त्रुटि <-` (` * tmp * `, मान = contr.funs [1 + isOF [nn]]) एक स्पष्ट रूप से सही डाटासेट

Question

मैं जब एक साधारण रेखीय मॉडल भागने की कोशिश कर अनुसंधान में कोई समस्या हुई है साथ predictor के रूप में एक स्पष्ट चर के साथ। ,

str(minimal) 
'data.frame': 330 obs. of 2 variables: 
$ swls  : num 5.2 NaN 7 6 NaN NaN NaN NaN NaN NaN ... 
$ exp.factor: Factor w/ 2 levels "erlebt","nicht erlebt": 1 1 1 1 2 2 2 2 NA 2 ... 

वहाँ भी लगता डेटा में पर्याप्त भिन्नता होने के लिए: जब मॉडल चल रहा है, आर त्रुटि

`Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : ` 

डेटा फेंकता है, तथापि, ठीक होने लगते हैं (डेटा सेट नीचे संलग्न) इसलिए समान धागे मैंने पाया यहाँ लागू नहीं है:

table(minimal$exp.factor) 

     erlebt nicht erlebt 
     148   163 

` हालांकि, एलएम() अभी भी काम करने के लिए मना कर दिया:

lm(swls ~ exp.factor, data = minimal) 
Fehler in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
    Kontraste können nur auf Faktoren mit 2 oder mehr Stufen angewendet werden 

वास्तव में अजीब है कि एल एम() अन्य कारकों (जैसे, लिंग)

कोई भी विचार यहाँ क्या गलत हो जाता है के साथ की उम्मीद के रूप में काम करता है?

डेटासेट:

structure(list(swls = c(5.2, NaN, 7, 6, NaN, NaN, NaN, NaN, NaN, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 
6.8, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 7, NaN, 
NaN, NaN, NaN, NaN, NaN, NaN, 5.8, 2.6, NaN, NaN, NaN, NaN, NaN, 
NaN, 5.4, NaN, 6.4, NaN, NaN, NaN, NaN, NaN, NaN, 6.8, NaN, NaN, 
NaN, NaN, NaN, NaN, 1.2, NaN, 6.2, 6.4, 5.2, NaN, 5.4, NaN, NaN, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 4.6, NaN, NaN, 
NaN, NaN, 5.8, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 5, NaN, 
NaN, NaN, 6.8, NaN, NaN, NaN, 6, 7, NaN, NaN, NaN, NaN, 6, NaN, 
6.4, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 6.8, NaN, NaN, 
6.8, NaN, NaN, NaN, NaN, NaN, 5, 5.6, NaN, NaN, NaN, NaN, NaN, 
NaN, NaN, 3, NaN, NaN, NaN, NaN, NaN, NaN, 1.2, 4.2, NaN, 5.4, 
NaN, NaN, NaN, NaN, NaN, NaN, 6.6, 5.8, NaN, NaN, NaN, 6.4, NaN, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.8, NaN, 4, NaN, NaN, NaN, 
6, 5, NaN, NaN, NaN, 4.4, NaN, 2, NaN, NaN, NaN, NaN, NaN, NaN, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.2, NaN, NaN, 
NaN, 7, NaN, NaN, NaN, 5.6, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 
NaN, NaN, NaN, 6.2, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 
5, NaN, NaN, 5.2, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 
NaN, 5.8, NaN, NaN, 3.6, 5.6, NaN, NaN, 2.8, NaN, NaN, NaN, 6.2, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, 5.8, 6.2, NaN, NaN, 5, 6.2, 
NaN, NaN, NaN, NaN, NaN, NaN, 4.8, NaN, NaN, NaN, NaN, 4.8, NaN, 
NaN, NaN, NaN, NaN, NaN, 4.4, NaN, NaN, 3, 5.2, NaN, 3.8, NaN, 
NaN, NaN, NaN, 3, NaN, NaN, NaN, NaN, 1.6, NaN, NaN, 6.6, NaN, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN), exp.factor = structure(c(1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, NA, 2L, 2L, NA, 2L, 2L, NA, 2L, 1L, 
2L, 2L, 2L, NA, 2L, 1L, NA, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
1L, 1L, NA, NA, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, NA, 2L, 1L, 
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, NA, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, NA, 1L, 2L, 
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
1L, 2L, 1L, 2L, NA, 2L, 1L, NA, 1L, 1L, 1L, 2L, 2L, NA, 1L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, NA, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 
1L, NA, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, NA, 2L, 2L, 1L, 
2L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 
NA, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, NA, 1L, 2L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L), .Label = c("erlebt", "nicht erlebt" 
), class = "factor")), .Names = c("swls", "exp.factor"), row.names = c(7L, 
9L, 19L, 36L, 50L, 63L, 67L, 75L, 84L, 85L, 94L, 100L, 109L, 
122L, 128L, 135L, 137L, 145L, 156L, 158L, 163L, 182L, 188L, 198L, 
204L, 213L, 221L, 240L, 251L, 254L, 258L, 261L, 271L, 284L, 286L, 
295L, 308L, 309L, 313L, 319L, 334L, 340L, 344L, 351L, 354L, 365L, 
372L, 382L, 385L, 391L, 398L, 404L, 427L, 431L, 435L, 438L, 441L, 
452L, 468L, 469L, 474L, 483L, 486L, 493L, 502L, 508L, 513L, 519L, 
524L, 528L, 537L, 543L, 546L, 557L, 572L, 578L, 591L, 606L, 611L, 
613L, 624L, 633L, 640L, 642L, 651L, 662L, 667L, 672L, 673L, 696L, 
703L, 709L, 718L, 722L, 732L, 735L, 747L, 749L, 753L, 770L, 780L, 
787L, 799L, 801L, 812L, 818L, 825L, 838L, 864L, 874L, 887L, 896L, 
897L, 906L, 909L, 920L, 923L, 929L, 944L, 959L, 964L, 973L, 978L, 
986L, 991L, 996L, 1001L, 1008L, 1014L, 1017L, 1033L, 1040L, 1046L, 
1067L, 1075L, 1085L, 1090L, 1100L, 1102L, 1113L, 1144L, 1145L, 
1150L, 1155L, 1158L, 1165L, 1175L, 1180L, 1189L, 1196L, 1198L, 
1203L, 1212L, 1217L, 1230L, 1235L, 1257L, 1264L, 1266L, 1279L, 
1285L, 1290L, 1299L, 1308L, 1320L, 1331L, 1338L, 1345L, 1350L, 
1366L, 1376L, 1381L, 1400L, 1403L, 1406L, 1409L, 1419L, 1424L, 
1456L, 1462L, 1467L, 1469L, 1490L, 1499L, 1501L, 1509L, 1515L, 
1518L, 1524L, 1531L, 1533L, 1538L, 1560L, 1571L, 1573L, 1578L, 
1587L, 1600L, 1602L, 1624L, 1626L, 1631L, 1637L, 1646L, 1656L, 
1661L, 1667L, 1677L, 1683L, 1692L, 1694L, 1699L, 1705L, 1712L, 
1714L, 1726L, 1739L, 1741L, 1750L, 1763L, 1768L, 1780L, 1795L, 
1811L, 1816L, 1821L, 1830L, 1864L, 1869L, 1883L, 1887L, 1891L, 
1904L, 1914L, 1917L, 1928L, 1934L, 1939L, 1941L, 1948L, 1950L, 
1961L, 1969L, 1975L, 1982L, 1992L, 1998L, 2004L, 2019L, 2025L, 
2040L, 2041L, 2046L, 2051L, 2068L, 2083L, 2085L, 2090L, 2098L, 
2103L, 2109L, 2116L, 2124L, 2131L, 2133L, 2138L, 2144L, 2154L, 
2160L, 2161L, 2183L, 2188L, 2190L, 2203L, 2218L, 2221L, 2229L, 
2234L, 2243L, 2252L, 2262L, 2265L, 2275L, 2280L, 2282L, 2286L, 
2289L, 2299L, 2308L, 2309L, 2319L, 2332L, 2335L, 2350L, 2353L, 
2360L, 2363L, 2366L, 2369L, 2376L, 2401L, 2406L, 2415L, 2426L, 
2429L, 2436L, 2447L, 2453L, 2459L, 2476L, 2478L, 2486L, 2492L, 
2499L, 2501L, 2511L, 2517L, 2522L, 2528L, 2541L, 2547L, 2552L, 
2554L, 2557L, 2566L, 2580L, 2587L, 2594L, 2603L, 2608L), class = "data.frame") 

और मेरे SessionInfo():

R version 3.1.0 (2014-04-10) 
Platform: x86_64-pc-linux-gnu (64-bit) 

locale: 
[1] LC_CTYPE=de_DE.UTF-8  LC_NUMERIC=C    LC_TIME=de_DE.UTF-8  LC_COLLATE=de_DE.UTF-8  LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 
[7] LC_PAPER=de_DE.UTF-8  LC_NAME=C     LC_ADDRESS=C    LC_TELEPHONE=C    LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C  

attached base packages: 
[1] splines grid  stats  graphics grDevices utils  datasets methods base  

other attached packages: 
[1] foreign_0.8-61 lavaan_0.5-15 plyr_1.8.1  xtable_1.7-3 Hmisc_3.14-4 Formula_1.1-1 survival_2.37-7 lattice_0.20-29 Lambda4_3.0  MBESS_3.3.3  

loaded via a namespace (and not attached): 
[1] cluster_1.15.2  latticeExtra_0.6-26 MASS_7.3-32   mnormt_1.4-7  pbivnorm_0.5-1  quadprog_1.5-5  RColorBrewer_1.0-5 Rcpp_0.11.1   
[9] stats4_3.1.0  tools_3.1.0   

Answer 1

समस्या यह है कि एक बार एनए मान डेटा सेट से छोड़े गए हैं, वहाँ किसी भी "Nicht erlebt नहीं हैं "टिप्पणियों छोड़ दिया:

summary(na.omit(minimal)) 
     swls    exp.factor 
Min. :1.200 erlebt  :64 
1st Qu.:4.400 nicht erlebt: 0 
Median :5.500      
Mean :5.119      
3rd Qu.:6.200      
Max. :7.000  

तो lm मुसीबत एक विधा फिटिंग के लिए जा रहा है केवल एक (शेष) स्तर के साथ एक कारक के लिए एल ...

तुम भी exp.factor और प्रतिक्रिया की is.na() के पार सारणीकरण को देखकर यह अनुमान लगा सकते हैं ...

with(minimal,table(exp.factor,is.na(swls))) 

exp.factor  FALSE TRUE 
    erlebt   64 84 
    nicht erlebt  0 163