[R-sig-Geo] "Zelig" function for Logistic regression

[Omar Faisel]

Dear All,
I have misleadings with "Zelig" function to process the data with Logistic regression.
Here is a glance at my data> head(data.variables, n=4)  target  F1 F2 F3 F41     1  103.6462  13.460189         15          52     1 1138.1763  14.049833         15          53     1  731.0000   8.321724         10          54     1  335.6171  21.928064          9          5
> cross.classification.lrm<-table(as.numeric(result.lrm$y),round(result.lrm$fitted.values),dnn=c("Observed","Predicted"))
Error in envRefInferField(x, what, getClass(class(x)), selfEnv) :   ‘y’ is not a valid field or method name for reference class “Zelig-logit”
Here is the function >result.lrm<-zelig(as.formula(paste(names(data.variables)[1],"~",paste(names(data.variables[,2:dim(data.variables)[2]]),collapse= "+"))), data=data.variables, model="logit")

Here the output names> names(result.lrm)1] "vignette.url"               "simx1"                      "summarise"          [4] "zelig#Zelig"                "setx.labels"                "bootstrap"                  [7] "bsetrange"                  "initialize"                 "show#Zelig"                 [10] "name"                       "mcfun"                      "year"                       [13] "simx"                       "callSuper"                  "range"                      [16] "signif.stars"               "packageauthors"             "get_qi"                     [19] "range1"                     "param"                      "zelig.out"                  [22] "sim.out"                    "acceptweights"              "setrange"                   [25] "json"                       "qi"                         "url"                        [28] "get_names"                  "packagename"                "from_zelig_model"           [31] "initialize#Zelig-glm"       "family"                     "get_df_residual"            [34] "signif.stars.default"       "linkinv"                    "num"                        [37] "originaldata"               "buildDataByWeights2"        "setforeveryby"              [40] "refs"                       "show#envRefClass"           "sim"                        [43] "cite"                       "toJSON"                     "get_se"                     [46] "setx"                       "getRefClass"                "model.call"                 [49] "graph"                      "import"                     "weights"                    [52] "copy"                       "simrange1"                  "export"                     [55] "show"                       "bsetx1"                     "simparam"                   [58] "matched"                    "initFields"                 "zeligauthors"               [61] "data"                       "get_coef"                   "category"                   [64] "by"                         "get_pvalue"                 "description"                [67] "mcunit"                     "getClass"                   "initialize#Zelig"           [70] "zelig"                      "usingMethods"               "test.statistics"            [73] "help"                       "mcformula"                  "outcome"                    [76] "get_predict"                "buildDataByBootstrap"       "bsetx"                      [79] "get_fitted"                 "initialize#Zelig-binchoice" "bsetrange1"                 [82] "trace"                      "get_model_data"             "avg"                        [85] "get_residuals"              "mi"                         "references"                 [88] "modelauthors"               "originalweights"            "zelig.call"                 [91] "link"                       "feedback"                   "summarize"                  [94] "setx.out"                   "formula"                    "simrange"                   [97] "simATT"                     "ATT"                        "bootstrap.num"             [100] "idx"                        "get_vcov"                   "untrace"                   [103] "set"                        "fn"                         "mcfun#Zelig"               [106] "buildDataByWeights"         "url.docs"                   "with.feedback"             [109] "field"                      "setx1"                      "setrange1"                 [112] "authors"                    "ljson"                      "explanatory"               [115] "wrapper"                    "mcunit.test"
for the predicted values, I used fitted(results.lrm), it works no problem."
I noticed there is no result called the logit function "y"
But my question: what is the value that can be used instead of "Y"?

Thank you,
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