gam: Interfaces for gam package for data science pipelines.
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Interfaces to gam functions that can be used
in a pipeline implemented by magrittr.
Usage
1
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
## Not run:
library(intubate)
library(gam)
data(kyphosis)
## Original function to interface
fit <- gam(Kyphosis ~ s(Age,4) + Number, family = binomial, data = kyphosis,
trace = TRUE)
summary(fit)
fit <- gam(Kyphosis ~ poly(Age,2) + s(Start), data = kyphosis,
family = binomial, subset = Number > 2)
summary(fit)
fit <- gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp),
data = airquality, na = na.gam.replace)
summary(fit)
## The interface reverses the order of data and formula
fit <- ntbt_gam(kyphosis, Kyphosis ~ s(Age,4) + Number,
family = binomial, trace = TRUE)
summary(fit)
fit <- ntbt_gam(data = kyphosis, Kyphosis ~ poly(Age,2) + s(Start),
family = binomial, subset = Number > 2)
summary(fit)
fit <- ntbt_gam(data = airquality, Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp),
na = na.gam.replace)
summary(fit)
## so it can be used easily in a pipeline.
library(magrittr)
kyphosis %>%
ntbt_gam(Kyphosis ~ s(Age,4) + Number,
family = binomial, trace = TRUE) %>%
summary()
kyphosis %>%
ntbt_gam(Kyphosis ~ poly(Age,2) + s(Start),
family = binomial, subset = Number > 2) %>%
summary()
airquality %>%
ntbt_gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp),
na = na.gam.replace) %>%
summary()
## End(Not run)
Example output
Loading required package: splines
Loading required package: foreach
Loaded gam 1.14-4
GAM s.wam loop 1: deviance = 66.42095
GAM s.wam loop 2: deviance = 63.77252
GAM s.wam loop 3: deviance = 63.25199
GAM s.wam loop 4: deviance = 63.13399
GAM s.wam loop 5: deviance = 63.11016
GAM s.wam loop 6: deviance = 63.10748
GAM s.wam loop 7: deviance = 63.10727
GAM s.wam loop 8: deviance = 63.10725
GAM s.wam loop 9: deviance = 63.10725
Call: gam(formula = Kyphosis ~ s(Age, 4) + Number, family = binomial,
data = kyphosis, trace = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7844 -0.7190 -0.3593 -0.1199 2.3198
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 83.2345 on 80 degrees of freedom
Residual Deviance: 63.1073 on 75 degrees of freedom
AIC: 75.1072
Number of Local Scoring Iterations: 9
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
s(Age, 4) 1 0.382 0.3818 0.4459 0.50632
Number 1 7.441 7.4412 8.6914 0.00426 **
Residuals 75 64.212 0.8562
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
s(Age, 4) 3 5.8993 0.1166
Number
Call: gam(formula = Kyphosis ~ poly(Age, 2) + s(Start), family = binomial,
data = kyphosis, subset = Number > 2)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79693 -0.41691 -0.19525 -0.02327 2.09833
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 77.0481 on 68 degrees of freedom
Residual Deviance: 44.5635 on 61.9998 degrees of freedom
AIC: 58.564
Number of Local Scoring Iterations: 10
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
poly(Age, 2) 2 6.363 3.1813 4.5712 0.014065 *
s(Start) 1 5.184 5.1838 7.4487 0.008251 **
Residuals 62 43.148 0.6959
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
poly(Age, 2)
s(Start) 3 6.9637 0.07308 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In na.action(mf) :
37 observations omitted due to missing values in the response
Call: gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality,
na.action = na.gam.replace)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.22276 -0.27667 -0.03453 0.32553 0.93450
(Dispersion Parameter for gaussian family taken to be 0.1953)
Null Deviance: 90.7149 on 115 degrees of freedom
Residual Deviance: 20.0523 on 102.6679 degrees of freedom
AIC: 154.2493
Number of Local Scoring Iterations: 2
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
lo(Solar.R) 1.00 15.515 15.5147 79.435 1.968e-14 ***
lo(Wind, Temp) 2.00 38.594 19.2972 98.802 < 2.2e-16 ***
Residuals 102.67 20.052 0.1953
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar F Pr(F)
(Intercept)
lo(Solar.R) 2.6 2.6590 0.06092 .
lo(Wind, Temp) 6.8 7.4772 4.027e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GAM s.wam loop 1: deviance = 66.42095
GAM s.wam loop 2: deviance = 63.77252
GAM s.wam loop 3: deviance = 63.25199
GAM s.wam loop 4: deviance = 63.13399
GAM s.wam loop 5: deviance = 63.11016
GAM s.wam loop 6: deviance = 63.10748
GAM s.wam loop 7: deviance = 63.10727
GAM s.wam loop 8: deviance = 63.10725
GAM s.wam loop 9: deviance = 63.10725
Call: gam(formula = Kyphosis ~ s(Age, 4) + Number, family = binomial,
data = kyphosis, trace = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7844 -0.7190 -0.3593 -0.1199 2.3198
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 83.2345 on 80 degrees of freedom
Residual Deviance: 63.1073 on 75 degrees of freedom
AIC: 75.1072
Number of Local Scoring Iterations: 9
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
s(Age, 4) 1 0.382 0.3818 0.4459 0.50632
Number 1 7.441 7.4412 8.6914 0.00426 **
Residuals 75 64.212 0.8562
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
s(Age, 4) 3 5.8993 0.1166
Number
Call: gam(formula = Kyphosis ~ poly(Age, 2) + s(Start), family = binomial,
data = kyphosis, subset = Number > 2)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79693 -0.41691 -0.19525 -0.02327 2.09833
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 77.0481 on 68 degrees of freedom
Residual Deviance: 44.5635 on 61.9998 degrees of freedom
AIC: 58.564
Number of Local Scoring Iterations: 10
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
poly(Age, 2) 2 6.363 3.1813 4.5712 0.014065 *
s(Start) 1 5.184 5.1838 7.4487 0.008251 **
Residuals 62 43.148 0.6959
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
poly(Age, 2)
s(Start) 3 6.9637 0.07308 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In na.action(mf) :
37 observations omitted due to missing values in the response
Call: gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality,
na.action = na.gam.replace)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.22276 -0.27667 -0.03453 0.32553 0.93450
(Dispersion Parameter for gaussian family taken to be 0.1953)
Null Deviance: 90.7149 on 115 degrees of freedom
Residual Deviance: 20.0523 on 102.6679 degrees of freedom
AIC: 154.2493
Number of Local Scoring Iterations: 2
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
lo(Solar.R) 1.00 15.515 15.5147 79.435 1.968e-14 ***
lo(Wind, Temp) 2.00 38.594 19.2972 98.802 < 2.2e-16 ***
Residuals 102.67 20.052 0.1953
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar F Pr(F)
(Intercept)
lo(Solar.R) 2.6 2.6590 0.06092 .
lo(Wind, Temp) 6.8 7.4772 4.027e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GAM s.wam loop 1: deviance = 66.42095
GAM s.wam loop 2: deviance = 63.77252
GAM s.wam loop 3: deviance = 63.25199
GAM s.wam loop 4: deviance = 63.13399
GAM s.wam loop 5: deviance = 63.11016
GAM s.wam loop 6: deviance = 63.10748
GAM s.wam loop 7: deviance = 63.10727
GAM s.wam loop 8: deviance = 63.10725
GAM s.wam loop 9: deviance = 63.10725
Call: gam(formula = Kyphosis ~ s(Age, 4) + Number, family = binomial,
data = ., trace = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7844 -0.7190 -0.3593 -0.1199 2.3198
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 83.2345 on 80 degrees of freedom
Residual Deviance: 63.1073 on 75 degrees of freedom
AIC: 75.1072
Number of Local Scoring Iterations: 9
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
s(Age, 4) 1 0.382 0.3818 0.4459 0.50632
Number 1 7.441 7.4412 8.6914 0.00426 **
Residuals 75 64.212 0.8562
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
s(Age, 4) 3 5.8993 0.1166
Number
Call: gam(formula = Kyphosis ~ poly(Age, 2) + s(Start), family = binomial,
data = ., subset = Number > 2)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79693 -0.41691 -0.19525 -0.02327 2.09833
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 77.0481 on 68 degrees of freedom
Residual Deviance: 44.5635 on 61.9998 degrees of freedom
AIC: 58.564
Number of Local Scoring Iterations: 10
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
poly(Age, 2) 2 6.363 3.1813 4.5712 0.014065 *
s(Start) 1 5.184 5.1838 7.4487 0.008251 **
Residuals 62 43.148 0.6959
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
poly(Age, 2)
s(Start) 3 6.9637 0.07308 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Call: gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = .,
na.action = na.gam.replace)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.22276 -0.27667 -0.03453 0.32553 0.93450
(Dispersion Parameter for gaussian family taken to be 0.1953)
Null Deviance: 90.7149 on 115 degrees of freedom
Residual Deviance: 20.0523 on 102.6679 degrees of freedom
AIC: 154.2493
Number of Local Scoring Iterations: 2
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
lo(Solar.R) 1.00 15.515 15.5147 79.435 1.968e-14 ***
lo(Wind, Temp) 2.00 38.594 19.2972 98.802 < 2.2e-16 ***
Residuals 102.67 20.052 0.1953
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar F Pr(F)
(Intercept)
lo(Solar.R) 2.6 2.6590 0.06092 .
lo(Wind, Temp) 6.8 7.4772 4.027e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In na.action(mf) :
37 observations omitted due to missing values in the response
intubate documentation built on May 2, 2019, 2:46 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) See Also Examples
Description
Interfaces to gam functions that can be used
in a pipeline implemented by magrittr.
Usage
1 |
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ## Not run:
library(intubate)
library(gam)
data(kyphosis)
## Original function to interface
fit <- gam(Kyphosis ~ s(Age,4) + Number, family = binomial, data = kyphosis,
trace = TRUE)
summary(fit)
fit <- gam(Kyphosis ~ poly(Age,2) + s(Start), data = kyphosis,
family = binomial, subset = Number > 2)
summary(fit)
fit <- gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp),
data = airquality, na = na.gam.replace)
summary(fit)
## The interface reverses the order of data and formula
fit <- ntbt_gam(kyphosis, Kyphosis ~ s(Age,4) + Number,
family = binomial, trace = TRUE)
summary(fit)
fit <- ntbt_gam(data = kyphosis, Kyphosis ~ poly(Age,2) + s(Start),
family = binomial, subset = Number > 2)
summary(fit)
fit <- ntbt_gam(data = airquality, Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp),
na = na.gam.replace)
summary(fit)
## so it can be used easily in a pipeline.
library(magrittr)
kyphosis %>%
ntbt_gam(Kyphosis ~ s(Age,4) + Number,
family = binomial, trace = TRUE) %>%
summary()
kyphosis %>%
ntbt_gam(Kyphosis ~ poly(Age,2) + s(Start),
family = binomial, subset = Number > 2) %>%
summary()
airquality %>%
ntbt_gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp),
na = na.gam.replace) %>%
summary()
## End(Not run)
|
Example output
Loading required package: splines
Loading required package: foreach
Loaded gam 1.14-4
GAM s.wam loop 1: deviance = 66.42095
GAM s.wam loop 2: deviance = 63.77252
GAM s.wam loop 3: deviance = 63.25199
GAM s.wam loop 4: deviance = 63.13399
GAM s.wam loop 5: deviance = 63.11016
GAM s.wam loop 6: deviance = 63.10748
GAM s.wam loop 7: deviance = 63.10727
GAM s.wam loop 8: deviance = 63.10725
GAM s.wam loop 9: deviance = 63.10725
Call: gam(formula = Kyphosis ~ s(Age, 4) + Number, family = binomial,
data = kyphosis, trace = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7844 -0.7190 -0.3593 -0.1199 2.3198
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 83.2345 on 80 degrees of freedom
Residual Deviance: 63.1073 on 75 degrees of freedom
AIC: 75.1072
Number of Local Scoring Iterations: 9
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
s(Age, 4) 1 0.382 0.3818 0.4459 0.50632
Number 1 7.441 7.4412 8.6914 0.00426 **
Residuals 75 64.212 0.8562
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
s(Age, 4) 3 5.8993 0.1166
Number
Call: gam(formula = Kyphosis ~ poly(Age, 2) + s(Start), family = binomial,
data = kyphosis, subset = Number > 2)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79693 -0.41691 -0.19525 -0.02327 2.09833
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 77.0481 on 68 degrees of freedom
Residual Deviance: 44.5635 on 61.9998 degrees of freedom
AIC: 58.564
Number of Local Scoring Iterations: 10
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
poly(Age, 2) 2 6.363 3.1813 4.5712 0.014065 *
s(Start) 1 5.184 5.1838 7.4487 0.008251 **
Residuals 62 43.148 0.6959
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
poly(Age, 2)
s(Start) 3 6.9637 0.07308 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In na.action(mf) :
37 observations omitted due to missing values in the response
Call: gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality,
na.action = na.gam.replace)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.22276 -0.27667 -0.03453 0.32553 0.93450
(Dispersion Parameter for gaussian family taken to be 0.1953)
Null Deviance: 90.7149 on 115 degrees of freedom
Residual Deviance: 20.0523 on 102.6679 degrees of freedom
AIC: 154.2493
Number of Local Scoring Iterations: 2
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
lo(Solar.R) 1.00 15.515 15.5147 79.435 1.968e-14 ***
lo(Wind, Temp) 2.00 38.594 19.2972 98.802 < 2.2e-16 ***
Residuals 102.67 20.052 0.1953
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar F Pr(F)
(Intercept)
lo(Solar.R) 2.6 2.6590 0.06092 .
lo(Wind, Temp) 6.8 7.4772 4.027e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GAM s.wam loop 1: deviance = 66.42095
GAM s.wam loop 2: deviance = 63.77252
GAM s.wam loop 3: deviance = 63.25199
GAM s.wam loop 4: deviance = 63.13399
GAM s.wam loop 5: deviance = 63.11016
GAM s.wam loop 6: deviance = 63.10748
GAM s.wam loop 7: deviance = 63.10727
GAM s.wam loop 8: deviance = 63.10725
GAM s.wam loop 9: deviance = 63.10725
Call: gam(formula = Kyphosis ~ s(Age, 4) + Number, family = binomial,
data = kyphosis, trace = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7844 -0.7190 -0.3593 -0.1199 2.3198
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 83.2345 on 80 degrees of freedom
Residual Deviance: 63.1073 on 75 degrees of freedom
AIC: 75.1072
Number of Local Scoring Iterations: 9
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
s(Age, 4) 1 0.382 0.3818 0.4459 0.50632
Number 1 7.441 7.4412 8.6914 0.00426 **
Residuals 75 64.212 0.8562
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
s(Age, 4) 3 5.8993 0.1166
Number
Call: gam(formula = Kyphosis ~ poly(Age, 2) + s(Start), family = binomial,
data = kyphosis, subset = Number > 2)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79693 -0.41691 -0.19525 -0.02327 2.09833
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 77.0481 on 68 degrees of freedom
Residual Deviance: 44.5635 on 61.9998 degrees of freedom
AIC: 58.564
Number of Local Scoring Iterations: 10
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
poly(Age, 2) 2 6.363 3.1813 4.5712 0.014065 *
s(Start) 1 5.184 5.1838 7.4487 0.008251 **
Residuals 62 43.148 0.6959
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
poly(Age, 2)
s(Start) 3 6.9637 0.07308 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In na.action(mf) :
37 observations omitted due to missing values in the response
Call: gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality,
na.action = na.gam.replace)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.22276 -0.27667 -0.03453 0.32553 0.93450
(Dispersion Parameter for gaussian family taken to be 0.1953)
Null Deviance: 90.7149 on 115 degrees of freedom
Residual Deviance: 20.0523 on 102.6679 degrees of freedom
AIC: 154.2493
Number of Local Scoring Iterations: 2
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
lo(Solar.R) 1.00 15.515 15.5147 79.435 1.968e-14 ***
lo(Wind, Temp) 2.00 38.594 19.2972 98.802 < 2.2e-16 ***
Residuals 102.67 20.052 0.1953
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar F Pr(F)
(Intercept)
lo(Solar.R) 2.6 2.6590 0.06092 .
lo(Wind, Temp) 6.8 7.4772 4.027e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GAM s.wam loop 1: deviance = 66.42095
GAM s.wam loop 2: deviance = 63.77252
GAM s.wam loop 3: deviance = 63.25199
GAM s.wam loop 4: deviance = 63.13399
GAM s.wam loop 5: deviance = 63.11016
GAM s.wam loop 6: deviance = 63.10748
GAM s.wam loop 7: deviance = 63.10727
GAM s.wam loop 8: deviance = 63.10725
GAM s.wam loop 9: deviance = 63.10725
Call: gam(formula = Kyphosis ~ s(Age, 4) + Number, family = binomial,
data = ., trace = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7844 -0.7190 -0.3593 -0.1199 2.3198
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 83.2345 on 80 degrees of freedom
Residual Deviance: 63.1073 on 75 degrees of freedom
AIC: 75.1072
Number of Local Scoring Iterations: 9
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
s(Age, 4) 1 0.382 0.3818 0.4459 0.50632
Number 1 7.441 7.4412 8.6914 0.00426 **
Residuals 75 64.212 0.8562
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
s(Age, 4) 3 5.8993 0.1166
Number
Call: gam(formula = Kyphosis ~ poly(Age, 2) + s(Start), family = binomial,
data = ., subset = Number > 2)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79693 -0.41691 -0.19525 -0.02327 2.09833
(Dispersion Parameter for binomial family taken to be 1)
Null Deviance: 77.0481 on 68 degrees of freedom
Residual Deviance: 44.5635 on 61.9998 degrees of freedom
AIC: 58.564
Number of Local Scoring Iterations: 10
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
poly(Age, 2) 2 6.363 3.1813 4.5712 0.014065 *
s(Start) 1 5.184 5.1838 7.4487 0.008251 **
Residuals 62 43.148 0.6959
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar Chisq P(Chi)
(Intercept)
poly(Age, 2)
s(Start) 3 6.9637 0.07308 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Call: gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = .,
na.action = na.gam.replace)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.22276 -0.27667 -0.03453 0.32553 0.93450
(Dispersion Parameter for gaussian family taken to be 0.1953)
Null Deviance: 90.7149 on 115 degrees of freedom
Residual Deviance: 20.0523 on 102.6679 degrees of freedom
AIC: 154.2493
Number of Local Scoring Iterations: 2
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
lo(Solar.R) 1.00 15.515 15.5147 79.435 1.968e-14 ***
lo(Wind, Temp) 2.00 38.594 19.2972 98.802 < 2.2e-16 ***
Residuals 102.67 20.052 0.1953
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova for Nonparametric Effects
Npar Df Npar F Pr(F)
(Intercept)
lo(Solar.R) 2.6 2.6590 0.06092 .
lo(Wind, Temp) 6.8 7.4772 4.027e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In na.action(mf) :
37 observations omitted due to missing values in the response
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.