EIglm: Perform a GLM on times of events
In eventInterval: Sequential Event Interval Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
Perform a GLM with the intervals between events as the response
and the times of the events as a predictor.
Usage
1
Arguments
event_times
A numeric vector of times of occurrence of events.
event_vars
Optional predictors to include in the GLM.
formula
A optional string that can be coerced to a valid formula.
Details
EIglm calculates the intervals between the times passed in
event_times and calls glm with family="Gamma".
If the times are not in increasing order, they will be sorted. If there are
any zero length intervals, the events that occur in the same time increment
are spread out over that time increment.
If more predictors are passed in event_vars these will be added to
the formula passed to glm. When passing predictors based on the events,
remember to discard the first value as the intervals are calculated between
events. This means that the interval is unknown for the first event.
EIglm only tests the effects of the variables specified and does not
test interactions. If a more complex model is to be tested, the user must
directly call glm and compose a formula for the model (see the last
example). Note that because there is no data argument in EIglm,
each term in the model must be specified explicitly.
Value
An object of class glm.
Author(s)
Jim Lemon
References
Blake, E.S., Rappaport, E.N. & Landsea, C.W. (2007)
The deadliest, costliest, and most intense United States tropical
cyclones from 1851 to 2006 (and other frequently requested hurricane facts).
NOAA Technical Memorandum NWS TPC-5, Miami: National Weather Service.
Florida Climate Center (2013)
Florida statewide averaged temperature data (in degrees Fahrenheit).
URL: http://climatecenter.fsu.edu/products-services/data/statewide-averages/temperature
accessed 28/5/2013
Examples
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## Not run:
# Florida hurricane analysis (20th century)
data(florida_hurr20)
oldloc<-Sys.setlocale("LC_TIME", "C")
fh_dates<-
as.Date(paste(florida_hurr20$day,florida_hurr20$month,
florida_hurr20$year,sep="-"),"%d-%B-%Y")
fh_days<-as.numeric(fh_dates)
fh_ints<-diff(fh_days)
fh_counts<-tabulate(florida_hurr20$year[-1]-1899,nbins=100)
# run a Poisson analysis on the counts
print(summary(glm(fh_counts~I(1900:1999),family="poisson")))
EIglm(fh_days)
data(florida_temp20)
plot(florida_temp20$year,florida_temp20$Annual,type="b",
main="Average temperature in Florida",xlab="Year",ylab="degrees F")
lines(supsmu(florida_temp20$year,florida_temp20$Annual),lwd=2)
# define a function to match values
findval<-
function(x,set) return(which(set %in% x))
ftemp_years<-florida_temp20$Annual[unlist(sapply(florida_hurr20$year,
FUN=findval,florida_temp20$year))]
EIglm(fh_days,list(ft=ftemp_years[-1]))
Sys.setlocale("LC_TIME", oldloc)
## End(Not run)
# manually performing an event interval GLM
# get the Florida hurricane data
data(florida_hurr20)
# create date values for the events
florida_hurr20$date<-
as.Date(paste(florida_hurr20$day,florida_hurr20$month,
florida_hurr20$year,sep="-"),"%d-%B-%Y")
# convert the dates to numeric values (offsets from 1/1/1970)
florida_hurr20$days<-as.numeric(florida_hurr20$date)
# calculate the intervals, adding NA for the first unknown value
florida_hurr20$ints<-c(NA,diff(florida_hurr20$days))
# first test the default model in EIglm, dropping the first event
summary(glm(ints~days,florida_hurr20[-1,],family="Gamma"))
# test a model predicting intervals with times of occurrence,
# the intensities of the hurricanes and their interaction,
# again dropping the first event
summary(glm(ints~days*category,florida_hurr20[-1,],family="Gamma"))
Example output
Call:
glm(formula = fh_counts ~ I(1900:1999), family = "poisson")
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2845 -1.1126 -1.0101 0.4797 2.2313
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.010385 8.484027 1.180 0.238
I(1900:1999) -0.005370 0.004361 -1.231 0.218
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 115.72 on 99 degrees of freedom
Residual deviance: 114.19 on 98 degrees of freedom
AIC: 217.76
Number of Fisher Scoring iterations: 5
formula: event_ints~event_times
Call:
glm(formula = formula, family = "Gamma")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3674 -1.4350 -0.2800 0.5792 1.2497
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.544e-03 1.977e-04 7.810 8.46e-11 ***
event_times -4.504e-08 1.798e-08 -2.505 0.0149 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7422684)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 83.429 on 62 degrees of freedom
AIC: 937.51
Number of Fisher Scoring iterations: 6
formula: event_ints~event_times+event_vars$ft
Call:
glm(formula = formula, family = "Gamma")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3824 -1.4196 -0.2258 0.6235 1.2417
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.331e-03 1.611e-02 -0.517 0.6069
event_times -4.744e-08 1.869e-08 -2.539 0.0137 *
event_vars$ft 1.394e-04 2.276e-04 0.612 0.5425
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7523581)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 83.147 on 61 degrees of freedom
AIC: 939.25
Number of Fisher Scoring iterations: 6
[1] "C"
Call:
glm(formula = ints ~ days, family = "Gamma", data = florida_hurr20[-1,
])
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3674 -1.4350 -0.2800 0.5792 1.2497
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.544e-03 1.977e-04 7.810 8.46e-11 ***
days -4.504e-08 1.798e-08 -2.505 0.0149 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7422684)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 83.429 on 62 degrees of freedom
AIC: 937.51
Number of Fisher Scoring iterations: 6
Call:
glm(formula = ints ~ days * category, family = "Gamma", data = florida_hurr20[-1,
])
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3537 -1.4185 -0.2857 0.5588 1.3339
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.823e-03 4.326e-04 4.213 8.58e-05 ***
days -9.674e-09 3.659e-08 -0.264 0.792
category -1.079e-04 1.478e-04 -0.730 0.468
days:category -1.556e-08 1.407e-08 -1.106 0.273
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7542537)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 82.243 on 60 degrees of freedom
AIC: 940.42
Number of Fisher Scoring iterations: 6
eventInterval documentation built on May 1, 2019, 9:22 p.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
Perform a GLM with the intervals between events as the response and the times of the events as a predictor.
Usage
1 |
Arguments
event_times |
A numeric vector of times of occurrence of events. |
event_vars |
Optional predictors to include in the GLM. |
formula |
A optional string that can be coerced to a valid formula. |
Details
EIglm calculates the intervals between the times passed in event_times and calls glm with family="Gamma". If the times are not in increasing order, they will be sorted. If there are any zero length intervals, the events that occur in the same time increment are spread out over that time increment.
If more predictors are passed in event_vars these will be added to the formula passed to glm. When passing predictors based on the events, remember to discard the first value as the intervals are calculated between events. This means that the interval is unknown for the first event.
EIglm only tests the effects of the variables specified and does not test interactions. If a more complex model is to be tested, the user must directly call glm and compose a formula for the model (see the last example). Note that because there is no data argument in EIglm, each term in the model must be specified explicitly.
Value
An object of class glm.
Author(s)
Jim Lemon
References
Blake, E.S., Rappaport, E.N. & Landsea, C.W. (2007) The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2006 (and other frequently requested hurricane facts). NOAA Technical Memorandum NWS TPC-5, Miami: National Weather Service.
Florida Climate Center (2013) Florida statewide averaged temperature data (in degrees Fahrenheit). URL: http://climatecenter.fsu.edu/products-services/data/statewide-averages/temperature accessed 28/5/2013
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 | ## Not run:
# Florida hurricane analysis (20th century)
data(florida_hurr20)
oldloc<-Sys.setlocale("LC_TIME", "C")
fh_dates<-
as.Date(paste(florida_hurr20$day,florida_hurr20$month,
florida_hurr20$year,sep="-"),"%d-%B-%Y")
fh_days<-as.numeric(fh_dates)
fh_ints<-diff(fh_days)
fh_counts<-tabulate(florida_hurr20$year[-1]-1899,nbins=100)
# run a Poisson analysis on the counts
print(summary(glm(fh_counts~I(1900:1999),family="poisson")))
EIglm(fh_days)
data(florida_temp20)
plot(florida_temp20$year,florida_temp20$Annual,type="b",
main="Average temperature in Florida",xlab="Year",ylab="degrees F")
lines(supsmu(florida_temp20$year,florida_temp20$Annual),lwd=2)
# define a function to match values
findval<-
function(x,set) return(which(set %in% x))
ftemp_years<-florida_temp20$Annual[unlist(sapply(florida_hurr20$year,
FUN=findval,florida_temp20$year))]
EIglm(fh_days,list(ft=ftemp_years[-1]))
Sys.setlocale("LC_TIME", oldloc)
## End(Not run)
# manually performing an event interval GLM
# get the Florida hurricane data
data(florida_hurr20)
# create date values for the events
florida_hurr20$date<-
as.Date(paste(florida_hurr20$day,florida_hurr20$month,
florida_hurr20$year,sep="-"),"%d-%B-%Y")
# convert the dates to numeric values (offsets from 1/1/1970)
florida_hurr20$days<-as.numeric(florida_hurr20$date)
# calculate the intervals, adding NA for the first unknown value
florida_hurr20$ints<-c(NA,diff(florida_hurr20$days))
# first test the default model in EIglm, dropping the first event
summary(glm(ints~days,florida_hurr20[-1,],family="Gamma"))
# test a model predicting intervals with times of occurrence,
# the intensities of the hurricanes and their interaction,
# again dropping the first event
summary(glm(ints~days*category,florida_hurr20[-1,],family="Gamma"))
|
Example output
Call:
glm(formula = fh_counts ~ I(1900:1999), family = "poisson")
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2845 -1.1126 -1.0101 0.4797 2.2313
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.010385 8.484027 1.180 0.238
I(1900:1999) -0.005370 0.004361 -1.231 0.218
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 115.72 on 99 degrees of freedom
Residual deviance: 114.19 on 98 degrees of freedom
AIC: 217.76
Number of Fisher Scoring iterations: 5
formula: event_ints~event_times
Call:
glm(formula = formula, family = "Gamma")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3674 -1.4350 -0.2800 0.5792 1.2497
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.544e-03 1.977e-04 7.810 8.46e-11 ***
event_times -4.504e-08 1.798e-08 -2.505 0.0149 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7422684)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 83.429 on 62 degrees of freedom
AIC: 937.51
Number of Fisher Scoring iterations: 6
formula: event_ints~event_times+event_vars$ft
Call:
glm(formula = formula, family = "Gamma")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3824 -1.4196 -0.2258 0.6235 1.2417
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.331e-03 1.611e-02 -0.517 0.6069
event_times -4.744e-08 1.869e-08 -2.539 0.0137 *
event_vars$ft 1.394e-04 2.276e-04 0.612 0.5425
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7523581)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 83.147 on 61 degrees of freedom
AIC: 939.25
Number of Fisher Scoring iterations: 6
[1] "C"
Call:
glm(formula = ints ~ days, family = "Gamma", data = florida_hurr20[-1,
])
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3674 -1.4350 -0.2800 0.5792 1.2497
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.544e-03 1.977e-04 7.810 8.46e-11 ***
days -4.504e-08 1.798e-08 -2.505 0.0149 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7422684)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 83.429 on 62 degrees of freedom
AIC: 937.51
Number of Fisher Scoring iterations: 6
Call:
glm(formula = ints ~ days * category, family = "Gamma", data = florida_hurr20[-1,
])
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3537 -1.4185 -0.2857 0.5588 1.3339
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.823e-03 4.326e-04 4.213 8.58e-05 ***
days -9.674e-09 3.659e-08 -0.264 0.792
category -1.079e-04 1.478e-04 -0.730 0.468
days:category -1.556e-08 1.407e-08 -1.106 0.273
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Gamma family taken to be 0.7542537)
Null deviance: 87.814 on 63 degrees of freedom
Residual deviance: 82.243 on 60 degrees of freedom
AIC: 940.42
Number of Fisher Scoring iterations: 6
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