Below is a SIMEX object that was generated with the "simex" function from the "simex" package applied to a logistic regression fit. From this mountain of information I would like to extract all of the values summarized in this line:
.. ..$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ... Can someone suggest how to go about doing this? I can extract the upper level results like fit.simex$coefficients but I have had no success getting at the lower levels. Tom > str(fit.simex) List of 24 $ coefficients : Named num [1:2] -17.1 3 ..- attr(*, "names")= chr [1:2] "(Intercept)" "x" $ SIMEX.estimates : num [1:6, 1:3] -1 0 0.5 1 1.5 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:3] "lambda" "(Intercept)" "x" $ lambda : num [1:5] 0 0.5 1 1.5 2 $ model :List of 31 ..$ coefficients : Named num [1:2] -13.27 2.32 .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "x" ..$ residuals : Named num [1:1615] -1.12 -1.42 -1.23 -1.07 -1.44 ... .. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... ..$ fitted.values : Named num [1:1615] 0.1032 0.2952 0.1847 0.0656 0.3062 ... .. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... ..$ effects : Named num [1:1615] 19.552 -9.275 -0.473 -0.283 -0.641 ... .. ..- attr(*, "names")= chr [1:1615] "(Intercept)" "x" "" "" ... ..$ R : num [1:2, 1:2] -15.6 0 -81 -4 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "(Intercept)" "x" .. .. ..$ : chr [1:2] "(Intercept)" "x" ..$ rank : int 2 ..$ qr :List of 5 .. ..$ qr : num [1:1615, 1:2] -15.6232 0.0292 0.0248 0.0159 0.0295 ... .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : chr [1:1615] "1" "2" "3" "4" ... .. .. .. ..$ : chr [1:2] "(Intercept)" "x" .. ..$ rank : int 2 .. ..$ qraux: num [1:2] 1.02 1.02 .. ..$ pivot: int [1:2] 1 2 .. ..$ tol : num 1e-11 .. ..- attr(*, "class")= chr "qr" ..$ family :List of 12 .. ..$ family : chr "binomial" .. ..$ link : chr "logit" .. ..$ linkfun :function (mu) .. ..$ linkinv :function (eta) .. ..$ variance :function (mu) .. ..$ dev.resids:function (y, mu, wt) .. ..$ aic :function (y, n, mu, wt, dev) .. ..$ mu.eta :function (eta) .. ..$ initialize: expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the binomial family, y must be a vector of 0 and 1's\n", "or a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) .. ..$ validmu :function (mu) .. ..$ valideta :function (eta) .. ..$ simulate :function (object, nsim) .. ..- attr(*, "class")= chr "family" ..$ linear.predictors: Named num [1:1615] -2.162 -0.87 -1.485 -2.656 -0.818 ... .. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... ..$ deviance : num 1521 ..$ aic : num 1525 ..$ null.deviance : num 1622 ..$ iter : int 4 ..$ weights : Named num [1:1615] 0.0926 0.208 0.1506 0.0613 0.2125 ... .. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... ..$ prior.weights : Named num [1:1615] 1 1 1 1 1 1 1 1 1 1 ... .. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... ..$ df.residual : int 1613 ..$ df.null : int 1614 ..$ y : Named num [1:1615] 0 0 0 0 0 0 0 0 0 0 ... .. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... ..$ converged : logi TRUE ..$ boundary : logi FALSE ..$ model :'data.frame': 1615 obs. of 2 variables: .. ..$ y: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... .. ..$ x: num [1:1615] 4.79 5.35 5.09 4.58 5.37 ... .. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 y ~ x .. .. .. ..- attr(*, "variables")= language list(y, x) .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1 .. .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. .. ..$ : chr [1:2] "y" "x" .. .. .. .. .. ..$ : chr "x" .. .. .. ..- attr(*, "term.labels")= chr "x" .. .. .. ..- attr(*, "order")= int 1 .. .. .. ..- attr(*, "intercept")= int 1 .. .. .. ..- attr(*, "response")= int 1 .. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> .. .. .. ..- attr(*, "predvars")= language list(y, x) .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "factor" "numeric" .. .. .. .. ..- attr(*, "names")= chr [1:2] "y" "x" ..$ x : num [1:1615, 1:2] 1 1 1 1 1 1 1 1 1 1 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:1615] "1" "2" "3" "4" ... .. .. ..$ : chr [1:2] "(Intercept)" "x" .. ..- attr(*, "assign")= int [1:2] 0 1 ..$ call : language glm(formula = y ~ x, family = binomial, x = TRUE, y = TRUE) ..$ formula :Class 'formula' length 3 y ~ x .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> ..$ terms :Classes 'terms', 'formula' length 3 y ~ x .. .. ..- attr(*, "variables")= language list(y, x) .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1 .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. ..$ : chr [1:2] "y" "x" .. .. .. .. ..$ : chr "x" .. .. ..- attr(*, "term.labels")= chr "x" .. .. ..- attr(*, "order")= int 1 .. .. ..- attr(*, "intercept")= int 1 .. .. ..- attr(*, "response")= int 1 .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> .. .. ..- attr(*, "predvars")= language list(y, x) .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "factor" "numeric" .. .. .. ..- attr(*, "names")= chr [1:2] "y" "x" ..$ data :<environment: R_GlobalEnv> ..$ offset : NULL ..$ control :List of 3 .. ..$ epsilon: num 1e-08 .. ..$ maxit : num 25 .. ..$ trace : logi FALSE ..$ method : chr "glm.fit" ..$ contrasts : NULL ..$ xlevels : Named list() ..- attr(*, "class")= chr [1:2] "glm" "lm" $ mc.matrix :List of 1 ..$ y: num [1:2, 1:2] 0.95 0.05 0.03 0.97 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "0" "1" .. .. ..$ : chr [1:2] "0" "1" $ B : num 800 $ extrapolation :List of 12 ..$ coefficients : num [1:3, 1:2] -13.258 3.299 -0.501 2.315 -0.593 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)" .. .. ..$ : chr [1:2] "(Intercept)" "x" ..$ residuals : num [1:5, 1:2] -0.01292 0.02805 -0.00663 -0.01922 0.01071 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:5] "1" "2" "3" "4" ... .. .. ..$ : chr [1:2] "(Intercept)" "x" ..$ effects : num [1:5, 1:2] 23.9505 3.63099 -0.46906 -0.00164 0.03846 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:5] "(Intercept)" "lambda" "I(lambda^2)" "" ... .. .. ..$ : chr [1:2] "(Intercept)" "x" ..$ rank : int 3 ..$ fitted.values: num [1:5, 1:2] -13.26 -11.73 -10.46 -9.44 -8.67 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:5] "1" "2" "3" "4" ... .. .. ..$ : chr [1:2] "(Intercept)" "x" ..$ assign : int [1:3] 0 1 2 ..$ qr :List of 5 .. ..$ qr : num [1:5, 1:3] -2.236 0.447 0.447 0.447 0.447 ... .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : chr [1:5] "1" "2" "3" "4" ... .. .. .. ..$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)" .. .. ..- attr(*, "assign")= int [1:3] 0 1 2 .. ..$ qraux: num [1:3] 1.45 1.12 1.78 .. ..$ pivot: int [1:3] 1 2 3 .. ..$ tol : num 1e-07 .. ..$ rank : int 3 .. ..- attr(*, "class")= chr "qr" ..$ df.residual : int 2 ..$ xlevels : Named list() ..$ call : language lm(formula = estimates ~ lambda + I(lambda^2)) ..$ terms :Classes 'terms', 'formula' length 3 estimates ~ lambda + I(lambda^2) .. .. ..- attr(*, "variables")= language list(estimates, lambda, I(lambda^2)) .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. ..$ : chr [1:3] "estimates" "lambda" "I(lambda^2)" .. .. .. .. ..$ : chr [1:2] "lambda" "I(lambda^2)" .. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)" .. .. ..- attr(*, "order")= int [1:2] 1 1 .. .. ..- attr(*, "intercept")= int 1 .. .. ..- attr(*, "response")= int 1 .. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124> .. .. ..- attr(*, "predvars")= language list(estimates, lambda, I(lambda^2)) .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.2" "numeric" "numeric" .. .. .. ..- attr(*, "names")= chr [1:3] "estimates" "lambda" "I(lambda^2)" ..$ model :'data.frame': 5 obs. of 3 variables: .. ..$ estimates : num [1:5, 1:2] -13.27 -11.71 -10.47 -9.46 -8.65 ... .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : NULL .. .. .. ..$ : chr [1:2] "(Intercept)" "x" .. ..$ lambda : num [1:5] 0 0.5 1 1.5 2 .. ..$ I(lambda^2):Class 'AsIs' num [1:5] 0 0.25 1 2.25 4 .. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 estimates ~ lambda + I(lambda^2) .. .. .. ..- attr(*, "variables")= language list(estimates, lambda, I(lambda^2)) .. .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 .. .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. .. ..$ : chr [1:3] "estimates" "lambda" "I(lambda^2)" .. .. .. .. .. ..$ : chr [1:2] "lambda" "I(lambda^2)" .. .. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)" .. .. .. ..- attr(*, "order")= int [1:2] 1 1 .. .. .. ..- attr(*, "intercept")= int 1 .. .. .. ..- attr(*, "response")= int 1 .. .. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124> .. .. .. ..- attr(*, "predvars")= language list(estimates, lambda, I(lambda^2)) .. .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.2" "numeric" "numeric" .. .. .. .. ..- attr(*, "names")= chr [1:3] "estimates" "lambda" "I(lambda^2)" ..- attr(*, "class")= chr [1:2] "mlm" "lm" $ fitting.method : chr "quad" $ SIMEXvariable : chr "y" $ call : language mcsimex(model = fit.naive, SIMEXvariable = "y", mc.matrix = P, lambda = c(0.5, 1, 1.5, 2), B = 800, fitting.method = "quadratic", ... $ theta :List of 2 ..$ (Intercept):'data.frame': 800 obs. of 4 variables: .. ..$ X1: num [1:800] -11.9 -12.4 -11.1 -11.4 -11 ... .. ..$ X2: num [1:800] -11.8 -10.05 -9.78 -10.97 -11.12 ... .. ..$ X3: num [1:800] -9 -8.71 -9.88 -8.73 -8.65 ... .. ..$ X4: num [1:800] -8.49 -8.7 -9.33 -8.23 -8.89 ... ..$ x :'data.frame': 800 obs. of 4 variables: .. ..$ X1: num [1:800] 2.07 2.16 1.93 1.97 1.9 ... .. ..$ X2: num [1:800] 2.07 1.73 1.69 1.9 1.94 ... .. ..$ X3: num [1:800] 1.56 1.49 1.72 1.49 1.48 ... .. ..$ X4: num [1:800] 1.47 1.51 1.63 1.42 1.54 ... $ fitted.values : Named num [1:1615] 0.0645 0.2685 0.1421 0.0351 0.2821 ... ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... $ residuals : Named num [1:1615] -0.0645 -0.2685 -0.1421 -0.0351 -0.2821 ... ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ... $ extrapolation.variance :List of 12 ..$ coefficients : num [1:3, 1:4] 1.668 -1.105 0.268 -0.321 0.209 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)" .. .. ..$ : NULL ..$ residuals : num [1:5, 1:4] 0.0159 -0.0385 0.0199 0.012 -0.0093 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:5] "1" "2" "3" "4" ... .. .. ..$ : NULL ..$ effects : num [1:5, 1:4] -2.1556 -0.9011 0.2503 -0.0154 -0.0461 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:5] "(Intercept)" "lambda" "I(lambda^2)" "" ... .. .. ..$ : NULL ..$ rank : int 3 ..$ fitted.values: num [1:5, 1:4] 1.668 1.182 0.83 0.612 0.528 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:5] "1" "2" "3" "4" ... .. .. ..$ : NULL ..$ assign : int [1:3] 0 1 2 ..$ qr :List of 5 .. ..$ qr : num [1:5, 1:3] -2.236 0.447 0.447 0.447 0.447 ... .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : chr [1:5] "1" "2" "3" "4" ... .. .. .. ..$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)" .. .. ..- attr(*, "assign")= int [1:3] 0 1 2 .. ..$ qraux: num [1:3] 1.45 1.12 1.78 .. ..$ pivot: int [1:3] 1 2 3 .. ..$ tol : num 1e-07 .. ..$ rank : int 3 .. ..- attr(*, "class")= chr "qr" ..$ df.residual : int 2 ..$ xlevels : Named list() ..$ call : language lm(formula = variance.jackknife ~ lambda + I(lambda^2)) ..$ terms :Classes 'terms', 'formula' length 3 variance.jackknife ~ lambda + I(lambda^2) .. .. ..- attr(*, "variables")= language list(variance.jackknife, lambda, I(lambda^2)) .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. ..$ : chr [1:3] "variance.jackknife" "lambda" "I(lambda^2)" .. .. .. .. ..$ : chr [1:2] "lambda" "I(lambda^2)" .. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)" .. .. ..- attr(*, "order")= int [1:2] 1 1 .. .. ..- attr(*, "intercept")= int 1 .. .. ..- attr(*, "response")= int 1 .. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124> .. .. ..- attr(*, "predvars")= language list(variance.jackknife, lambda, I(lambda^2)) .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.4" "numeric" "numeric" .. .. .. ..- attr(*, "names")= chr [1:3] "variance.jackknife" "lambda" "I(lambda^2)" ..$ model :'data.frame': 5 obs. of 3 variables: .. ..$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ... .. ..$ lambda : num [1:5] 0 0.5 1 1.5 2 .. ..$ I(lambda^2) :Class 'AsIs' num [1:5] 0 0.25 1 2.25 4 .. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 variance.jackknife ~ lambda + I(lambda^2) .. .. .. ..- attr(*, "variables")= language list(variance.jackknife, lambda, I(lambda^2)) .. .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 .. .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. .. ..$ : chr [1:3] "variance.jackknife" "lambda" "I(lambda^2)" .. .. .. .. .. ..$ : chr [1:2] "lambda" "I(lambda^2)" .. .. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)" .. .. .. ..- attr(*, "order")= int [1:2] 1 1 .. .. .. ..- attr(*, "intercept")= int 1 .. .. .. ..- attr(*, "response")= int 1 .. .. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124> .. .. .. ..- attr(*, "predvars")= language list(variance.jackknife, lambda, I(lambda^2)) .. .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.4" "numeric" "numeric" .. .. .. .. ..- attr(*, "names")= chr [1:3] "variance.jackknife" "lambda" "I(lambda^2)" ..- attr(*, "class")= chr [1:2] "mlm" "lm" $ variance.jackknife : num [1:2, 1:2] 3.04 -0.58 -0.58 0.111 ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:2] "(Intercept)" "x" .. ..$ : chr [1:2] "(Intercept)" "x" $ variance.jackknife.lambda: num [1:6, 1:5] -1 0 0.5 1 1.5 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:6] "1" "" "" "" ... .. ..$ : NULL $ PSI : num [1:1615, 1:10] -0.1032 -0.2952 -0.1847 -0.0656 -0.3062 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:1615] "1" "2" "3" "4" ... .. ..$ : chr [1:10] "(Intercept)" "x" "(Intercept)" "x" ... $ c11 : num [1:10, 1:10] 0.151 0.78 0.144 0.748 0.139 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:10] "(Intercept)" "x" "(Intercept)" "x" ... .. ..$ : chr [1:10] "(Intercept)" "x" "(Intercept)" "x" ... $ a11 : num [1:10, 1:10] -0.151 -0.784 0 0 0 ... $ sigma : num [1:10, 1:10] 2979 -572 2482 -478 2132 ... $ sigma.gamma : num [1:6, 1:6] 2961 -1030 192 -569 195 ... $ g : num [1:6, 1:2] 1 -1 1 0 0 0 0 0 0 1 ... $ s : num [1:6, 1:10] -1 0 0 0 0 0 0 0 0 -1 ... $ variance.asymptotic : num [1:2, 1:2] 3.697 -0.704 -0.704 0.134 ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:2] "(Intercept)" "x" .. ..$ : chr [1:2] "(Intercept)" "x" - attr(*, "class")= chr "mcsimex" -- View this message in context: http://r.789695.n4.nabble.com/How-to-Extract-Information-from-SIMEX-Output-tp3459082p3459082.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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