I'm working with betareg package for beta regression but receive the below error:
Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : non-finite value supplied by optim
I can trace this error to creating the initial values for optim. Specifically, these lines of betareg.fit, which uses lm.wfit to generate starting values.
It turns out that one of the starting values is returned as NA for my dataset. I'm unsure why this is the case, since there are no missing values in the data / input to lm.wfit.
## data -- a sample of 100 obs from my actual data
nobs <- 100L
w <- rep(1, nobs)
offset <- rep(0, nobs)
y <- stats::rbeta(nobs, 0.75, 1.658)
x <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0165928242550604,
0.0984749494334759, 0.05517578125, 0.0185352577155742, 0.168701442841287,
0.0514759697487192, 0.026507054296708, 0.0188496858385694, 0.108620689655172,
0.0722387772757858, 0.0272373540856031, 0.0538907902524382, 0.0295235311312482,
0.0318257956448911, 0.231788079470199, 0.0674772036474164, 0.14846108458939,
0.0969908238068386, 0.0441553321506012, 0.154121863799283, 0,
0.110460389247421, 0.0292207792207792, 0.0522853185595568, 0.205288796102992,
0.00961124552835874, 0.0546908714289824, 0.0268199233716475,
0.0253164556962025, 0.181780542384243, 0.0551724137931034, 0.128842504743833,
0.0751429349305745, 0.217853751187085, 0.0510314875135722, 0.108407709439207,
0.04, 0.0638009815535624, 0.128329297820823, 0.0398115958281933,
0.0513258247605534, 0.0520833333333333, 0.0956239870340357, 0.0742899497995351,
0.144527098831031, 0.0723209169054441, 0.140116763969975, 0.172426847735821,
0.00830471112933819, 0.0548386400835806, 0.0372010221576987,
0.0549927641099855, 0.0386658431130327, 0.0256367439122648, 0.0166402535657686,
0.0769230769230769, 0.0130681818181818, 0.0229684699649666, 0.0344827586206897,
0.0135106607557526, 0.0581090909090909, 0.0321364452423698, 0.0141176470588235,
0.0203003337041157, 0.0948080795499367, 0.0202898550724638, 0.0443828016643551,
0.105830475257227, 0.0482315112540193, 0.0394736842105263, 0,
0.071608040201005, 0.0416666666666667, 0.268330928934329, 0.0422895357985838,
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1, 1, 0), .Dim = c(100L, 35L), .Dimnames = list(c("2801", "2316",
"382", "8062", "2687", "2731", "8019", "5652", "8429", "3479",
"7753", "9001", "2188", "8121", "8478", "5817", "1528", "2460",
"3946", "3531", "3421", "2802", "1975", "3639", "2894", "5897",
"9331", "9490", "7135", "5858", "7724", "9414", "9095", "6601",
"5064", "7111", "3593", "7322", "9522", "7116", "6922", "5172",
"2458", "5199", "1387", "3878", "6119", "8722", "6378", "4661",
"6109", "3682", "5751", "9390", "7915", "5268", "1029", "5953",
"242", "2912", "8798", "9607", "9768", "2222", "8260", "851",
"4205", "1823", "5063", "4189", "7541", "608", "6849", "7220",
"2889", "6770", "7064", "646", "4919", "1404", "120", "9716",
"7722", "7700", "6638", "8176", "5745", "6", "9481", "2233",
"341", "228", "1543", "553", "9709", "9493", "881", "7647", "6039",
"2925"), c("(Intercept)", "x 1", "x 2", "x 3", "x 4", "x 5",
"x 6", "x 7", "x 8", "x 9", "x 10", "x 11", "x 12", "x 13", "x 14",
"x 15", "x 16", "x 17", "x 18", "x 19", "x 20", "x 21", "x 22",
"x 23", "x 24", "x 25", "x 26", "x 27", "x 28", "x 29", "x 30",
"x 31", "x 32", "x 33", "x 34")))
betareg: the NA that causes the problemlinkfun <- function(mu) {.Call(stats:::C_logit_link, mu)}
auxreg_test <- lm.wfit(x, linkfun(y), w, offset)
# problem:
(beta <- auxreg_test$coefficients)
is.na(beta['x 8'])
> beta['x 8']
x 8
NA
I originally thought this might be related to using the CRAN version of betareg (3.1-0). But I updated to the rforge version (3.2-0) via devtools::install_github("rforge/betareg/pkg") and still have the same problem.
If I remove the offending predictor from my formula, betareg runs fine; however, the predictor is a necessary one.
NA coefficients from glm / lm / lm.fit / .lm.fit / lm.wfit imply the model matrix to be rank-deficient. They are just 0 with 0 standard error (i.e., fixed at 0).
I appreciated that you have done much debugging work and located the source of the error, but giving us a model matrix x directly is less informative for us to investigate. It would be good if you show us the model formula and the data frame.
Anyway, I have (with some pain) found the collinearity problem from your model matrix.
rowSums(, x[, 2:9])
#2801 2316 382 8062 2687 2731 8019 5652 8429 3479 7753 9001 2188 8121 8478 5817
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#1528 2460 3946 3531 3421 2802 1975 3639 2894 5897 9331 9490 7135 5858 7724 9414
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#9095 6601 5064 7111 3593 7322 9522 7116 6922 5172 2458 5199 1387 3878 6119 8722
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#6378 4661 6109 3682 5751 9390 7915 5268 1029 5953 242 2912 8798 9607 9768 2222
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#8260 851 4205 1823 5063 4189 7541 608 6849 7220 2889 6770 7064 646 4919 1404
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# 120 9716 7722 7700 6638 8176 5745 6 9481 2233 341 228 1543 553 9709 9493
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# 881 7647 6039 2925
# 1 1 1 1
Columns x1 to x8, if all included, has collinearity problem with the intercept (strange; those columns are not dummy ones so they are not from factor variables). If you don't want to drop any of them, drop intercept instead.