I have the following data frame:
obs zip age bed bath size lot exter garage fp price
1 1 1 3 21 3 3.0 951 64904 other 0 0 30000
2 2 2 3 21 3 2.0 1036 217800 frame 0 0 39900
3 3 3 4 7 1 1.0 676 54450 other 2 0 46500
4 4 4 3 6 3 2.0 1456 51836 other 0 1 48600
5 5 5 1 51 3 1.0 1186 10857 other 1 0 51500
6 6 6 2 19 3 2.0 1456 40075 frame 0 0 56990
7 7 7 3 8 3 2.0 1368 . frame 0 0 59900
8 8 8 4 27 3 1.0 994 11016 frame 1 0 62500
9 9 9 1 51 2 1.0 1176 6259 frame 1 1 65500
10 10 10 3 1 3 2.0 1216 11348 other 0 0 69000
11 11 11 4 32 3 2.0 1410 25450 brick 0 0 76900
12 12 12 3 2 3 2.0 1344 . other 0 1 79000
13 13 13 3 25 2 2.0 1064 218671 other 0 0 79900
14 14 14 1 31 3 1.5 1770 19602 brick 0 1 79950
15 15 15 4 29 3 2.0 1524 12720 brick 2 1 82900
16 16 16 3 16 3 2.0 1750 130680 frame 0 0 84900
17 17 17 3 20 3 2.0 1152 104544 other 2 0 85000
18 18 18 3 18 4 2.0 1770 10640 other 0 0 87900
19 19 19 4 28 3 2.0 1624 12700 brick 2 1 89900
20 20 20 2 27 3 2.0 1540 5679 brick 2 1 89900
with the following structure:
str(df)
'data.frame': 69 obs. of 12 variables:
$ Obs : int 1 2 3 4 5 6 7 8 9 10 ...
$ obs : int 1 2 3 4 5 6 7 8 9 10 ...
$ zip : int 3 3 4 3 1 2 3 4 1 3 ...
$ age : int 21 21 7 6 51 19 8 27 51 1 ...
$ bed : int 3 3 1 3 3 3 3 3 2 3 ...
$ bath : num 3 2 1 2 1 2 2 1 1 2 ...
$ size : Factor w/ 66 levels ".","1036","1064",..: 65 2 64 14 6 14 10 66 5 7 ...
$ lot : Factor w/ 60 levels ".","10295","10400",..: 47 28 43 39 9 35 1 11 46 13 ...
$ exter : Factor w/ 3 levels "brick","frame",..: 3 2 3 3 3 2 2 2 2 3 ...
$ garage: int 0 0 2 0 1 0 0 1 1 0 ...
$ fp : int 0 0 0 1 0 0 0 0 1 0 ...
$ price : int 30000 39900 46500 48600 51500 56990 59900 62500 65500 69000 ...
As you can be seen the "lot" variable appears as a factor. I have the following questions about this data:
Why does R read this variable "lot" as a factor?
When I tried:
df$lot[df$lot == "."] <- NA all dots (.) were replaced with <NA> and not as NA as I wanted.
I then tried df$lot <- as.numeric(df$lot) but the numerical values of this variable have changed completely, with the (.) being replaced by 1. What happened when I changed the variable's type?
How may I replace all dots (.) with NA?
Related
I am working on a dataframe of baseball data called mlb_team_logs. A random sample lies below.
Date Team season AB PA H X1B X2B X3B HR R RBI BB IBB SO HBP SF SH GDP
1 2015-04-06 ARI 2015 34 39 9 7 1 1 0 4 4 3 0 6 2 0 0 2
2 2015-04-07 ARI 2015 31 36 8 4 1 1 2 7 7 5 0 7 0 0 0 1
3 2015-04-08 ARI 2015 32 35 5 3 2 0 0 2 1 2 0 7 1 0 0 0
4 2015-04-10 ARI 2015 35 38 7 6 0 0 1 4 4 3 0 10 0 0 0 0
5 2015-04-11 ARI 2015 32 35 10 9 0 0 1 6 6 3 0 7 0 0 0 1
6 2015-04-12 ARI 2015 36 38 10 7 3 0 0 4 4 1 0 11 0 0 1 1
7 2015-04-13 ARI 2015 39 44 12 8 3 1 0 8 7 4 0 11 0 0 1 0
8 2015-04-14 ARI 2015 28 32 3 1 2 0 0 1 1 3 0 4 1 0 0 2
9 2015-04-15 ARI 2015 33 34 9 7 1 0 1 2 2 1 0 8 0 0 0 1
10 2015-04-16 ARI 2015 47 51 11 6 2 0 3 7 7 3 1 8 1 0 0 0
240 2015-07-03 ATL 2015 30 32 7 4 1 0 2 2 2 2 0 6 0 0 0 1
241 2015-07-04 ATL 2015 34 40 10 6 3 0 1 9 9 5 0 5 0 0 1 0
242 2015-07-05 ATL 2015 35 37 7 6 1 0 0 0 0 1 0 10 1 0 0 1
243 2015-07-06 ATL 2015 40 44 15 10 4 0 1 5 5 3 0 7 0 0 1 1
244 2015-07-07 ATL 2015 34 37 10 7 1 1 1 4 4 2 0 4 0 0 1 1
245 2015-07-08 ATL 2015 31 38 7 4 1 0 2 5 5 5 1 7 0 0 2 1
246 2015-07-09 ATL 2015 34 37 10 8 2 0 0 3 3 1 0 9 0 1 1 2
247 2015-07-10 ATL 2015 32 35 8 7 0 0 1 3 3 2 0 5 1 0 0 2
248 2015-07-11 ATL 2015 33 38 6 3 1 0 2 2 2 5 1 8 0 0 0 0
249 2015-07-12 ATL 2015 34 41 8 6 2 0 0 3 3 7 1 10 0 0 0 1
250 2015-07-17 ATL 2015 30 36 7 4 3 0 0 4 4 5 1 7 0 0 0 0
In total, the df has 43 total columns. My objective is to sum columns 4 (AB) to 43 on two criteria:
the team
the date is within 7 days of the entry in "Date" (ie Date - 7 to Date - 1)
Eventually, I would like these columns to be appended to mlb_team_logs as l7_AB, l7_PA, etc (but I know how to do that if the output will be a new dataframe). Any help is appreciated!
EDIT I altered the sample to allow for more easily tested results
You might be able to use a data.table non-equi join here. The idea would be to create a lower date bound (below, I've named this date_lb), and then join the table on itself, matching on Team = Team, Date < Date, and Date >= date_lb. Then use lapply with .SDcols to sum the columns of interest.
load library and set your frame to data.table
library(data.table)
setDT(mlb_team_logs)
Identify the columns you want to sum, in a character vector (change to 4:43 in your full dataset)
sum_cols = names(mlb_team_logs)[4:19]
Add a lower bound on date
df[, date_lb := Date-7]
Join the table on itself, and use lapply(.SD, sum) on the columns of interest
result = mlb_team_logs[mlb_team_logs[, .(Team, Date, date_lb)], on=.(Team, Date<Date, Date>=date_lb)] %>%
.[, lapply(.SD, sum), by=.(Date,Team), .SDcols = sumcols ]
Set the new names (inplace, using setnames())
setnames(result, old=sumcols, new=paste0("I7_",sumcols))
Output:
Date Team I7_AB I7_PA I7_H I7_X1B I7_X2B I7_X3B I7_HR I7_R I7_RBI I7_BB I7_IBB I7_SO I7_HBP I7_SF I7_SH I7_GDP
<IDat> <char> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1: 2015-04-06 ARI NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
2: 2015-04-07 ARI 34 39 9 7 1 1 0 4 4 3 0 6 2 0 0 2
3: 2015-04-08 ARI 65 75 17 11 2 2 2 11 11 8 0 13 2 0 0 3
4: 2015-04-10 ARI 97 110 22 14 4 2 2 13 12 10 0 20 3 0 0 3
5: 2015-04-11 ARI 132 148 29 20 4 2 3 17 16 13 0 30 3 0 0 3
6: 2015-04-12 ARI 164 183 39 29 4 2 4 23 22 16 0 37 3 0 0 4
7: 2015-04-13 ARI 200 221 49 36 7 2 4 27 26 17 0 48 3 0 1 5
8: 2015-04-14 ARI 205 226 52 37 9 2 4 31 29 18 0 53 1 0 2 3
9: 2015-04-15 ARI 202 222 47 34 10 1 2 25 23 16 0 50 2 0 2 4
10: 2015-04-16 ARI 203 221 51 38 9 1 3 25 24 15 0 51 1 0 2 5
11: 2015-07-03 ATL NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
12: 2015-07-04 ATL 30 32 7 4 1 0 2 2 2 2 0 6 0 0 0 1
13: 2015-07-05 ATL 64 72 17 10 4 0 3 11 11 7 0 11 0 0 1 1
14: 2015-07-06 ATL 99 109 24 16 5 0 3 11 11 8 0 21 1 0 1 2
15: 2015-07-07 ATL 139 153 39 26 9 0 4 16 16 11 0 28 1 0 2 3
16: 2015-07-08 ATL 173 190 49 33 10 1 5 20 20 13 0 32 1 0 3 4
17: 2015-07-09 ATL 204 228 56 37 11 1 7 25 25 18 1 39 1 0 5 5
18: 2015-07-10 ATL 238 265 66 45 13 1 7 28 28 19 1 48 1 1 6 7
19: 2015-07-11 ATL 240 268 67 48 12 1 6 29 29 19 1 47 2 1 6 8
20: 2015-07-12 ATL 239 266 63 45 10 1 7 22 22 19 2 50 2 1 5 8
21: 2015-07-17 ATL 99 114 22 16 3 0 3 8 8 14 2 23 1 0 0 3
Date Team I7_AB I7_PA I7_H I7_X1B I7_X2B I7_X3B I7_HR I7_R I7_RBI I7_BB I7_IBB I7_SO I7_HBP I7_SF I7_SH I7_GDP
I have a dataset with over 15,000 observations. I've dropped all variables but three (3).
One is the individual's origin or, the other is the individual's destination dest, and the third is weight of that individual wgt.
Origin and destination are categorical variables.
The weights I have are used as analytic weights in Stata. However, Stata can't handle the number of columns I generate when making tables. R generates them with ease. However, I can't figure out how to apply weights into the generated table.
I tried using wtd.tables(), but the following error appears.
wtd.table(NonHSGrad$b206reg, NonHSGrad$c305reg, weights=NonHSGrad$ind_wgts)
Error in proxy[, ..., drop = FALSE] : incorrect number of dimensions
When I use only the table(), this comes out:
table(NonHSGrad$b206reg, NonHSGrad$c305reg)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 285 38 20 8 6 3 1 2 0 1 0 10 38 46 0 2 14
2 32 312 26 3 1 0 2 1 1 0 1 1 22 51 0 0 8
3 17 35 325 12 12 2 3 7 0 2 3 5 52 13 1 1 25
4 3 5 27 224 19 5 2 10 1 1 1 2 51 4 0 3 35
5 4 9 44 81 778 6 7 22 1 4 5 5 155 5 0 5 47
6 4 5 22 21 10 547 24 12 32 21 32 81 86 5 3 15 58
7 5 4 12 17 20 21 558 20 31 99 93 33 59 1 3 67 15
8 8 9 41 49 17 11 24 919 5 8 37 10 151 2 0 52 19
9 0 1 7 9 1 4 26 5 466 66 19 17 17 2 24 24 7
10 1 2 3 4 2 3 27 8 41 528 21 17 13 2 11 36 2
11 3 0 3 10 1 5 11 5 6 17 519 59 7 1 2 49 1
12 0 1 1 2 0 1 5 2 2 10 39 318 10 0 14 17 1
13 15 9 26 34 25 21 12 42 2 5 3 5 187 2 1 6 15
14 14 47 7 5 0 0 0 1 1 0 0 0 9 475 0 0 0
15 0 0 3 1 2 2 4 2 22 9 3 60 9 2 342 2 3
16 0 2 6 10 3 2 11 21 3 33 29 4 34 0 3 404 5
17 1 1 7 15 2 6 1 2 0 1 1 0 34 0 0 2 463
99 0 0 1 1 0 0 0 1 0 1 0 0 0 1 2 0 1
I am also going to use the table for a chord diagram to show flows.
I'm trying to create a mixed effects model with lmer. The SubPlot should be nested with Plot ja Treatment should be nested with Subplot. So there's 3 to 7 Treatment in SubPlots ja always 3 SubPlot in a Plot. I created a following model:
model <- lmer(Depth ~ Mass + (1|Plot:SubPlot:Treatment), data=mydata)
But this gives me an error:
Error: number of levels of each grouping factor must be < number of observations (problems: Plot:SubPlot:Treatment)
'data.frame': 147 obs. of 6 variables:
$ Plot : int 1 1 1 1 1 1 1 1 1 1 ...
$ SubPlot : int 1 1 1 1 1 1 1 2 2 2 ...
$ Treatment : int 1 2 3 4 5 6 7 1 2 3 ...
$ Depth : num 0 4 4.5 5.5 6 6 6 3 4.5 6.5 ...
$ Mass : int 21 50 78 103 128 147 172 21 49 77 ...
Here's some data:
Plot SubPlot Treatment Depth Mass
1 1 1 0 21
1 1 2 4 50
1 1 3 4.5 78
1 1 4 5.5 103
1 1 5 6 128
1 1 6 6 147
1 1 7 6 172
1 2 1 3 21
1 2 2 4.5 49
1 2 3 6.5 77
1 2 4 7 102
1 2 5 8 127
1 2 6 9 146
1 2 7 10.5 171
1 3 1 3 21
1 3 2 1.5 49
1 3 3 1.5 77
1 3 4 1.5 102
1 3 5 1.5 127
1 3 6 1.5 146
1 3 7 1.5 171
2 1 1 3 21
2 1 2 5 50
2 1 3 5 78
2 1 4 7 103
2 1 5 9 128
2 1 6 9.5 146
2 1 7 10 171
2 2 1 1.5 21
2 2 2 4 50
2 2 3 5 78
2 2 4 9 103
2 2 5 10 128
2 2 6 10.5 146
2 2 7 10.5 171
2 3 1 0 21
2 3 2 0 50
2 3 3 0 78
2 3 4 0 103
2 3 5 0 128
2 3 6 0 146
2 3 7 0 171
Any ideas how to proceed?
This question already has answers here:
How to change factor labels into string in a data frame
(3 answers)
Closed 5 years ago.
I have this table (all football games from greek league, where one team won from behind - ht)
Date HomeTeam AwayTeam FTHG FTAG FTR HTHG HTAG HTR
8 24/08/15 Panetolikos Panathinaikos 1 2 A 1 0 H
16 31/08/15 Platanias Atromitos 1 2 A 1 0 H
40 28/09/15 Veria AEK 1 2 A 1 0 H
42 03/10/15 Panthrakikos Levadeiakos 1 3 A 1 0 H
68 01/11/15 Asteras Tripolis PAOK 2 1 H 0 1 A
97 05/12/15 Asteras Tripolis Iraklis 1 2 A 1 0 H
120 21/12/15 AEK Levadeiakos 1 2 A 1 0 H
138 17/01/16 Asteras Tripolis Kallonis 3 1 H 0 1 A
196 06/03/16 Panthrakikos PAOK 2 1 H 0 1 A
203 13/03/16 Atromitos Asteras Tripolis 2 1 H 0 1 A
233 17/04/16 Asteras Tripolis Veria 2 1 H 0 1 A
and I want to create a new column, let's call it tempWinner which has the name of the winner. I am using the following formula, which uses excel's rational and unfortunately fails to give me the correct result. I have searched how to just "copy" a cell using a condition, but I was not able to find anything relevant.
anatropes$tempWinner <- ifelse (anatropes$FTR == "H", anatropes$HomeTeam , anatropes$AwayTeam)
Any idea? What I want to do eventually is count how many times each team has won from behind (either being home or away team).
edit:
str(anatropes) returns:
'data.frame': 11 obs. of 9 variables:
$ Date : Factor w/ 85 levels "","01/11/15",..: 67 84 75 7 2 12 57 41 14 32 ...
$ HomeTeam: Factor w/ 17 levels "","AEK","Asteras Tripolis",..: 11 15 16 13 3 3 2 3 13 4 ...
$ AwayTeam: Factor w/ 17 levels "","AEK","Asteras Tripolis",..: 10 4 2 8 14 6 8 7 14 3 ...
$ FTHG : int 1 1 1 1 2 1 1 3 2 2 ...
$ FTAG : int 2 2 2 3 1 2 2 1 1 1 ...
$ FTR : Factor w/ 4 levels "","A","D","H": 2 2 2 2 4 2 2 4 4 4 ...
$ HTHG : int 1 1 1 1 0 1 1 0 0 0 ...
$ HTAG : int 0 0 0 0 1 0 0 1 1 1 ...
$ HTR : Factor w/ 4 levels "","A","D","H": 4 4 4 4 2 4 4 2 2 2 ...
There is no error in my method, I just get the following data frame:
> anatropes
Date HomeTeam AwayTeam FTHG FTAG FTR HTHG HTAG HTR
8 24/08/15 Panetolikos Panathinaikos 1 2 A 1 0 H
16 31/08/15 Platanias Atromitos 1 2 A 1 0 H
40 28/09/15 Veria AEK 1 2 A 1 0 H
42 03/10/15 Panthrakikos Levadeiakos 1 3 A 1 0 H
68 01/11/15 Asteras Tripolis PAOK 2 1 H 0 1 A
97 05/12/15 Asteras Tripolis Iraklis 1 2 A 1 0 H
120 21/12/15 AEK Levadeiakos 1 2 A 1 0 H
138 17/01/16 Asteras Tripolis Kallonis 3 1 H 0 1 A
196 06/03/16 Panthrakikos PAOK 2 1 H 0 1 A
203 13/03/16 Atromitos Asteras Tripolis 2 1 H 0 1 A
233 17/04/16 Asteras Tripolis Veria 2 1 H 0 1 A
tempWinner
8 10
16 4
40 2
42 8
68 3
97 6
120 8
138 3
196 13
203 4
233 3
as #Sotos suggested - use as.character() since HomeTeam and AwayTeam are both factors. what you are getting is the id of the level instead of the string value.
I am running a zero inflated negative binomial model with probit link on R (http://www.ats.ucla.edu/stat/r/dae/zinbreg.htm) and Stata (http://www.ats.ucla.edu/stat/stata/dae/zinb.htm).
There is a Vuong test to compare whether this specification is better than an ordinary negative binomial model. Where R tells me I am better off using the latter, Stata says a ZINB is the preferable choice. In both instances I assume that the process leading to the excess zeros is the same as for the negative binomial distributed non-zero observations. Coefficients are indeed the same (except that Stata prints one digit more).
In R I run (data code is below)
require(pscl)
ZINB <- zeroinfl(Two.Year ~ length + numAuth + numAck,
data=Master,
dist="negbin", link="probit"
)
NB <- glm.nb(Two.Year ~ length + numAuth + numAck,
data=Master
)
Comparing both with vuong(ZINB, NB) from the same package yields
Vuong Non-Nested Hypothesis Test-Statistic: -10.78337
(test-statistic is asymptotically distributed N(0,1) under the
null that the models are indistinguishible)
in this case:
model2 > model1, with p-value < 2.22e-16
Hence: NB is better than ZINB.
In Stata I run
zinb twoyear numauth length numack, inflate(numauth length numack) probit vuong
and receive (iteration fitting suppressed)
Zero-inflated negative binomial regression Number of obs = 714
Nonzero obs = 433
Zero obs = 281
Inflation model = probit LR chi2(3) = 74.19
Log likelihood = -1484.763 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
twoyear | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
twoyear |
numauth | .1463257 .0667629 2.19 0.028 .0154729 .2771785
length | .038699 .006077 6.37 0.000 .0267883 .0506097
numack | .0333765 .010802 3.09 0.002 .0122049 .0545481
_cons | -.4588568 .2068824 -2.22 0.027 -.8643389 -.0533747
-------------+----------------------------------------------------------------
inflate |
numauth | .2670777 .1141893 2.34 0.019 .0432708 .4908846
length | .0147993 .0105611 1.40 0.161 -.0059001 .0354987
numack | .0177504 .0150118 1.18 0.237 -.0116722 .0471729
_cons | -2.057536 .5499852 -3.74 0.000 -3.135487 -.9795845
-------------+----------------------------------------------------------------
/lnalpha | .0871077 .1608448 0.54 0.588 -.2281424 .4023577
-------------+----------------------------------------------------------------
alpha | 1.091014 .175484 .7960109 1.495346
------------------------------------------------------------------------------
Vuong test of zinb vs. standard negative binomial: z = 2.36 Pr>z = 0.0092
In the very last line Stata tells me that in this case ZINB is better than NB: Both test statistic and p-value differ. How come?
Data (R code)
Master <- <-read.table(text="
Two.Year numAuth length numAck
0 1 4 6
3 3 28 3
3 1 18 4
0 1 42 4
0 2 17 0
2 1 10 3
1 2 20 0
0 1 28 3
1 1 23 7
0 2 34 3
2 2 24 2
0 2 18 0
0 1 23 7
0 1 35 11
4 2 33 13
0 2 24 4
0 2 21 9
1 4 21 0
1 1 8 6
2 1 18 1
0 3 28 2
0 2 17 2
1 1 30 6
4 2 28 16
1 4 35 1
2 3 19 2
0 1 24 2
1 3 26 6
1 1 17 7
0 3 42 4
0 3 32 8
3 1 33 23
7 2 24 9
0 2 25 6
1 1 7 1
0 1 15 2
2 2 16 2
0 1 23 6
2 3 18 7
0 1 28 5
0 1 12 2
1 1 25 4
0 4 18 1
1 2 32 6
1 1 15 2
2 2 14 4
0 2 24 9
0 3 30 9
0 2 19 9
0 2 14 2
2 2 23 3
0 2 18 0
1 3 13 4
0 1 10 4
0 1 24 8
0 2 22 9
2 3 29 5
2 1 25 5
0 2 17 4
1 2 24 0
0 2 26 0
2 2 33 12
1 4 17 2
1 1 25 8
3 1 36 11
0 1 10 4
9 2 60 22
0 2 18 3
2 3 19 6
2 2 23 7
2 2 26 0
1 1 20 5
4 2 31 4
0 2 21 2
0 1 24 12
1 1 12 1
1 3 26 5
1 4 32 8
2 3 21 1
3 3 26 3
4 2 36 6
3 3 28 2
1 3 27 1
0 2 12 5
0 3 24 4
0 2 35 1
0 2 17 2
3 2 28 3
0 3 29 8
0 2 20 3
3 2 28 0
11 1 30 2
0 3 22 2
21 3 59 24
0 2 15 5
0 2 22 2
5 4 33 0
0 2 21 2
4 2 21 0
0 3 25 9
2 2 31 5
1 2 23 1
2 3 25 0
0 1 13 3
0 1 22 7
0 1 16 3
6 1 18 4
2 2 19 7
3 2 22 10
0 1 12 6
0 1 23 8
1 1 23 9
1 2 32 15
1 3 26 8
1 3 15 2
0 3 16 2
0 4 29 2
2 3 24 3
2 3 32 1
2 1 29 13
1 3 26 0
5 1 23 4
3 2 21 2
4 2 19 4
4 3 19 2
2 1 29 0
0 1 13 6
0 2 28 2
0 3 33 1
0 1 20 2
0 1 30 8
1 2 19 2
17 2 30 7
5 3 39 17
21 3 30 5
1 3 29 24
1 1 31 4
4 3 26 13
4 2 14 16
2 3 31 14
5 3 37 10
15 2 52 13
1 1 6 5
2 1 24 13
17 3 17 3
3 2 29 5
2 1 26 7
3 3 34 9
5 2 39 2
3 1 26 7
1 2 32 12
2 3 26 4
9 3 28 8
1 3 29 1
4 1 24 7
9 1 40 13
1 2 27 21
2 2 27 13
5 3 31 10
10 2 29 15
10 2 41 15
8 1 24 17
2 4 16 5
17 2 26 20
3 2 31 3
2 2 18 1
6 3 32 9
2 1 32 11
4 3 34 8
4 1 16 1
5 1 33 5
0 2 17 11
17 2 48 8
2 1 11 2
5 3 33 18
4 2 25 9
10 2 17 5
1 1 25 8
3 3 41 16
2 1 40 13
4 3 25 2
16 4 32 13
10 1 33 18
5 2 25 3
3 2 20 3
2 3 14 7
3 2 23 4
2 2 28 4
3 2 25 19
0 2 14 6
3 1 28 18
8 3 27 11
1 3 25 17
21 2 33 15
9 2 24 2
1 1 16 14
1 1 38 10
16 2 37 13
16 2 41 1
7 2 24 18
4 2 17 5
4 1 37 32
3 1 37 8
13 2 35 6
15 1 23 11
7 1 47 11
3 1 16 6
12 2 36 6
7 1 24 17
4 2 24 8
14 2 24 9
15 2 24 11
0 3 19 4
0 4 28 9
1 1 5 3
11 1 28 15
5 1 33 5
10 2 21 9
3 3 28 8
2 3 13 2
11 2 41 8
4 2 24 11
3 1 32 11
4 2 31 11
7 2 34 3
11 6 33 6
7 3 33 7
2 2 37 13
7 3 19 9
1 2 14 3
6 2 15 11
11 3 37 12
0 2 20 5
7 4 13 6
17 1 52 14
9 3 47 30
1 2 32 27
30 3 36 19
2 2 12 5
3 1 30 7
4 2 19 11
32 3 45 14
13 1 17 7
16 2 24 4
5 1 32 13
7 3 29 14
5 2 46 2
1 2 21 6
1 3 13 17
11 1 41 16
6 2 33 1
7 1 31 20
0 1 16 13
6 3 26 8
11 2 46 7
8 2 20 5
8 1 44 7
2 2 33 12
1 3 22 5
0 4 14 2
4 1 25 8
5 3 24 11
1 1 21 18
5 1 28 5
2 1 51 19
2 1 16 4
17 2 35 2
4 1 35 1
9 3 48 8
2 1 33 16
0 3 24 7
18 2 33 12
11 1 41 5
5 2 17 3
8 1 19 7
4 3 38 2
23 2 27 10
22 3 46 13
5 3 21 1
5 2 38 10
1 2 20 5
2 2 24 8
0 3 30 9
7 2 44 16
7 1 21 7
0 1 20 10
10 2 33 11
4 2 18 2
11 1 45 17
7 2 32 7
7 2 28 6
5 2 25 10
3 2 57 6
8 1 16 2
7 2 34 4
5 2 22 8
2 2 21 7
4 2 37 15
2 4 36 7
1 1 17 4
0 2 23 9
12 2 48 4
8 3 29 13
0 1 29 7
0 2 27 12
1 1 53 10
3 3 15 5
8 1 40 29
2 2 22 11
10 2 20 7
4 4 27 3
4 1 24 4
2 2 24 5
1 2 19 6
10 3 41 10
57 3 46 9
5 1 20 11
6 2 30 4
0 2 20 5
16 3 35 8
1 2 44 1
2 4 24 8
1 1 20 9
5 3 19 11
5 3 29 15
3 1 21 8
3 3 19 3
8 3 44 0
11 3 34 15
2 2 31 1
11 1 39 11
0 3 24 3
4 2 35 6
2 1 14 6
10 1 30 10
6 2 21 4
9 2 32 3
0 1 34 10
6 2 32 3
7 2 50 11
11 1 35 15
4 1 27 9
1 2 32 27
8 2 54 2
0 3 15 8
2 1 31 13
0 1 31 11
0 4 14 5
0 2 37 15
0 2 51 12
0 2 34 1
0 3 29 12
0 2 22 11
0 2 19 15
0 2 39 13
0 3 25 12
0 1 46 2
0 4 42 10
0 1 38 5
0 3 31 4
0 3 33 1
0 2 24 11
0 1 28 16
0 2 28 13
0 1 29 17
0 1 23 13
0 3 36 21
0 2 30 15
0 2 25 12
0 2 26 17
0 3 19 2
0 2 37 5
0 2 47 12
0 1 21 20
0 3 27 21
0 2 16 7
0 1 35 5
0 2 32 24
0 3 31 6
0 3 36 13
0 2 26 20
0 1 31 13
0 2 46 6
0 2 34 12
0 1 18 13
0 1 29 3
0 3 40 9
0 1 25 3
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0 2 20 22
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2 2 12 7
0 3 18 1
0 1 19 1
8 2 21 2
1 2 23 5
7 2 19 6
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3 2 24 2
6 1 32 21
0 1 16 0
1 2 15 0
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1 2 21 2
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1 1 18 8
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1 1 12 1
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1 1 17 7
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1 1 18 2
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0 1 12 2
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1 1 11 4
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8 2 20 7
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6 1 18 6
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4 1 17 6
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5 1 37 3
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6 3 30 11
1 1 30 9
4 3 31 26
1 2 14 1
10 1 35 27
1 1 36 7
5 1 32 8
2 1 28 6
3 1 34 16
3 2 32 5
1 3 11 0
2 2 42 5
0 2 30 7
0 1 32 9
3 3 43 2
7 2 43 6
1 2 21 5
2 1 27 20
1 2 37 7
2 1 37 8
0 1 19 3
0 3 28 5
2 2 33 3
3 1 41 6
13 2 41 9
2 1 38 3
4 1 32 5
2 1 34 8
1 1 27 9
8 1 29 7
4 1 17 6
0 1 20 8
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1 1 16 11
4 2 33 5
0 2 15 6
1 1 27 4
2 3 15 8
1 1 30 8
3 2 41 20
0 1 25 15
1 3 35 24
4 2 30 21
6 2 30 6
16 2 33 21
2 3 37 3
2 2 30 12
4 1 57 11
0 2 18 16
4 4 20 13
3 1 43 10
3 1 25 15
7 2 31 11
2 1 31 3
5 2 40 11
3 2 28 7
4 2 27 10
0 1 26 6
4 2 24 14
4 2 23 8
0 2 25 11
21 2 33 12
1 3 37 0
3 2 28 7
4 2 27 10
1 2 41 15
2 2 30 16
2 2 28 7
6 1 19 8
4 4 22 19
0 2 38 33
1 1 29 11
1 2 27 2
4 2 24 6
2 1 22 5
",header=TRUE,sep="")
The above problem occurred with pcsl version 1.4.6. I spoke to the author since and in version 1.4.7 he fixed the bug. The actual version in February 2015 is 1.4.8.