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"
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