R çıkışını bellekten matrise oku (paket tıklatma)

Question

Geçiş olasılıklarını tahmin etmek için R paketi tıklama akışını kullanıyorum.

çıktı şuna benzer: Şimdi

Higher-Order Markov Chain (order=2) 

Transition Probabilities: 

Lag: 1 
lambda: 1 
            A Conversion      D 
A   0,25999823819591261209183   0 0,0605291772381297554784 
Conversion 0,02034883720930232703372   0 0,0021747009786154403166 
D   0,00162966878083157162491   0 0,6451612903225806272545 
E   0,00000000000000000000000   0 0,0000000000000000000000 
F   0,00132135306553911205212   0 0,0007249003262051467722 
N   0,00603417899929527875441   0 0,0181225081551286693049 
Null  0,62182875264270609516615   1 0,1808626313881841196629 
O   0,00792811839323467187901   0 0,0094237042406669080385 
P   0,00017618040873854828084   0 0,0000000000000000000000 
Start  0,00000000000000000000000   0 0,0000000000000000000000 
T   0,08073467230443974462784   0 0,0830010873504893054164 
           E      F       N Null 
A   0,035714285714285712303 0,0675675675675675713183 0,01131221719457013545618 0 
Conversion 0,071428571428571424606 0,0405405405405405427910 0,00061703002879473468293 0 
D   0,000000000000000000000 0,0022522522522522522327 0,00102838338132455776874 0 
E   0,392857142857142849213 0,0000000000000000000000 0,00000000000000000000000 0 
F   0,000000000000000000000 0,3355855855855855995884 0,0
N   0,000000000000000000000 0,0090090090090090089309 0,09543397778691896371495 0 
Null  0,214285714285714273819 0,4797297297297297147267 0,86960098724804602010607 0 
O   0,000000000000000000000 0,0045045045045045044654 0,00143973673385438096298 0 
P   0,000000000000000000000 0,0000000000000000000000 0,00020567667626491157001 0 
Start  0,000000000000000000000 0,0000000000000000000000 0,00000000000000000000000 0 
T   0,285714285714285698425 0,0608108108108108141865 0,01912793089263677484557 0 
            O      P      Start 
A   0,05175117616309461709978 0,059999999999999997780 0,23964753542773373218289 
Conversion 0,01568217459487715459421 0,020000000000000000416 0,00000000000000000000000 
D   0,00209095661265028742379 0,000000000000000000000 0,01343022430996705478268 
E   0,00026136957658128592797 0,000000000000000000000 0,00022068443701823799359 
F   0,00156821745948771567626 0,000000000000000000000 0,00345213512192815144206 
N   0,00836382645060114969515 0,020000000000000000416 0,06317880168350699376933 
Null  0,62859383167799265379472 0,520000000000000017764 0,00000000000000000000000 
O   0,20987976999477261919047 0,000000000000000000000 0,04090543671873768383218 
P   0,00000000000000000000000 0,320000000000000006661 0,00045713204810920727824 
Start  0,00000000000000000000000 0,000000000000000000000 0,00000000000000000000000 
T   0,08180867746994249789516 0,059999999999999997780 0,63870805025299892676571 
            T 
A   0,01751190166393681163504 
Conversion 0,00991932891453763294909 
D   0,00116337808256922864945 
E   0,00003061521269919022833 
F   0,00048984340318704365329 
N   0,00254106265403278904630 
Null  0,62369311310790331859266 
O   0,00326052015246375932397 
P   0,00000000000000000000000 
Start  0,00000000000000000000000 
T   0,34139023680867025545282 

Lag: 2 
lambda: 0 
            A Conversion       D 
A   0,31341719077568136464507   0 0,05353982300884956024856 
Conversion 0,02154670393664104288689   0 0,00353982300884955747300 
D   0,00384346610761705093084   0 0,63628318584070797658825 
E   0,00000000000000000000000   0 0,00000000000000000000000 
F   0,00279524807826694605267   0 0,00044247787610619468412 
N   0,00815280689494526054906   0 0,01548672566371681394437 
Null  0,48742138364779874493138   0 0,18008849557522124684716 
O   0,01059864896342883715252   0 0,01061946902654867241900 
P   0,00058234334963894716659   0 0,00000000000000000000000 
Start  0,00000000000000000000000   0 0,00000000000000000000000 
T   0,15164220824598181880383   0 0,10000000000000000555112 
... 

Yaptığımın

sink("mc_output.txt") 
mc 
sink() 


# Lag 1 
mydat11 = read.table("mc_output.txt", skip = 6, nrows = 11) 
mydat12 = read.table("mc_output.txt", skip = 18, nrows = 11) 
mydat13 = read.table("mc_output.txt", skip = 30, nrows = 11) 
mydat14 = read.table("mc_output.txt", skip = 42, nrows = 11) 
... 

Bu yaklaşım bir matris olarak okumak daha sonra bir metin dosyasına çıktı yazmak ve oldukça hata eğilimli Matrisin boyutları değiştiğinde. Fikir, çıktıyı doğrudan bellekten okumaktı. Bunu başarmak için herhangi bir akıllı çözüm var mı?

Answer 1

library(clickstream) 
clickstreams <- c("User1,h,c,c,p,c,h,c,p,p,c,p,p,o", 
"User2,i,c,i,c,c,c,d", 
"User3,h,i,c,i,c,p,c,c,p,c,c,i,d", 
"User4,c,c,p,c,d", 
"User5,h,c,c,p,p,c,p,p,p,i,p,o", 
"User6,i,h,c,c,p,p,c,p,c,d") 
csf <- tempfile() 
writeLines(clickstreams, csf) 
cls <- readClickstreams(csf, header = TRUE) 
mc <- fitMarkovChain(cls) 

örneğin kullanarak geçiş matrisi erişebilir Verilen mc nesne çok başka yuvalar bulunmaktadır

mc@transitions[[1]] 
#  c d h   i o   p 
# c 0.32 0 0.8 0.5714286 0 0.47058824 
# d 0.12 0 0.0 0.1428571 0 0.00000000 
# h 0.04 0 0.0 0.1428571 0 0.00000000 
# i 0.12 0 0.2 0.0000000 0 0.05882353 
# o 0.00 0 0.0 0.0000000 0 0.11764706 
# p 0.40 0 0.0 0.1428571 0 0.35294118 

: Ben arıyordu Tam olarak ne

slotNames(mc) 
# [1] "states"     "order"     "transitions"   "lambda"     
# [5] "logLikelihood"   "observations"   "start"     "end"     
# [9] "transientStates"  "absorbingStates"  "absorbingProbabilities"