Fehler im lm.fit(x, y, offset = offset in der Einzahl.ok = singular.ok, ...) 0 nicht-na Fällen

Habe ich bereits überprüft, die anderen Fragen zu diesem Thema, aber da das problem sehr spezifisch sein scheint, Sie waren nicht hilfreich.

Ich habe einen dataframe, wie (dies ist nur ein schnelles Beispiel, Beispiel Daten von dput() ist nachfolgend angegeben):

year species abundance site county
2005 A       2         SH1  Göttingen
2006 A       0         SH1  Göttingen
2007 A       NA        SH1  Göttingen
2008 A       2         SH1  Göttingen
2009 A       NA        SH1  Göttingen
2010 A       2         SH1  Göttingen
2011 A       NA        SH1  Göttingen
2005 B       2         SH1  Göttingen
2006 B       0         SH1  Göttingen
2007 B       NA        SH1  Göttingen
2008 B       2         SH1  Göttingen
2009 B       NA        SH1  Göttingen
2010 B       2         SH1  Göttingen
2011 B       NA        SH1  Göttingen
2005 A       2         SH1  Göttingen
2006 A       0         SH1  Göttingen
2007 A       NA        SH1  Göttingen
2008 A       2         SH1  Göttingen
2009 A       NA        SH1  Göttingen
2010 A       2         SH1  Göttingen
2011 A       NA        SH1  Göttingen
2005 A       2         SH2  Göttingen
2006 A       0         SH2  Göttingen
2007 A       NA        SH2  Göttingen
2008 A       2         SH2  Göttingen
2009 A       NA        SH2  Göttingen
2010 A       2         SH2  Göttingen
2011 A       NA        SH2  Göttingen

Es enthält die fülle für 11 Arten auf mehreren verschiedenen Webseiten pro county (mehr als 1500 Standorten in über 400ish Landkreise) für jedes Jahr 2005-2011. für jeden Ort, in jeder Provinz, jedes Jahr, alle Arten berücksichtigt worden, so ist es entweder ein NA oder eine Zahl, in Hülle und fülle für jedes Jahr. Die Anzahl der Seiten variiert je nach county.

Ich würde gerne die folgende Schleife zu legen, die fülle in mehrere Spalten: Es sollte erstellen Sie ein lineares Modell zu berechnen, die Bevölkerungsentwicklung in diesen Jahren, und speichern Sie die Ausgabe in eine weitere Zeile. Am Ende hätte ich gerne noch ein trend für jede Art auf jeder Website, die über die Jahre:

alldata_lm$slope_abundance_plot <- NA
alldata_lm$p_slope_abundance_plot <- NA

species <- unique(alldata_lm$species)
sites <- unique(alldata_lm$site)

for (i in (1:length(species))) {
  for (k in(1:length(sites))) {        
    print(c(i,k))
    lm1 <-  lm(abundance ~ year, data = alldata_lm[alldata_lm$species == species[i] & alldata_lm$site == sites[k],], na.action=na.omit)
    alldata_lm$slope_abundance_plot[alldata_lm$species == species[i] & alldata_lm$site == sites[k]] <- coefficients(lm1)[2]
    if (nrow(coef(summary(lm1)))>1){ alldata_lm$p_slope_abundance_plot[alldata_lm$species == species[i] & alldata_lm$site == sites[k]] <- coef(summary(lm1))[2,4]}
  }  
}

Allerdings, wenn ich tun, es gibt die folgende Fehlermeldung zurück:

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  alle Fälle NA

Den gleichen loop funktioniert einwandfrei mit einem sehr ähnlichen dataframe, der einzige Unterschied ist, dass die aktuellen dataframe enthält weit mehr, NA.

Löschen die NA ' s vor der Ausführung der Schleife nicht helfen. Ich erhalte die Fehlermeldung, egal, ob es irgendwelche NA ' s in der fülle Spalte oder nicht. Ich denke der Fehler kann auftreten, irgendwo anders. Jahr Spalte ist nie enthalten keine fehlenden Werte.

Ich würde schätzen jede Hilfe!!! Dank

BEISPIEL DATEN

 structure(list(site = structure(c(700L, 700L, 700L, 700L, 700L, 
700L, 700L), .Label = c("bb1", "bb100", "bb101", "bb104", "bb107", 
"bb108", "bb109", "bb11", "bb111", "bb113", "bb115", "bb116", 
"bb117", "bb118", "bb119", "bb120", "bb121", "bb122", "bb123", 
"bb124", "bb125", "bb126", "bb127", "bb129", "bb130", "bb131", 
"bb132", "bb134", "bb135", "bb138", "bb139", "bb14", "bb140", 
"bb143", "bb147", "bb15", "bb150", "bb152", "bb154", "bb155", 
"bb156", "bb157", "bb158", "bb159", "bb163", "bb164", "bb166", 
"bb167", "bb169", "bb170", "bb171", "bb172", "bb173", "bb174", 
"bb175", "bb176", "bb177", "bb178", "bb179", "bb180", "bb181", 
"bb183", "bb186", "bb187", "bb188", "bb19", "bb191", "bb192", 
"bb193", "bb194", "bb197", "bb198", "bb199", "bb20", "bb200", 
"bb201", "bb202", "bb203", "bb204", "bb205", "bb207", "bb208", 
"bb209", "bb21", "bb210", "bb211", "bb212", "bb213", "bb215", 
"bb216", "bb217", "bb218", "bb219", "bb220", "bb221", "bb224", 
"bb225", "bb228", "bb23", "bb230", "bb232", "bb234", "bb237", 
"bb239", "bb242", "bb27", "bb32", "bb35", "bb37", "bb38", "bb39", 
"bb4", "bb40", "bb41", "bb47", "bb49", "bb53", "bb55", "bb58", 
"bb59", "bb6", "bb60", "bb63", "bb65", "bb66", "bb7", "bb70", 
"bb72", "bb73", "bb76", "bb77", "bb79", "bb8", "bb80", "bb81", 
"bb82", "bb84", "bb85", "bb87", "bb89", "bb9", "bb90", "bb91", 
"bb92", "bb93", "bb94", "bb96", "bb97", "bb98", "be14", "be15", 
"be17", "be30", "bw10", "bw100", "bw104", "bw108", "bw111", "bw112", 
"bw12", "bw120", "bw124", "bw126", "bw13", "bw144", "bw146", 
"bw175", "bw183", "bw192", "bw193", "bw198", "bw199", "bw200", 
"bw202", "bw208", "bw210", "bw211", "bw213", "bw215", "bw219", 
"bw225", "bw226", "bw229", "bw236", "bw243", "bw257", "bw262", 
"bw266", "bw268", "bw283", "bw294", "bw3", "bw30", "bw307", "bw326", 
"bw327", "bw338", "bw339", "bw341", "bw35", "bw36", "bw360", 
"bw368", "bw380", "bw381", "bw397", "bw405", "bw42", "bw53", 
"bw58", "bw6", "bw7", "bw84", "bw89", "bw91", "bw92", "bw96", 
"bw97", "by10", "by103", "by109", "by11", "by110", "by111", "by112", 
"by113", "by114", "by115", "by116", "by117", "by118", "by120", 
"by122", "by125", "by126", "by127", "by128", "by129", "by130", 
"by134", "by137", "by14", "by142", "by144", "by146", "by147", 
"by150", "by151", "by152", "by153", "by154", "by155", "by156", 
"by157", "by158", "by159", "by163", "by164", "by166", "by167", 
"by169", "by170", "by171", "by173", "by175", "by176", "by177", 
"by178", "by18", "by180", "by182", "by186", "by187", "by188", 
"by19", "by192", "by193", "by194", "by197", "by200", "by203", 
"by205", "by210", "by212", "by215", "by22", "by222", "by223", 
"by225", "by226", "by229", "by230", "by231", "by233", "by234", 
"by236", "by238", "by239", "by24", "by240", "by241", "by242", 
"by243", "by247", "by248", "by25", "by250", "by251", "by255", 
"by257", "by267", "by268", "by271", "by272", "by274", "by275", 
"by278", "by279", "by28", "by280", "by283", "by284", "by285", 
"by286", "by287", "by289", "by29", "by290", "by291", "by292", 
"by293", "by294", "by295", "by298", "by30", "by303", "by305", 
"by307", "by308", "by310", "by32", "by321", "by322", "by323", 
"by324", "by326", "by331", "by333", "by334", "by337", "by34", 
"by341", "by346", "by347", "by350", "by352", "by356", "by357", 
"by36", "by368", "by37", "by370", "by376", "by378", "by39", "by4", 
"by40", "by41", "by43", "by451", "by452", "by47", "by5", "by52", 
"by53", "by56", "by63", "by64", "by65", "by66", "by67", "by68", 
"by69", "by7", "by72", "by74", "by76", "by79", "by8", "by80", 
"by87", "by88", "by89", "by90", "by91", "by92", "by96", "by97", 
"by98", "hb16", "hb17", "hb18", "hb21", "hb30", "hb5", "hb6", 
"hb7", "hb9", "he100", "he103", "he106", "he107", "he108", "he109", 
"he110", "he111", "he113", "he114", "he115", "he116", "he119", 
"he120", "he122", "he124", "he13", "he130", "he137", "he14", 
"he144", "he145", "he150", "he154", "he18", "he37", "he42", "he46", 
"he47", "he51", "he52", "he66", "he68", "he7", "he70", "he72", 
"he73", "he75", "he82", "he83", "he84", "he85", "he89", "he9", 
"he91", "he93", "he94", "he96", "he97", "hh10", "hh28", "hh29", 
"hh30", "hh41", "hh44", "mv108", "mv109", "mv110", "mv122", "mv124", 
"mv125", "mv126", "mv141", "mv143", "mv15", "mv153", "mv156", 
"mv160", "mv17", "mv24", "mv29", "mv40", "mv41", "mv50", "mv55", 
"mv61", "mv63", "mv76", "mv82", "ni10", "ni100", "ni101", "ni102", 
"ni11", "ni110", "ni111", "ni119", "ni122", "ni125", "ni126", 
"ni13", "ni131", "ni134", "ni135", "ni136", "ni138", "ni14", 
"ni142", "ni146", "ni147", "ni149", "ni15", "ni150", "ni152", 
"ni162", "ni163", "ni166", "ni167", "ni168", "ni169", "ni170", 
"ni171", "ni172", "ni175", "ni182", "ni187", "ni188", "ni189", 
"ni191", "ni192", "ni193", "ni198", "ni2", "ni20", "ni206", "ni215", 
"ni218", "ni225", "ni226", "ni227", "ni231", "ni236", "ni239", 
"ni240", "ni241", "ni242", "ni243", "ni244", "ni246", "ni252", 
"ni257", "ni26", "ni260", "ni263", "ni272", "ni274", "ni282", 
"ni286", "ni290", "ni291", "ni297", "ni298", "ni299", "ni3", 
"ni303", "ni32", "ni34", "ni37", "ni38", "ni39", "ni4", "ni40", 
"ni41", "ni43", "ni45", "ni453", "ni455", "ni46", "ni47", "ni49", 
"ni50", "ni52", "ni55", "ni6", "ni61", "ni63", "ni66", "ni68", 
"ni71", "ni72", "ni73", "ni76", "ni77", "ni85", "ni87", "ni88", 
"ni89", "ni90", "ni91", "ni92", "ni93", "ni95", "ni97", "nw10", 
"nw108", "nw110", "nw112", "nw126", "nw13", "nw130", "nw140", 
"nw142", "nw143", "nw149", "nw154", "nw156", "nw173", "nw182", 
"nw20", "nw25", "nw38", "nw41", "nw5", "nw50", "nw52", "nw53", 
"nw54", "nw55", "nw6", "nw60", "nw63", "nw7", "nw72", "nw73", 
"nw74", "nw84", "nw86", "nw92", "rp101", "rp102", "rp103", "rp106", 
"rp108", "rp109", "rp116", "rp117", "rp120", "rp130", "rp131", 
"rp139", "rp140", "rp143", "rp144", "rp146", "rp21", "rp22", 
"rp23", "rp24", "rp36", "rp4", "rp84", "rp94", "rp99", "sh100", 
"sh101", "sh102", "sh103", "sh106", "sh109", "sh11", "sh111", 
"sh112", "sh117", "sh12", "sh121", "sh123", "sh125", "sh128", 
"sh130", "sh132", "sh14", "sh140", "sh17", "sh18", "sh19", "sh20", 
"sh25", "sh26", "sh27", "sh30", "sh33", "sh34", "sh35", "sh36", 
"sh37", "sh39", "sh42", "sh43", "sh44", "sh45", "sh46", "sh47", 
"sh51", "sh52", "sh54", "sh55", "sh58", "sh59", "sh60", "sh61", 
"sh62", "sh63", "sh65", "sh66", "sh67", "sh69", "sh70", "sh71", 
"sh72", "sh73", "sh74", "sh75", "sh76", "sh78", "sh79", "sh8", 
"sh84", "sh86", "sh88", "sh89", "sh91", "sh93", "sh95", "sh96", 
"sh98", "sh99", "sn104", "sn112", "sn117", "sn120", "sn123", 
"sn131", "sn141", "sn144", "sn145", "sn15", "sn151", "sn158", 
"sn159", "sn16", "sn162", "sn164", "sn165", "sn167", "sn18", 
"sn25", "sn27", "sn28", "sn30", "sn35", "sn40", "sn44", "sn45", 
"sn5", "sn56", "sn69", "sn7", "sn72", "sn74", "sn79", "sn83", 
"sn87", "sn89", "sn9", "sn91", "sn92", "sn93", "sn99", "st1", 
"st100", "st101", "st103", "st105", "st107", "st108", "st109", 
"st11", "st110", "st111", "st112", "st113", "st114", "st115", 
"st116", "st118", "st119", "st120", "st121", "st125", "st126", 
"st127", "st13", "st130", "st134", "st135", "st137", "st139", 
"st140", "st141", "st143", "st144", "st145", "st146", "st147", 
"st148", "st150", "st151", "st153", "st158", "st159", "st160", 
"st162", "st166", "st21", "st24", "st25", "st26", "st27", "st28", 
"st30", "st33", "st34", "st4", "st43", "st47", "st49", "st50", 
"st55", "st56", "st57", "st59", "st60", "st61", "st64", "st69", 
"st70", "st72", "st75", "st77", "st79", "st84", "st87", "st88", 
"st91", "st92", "st97", "st98", "st99", "th100", "th103", "th104", 
"th105", "th112", "th118", "th121", "th123", "th15", "th16", 
"th18", "th19", "th20", "th22", "th26", "th27", "th31", "th32", 
"th36", "th39", "th4", "th40", "th44", "th45", "th49", "th50", 
"th51", "th52", "th55", "th56", "th58", "th59", "th60", "th61", 
"th63", "th64", "th73", "th76", "th8", "th81", "th83", "th85", 
"th91", "th94", "th95", "th96", "th98", "th99"), class = "factor"), 
    year = 2005:2011, species = structure(c(8L, 8L, 8L, 8L, 8L, 
    8L, 8L), .Label = c("common linnet", "common whitethroat", 
    "corn bunting", "eurasian skylark", "northern lapwing", "red-backed shrike", 
    "tree sparrow", "western yellow wagtail", "whinchat", "woodlark", 
    "yellowhammer"), class = "factor"), abundance = c(NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_), county = structure(c(48L, 48L, 48L, 48L, 48L, 
    48L, 48L), .Label = c("Aichach-Friedberg, Landkreis", "Alb-Donau-Kreis", 
    "Altmarkkreis Salzwedel", "Alzey-Worms, Landkreis", "Amberg-Sulzbach, Landkreis", 
    "Ammerland, Landkreis", "Anhalt-Bitterfeld, Landkreis", "Ansbach, Landkreis", 
    "Aschaffenburg, Landkreis", "Augsburg, Landkreis", "Aurich, Landkreis", 
    "Böblingen, Landkreis", "Börde, Landkreis", "Bad Dürkheim, Landkreis", 
    "Bad Kissingen, Landkreis", "Bad Kreuznach, Landkreis", "Bad Tölz-Wolfratshausen, Landkreis", 
    "Barnim, Landkreis", "Bautzen, Landkreis", "Bayreuth", "Bayreuth, Landkreis", 
    "Bergstraße, Landkreis", "Berlin", "Biberach, Landkreis", 
    "Bodenseekreis", "Borken, Kreis", "Brandenburg an der Havel, Kreisfreie Stadt", 
    "Breisgau-Hochschwarzwald, Landkreis", "Bremen, Kreisfreie Stadt", 
    "Bremerhaven, Kreisfreie Stadt", "Burgenlandkreis", "Calw, Landkreis", 
    "Celle, Landkreis", "Cham, Landkreis", "Chemnitz, Stadt", 
    "Cloppenburg, Landkreis", "Coburg, Landkreis", "Cottbus, Kreisfreie Stadt", 
    "Cuxhaven, Landkreis", "Dachau, Landkreis", "Dahme-Spreewald, Landkreis", 
    "Darmstadt-Dieburg, Landkreis", "Deggendorf, Landkreis", 
    "Dessau-Roßlau, Kreisfreie Stadt", "Diepholz, Landkreis", 
    "Dillingen a.d.Donau, Landkreis", "Dingolfing-Landau, Landkreis", 
    "Dithmarschen, Landkreis", "Donau-Ries, Landkreis", "Donnersbergkreis", 
    "Dresden, Stadt", "Ebersberg, Landkreis", "Eichsfeld, Kreis", 
    "Eichstätt, Landkreis", "Eisenach, krsfr. Stadt", "Elbe-Elster, Landkreis", 
    "Emmendingen, Landkreis", "Emsland, Landkreis", "Ennepe-Ruhr-Kreis", 
    "Enzkreis", "Erding, Landkreis", "Erfurt, krsfr. Stadt", 
    "Erlangen-Höchstadt, Landkreis", "Erzgebirgskreis", "Esslingen, Landkreis", 
    "Fürstenfeldbruck, Landkreis", "Fürth, Landkreis", "Forchheim, Landkreis", 
    "Frankfurt (Oder), Kreisfreie Stadt", "Frankfurt am Main, Kreisfreie Stadt", 
    "Freising, Landkreis", "Freudenstadt, Landkreis", "Freyung-Grafenau, Landkreis", 
    "Friesland, Landkreis", "Fulda, Landkreis", "Göppingen, Landkreis", 
    "Görlitz, Landkreis", "Göttingen, Landkreis", "Garmisch-Partenkirchen, Landkreis", 
    "Gießen, Landkreis", "Gifhorn, Landkreis", "Goslar, Landkreis", 
    "Gotha, Kreis", "Grafschaft Bentheim, Landkreis", "Greiz, Kreis", 
    "Höxter, Kreis", "Haßberge, Landkreis", "Halle (Saale), Kreisfreie Stadt", 
    "Hamburg", "Hameln-Pyrmont, Landkreis", "Hamm, Kreisfreie Stadt", 
    "Harburg, Landkreis", "Harz, Landkreis", "Havelland, Landkreis", 
    "Heidekreis, Landkreis", "Heidelberg, Kreisfreie Stadt", 
    "Heidenheim, Landkreis", "Heilbronn, Landkreis", "Heinsberg, Kreis", 
    "Hersfeld-Rotenburg, Landkreis", "Herzogtum Lauenburg, Landkreis", 
    "Hildburghausen, Kreis", "Hildesheim, Landkreis", "Hochsauerlandkreis", 
    "Hochtaunuskreis", "Hof, Landkreis", "Holzminden, Landkreis", 
    "Ilm-Kreis", "Ingolstadt", "Jerichower Land, Landkreis", 
    "Köln, Kreisfreie Stadt", "Kaiserslautern, Landkreis", "Karlsruhe, Kreisfreie Stadt", 
    "Karlsruhe, Landkreis", "Kassel, Landkreis", "Kelheim, Landkreis", 
    "Kitzingen, Landkreis", "Kleve, Kreis", "Konstanz, Landkreis", 
    "Kronach, Landkreis", "Kulmbach, Landkreis", "Kusel, Landkreis", 
    "Kyffhäuserkreis", "Lörrach, Landkreis", "Lüchow-Dannenberg, Landkreis", 
    "Lüneburg, Landkreis", "Lahn-Dill-Kreis", "Landkreis Ludwigslust-Parchim", 
    "Landkreis Mecklenburgische Seenplatte", "Landkreis Nordwestmecklenburg", 
    "Landkreis Rostock", "Landkreis Vorpommern-Greifswald", "Landkreis Vorpommern-Rügen", 
    "Landsberg am Lech, Landkreis", "Landshut, Landkreis", "Leer, Landkreis", 
    "Leipzig, Landkreis", "Lichtenfels, Landkreis", "Limburg-Weilburg, Landkreis", 
    "Lippe, Kreis", "Ludwigsburg, Landkreis", "Märkisch-Oderland, Landkreis", 
    "Märkischer Kreis", "Mühldorf a.Inn, Landkreis", "München, Landeshauptstadt", 
    "München, Landkreis", "Main-Spessart, Landkreis", "Main-Tauber-Kreis", 
    "Main-Taunus-Kreis", "Mainz-Bingen, Landkreis", "Mannheim, Universitätsstadt, Kreisfreie Stadt", 
    "Mansfeld-Südharz, Landkreis", "Marburg-Biedenkopf, Landkreis", 
    "Mayen-Koblenz, Landkreis", "Meißen, Landkreis", "Mettmann, Kreis", 
    "Minden-Lübbecke, Kreis", "Mittelsachsen, Landkreis", "Nürnberg", 
    "Nürnberger Land, Landkreis", "Neckar-Odenwald-Kreis", "Neu-Ulm, Landkreis", 
    "Neuburg-Schrobenhausen, Landkreis", "Neumarkt i.d.OPf., Landkreis", 
    "Neustadt a.d.Aisch-Bad Windsheim, Landkreis", "Neustadt a.d.Waldnaab, Landkreis", 
    "Neustadt an der Weinstraße, Kreisfreie Stadt", "Neuwied, Landkreis", 
    "Nienburg (Weser), Landkreis", "Nordfriesland, Landkreis", 
    "Nordhausen, Kreis", "Nordsachsen, Landkreis", "Northeim, Landkreis", 
    "Oberallgäu, Landkreis", "Oberhavel, Landkreis", "Oberspreewald-Lausitz, Landkreis", 
    "Odenwaldkreis", "Oder-Spree, Landkreis", "Offenbach, Landkreis", 
    "Oldenburg, Landkreis", "Olpe, Kreis", "Ortenaukreis", "Osnabrück, Landkreis", 
    "Ostalbkreis", "Ostallgäu, Landkreis", "Osterholz, Landkreis", 
    "Osterode am Harz, Landkreis", "Ostholstein, Landkreis", 
    "Ostprignitz-Ruppin, Landkreis", "Passau, Landkreis", "Peine, Landkreis", 
    "Pfaffenhofen a.d.Ilm, Landkreis", "Pinneberg, Landkreis", 
    "Plön, Landkreis", "Potsdam-Mittelmark, Landkreis", "Potsdam, Kreisfreie Stadt", 
    "Prignitz, Landkreis", "Rastatt, Landkreis", "Ravensburg, Landkreis", 
    "Regen, Landkreis", "Regensburg, Landkreis", "Region Hannover, Landkreis", 
    "Rems-Murr-Kreis", "Rendsburg-Eckernförde, Landkreis", "Reutlingen, Landkreis", 
    "Rhön-Grabfeld, Landkreis", "Rhein-Kreis Neuss", "Rhein-Neckar-Kreis", 
    "Rhein-Sieg-Kreis", "Rheingau-Taunus-Kreis", "Rheinisch-Bergischer Kreis", 
    "Rosenheim, Landkreis", "Rotenburg (Wümme), Landkreis", 
    "Roth, Landkreis", "Rottweil, Landkreis", "Sächsische Schweiz-Osterzgebirge, Landkreis", 
    "Sömmerda, Kreis", "Südliche Weinstraße, Landkreis", "Südwestpfalz, Landkreis", 
    "Saale-Holzland-Kreis", "Saale-Orla-Kreis", "Saalekreis", 
    "Saalfeld-Rudolstadt, Kreis", "Salzlandkreis", "Schaumburg, Landkreis", 
    "Schleswig-Flensburg, Landkreis", "Schmalkalden-Meiningen, Kreis", 
    "Schwabach", "Schwandorf, Landkreis", "Schweinfurt", "Schweinfurt, Landkreis", 
    "Segeberg, Landkreis", "Siegen-Wittgenstein, Kreis", "Sigmaringen, Landkreis", 
    "Soest, Kreis", "Sonneberg, Kreis", "Spree-Neiße, Landkreis", 
    "Stade, Landkreis", "Starnberg, Landkreis", "Steinburg, Landkreis", 
    "Steinfurt, Kreis", "Stendal, Landkreis", "Stormarn, Landkreis", 
    "Straubing-Bogen, Landkreis", "Stuttgart, Landeshauptstadt, Kreisfreie Stadt", 
    "Tübingen, Landkreis", "Teltow-Fläming, Landkreis", "Traunstein, Landkreis", 
    "Uckermark, Landkreis", "Uelzen, Landkreis", "Unstrut-Hainich-Kreis", 
    "Unterallgäu, Landkreis", "Vechta, Landkreis", "Verden, Landkreis", 
    "Viersen, Kreis", "Vogelsbergkreis", "Würzburg, Landkreis", 
    "Waldeck-Frankenberg, Landkreis", "Wartburgkreis", "Weißenburg-Gunzenhausen, Landkreis", 
    "Weilheim-Schongau, Landkreis", "Weimar, krsfr. Stadt", "Weimarer Land, Kreis", 
    "Werra-Meißner-Kreis", "Wesel, Kreis", "Wesermarsch, Landkreis", 
    "Westerwaldkreis", "Wetteraukreis", "Wiesbaden, Landeshauptstadt, Kreisfreie Stadt", 
    "Wittenberg, Landkreis", "Wittmund, Landkreis", "Wolfenbüttel, Landkreis", 
    "Wuppertal, Kreisfreie Stadt", "Zollernalbkreis", "Zwickau, Landkreis"
    ), class = "factor"), slope_abundance_plot = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
    ), p_slope_abundance_plot = c(NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_)), .Names = c("site", 
"year", "species", "abundance", "county", "slope_abundance_plot", 
"p_slope_abundance_plot"), row.names = c(61L, 75L, 76L, 91L, 
92L, 93L, 134L), class = "data.frame")

InformationsquelleAutor thecrashlandingdodo | 2014-10-17