R Under development (unstable) (2024-12-13 r87441 ucrt) -- "Unsuffered Consequences"
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> options(na.action=na.exclude) # preserve missings
> options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
> library(survival)
> mdy.date <- function(m, d, y) {
+     y <- ifelse(y<100, y+1900, y)
+     as.Date(paste(m,d,y, sep='/'), "%m/%d/%Y")
+ }
> 
> # 
> # Simple case: a single male subject, born 6/6/36 and entered on study 6/6/55.
> #
> 
> temp1 <- mdy.date(6,6,36)
> temp2 <- mdy.date(6,6,55)# Now compare the results from person-years
> #
> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:31 * 365.24))),
+ 	labels=c('0-18', paste(18:30, 19:31, sep='-')))
> temp.yr  <- tcut(temp2, mdy.date(1,1,1954:1965), labels=1954:1964)
> temp.time <- 3700   #total days of fu
> py1 <- pyears(temp.time ~ temp.age + temp.yr, scale=1) #output in days
> 
> # The subject should appear in 20 cells
> #    6/6/55 - 12/31/55, 209 days, age 19-20, 1955
> #    1/1/56 -  6/ 4/56, 156 days, age 19-20, 1956
> #    6/5/56 - 12/31/56, 210 days, age 20-21, 1956   (a leap year, and his
> #			birthday computes one day earlier)
> #    1/1/57 -  6/ 5/57, 156 days, age 20-21, 1957
> #    6/6/57 - 12/31/57, 209 days, age 21-22, 1957
> # and etc
> #   with 203 days "off table", ie, beyond the last cell of the table
> #
> # It is a nuisance, but tcut follows 'cut' in that we give the ENDS of
> #  the intervals, whereas the survival tables use the starts of intervals.
> #  Thus this breakdown does not match that in doexpect.s
> #
> xx <- matrix(0, nrow=14, ncol=11)
> xx[cbind(3:11, 3:11)] <- 156
> xx[cbind(3:12, 2:11)] <- c(209, 210, rep(c(209, 209, 209, 210),2))
> dimnames(xx) <- list(temp.age= c('0-18', paste(18:30, 19:31, sep='-')), 
+                      temp.yr = 1954:1964)
> all.equal(xx, py1$pyears)
[1] TRUE
> all.equal(203, py1$offtable)
[1] TRUE
> all.equal(1*(xx>0), py1$n)
[1] TRUE
> 
> #
> # Now with expecteds
> #
> py2 <- pyears(temp.time ~ temp.age + temp.yr,
+ 		rmap=list(age=temp2-temp1, year=temp2, sex=1),
+ 	     scale=1, ratetable=survexp.us ) #output in days
> all.equal(xx, py2$pyears)
[1] TRUE
> all.equal(203, py2$offtable)
[1] TRUE
> all.equal(1*(xx>0), py2$n)
[1] TRUE
> 
> 
> py3 <-  pyears(temp.time ~ temp.age + temp.yr,
+ 		rmap=list(age=temp2-temp1, year=temp2, sex=1),
+ 	     scale=1, ratetable=survexp.us , expect='pyears')
> all.equal(py2$n, py3$n)
[1] TRUE
> all.equal(py2$pyear, py3$pyear)
[1] TRUE
> all.equal(py3$n, 1*(py3$expect>0))
[1] TRUE
> 
> # Now, compute the py3 result "by hand".  Since there is only one person
> #   it can be derived from py2.
> #
> xx1 <- py2$expect[py2$n>0]   		# the hazard over each interval
> cumhaz <- cumsum(c(0, xx1[-length(xx1)]))     # the cumulative hazard	
> xx2 <- py3$expect[py3$n>0]   		# the expected number of person days
> xx3 <- py3$pyears[py3$n>0]   		# the potential number of person days
> 
> # This is the integral of the curve "exp(-haz *t)" over the interval
> integral <- xx3 * exp(-cumhaz)* (1- exp(-xx1))/ xx1
> # They might not be exactly equal, since the C code tracks changes in the
> #   rate tables that occur -within- an interval.  So try for 6 digits
> all.equal(round(integral,3), round(xx2,3))
[1] TRUE
> 
> # Cut off the bottom of the table, instead of the side
> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:27 * 365.24))),
+ 	labels=c('0-18', paste(18:26, 19:27, sep='-')))
> 
> py4 <- eval(py3$call)
> all.equal(py4$pyear, py3$pyear[1:10,])
[1] TRUE
> all.equal(py4$expect, py3$expect[1:10,])
[1] TRUE
> 
> 
> rm(temp.age, integral, xx1, xx2, xx3, cumhaz, py1, py2, py3, py4)
> rm(temp1, temp2, temp.yr, temp.time, xx)
> 
> 
> 
> 
> # 
> # Simple case: a single male subject, born 6/6/36 and entered on study 6/6/55.
> #
> 
> temp1 <- mdy.date(6,6,36)
> temp2 <- mdy.date(6,6,55)# Now compare the results from person-years
> #
> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:31 * 365.24))),
+ 	labels=c('0-18', paste(18:30, 19:31, sep='-')))
> temp.yr  <- tcut(temp2, mdy.date(1,1,1954:1965), labels=1954:1964)
> temp.time <- 3700   #total days of fu
> py1 <- pyears(temp.time ~ temp.age + temp.yr, scale=1) #output in days
> 
> # The subject should appear in 20 cells
> #    6/6/55 - 12/31/55, 209 days, age 19-20, 1955
> #    1/1/56 -  6/ 4/56, 156 days, age 19-20, 1956
> #    6/5/56 - 12/31/56, 210 days, age 20-21, 1956   (a leap year, and his
> #			birthday computes one day earlier)
> #    1/1/57 -  6/ 5/57, 156 days, age 20-21, 1957
> #    6/6/57 - 12/31/57, 209 days, age 21-22, 1957
> # and etc
> #   with 203 days "off table", ie, beyond the last cell of the table
> #
> # It is a nuisance, but tcut follows 'cut' in that we give the ENDS of
> #  the intervals, whereas the survival tables use the starts of intervals.
> #
> xx <- matrix(0, nrow=14, ncol=11)
> xx[cbind(3:11, 3:11)] <- 156
> xx[cbind(3:12, 2:11)] <- c(209, 210, rep(c(209, 209, 209, 210),2))
> dimnames(xx) <- list(temp.age= c('0-18', paste(18:30, 19:31, sep='-')), 
+                      temp.yr = 1954:1964)
> all.equal(xx, py1$pyears)
[1] TRUE
> all.equal(203, py1$offtable)
[1] TRUE
> all.equal(1*(xx>0), py1$n)
[1] TRUE
> 
> #
> # Now with expecteds
> #
> py2 <- pyears(temp.time ~ temp.age + temp.yr,
+ 	   rmap= list(age=temp2-temp1, year=temp2, sex=1),
+ 	     scale=1, ratetable=survexp.us ) #output in days
> all.equal(xx, py2$pyears)
[1] TRUE
> all.equal(203, py2$offtable)
[1] TRUE
> all.equal(1*(xx>0), py2$n)
[1] TRUE
> 
> 
> py3 <-  pyears(temp.time ~ temp.age + temp.yr,
+ 		rmap= list(age=temp2-temp1, year=temp2, sex=1),
+ 	     scale=1, ratetable=survexp.us , expect='pyears')
> all.equal(py2$n, py3$n)
[1] TRUE
> all.equal(py2$pyear, py3$pyear)
[1] TRUE
> all.equal(py3$n, 1*(py3$expect>0))
[1] TRUE
> 
> # Now, compute the py3 result "by hand".  Since there is only one person
> #   it can be derived from py2.
> #
> xx1 <- py2$expect[py2$n>0]   		# the hazard over each interval
> cumhaz <- cumsum(c(0, xx1[-length(xx1)]))     # the cumulative hazard	
> xx2 <- py3$expect[py3$n>0]   		# the expected number of person days
> xx3 <- py3$pyears[py3$n>0]   		# the potential number of person days
> 
> # This is the integral of the curve "exp(-haz *t)" over the interval
> integral <- xx3 * exp(-cumhaz)* (1- exp(-xx1))/ xx1
> # They might not be exactly equal, since the C code tracks changes in the
> #   rate tables that occur -within- an interval.  So try for 6 digits
> all.equal(round(integral,3), round(xx2,3))
[1] TRUE
> 
> # Cut off the bottom of the table, instead of the side
> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:27 * 365.24))),
+ 	labels=c('0-18', paste(18:26, 19:27, sep='-')))
> 
> py4 <- eval(py3$call)
> all.equal(py4$pyear, py3$pyear[1:10,])
[1] TRUE
> all.equal(py4$expect, py3$expect[1:10,])
[1] TRUE
> 
> 
> rm(temp.age, integral, xx1, xx2, xx3, cumhaz, py1, py2, py3, py4)
> rm(temp1, temp2, temp.yr, temp.time, xx)
> 
> 
> 
> 
> #
> # Create a "user defined" rate table, using the smoking data
> #
> temp <- scan("data.smoke")/100000
Read 224 items
> temp <-  matrix(temp, ncol=8, byrow=T)
> smoke.rate <- c(rep(temp[,1],6), rep(temp[,2],6), temp[,3:8])
> attributes(smoke.rate) <- list(
+ 	dim=c(7,2,2,6,3),
+ 	dimnames=list(c("45-49","50-54","55-59","60-64","65-69","70-74","75-79"),
+ 		      c("1-20", "21+"),
+ 		      c("Male","Female"),
+ 		      c("<1", "1-2", "3-5", "6-10", "11-15", ">=16"),
+ 		      c("Never", "Current", "Former")),
+ 	dimid=c("age", "amount", "sex", "duration", "status"),
+ 	factor=c(0,1,1,0,1),
+ 	cutpoints=list(c(45,50,55,60,65,70,75),NULL, NULL,
+ 				     c(0,1,3,6,11,16),NULL),
+ 	class='ratetable'
+ 	)
> rm(temp)
> 
> is.ratetable(smoke.rate)
[1] TRUE
> summary(smoke.rate)
 Rate table with 5 dimensions:
	age ranges from 45 to 75; with 7 categories
	amount has levels of: 1-20 21+
	sex has levels of: Male Female
	duration ranges from 0 to 16; with 6 categories
	status has levels of: Never Current Former
> print(smoke.rate)
Rate table with dimension(s): age amount sex duration status 
, , Male, <1, Never

          1-20      21+
45-49 0.001860 0.001860
50-54 0.002556 0.002556
55-59 0.004489 0.004489
60-64 0.007337 0.007337
65-69 0.011194 0.011194
70-74 0.020705 0.020705
75-79 0.036753 0.036753

, , Female, <1, Never

          1-20      21+
45-49 0.001257 0.001257
50-54 0.001773 0.001773
55-59 0.002448 0.002448
60-64 0.003977 0.003977
65-69 0.006921 0.006921
70-74 0.011600 0.011600
75-79 0.020708 0.020708

, , Male, 1-2, Never

          1-20      21+
45-49 0.001860 0.001860
50-54 0.002556 0.002556
55-59 0.004489 0.004489
60-64 0.007337 0.007337
65-69 0.011194 0.011194
70-74 0.020705 0.020705
75-79 0.036753 0.036753

, , Female, 1-2, Never

          1-20      21+
45-49 0.001257 0.001257
50-54 0.001773 0.001773
55-59 0.002448 0.002448
60-64 0.003977 0.003977
65-69 0.006921 0.006921
70-74 0.011600 0.011600
75-79 0.020708 0.020708

, , Male, 3-5, Never

          1-20      21+
45-49 0.001860 0.001860
50-54 0.002556 0.002556
55-59 0.004489 0.004489
60-64 0.007337 0.007337
65-69 0.011194 0.011194
70-74 0.020705 0.020705
75-79 0.036753 0.036753

, , Female, 3-5, Never

          1-20      21+
45-49 0.001257 0.001257
50-54 0.001773 0.001773
55-59 0.002448 0.002448
60-64 0.003977 0.003977
65-69 0.006921 0.006921
70-74 0.011600 0.011600
75-79 0.020708 0.020708

, , Male, 6-10, Never

          1-20      21+
45-49 0.001860 0.001860
50-54 0.002556 0.002556
55-59 0.004489 0.004489
60-64 0.007337 0.007337
65-69 0.011194 0.011194
70-74 0.020705 0.020705
75-79 0.036753 0.036753

, , Female, 6-10, Never

          1-20      21+
45-49 0.001257 0.001257
50-54 0.001773 0.001773
55-59 0.002448 0.002448
60-64 0.003977 0.003977
65-69 0.006921 0.006921
70-74 0.011600 0.011600
75-79 0.020708 0.020708

, , Male, 11-15, Never

          1-20      21+
45-49 0.001860 0.001860
50-54 0.002556 0.002556
55-59 0.004489 0.004489
60-64 0.007337 0.007337
65-69 0.011194 0.011194
70-74 0.020705 0.020705
75-79 0.036753 0.036753

, , Female, 11-15, Never

          1-20      21+
45-49 0.001257 0.001257
50-54 0.001773 0.001773
55-59 0.002448 0.002448
60-64 0.003977 0.003977
65-69 0.006921 0.006921
70-74 0.011600 0.011600
75-79 0.020708 0.020708

, , Male, >=16, Never

          1-20      21+
45-49 0.001860 0.001860
50-54 0.002556 0.002556
55-59 0.004489 0.004489
60-64 0.007337 0.007337
65-69 0.011194 0.011194
70-74 0.020705 0.020705
75-79 0.036753 0.036753

, , Female, >=16, Never

          1-20      21+
45-49 0.001257 0.001257
50-54 0.001773 0.001773
55-59 0.002448 0.002448
60-64 0.003977 0.003977
65-69 0.006921 0.006921
70-74 0.011600 0.011600
75-79 0.020708 0.020708

, , Male, <1, Current

          1-20      21+
45-49 0.004392 0.006100
50-54 0.007027 0.009156
55-59 0.011324 0.013910
60-64 0.019811 0.023934
65-69 0.030030 0.034979
70-74 0.046975 0.058613
75-79 0.073406 0.062500

, , Female, <1, Current

          1-20      21+
45-49 0.002256 0.002779
50-54 0.003538 0.005179
55-59 0.005428 0.008235
60-64 0.008580 0.013029
65-69 0.014962 0.019349
70-74 0.020848 0.028270
75-79 0.033195 0.042731

, , Male, 1-2, Current

          1-20      21+
45-49 0.004392 0.006100
50-54 0.007027 0.009156
55-59 0.011324 0.013910
60-64 0.019811 0.023934
65-69 0.030030 0.034979
70-74 0.046975 0.058613
75-79 0.073406 0.062500

, , Female, 1-2, Current

          1-20      21+
45-49 0.002256 0.002779
50-54 0.003538 0.005179
55-59 0.005428 0.008235
60-64 0.008580 0.013029
65-69 0.014962 0.019349
70-74 0.020848 0.028270
75-79 0.033195 0.042731

, , Male, 3-5, Current

          1-20      21+
45-49 0.004392 0.006100
50-54 0.007027 0.009156
55-59 0.011324 0.013910
60-64 0.019811 0.023934
65-69 0.030030 0.034979
70-74 0.046975 0.058613
75-79 0.073406 0.062500

, , Female, 3-5, Current

          1-20      21+
45-49 0.002256 0.002779
50-54 0.003538 0.005179
55-59 0.005428 0.008235
60-64 0.008580 0.013029
65-69 0.014962 0.019349
70-74 0.020848 0.028270
75-79 0.033195 0.042731

, , Male, 6-10, Current

          1-20      21+
45-49 0.004392 0.006100
50-54 0.007027 0.009156
55-59 0.011324 0.013910
60-64 0.019811 0.023934
65-69 0.030030 0.034979
70-74 0.046975 0.058613
75-79 0.073406 0.062500

, , Female, 6-10, Current

          1-20      21+
45-49 0.002256 0.002779
50-54 0.003538 0.005179
55-59 0.005428 0.008235
60-64 0.008580 0.013029
65-69 0.014962 0.019349
70-74 0.020848 0.028270
75-79 0.033195 0.042731

, , Male, 11-15, Current

          1-20      21+
45-49 0.004392 0.006100
50-54 0.007027 0.009156
55-59 0.011324 0.013910
60-64 0.019811 0.023934
65-69 0.030030 0.034979
70-74 0.046975 0.058613
75-79 0.073406 0.062500

, , Female, 11-15, Current

          1-20      21+
45-49 0.002256 0.002779
50-54 0.003538 0.005179
55-59 0.005428 0.008235
60-64 0.008580 0.013029
65-69 0.014962 0.019349
70-74 0.020848 0.028270
75-79 0.033195 0.042731

, , Male, >=16, Current

          1-20      21+
45-49 0.004392 0.006100
50-54 0.007027 0.009156
55-59 0.011324 0.013910
60-64 0.019811 0.023934
65-69 0.030030 0.034979
70-74 0.046975 0.058613
75-79 0.073406 0.062500

, , Female, >=16, Current

          1-20      21+
45-49 0.002256 0.002779
50-54 0.003538 0.005179
55-59 0.005428 0.008235
60-64 0.008580 0.013029
65-69 0.014962 0.019349
70-74 0.020848 0.028270
75-79 0.033195 0.042731

, , Male, <1, Former

          1-20      21+
45-49 0.002344 0.004975
50-54 0.005447 0.004828
55-59 0.009452 0.017571
60-64 0.011777 0.015784
65-69 0.022449 0.023018
70-74 0.042553 0.031746
75-79 0.058824 0.040000

, , Female, <1, Former

          1-20      21+
45-49 0.000000 0.002667
50-54 0.001168 0.001387
55-59 0.002874 0.004736
60-64 0.010163 0.011148
65-69 0.011080 0.023196
70-74 0.006452 0.046358
75-79 0.000000 0.024096

, , Male, 1-2, Former

          1-20      21+
45-49 0.003658 0.002517
50-54 0.004310 0.005007
55-59 0.007288 0.009535
60-64 0.015892 0.018472
65-69 0.033803 0.037766
70-74 0.050830 0.029740
75-79 0.065972 0.044248

, , Female, 1-2, Former

          1-20      21+
45-49 0.004339 0.001027
50-54 0.000921 0.004668
55-59 0.002595 0.006020
60-64 0.003650 0.008621
65-69 0.013485 0.012500
70-74 0.014831 0.025172
75-79 0.025806 0.057692

, , Male, 3-5, Former

          1-20      21+
45-49 0.001596 0.004175
50-54 0.004548 0.004889
55-59 0.007294 0.010258
60-64 0.013165 0.017901
65-69 0.023749 0.020810
70-74 0.044850 0.037129
75-79 0.077075 0.073298

, , Female, 3-5, Former

          1-20      21+
45-49 0.002120 0.001786
50-54 0.002895 0.002701
55-59 0.003759 0.003610
60-64 0.006509 0.006996
65-69 0.012632 0.016880
70-74 0.012500 0.016873
75-79 0.025907 0.031250

, , Male, 6-10, Former

          1-20      21+
45-49 0.002169 0.001226
50-54 0.003497 0.004029
55-59 0.005902 0.007440
60-64 0.012669 0.012207
65-69 0.018202 0.027664
70-74 0.038887 0.039888
75-79 0.049451 0.063830

, , Female, 6-10, Former

          1-20      21+
45-49 0.001072 0.002247
50-54 0.002009 0.001902
55-59 0.001658 0.004545
60-64 0.004708 0.005417
65-69 0.008648 0.008287
70-74 0.011263 0.028487
75-79 0.039604 0.029787

, , Male, 11-15, Former

          1-20      21+
45-49 0.001674 0.001983
50-54 0.002140 0.003939
55-59 0.004473 0.006685
60-64 0.008756 0.011000
65-69 0.016691 0.022681
70-74 0.031843 0.032686
75-79 0.056180 0.076661

, , Female, 11-15, Former

          1-20      21+
45-49 0.001359 0.001421
50-54 0.001213 0.001168
55-59 0.002022 0.004122
60-64 0.005706 0.003731
65-69 0.005866 0.007979
70-74 0.010705 0.016212
75-79 0.016667 0.028037

, , Male, >=16, Former

          1-20      21+
45-49 0.001595 0.001934
50-54 0.002504 0.003543
55-59 0.004366 0.005378
60-64 0.007030 0.009933
65-69 0.011592 0.012307
70-74 0.021949 0.024689
75-79 0.041289 0.050481

, , Female, >=16, Former

          1-20      21+
45-49 0.000910 0.001388
50-54 0.001721 0.000830
55-59 0.002472 0.001821
60-64 0.003197 0.003564
65-69 0.006180 0.005815
70-74 0.012721 0.013634
75-79 0.018615 0.021954

> 
> summary(smoke.rate[1:3,,1,,])  #test subscripting
 Rate table with 4 dimensions:
	age ranges from 45 to 55; with 3 categories
	amount has levels of: 1-20 21+
	duration ranges from 0 to 16; with 6 categories
	status has levels of: Never Current Former
> 
> proc.time()
   user  system elapsed 
   0.85    0.04    0.90