search for: nonwhites

...there are observations collected in many cities, represented by a variable PLACE. We are comparing the effect of several variables on a response for each of the values of a RACE variable. RACE is dichotomized into "White" and "Nonwhite" by the people who collect the data. For Nonwhites only, we can estimate the effect of individual level predictors (x) on the output (y). fm1 <- lmer( y ~ x + (1 | PLACE), data=dat, , family=binomial, subset= Race %in% c("Nonwhite")) The random effect in this model indicates the variance caused by a Nonwhite's location on the res...

...: num 3 2 3 3 2 2 3 3 3 3 ... $ family : num 3 2 2 2 3 2 2 1 2 1 ... $ special : num 3 3 4 4 3 3 4 4 3 4 ... $ immigrants: num 3 8 3 8 3 3 4 1 1 2 ... $ wedlock : num 3 3 3 3 3 2 2 8 2 3 ... $ crime : num 3 2 2 1 2 3 1 8 2 1 ... $ breakdown : num 3 3 3 2 2 4 8 2 2 4 ... $ nonwhites : num 2 4 3 3 2 2 3 4 3 3 ... *RECODE social$nonwhites<-recode(social$nonwhites, "1=4; 2=3; 3=2; 4=1; 8=NA; -9=NA") *ERROR Error in recode.default(social$nonwhites, "1=4; 2=3; 3=2; 4=1; 8=NA; -9=NA") : object '.data' not found *********************************...

If I use latex(summary(X)) where X is a data frame with four variables I get something like Rainfall Education Popden Nonwhite Min. :10.00 Min. : 9.00 Min. :1441 Min. : 0.80 1st Qu.:32.75 1st Qu.:10.40 1st Qu.:3104 1st Qu.: 4.95 Median :38.00 Median :11.05 Median :3567 Median :10.40 Mean :37.37 Mean :10.97 Mean :3866

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Dear users, In my psychometric test i have applied logistic regression on my data. My data consists of 50 predictors (22 continuous and 28 categorical) plus a binary response. Using glm(), stepAIC() i didn't get satisfactory result as misclassification rate is too high. I think categorical variables are responsible for this debacle. Some of them have more than 6 level (one has 10 level).