3 light formatting

I have the following result from running glm function.

How can I interpret the following values:

• Null deviance
• Residual deviance
• AIC

Do they have something to do with the goodness of fit? Can I calculate some goodness of fit measure from these result such as R-square or any other measure?

Call:
glm(formula = tmpData$$Y ~ tmpData$$X1 + tmpData$$X2+ tmpData$$$$X2 + tmpData$$X3 +
as.numeric(tmpData$$X4) + tmpData$$X5 +
tmpData$$X6 + tmpData$$X7)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-0.52628  -0.24781  -0.02916   0.25581   0.48509

Coefficients:
Estimate Std. Error  t value Pr(>|t|)
(Intercept         -1.305e-01  1.391e-01   -0.938   0.3482
tmpData$$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$$X2         -1.001e+00  1.104e-03 -906.787   <2e-16 ***
tmpData$$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$$X4         -1.825e-05  2.716e-05   -0.672   0.5017
tmpData$$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$$X6          1.002e+00  1.452e-03  690.211   <2e-16 ***
tmpData$X7 6.128e-04 3.035e-04 2.019 0.0436 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 0.08496843) Null deviance: 109217.71 on 3006 degrees of freedom Residual deviance: 254.82 on 2999 degrees of freedom (4970 observations deleted due to missingness) AIC: 1129.8 Number of Fisher Scoring iterations: 2  I have the following result from running glm function. How can I interpret the following values: • Null deviance • Residual deviance • AIC Do they have something to do with the goodness of fit? Can I calculate some goodness of fit measure from these result such as R-square or any other measure? Call: glm(formula = tmpData$$Y ~ tmpData$$X1 + tmpData$$X2+ tmpData$$X3 + as.numeric(tmpData$$X4) + tmpData$$X5 + tmpData$$X6 + tmpData$$X7) Deviance Residuals: Min 1Q Median 3Q Max -0.52628 -0.24781 -0.02916 0.25581 0.48509 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept -1.305e-01 1.391e-01 -0.938 0.3482 tmpData$$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$$X2 -1.001e+00 1.104e-03 -906.787 <2e-16 *** tmpData$$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$$X4 -1.825e-05 2.716e-05 -0.672 0.5017 tmpData$$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$$X6 1.002e+00 1.452e-03 690.211 <2e-16 *** tmpData$X7          6.128e-04  3.035e-04    2.019   0.0436 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.08496843)

Null deviance: 109217.71  on 3006  degrees of freedom
Residual deviance:    254.82  on 2999  degrees of freedom
(4970 observations deleted due to missingness)
AIC: 1129.8

Number of Fisher Scoring iterations: 2


I have the following result from running glm function.

How can I interpret the following values:

• Null deviance
• Residual deviance
• AIC

Do they have something to do with the goodness of fit? Can I calculate some goodness of fit measure from these result such as R-square or any other measure?

Call:
glm(formula = tmpData$$Y ~ tmpData$$X1 + tmpData$$X2 + tmpData$$X3 +
as.numeric(tmpData$$X4) + tmpData$$X5 + tmpData$$X6 + tmpData$$X7)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-0.52628  -0.24781  -0.02916   0.25581   0.48509

Coefficients:
Estimate Std. Error  t value Pr(>|t|)
(Intercept         -1.305e-01  1.391e-01   -0.938   0.3482
tmpData$$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$$X2         -1.001e+00  1.104e-03 -906.787   <2e-16 ***
tmpData$$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$$X4         -1.825e-05  2.716e-05   -0.672   0.5017
tmpData$$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$$X6          1.002e+00  1.452e-03  690.211   <2e-16 ***
tmpData$X7 6.128e-04 3.035e-04 2.019 0.0436 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 0.08496843) Null deviance: 109217.71 on 3006 degrees of freedom Residual deviance: 254.82 on 2999 degrees of freedom (4970 observations deleted due to missingness) AIC: 1129.8 Number of Fisher Scoring iterations: 2  2 deleted 3 characters in body I have the following result from running glm function. How can I interpret the following values: -Null deviance -residual deviance and -AIC. Do thay • Null deviance • Residual deviance • AIC Do they have something to do with the goodness of fit. ? Can I calculate some goodness of fit measure from these result such as R-square or any other measure.? Call: glm(formula = tmpData$$Y ~ tmpData$$X1 + tmpData$$X2+ tmpData$$X3 + as.numeric(tmpData$$X4) + tmpData$$X5 + tmpData$$X6 + tmpData$$X7) Deviance Residuals: Min 1Q Median 3Q Max -0.52628 -0.24781 -0.02916 0.25581 0.48509 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept -1.305e-01 1.391e-01 -0.938 0.3482 tmpData$$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$$X2 -1.001e+00 1.104e-03 -906.787 <2e-16 *** tmpData$$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$$X4 -1.825e-05 2.716e-05 -0.672 0.5017 tmpData$$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$$X6 1.002e+00 1.452e-03 690.211 <2e-16 *** tmpData$X7          6.128e-04  3.035e-04    2.019   0.0436 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.08496843)

Null deviance: 109217.71  on 3006  degrees of freedom
Residual deviance:    254.82  on 2999  degrees of freedom
(4970 observations deleted due to missingness)
AIC: 1129.8

Number of Fisher Scoring iterations: 2


I have the following result from running glm function.

How can I interpret the following values: -Null deviance -residual deviance and -AIC. Do thay have something to do with the goodness of fit.

Can I calculate some goodness of fit measure from these result such as R-square or any other measure.

Call:
glm(formula = tmpData$$Y ~ tmpData$$X1 + tmpData$$X2+ tmpData$$X3 + as.numeric(tmpData$$X4) + tmpData$$X5 +
tmpData$$X6 + tmpData$$X7)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-0.52628  -0.24781  -0.02916   0.25581   0.48509

Coefficients:
Estimate Std. Error  t value Pr(>|t|)
(Intercept         -1.305e-01  1.391e-01   -0.938   0.3482
tmpData$$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$$X2         -1.001e+00  1.104e-03 -906.787   <2e-16 ***
tmpData$$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$$X4         -1.825e-05  2.716e-05   -0.672   0.5017
tmpData$$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$$X6          1.002e+00  1.452e-03  690.211   <2e-16 ***
tmpData$X7 6.128e-04 3.035e-04 2.019 0.0436 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 0.08496843) Null deviance: 109217.71 on 3006 degrees of freedom Residual deviance: 254.82 on 2999 degrees of freedom (4970 observations deleted due to missingness) AIC: 1129.8 Number of Fisher Scoring iterations: 2  I have the following result from running glm function. How can I interpret the following values: • Null deviance • Residual deviance • AIC Do they have something to do with the goodness of fit? Can I calculate some goodness of fit measure from these result such as R-square or any other measure? Call: glm(formula = tmpData$$Y ~ tmpData$$X1 + tmpData$$X2+ tmpData$$X3 + as.numeric(tmpData$$X4) + tmpData$$X5 + tmpData$$X6 + tmpData$$X7) Deviance Residuals: Min 1Q Median 3Q Max -0.52628 -0.24781 -0.02916 0.25581 0.48509 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept -1.305e-01 1.391e-01 -0.938 0.3482 tmpData$$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$$X2 -1.001e+00 1.104e-03 -906.787 <2e-16 *** tmpData$$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$$X4 -1.825e-05 2.716e-05 -0.672 0.5017 tmpData$$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$$X6 1.002e+00 1.452e-03 690.211 <2e-16 *** tmpData$X7          6.128e-04  3.035e-04    2.019   0.0436 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.08496843)

Null deviance: 109217.71  on 3006  degrees of freedom
Residual deviance:    254.82  on 2999  degrees of freedom
(4970 observations deleted due to missingness)
AIC: 1129.8

Number of Fisher Scoring iterations: 2

Post Migrated Here from stackoverflow.com
1

# How to calculate goodness of fit in glm (R)

I have the following result from running glm function.

How can I interpret the following values: -Null deviance -residual deviance and -AIC. Do thay have something to do with the goodness of fit.

Can I calculate some goodness of fit measure from these result such as R-square or any other measure.

Call:
glm(formula = tmpData$$Y ~ tmpData$$X1 + tmpData$$X2+ tmpData$$X3 + as.numeric(tmpData$$X4) + tmpData$$X5 +
tmpData$$X6 + tmpData$$X7)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-0.52628  -0.24781  -0.02916   0.25581   0.48509

Coefficients:
Estimate Std. Error  t value Pr(>|t|)
(Intercept         -1.305e-01  1.391e-01   -0.938   0.3482
tmpData$$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$$X2         -1.001e+00  1.104e-03 -906.787   <2e-16 ***
tmpData$$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$$X4         -1.825e-05  2.716e-05   -0.672   0.5017
tmpData$$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$$X6          1.002e+00  1.452e-03  690.211   <2e-16 ***
tmpData\$X7          6.128e-04  3.035e-04    2.019   0.0436 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.08496843)

Null deviance: 109217.71  on 3006  degrees of freedom
Residual deviance:    254.82  on 2999  degrees of freedom
(4970 observations deleted due to missingness)
AIC: 1129.8

Number of Fisher Scoring iterations: 2