Not sure, but one possible candidate problem is that in your simulations one iteration ended up with fewer levels of a factor than the overall dataset and that caused the error.
There is no recode function in the default packages, there are at least 6 recode functions in other packages, we cannot tell which you were using from the code below. -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.s...@imail.org 801.408.8111 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r- > project.org] On Behalf Of Mike Harwood > Sent: Monday, January 10, 2011 6:29 AM > To: r-help@r-project.org > Subject: [R] debug biglm response error on bigglm model > > G'morning > > What does the error message "Error in x %*% coef(object) : non- > conformable arguments" indicate when calculating the response values > for > newdata with a model from bigglm (in package biglm), and how can I > debug it? I am attempting to do Monte Carlo simulations, which may > explain the loop in the code that follows. After the code I > have included the output, which shows that the simulations are > changing the response and input values, and that there are not any > atypical values for the > factors in the seventh iteration. At the end of the output is the > aforementioned error message. Finally, I have included the model from > biglm. > > Thanks in advance! > > Code: > ======= > iter <- nrow(nov.2010) > predict.nov.2011 <- vector(mode='numeric', length=iter) > for (i in 1:iter) { > iter.df <- nov.2010 > ##---------- Update values of dynamic variables ------------------ > iter.df$age <- iter.df$age + 12 > iter.df$pct_utilize <- > iter.df$pct_utilize + mc.util.delta[i] > > iter.df$updated_varname1 <- > ceiling(iter.df$updated_varname1 + mc.varname1.delta[i]) > > if(iter.df$state=="WI") > iter.df$varname3 <- iter.df$varname3 + mc.wi.varname3.delta[i] > if(iter.df$state=="MN") > iter.df$varname3 <- iter.df$varname3 + mc.mn.varname3.delta[i] > if(iter.df$state=="IL") > iter.df$varname3 <- iter.df$varname3 + mc.il.varname3.delta[i] > if(iter.df$state=="US") > iter.df$varname3 <- iter.df$varname3 + mc.us.varname3.delta[i] > > ##--- Bin Variables ------------------ > iter.df$bin_varname1 <- as.factor(recode(iter.df$updated_varname1, > "300:499 = '300 - 499'; > 500:549 = '500 - 549'; > 550:599 = '550 - 599'; > 600:649 = '600 - 649'; > 650:699 = '650 - 699'; > 700:749 = '700 - 749'; > 750:799 = '750 - 799'; 800:849 = 'GE 800'; else = > 'missing'; > ")) > iter.df$bin_age <- as.factor(recode(iter.df$age, > "0:23 = ' < 24mo.'; > 24:72 = '24 - 72mo.'; > 72:300 = '72 - 300mo'; else = 'missing'; > ")) > iter.df$bin_util <- as.factor(recode(iter.df$pct_utilize, > "0.0:0.2 = ' 0 - 20%'; > 0.2:0.4 = ' 20 - 40%'; > 0.4:0.6 = ' 40 - 60%'; > 0.6:0.8 = ' 60 - 80%'; > 0.8:1.0 = ' 80 - 100%'; > 1.0:1.2 = '100 - 120%'; else = 'missing'; > ")) > iter.df$bin_varname2 <- as.factor(recode(iter.df$varname2_prop, > "0:70 = ' < 70%'; > 70:85 = ' 70 - 85%'; > 85:95 = ' 85 - 95%'; > 95:110 = '95 - 110%'; else = 'missing'; > ")) > iter.df$bin_varname1 <- relevel(iter.df$bin_varname1, 'missing') > iter.df$bin_age <- relevel(iter.df$bin_age, 'missing') > iter.df$bin_util <- relevel(iter.df$bin_util, 'missing') > iter.df$bin_varname2 <- relevel(iter.df$bin_varname2, 'missing') > > #~ print(head(iter.df)) > if (i>=6 & i<=8){ > print('---------------------------------') > browser() > print(i) > print(table(iter.df$bin_varname1)) > print(table(iter.df$bin_age)) > print(table(iter.df$bin_util)) > print(table(iter.df$bin_varname2)) > #~ debug(predict.nov.2011[i] <- > #~ sum(predict(logModel.1, newdata=iter.df, > type='response'))) > } > > predict.nov.2011[i] <- > sum(predict(logModel.1, newdata=iter.df, type='response')) > > print(predict.nov.2011[i]) > > } > > Output > ========== > [1] 36.56073 > [1] 561.4516 > [1] 4.83483 > [1] 5.01398 > [1] 7.984146 > [1] "---------------------------------" > Called from: top level > Browse[1]> > [1] 6 > > missing 300 - 499 500 - 549 550 - 599 600 - 649 650 - 699 700 - 749 > 750 - 799 GE 800 > 842 283 690 1094 1695 3404 > 6659 18374 21562 > > missing < 24mo. 24 - 72mo. 72 - 300mo > 16 2997 19709 31881 > > missing 0 - 20% 20 - 40% 40 - 60% 60 - 80% 80 - 100% 100 > - 120% > 17906 4832 4599 5154 7205 > 14865 42 > > missing < 70% 70 - 85% 85 - 95% 95 - 110% > 10423 19429 10568 8350 5833 > [1] 11.04090 > [1] "---------------------------------" > Called from: top level > Browse[1]> > [1] 7 > > missing 300 - 499 500 - 549 550 - 599 600 - 649 650 - 699 700 - 749 > 750 - 799 > 847 909 1059 1586 3214 6304 > 16349 24335 > > missing < 24mo. 24 - 72mo. 72 - 300mo > 16 2997 19709 31881 > > missing 0 - 20% 20 - 40% 40 - 60% 60 - 80% 80 - 100% 100 > - 120% > 17145 4972 4617 5020 6634 > 16139 76 > > missing < 70% 70 - 85% 85 - 95% 95 - 110% > 10423 19429 10568 8350 5833 > Error in x %*% coef(object) : non-conformable arguments > > Model > ======= > Large data regression model: bigglm(outcome ~ bin_varname1 + > bin_varname2 + bin_age + bin_util + > state + varname3 + varname3:state, family = binomial(link = > "logit"), > data = dev.data, maxit = 75, sandwich = FALSE) > Sample size = 1372250 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
