Re: [R] How to Extract Information from SIMEX Output

From: Peter Ehlers <ehlers_at_ucalgary.ca>
Date: Tue, 19 Apr 2011 01:44:51 -0700

On 2011-04-18 17:52, Tom La Bone wrote:
> 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

This should do it:

  fit.simex$extrapolation.variance$model$variance.jackknife

or

  z <- fit.simex[['extrapolation.variance']]   z[['model']][['variance.jackknife']]

Peter Ehlers

>
>
>> 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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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. Received on Tue 19 Apr 2011 - 08:47:36 GMT

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