I ran into a problem in R when trying to run a simple linear model with a categorical variable as a predictor. When starting the model R produces an error
`Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : `
The data, however, looks fine (the dataset is attached below):
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 ...
There seems to be a fair amount of change in the data, too, so similar threads I found don't apply here:
table(minimal$exp.factor) erlebt nicht erlebt 148 163
`However, lm () still refuses to work:
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
It's really strange that lm () works as expected with other factors (like sex)
Any ideas what is wrong here?
Data set:
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")
And my 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