R: Summary informations for a fitted 'threshpt' object.
summary.threshpt
R Documentation
Summary informations for a fitted threshpt object.
Description
summary provides summary statistics for a threshpt object produced by threshpt()
Usage
## S3 method for class 'threshpt'
summary(object, ...)
Arguments
object
a fitted threshpt object produced by threshpt()
...
Not used
Value
summary.threshpt produces a list of summary informations for a fitted threshpt object with components
Formula
the formula which is used in the threshpt function
Best fit
estimated parameter coefficients of model with the minimum deviance
Deviance
deviance of a fitted threshpt model
Threshold
threshold value of the model with the minimum deviance
Author(s)
Youn-Hee Lim, Il-Sang Ohn, and Ho Kim
Examples
# read the Seoul data set and create lag variables
data(mort)
seoul = read6city(mort, 11)
seoul_lag = lagdata(seoul, c("meantemp", "mintemp", "meanpm10", "meanhumi"), 5)
# find a optimal threshold and conduct piecewise linear regression
mythresh = threshpt(nonacc ~ meantemp_m3 + meanpm10_m2 + meanhumi + ns(sn, 4*10) + factor(dow),
expvar = "meantemp_m3", family = "poisson", data = seoul_lag,
startrng = 23, endrng = 33, searchunit = 0.2)
# provide summary informations
summary(mythresh)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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> library(HEAT)
Loading required package: splines
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HEAT/summary.threshpt.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summary.threshpt
> ### Title: Summary informations for a fitted 'threshpt' object.
> ### Aliases: summary.threshpt
>
> ### ** Examples
>
>
> # read the Seoul data set and create lag variables
> data(mort)
> seoul = read6city(mort, 11)
> seoul_lag = lagdata(seoul, c("meantemp", "mintemp", "meanpm10", "meanhumi"), 5)
>
> # find a optimal threshold and conduct piecewise linear regression
> mythresh = threshpt(nonacc ~ meantemp_m3 + meanpm10_m2 + meanhumi + ns(sn, 4*10) + factor(dow),
+ expvar = "meantemp_m3", family = "poisson", data = seoul_lag,
+ startrng = 23, endrng = 33, searchunit = 0.2)
>
> # provide summary informations
> summary(mythresh)
Call:
threshpt(formula = nonacc ~ meantemp_m3 + meanpm10_m2 + meanhumi +
ns(sn, 4 * 10) + factor(dow), family = "poisson", data = seoul_lag,
expvar = "meantemp_m3", startrng = 23, endrng = 33, searchunit = 0.2)
Formula:
nonacc ~ meantemp_m3 + meanpm10_m2 + meanhumi + ns(sn, 4 * 10) +
factor(dow) + meantemp_m3_2
<environment: 0x3a79188>
Coefficients:
Estimate Std. Error Pr(>|z|)
(Intercept) 4.7454e+00 0.0262 < 2.2e-16 ***
<Threshold -5.1002e-04 0.0009 0.57627
>=Threshold 1.8416e-02 0.0032 1.181e-08 ***
meanpm10_m2 3.2581e-04 0.0001 0.00018 ***
meanhumi -8.1887e-05 0.0002 0.60425
factor(dow)2 3.6208e-02 0.0072 4.345e-07 ***
factor(dow)3 1.6617e-02 0.0072 0.02099 *
factor(dow)4 1.2795e-02 0.0072 0.07593 .
factor(dow)5 1.6796e-02 0.0072 0.01968 *
factor(dow)6 2.1021e-03 0.0072 0.77111
factor(dow)7 1.7900e-03 0.0072 0.80423
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Optimum threshold: 24.2
Deviance of the model with optimum threshold: 3147.556
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> dev.off()
null device
1
>