Alejandro Buren
2008-Aug-01 18:57 UTC
[R] multinomRob: Error in eigen [..] infinite or missing values in 'x'
I'm interested in analysing some of my data using multinomial regression. I have been using nnet's multinom so far. However, I found that some of the data shows overdispersion and hence want to change to robust multinomial regression, package: multinomRob I have succesfully implemented Agresti's (2002) alligator example (chapter 7, p 268) using the option MLEonly=TRUE as there are too few observations to estimate overdispersed MNL (commented code provided below). However, when I tried to run my data, I get the error mesagge (commented code provided below) 'Error in eigen(hess2, symmetric = TRUE, only.values = TRUE) : infinite or missing values in 'x' ' Help on how to overcome this isuue will be most appreciated Reference: Agresti, A. 2002. Categorical Data Analysis. John Wiley & Sons Operating system: Windows XP Professional Version 2002 Service Pack 2 R version 2.7.0 (2008-04-22) i386-pc-mingw32 locale: "LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252" Package: multinomRob Version: 1.8-2 Packaged: Fri Feb 9 02:14:07 2007; sekhon Built: R 2.7.1; i386-pc-mingw32; 2008-06-15 22:52:42; windows ------------ Alejandro Buren Psychology Department Memorial University of Newfoundland # ---------- Diet Analysis BEGINS-------------------- # I am modeling prey choice of a marine predator # the prey choice depends upon # A. length of the predator (categorical[1=small, 2=medium or 3=large]) # B. year (continuous), # C. depth (shallow or deep) and # D. position (north or south) # I am considering a suite of 13 prey, prey #3 is the most common and therefore used as baseline # the data is provided in a dataframe with 4 columns containing the values of the explanatory variables # and 13 columns that contain the frequency of each prey (one column per level of the dependent variable) # The data frame is provided after the code to run the model as it is quite large # and would interrupt the flow of the reading # I try running multinomRob but get the error mesagge: # 'Error in eigen(hess2, symmetric = TRUE, only.values = TRUE) : # infinite or missing values in 'x' ' library(multinomRob) mlRobDiet <- multinomRob(list(p3 ~0, p1 ~ length+depth+position+year, p2 ~ length+depth+position+year, p4 ~ length+depth+position+year, p5 ~ length+depth+position+year, p6 ~ length+depth+position+year, p7 ~ length+depth+position+year, p8 ~ length+depth+position+year, p9 ~ length+depth+position+year, p10 ~ length+depth+position+year, p11 ~ length+depth+position+year, p12 ~ length+depth+position+year, p13 ~ length+depth+position+year), data=diet) `diet` <- structure(list(year = c(1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 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1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L ), length = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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1059L, 1060L, 1061L, 1062L, 1063L, 1064L, 1065L, 1066L, 1080L, 1081L, 1082L, 1083L, 1084L, 1085L, 1086L, 1087L, 1088L, 1089L, 1090L, 1091L, 1092L, 1093L, 1094L, 1095L, 1096L, 1097L, 1098L, 1099L, 1100L, 1101L, 1102L, 1103L, 1104L, 1105L, 1119L, 1120L, 1121L, 1122L, 1123L, 1124L, 1125L, 1126L, 1127L, 1128L, 1129L, 1130L, 1131L, 1132L, 1133L, 1134L, 1135L, 1136L, 1137L, 1138L, 1139L, 1140L, 1141L, 1142L, 1143L, 1144L, 1158L, 1159L, 1160L, 1161L, 1162L, 1163L, 1164L, 1165L, 1166L, 1167L, 1168L, 1169L, 1170L), class = "data.frame") # ---------- Diet Analysis ENDS-------------------- # -----------Agresti's (2002) alligator example BEGINS------------ # The measured response is the category of primary food choice of 219 alligators caught # in four Florida lakes (fish, invertebrate, reptile, bird, other). The explanatory variables # are categorical: L = lake of capture, G =gender, S = size ( . 2.3 m, > 2.3 m) # After performing model selection, Agresti concludes that the example can be simpified by collapsing over gender # Therefore, the model is (FoodChoice ~ Lake + Size) # Fish was the most common food choice and is therefore used as baseline category # Create Table 7.3 in Agresti 2002 (p 270) size.labs<-factor(c("<2.3",">2.3"),levels=c(">2.3","<2.3")) lake.labs<-factor(c("hancock","oklawaha","trafford","george"),levels=c("george", "hancock", "oklawaha","trafford")) table<-expand.grid(lake=lake.labs,size=size.labs) fish<-c(23,7,5,13,5,8,16,17) invert<-c(4,0,11,8,11,7,19,1) reptile<-c(2,1,1,6,2,6,1,0) bird<-c(2,3,0,1,1,3,2,1) other<-c(8,5,3,0,5,5,3,3) table.7.3<-cbind(table,fish,invert,reptile,bird,other) # load multinomRob package library(multinomRob) # fit multinomial logistic model mlRobAgresti <- multinomRob(model=list(fish~0, invert~lake+size, reptile~lake+size, bird~lake+size, other~lake+size), data=table.7.3, MLEonly=TRUE) # -----------Agresti's (2002) alligator example ENDS------------