i]));
}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/a6428f7d/scripts/algorithms/LinearRegCG.dml
----------------------------------------------------------------------
diff --git a/scripts/algorithms/LinearRegCG.dml b/scripts/algorithms/LinearRegCG.dml
index 25e5862..9ba7e89 100644
--- a/scripts/algorithms/LinearRegCG.dml
+++ b/scripts/algorithms/LinearRegCG.dml
@@ -59,11 +59,11 @@
# AVG_RES_Y Average of the residual Y - pred(Y|X), i.e. residual bias
# STDEV_RES_Y Standard Deviation of the residual Y - pred(Y|X)
# DISPERSION GLM-style dispersion, i.e. residual sum of squares / # deg. fr.
-# PLAIN_R2 Plain R^2 of residual with bias included vs. total average
+# R2 R^2 of residual with bias included vs. total average
# ADJUSTED_R2 Adjusted R^2 of residual with bias included vs. total average
-# PLAIN_R2_NOBIAS Plain R^2 of residual with bias subtracted vs. total average
+# R2_NOBIAS R^2 of residual with bias subtracted vs. total average
# ADJUSTED_R2_NOBIAS Adjusted R^2 of residual with bias subtracted vs. total average
-# PLAIN_R2_VS_0 * Plain R^2 of residual with bias included vs. zero constant
+# R2_VS_0 * R^2 of residual with bias included vs. zero constant
# ADJUSTED_R2_VS_0 * Adjusted R^2 of residual with bias included vs. zero constant
# -------------------------------------------------------------------------------------
# * The last two statistics are only printed if there is no intercept (icpt=0)
@@ -223,7 +223,7 @@ avg_res = sum (y_residual) / n;
ss_res = sum (y_residual ^ 2);
ss_avg_res = ss_res - n * avg_res ^ 2;
-plain_R2 = 1 - ss_res / ss_avg_tot;
+R2 = 1 - ss_res / ss_avg_tot;
if (n > m_ext) {
dispersion = ss_res / (n - m_ext);
adjusted_R2 = 1 - dispersion / (ss_avg_tot / (n - 1));
@@ -232,7 +232,7 @@ if (n > m_ext) {
adjusted_R2 = 0.0 / 0.0;
}
-plain_R2_nobias = 1 - ss_avg_res / ss_avg_tot;
+R2_nobias = 1 - ss_avg_res / ss_avg_tot;
deg_freedom = n - m - 1;
if (deg_freedom > 0) {
var_res = ss_avg_res / deg_freedom;
@@ -243,7 +243,7 @@ if (deg_freedom > 0) {
print ("Warning: zero or negative number of degrees of freedom.");
}
-plain_R2_vs_0 = 1 - ss_res / ss_tot;
+R2_vs_0 = 1 - ss_res / ss_tot;
if (n > m) {
adjusted_R2_vs_0 = 1 - (ss_res / (n - m)) / (ss_tot / n);
} else {
@@ -255,12 +255,12 @@ str = append (str, "STDEV_TOT_Y," + sqrt (var_tot)); # Standard Dev
str = append (str, "AVG_RES_Y," + avg_res); # Average of the residual Y - pred(Y|X), i.e. residual bias
str = append (str, "STDEV_RES_Y," + sqrt (var_res)); # Standard Deviation of the residual Y - pred(Y|X)
str = append (str, "DISPERSION," + dispersion); # GLM-style dispersion, i.e. residual sum of squares / # d.f.
-str = append (str, "PLAIN_R2," + plain_R2); # Plain R^2 of residual with bias included vs. total average
+str = append (str, "R2," + R2); # R^2 of residual with bias included vs. total average
str = append (str, "ADJUSTED_R2," + adjusted_R2); # Adjusted R^2 of residual with bias included vs. total average
-str = append (str, "PLAIN_R2_NOBIAS," + plain_R2_nobias); # Plain R^2 of residual with bias subtracted vs. total average
+str = append (str, "R2_NOBIAS," + R2_nobias); # R^2 of residual with bias subtracted vs. total average
str = append (str, "ADJUSTED_R2_NOBIAS," + adjusted_R2_nobias); # Adjusted R^2 of residual with bias subtracted vs. total average
if (intercept_status == 0) {
- str = append (str, "PLAIN_R2_VS_0," + plain_R2_vs_0); # Plain R^2 of residual with bias included vs. zero constant
+ str = append (str, "R2_VS_0," + R2_vs_0); # R^2 of residual with bias included vs. zero constant
str = append (str, "ADJUSTED_R2_VS_0," + adjusted_R2_vs_0); # Adjusted R^2 of residual with bias included vs. zero constant
}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/a6428f7d/scripts/algorithms/LinearRegDS.dml
----------------------------------------------------------------------
diff --git a/scripts/algorithms/LinearRegDS.dml b/scripts/algorithms/LinearRegDS.dml
index 2f55b41..10def79 100644
--- a/scripts/algorithms/LinearRegDS.dml
+++ b/scripts/algorithms/LinearRegDS.dml
@@ -55,11 +55,11 @@
# AVG_RES_Y Average of the residual Y - pred(Y|X), i.e. residual bias
# STDEV_RES_Y Standard Deviation of the residual Y - pred(Y|X)
# DISPERSION GLM-style dispersion, i.e. residual sum of squares / # deg. fr.
-# PLAIN_R2 Plain R^2 of residual with bias included vs. total average
+# R2 R^2 of residual with bias included vs. total average
# ADJUSTED_R2 Adjusted R^2 of residual with bias included vs. total average
-# PLAIN_R2_NOBIAS Plain R^2 of residual with bias subtracted vs. total average
+# R2_NOBIAS R^2 of residual with bias subtracted vs. total average
# ADJUSTED_R2_NOBIAS Adjusted R^2 of residual with bias subtracted vs. total average
-# PLAIN_R2_VS_0 * Plain R^2 of residual with bias included vs. zero constant
+# R2_VS_0 * R^2 of residual with bias included vs. zero constant
# ADJUSTED_R2_VS_0 * Adjusted R^2 of residual with bias included vs. zero constant
# -------------------------------------------------------------------------------------
# * The last two statistics are only printed if there is no intercept (icpt=0)
@@ -165,7 +165,7 @@ avg_res = sum (y_residual) / n;
ss_res = sum (y_residual ^ 2);
ss_avg_res = ss_res - n * avg_res ^ 2;
-plain_R2 = 1 - ss_res / ss_avg_tot;
+R2 = 1 - ss_res / ss_avg_tot;
if (n > m_ext) {
dispersion = ss_res / (n - m_ext);
adjusted_R2 = 1 - dispersion / (ss_avg_tot / (n - 1));
@@ -174,7 +174,7 @@ if (n > m_ext) {
adjusted_R2 = 0.0 / 0.0;
}
-plain_R2_nobias = 1 - ss_avg_res / ss_avg_tot;
+R2_nobias = 1 - ss_avg_res / ss_avg_tot;
deg_freedom = n - m - 1;
if (deg_freedom > 0) {
var_res = ss_avg_res / deg_freedom;
@@ -185,7 +185,7 @@ if (deg_freedom > 0) {
print ("Warning: zero or negative number of degrees of freedom.");
}
-plain_R2_vs_0 = 1 - ss_res / ss_tot;
+R2_vs_0 = 1 - ss_res / ss_tot;
if (n > m) {
adjusted_R2_vs_0 = 1 - (ss_res / (n - m)) / (ss_tot / n);
} else {
@@ -197,12 +197,12 @@ str = append (str, "STDEV_TOT_Y," + sqrt (var_tot)); # Standard Dev
str = append (str, "AVG_RES_Y," + avg_res); # Average of the residual Y - pred(Y|X), i.e. residual bias
str = append (str, "STDEV_RES_Y," + sqrt (var_res)); # Standard Deviation of the residual Y - pred(Y|X)
str = append (str, "DISPERSION," + dispersion); # GLM-style dispersion, i.e. residual sum of squares / # d.f.
-str = append (str, "PLAIN_R2," + plain_R2); # Plain R^2 of residual with bias included vs. total average
+str = append (str, "R2," + R2); # R^2 of residual with bias included vs. total average
str = append (str, "ADJUSTED_R2," + adjusted_R2); # Adjusted R^2 of residual with bias included vs. total average
-str = append (str, "PLAIN_R2_NOBIAS," + plain_R2_nobias); # Plain R^2 of residual with bias subtracted vs. total average
+str = append (str, "R2_NOBIAS," + R2_nobias); # R^2 of residual with bias subtracted vs. total average
str = append (str, "ADJUSTED_R2_NOBIAS," + adjusted_R2_nobias); # Adjusted R^2 of residual with bias subtracted vs. total average
if (intercept_status == 0) {
- str = append (str, "PLAIN_R2_VS_0," + plain_R2_vs_0); # Plain R^2 of residual with bias included vs. zero constant
+ str = append (str, "R2_VS_0," + R2_vs_0); # R^2 of residual with bias included vs. zero constant
str = append (str, "ADJUSTED_R2_VS_0," + adjusted_R2_vs_0); # Adjusted R^2 of residual with bias included vs. zero constant
}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/a6428f7d/scripts/algorithms/StepLinearRegDS.dml
----------------------------------------------------------------------
diff --git a/scripts/algorithms/StepLinearRegDS.dml b/scripts/algorithms/StepLinearRegDS.dml
index 2efb2bb..00f50ee 100644
--- a/scripts/algorithms/StepLinearRegDS.dml
+++ b/scripts/algorithms/StepLinearRegDS.dml
@@ -57,11 +57,11 @@
# AVG_RES_Y Average of the residual Y - pred(Y|X), i.e. residual bias
# STDEV_RES_Y Standard Deviation of the residual Y - pred(Y|X)
# DISPERSION GLM-style dispersion, i.e. residual sum of squares / # deg. fr.
-# PLAIN_R2 Plain R^2 of residual with bias included vs. total average
+# R2 R^2 of residual with bias included vs. total average
# ADJUSTED_R2 Adjusted R^2 of residual with bias included vs. total average
-# PLAIN_R2_NOBIAS Plain R^2 of residual with bias subtracted vs. total average
+# R2_NOBIAS R^2 of residual with bias subtracted vs. total average
# ADJUSTED_R2_NOBIAS Adjusted R^2 of residual with bias subtracted vs. total average
-# PLAIN_R2_VS_0 * Plain R^2 of residual with bias included vs. zero constant
+# R2_VS_0 * R^2 of residual with bias included vs. zero constant
# ADJUSTED_R2_VS_0 * Adjusted R^2 of residual with bias included vs. zero constant
# -------------------------------------------------------------------------------------
# * The last two statistics are only printed if there is no intercept (icpt=0)
@@ -271,7 +271,7 @@ linear_regression = function (Matrix[Double] X, Matrix[Double] y, Double m_orig,
# ss_res = sum (y_residual ^ 2);
ss_avg_res = ss_res - n * avg_res ^ 2;
- plain_R2 = 1 - ss_res / ss_avg_tot;
+ R2 = 1 - ss_res / ss_avg_tot;
if (n > m_ext) {
dispersion = ss_res / (n - m_ext);
adjusted_R2 = 1 - dispersion / (ss_avg_tot / (n - 1));
@@ -280,7 +280,7 @@ linear_regression = function (Matrix[Double] X, Matrix[Double] y, Double m_orig,
adjusted_R2 = 0.0 / 0.0;
}
- plain_R2_nobias = 1 - ss_avg_res / ss_avg_tot;
+ R2_nobias = 1 - ss_avg_res / ss_avg_tot;
deg_freedom = n - m - 1;
if (deg_freedom > 0) {
var_res = ss_avg_res / deg_freedom;
@@ -291,7 +291,7 @@ linear_regression = function (Matrix[Double] X, Matrix[Double] y, Double m_orig,
print ("Warning: zero or negative number of degrees of freedom.");
}
- plain_R2_vs_0 = 1 - ss_res / ss_tot;
+ R2_vs_0 = 1 - ss_res / ss_tot;
if (n > m) {
adjusted_R2_vs_0 = 1 - (ss_res / (n - m)) / (ss_tot / n);
} else {
@@ -303,12 +303,12 @@ linear_regression = function (Matrix[Double] X, Matrix[Double] y, Double m_orig,
str = append (str, "AVG_RES_Y," + avg_res); # Average of the residual Y - pred(Y|X), i.e. residual bias
str = append (str, "STDEV_RES_Y," + sqrt (var_res)); # Standard Deviation of the residual Y - pred(Y|X)
str = append (str, "DISPERSION," + dispersion); # GLM-style dispersion, i.e. residual sum of squares / # d.f.
- str = append (str, "PLAIN_R2," + plain_R2); # Plain R^2 of residual with bias included vs. total average
+ str = append (str, "R2," + R2); # R^2 of residual with bias included vs. total average
str = append (str, "ADJUSTED_R2," + adjusted_R2); # Adjusted R^2 of residual with bias included vs. total average
- str = append (str, "PLAIN_R2_NOBIAS," + plain_R2_nobias); # Plain R^2 of residual with bias subtracted vs. total average
+ str = append (str, "R2_NOBIAS," + R2_nobias); # R^2 of residual with bias subtracted vs. total average
str = append (str, "ADJUSTED_R2_NOBIAS," + adjusted_R2_nobias); # Adjusted R^2 of residual with bias subtracted vs. total average
if (intercept_status == 0) {
- str = append (str, "PLAIN_R2_VS_0," + plain_R2_vs_0); # Plain R^2 of residual with bias included vs. zero constant
+ str = append (str, "R2_VS_0," + R2_vs_0); # R^2 of residual with bias included vs. zero constant
str = append (str, "ADJUSTED_R2_VS_0," + adjusted_R2_vs_0); # Adjusted R^2 of residual with bias included vs. zero constant
}