| Title: | Logistic Regression with Misclassification in Dependent Variables |
|---|---|
| Description: | Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables. |
| Authors: | Haiyan Liu and Zhiyong Zhang |
| Maintainer: | Zhiyong Zhang <[email protected]> |
| License: | GPL |
| Version: | 1.6 |
| Built: | 2025-02-12 02:38:55 UTC |
| Source: | https://github.com/cran/logistic4p |
Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
The DESCRIPTION file:
| Package: | logistic4p |
| Type: | Package |
| Title: | Logistic Regression with Misclassification in Dependent Variables |
| Version: | 1.6 |
| Date: | 2023-10-20 |
| Depends: | R (>= 2.10), MASS |
| Author: | Haiyan Liu and Zhiyong Zhang |
| Maintainer: | Zhiyong Zhang <[email protected]> |
| Description: | Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables. |
| License: | GPL |
| LazyLoad: | yes |
| NeedsCompilation: | no |
| Packaged: | 2023-10-21 15:05:54 UTC; zzhang4 |
| Date/Publication: | 2023-10-21 15:40:02 UTC |
| Repository: | https://johnnyzhz.r-universe.dev |
| RemoteUrl: | https://github.com/cran/logistic4p |
| RemoteRef: | HEAD |
| RemoteSha: | 8736039aaf0937eb699e002b41463416c45ef978 |
Index of help topics:
logistic Logistic Regression
logistic4p Logistic Regressions with Misclassification
Correction
logistic4p-package Logistic Regression with Misclassification in
Dependent Variables
logistic4p.e Logistic regressions with constrained FP and FN
misclassifications
logistic4p.fn Logistic Regression Model with FN
Misclassification Correction
logistic4p.fp Logistic Regression with FP Misclassification
Correction
logistic4p.fp.fn Logistic Regression with both FP and FN
Misclassification Correction
nlsy An example data set
print.logistic4p Printing Outputs of Logistic Regression with
Misclassification Parameters
Haiyan Liu and Zhiyong Zhang
Maintainer: Zhiyong Zhang <[email protected]>
Liu, H. and Zhang, Z. (2016) Logistic Regression with Misclassification in Dependent Variables: Method and Software.(In preparation.)
## Not run: data(nlsy) x=nlsy[, -1] y=nlsy[,1] mod=logistic4p(x, y, model='fn') ## End(Not run)## Not run: data(nlsy) x=nlsy[, -1] y=nlsy[,1] mod=logistic4p(x, y, model='fn') ## End(Not run)
Fit a logistic regression model.
logistic(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)logistic(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)
x, y
|
x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y. |
initial |
a vector of starting values for the parameters in the linear predictor; if not specified, the default initials are 0 for all parameters. |
max.iter |
a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop. |
epsilon |
a positive convergence tolerance epsilon. |
detail |
logical indicating if output should be printed for each iteration. |
estimates |
a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values. |
n.iter |
an integer giving the number of iteration used |
d |
the actual max absolute difference of the parameters of the last two iterations. |
loglike |
loglikelihood evaluated at the parameter estimates. |
AIC |
Akaike Information Criterion. |
BIC |
Bayesian Information Criterion. |
converged |
logical indicating whether the current procedure converged or not. |
Haiyan Liu and Zhiyong Zhang
## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod=logistic(x,y) ## End(Not run)## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod=logistic(x,y) ## End(Not run)
logistic4p is used to fit logistic regressions with correction of the misclassifications in the binary dependent variable. It is specified by
logistic4p(x, y, initial, model = c("lg", "fp.fn", "fp", "fn", "equal"), max.iter = 1000, epsilon = 1e-06, detail = FALSE)logistic4p(x, y, initial, model = c("lg", "fp.fn", "fp", "fn", "equal"), max.iter = 1000, epsilon = 1e-06, detail = FALSE)
x, y
|
x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y. |
initial |
starting values for the parameters in the model(FP,FN misclassification parameters and those in the linear predictor); if not specified, the default initials are 0 for the misclassification parameters and estimates obtained from the logistic regression for the parameters in the linear predictor. |
model |
a character string specifying the model to be used in the analysis. Currently available options are "lg" (logistic regression), "fp.fn" (logistic regression with both FP and FN parameters), "fp" (logistic regression with the FP parameter), "fn" (logistic regression with the FN parameter), "equal" (logistic regression with FN=FN). If it is not specified, the default one ('lg') will be used. |
max.iter |
a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop. |
epsilon |
a positive convergence tolerance epsilon. |
detail |
logical indicating if the itermediate output should be printed after each iteration. |
This package implements the logistic regressions with misclassification corrections. There are five different models which can be specified by 'model'.
In the specification, x is a matrix of data frame of predictors fitted to the model; y is a numeric vector taking either 0 or 1.
The 'initial' is the vector of starting values for both misclassification and regression coefficients parameters in the model. It is suggested to provide 'initial', however if not, the default one will be used.
For the background to warning messages about 'fitted probabilities numerically 0 or 1 occurred', when the fitted probabilities of some individuals are either 0 or 1.
The package cannot handle missing data problems currently. If there are missing values in either x or y, there will be warning message.
logistic4p returns a list of values inheriting from "logistic4p".
estimates |
a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values. |
n.iter |
an integer giving the number of iteration used |
d |
the actual max absolute difference of the parameters of the last two iterations. |
loglike |
loglikelihood evaluated at the parameter estimates. |
AIC |
Akaike Information Criterion. |
BIC |
Bayesian Information Criterion. |
converged |
logical indicating whether the current procedure converged or not. |
Haiyan Liu and Zhiyong Zhang
Liu, H. and Zhang, Z. (2016) Logistic Regression with Misclassification in Dependent Variables: Method and Software.(In preparation.)
## Not run: data(nlsy) y=nlsy[, 1] x=nlsy[,-1] mod1=logistic4p(x,y) mod1 mod1$estimates mod2=logistic4p(x,y, model='fp.fn') mod3=logistic4p(x,y, model='fn') ## End(Not run)## Not run: data(nlsy) y=nlsy[, 1] x=nlsy[,-1] mod1=logistic4p(x,y) mod1 mod1$estimates mod2=logistic4p(x,y, model='fp.fn') mod3=logistic4p(x,y, model='fn') ## End(Not run)
Fit logistic regressions with misclassification correction. The FP and FN parameters are constrained to be equal.
logistic4p.e(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)logistic4p.e(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)
x, y
|
x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y. |
initial |
starting values for the parameters in the model(the misclassification parameter and those in the linear predictor); if not specified, the default initials are 0 for the misclassification parameters and estimates obtained from the logistic regression for the parameters in the linear predictor. |
max.iter |
a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop. |
epsilon |
a positive convergence tolerance epsilon. |
detail |
logical indicating if the itermediate output should be printed after each iteration. |
estimates |
a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values. |
n.iter |
an integer giving the number of iteration used |
d |
the actual max absolute difference of the parameters of the last two iterations. |
loglike |
loglikelihood evaluated at the parameter estimates. |
AIC |
Akaike Information Criterion. |
BIC |
Bayesian Information Criterion. |
converged |
logical indicating whether the current procedure converged or not. |
Haiyan Liu and Zhiyong Zhang
## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod=logistic4p.e(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE) ## End(Not run)## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod=logistic4p.e(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE) ## End(Not run)
logistic4p.fn is used to fit logistic regressions with the false negative parameter in the model.
logistic4p.fn(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)logistic4p.fn(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)
x, y
|
x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y. |
initial |
starting values for the parameters in the model(FP and those in the linear predictor); if not specified, the default initials are 0 for the misclassification parameter and estimates obtained from the logistic regression for the parameters in the linear predictor. |
max.iter |
a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop. |
epsilon |
a positive convergence tolerance epsilon. |
detail |
logical indicating if output should be printed for each iteration. |
estimates |
a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values. |
n.iter |
an integer giving the number of iteration used |
d |
the actual max absolute difference of the parameters of the last two iterations. |
loglike |
loglikelihood evaluated at the parameter estimates. |
AIC |
Akaike Information Criterion. |
BIC |
Bayesian Information Criterion. |
converged |
logical indicating whether the current procedure converged or not. |
Haiyan Liu and Zhiyong Zhang
## Not run: data(nlsy) y=nlsy[,1] x=nlsy[,-1] mod=logistic4p.fn(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE) ## End(Not run)## Not run: data(nlsy) y=nlsy[,1] x=nlsy[,-1] mod=logistic4p.fn(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE) ## End(Not run)
logistic4p.fp is used to fit logistic regression models with correction of the false positive misclassification in the binary dependent variable.
logistic4p.fp(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)logistic4p.fp(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)
x, y
|
x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y. |
initial |
starting values for the parameters in the model(FP,FN misclassification parameters and those in the linear predictor); if not specified, the default initials are 0 for the misclassification parameters and estimates obtained from the logistic regression for the parameters in the linear predictor. |
max.iter |
a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop. |
epsilon |
a positive convergence tolerance epsilon. |
detail |
logical indicating if output should be printed for each iteration. |
estimates |
a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values. |
n.iter |
an integer giving the number of iteration used |
d |
the actual max absolute difference of the parameters of the last two iterations. |
loglike |
loglikelihood evaluated at the parameter estimates. |
AIC |
Akaike Information Criterion. |
BIC |
Bayesian Information Criterion. |
converged |
logical indicating whether the current procedure converged or not. |
Haiyan Liu and Zhiyong Zhang
## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod.fp=logistic4p.fp(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE) ## End(Not run)## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod.fp=logistic4p.fp(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE) ## End(Not run)
logistic4p.fp.fn is used to fit a logistic regression model with both FP and FN misclassification parameters to a binary dependent variable.
logistic4p.fp.fn(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)logistic4p.fp.fn(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)
x, y
|
x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y. |
initial |
starting values for the parameters in the model(FP,FN misclassification parameters and those in the linear predictor); if not specified, the default initials are 0 for the misclassification parameters and estimates obtained from the logistic regression for the parameters in the linear predictor. |
max.iter |
a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop. |
epsilon |
a positive convergence tolerance epsilon. |
detail |
logical indicating if the output should be printed for each iteration. |
estimates |
a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values. |
n.iter |
an integer giving the number of iteration used |
d |
the actual max absolute difference of the parameters of the last two iterations. |
loglike |
loglikelihood evaluated at the parameter estimates. |
AIC |
Akaike Information Criterion. |
BIC |
Bayesian Information Criterion. |
converged |
logical indicating whether the current procedure converged or not. |
Haiyan Liu and Zhiyong Zhang
## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod=logistic4p.fp.fn(x,y) ## End(Not run)## Not run: data(nlsy) y=nlsy[,1] x=nlsy[, -1] mod=logistic4p.fp.fn(x,y) ## End(Not run)
Data set used in Liu & Zhang (2016).
marijuana: binary; 1=used, 0=not used
gender: binary; 1=female, 0=male
smoke: binary; 1=smoke, 0=not smoke
residence: binary; 1=urban areas, 0=rural areas
peer: comprised score on peers life style; the higher score, the healthier the peers live.
data(nlsy)data(nlsy)
This is an function to print the inherit outputs of. logistic4p
## S3 method for class 'logistic4p' print(x, ...)## S3 method for class 'logistic4p' print(x, ...)
x |
An object of class 'logistic4p'. |
... |
further arguments passed to or from other methods. |
Haiyan Liu and Zhiyong Zhang
## Not run: data(nlsy) y=nlsy[,1] x=nlsy[,-1] mod=logistic4p(x,y) print(mod) ## End(Not run)## Not run: data(nlsy) y=nlsy[,1] x=nlsy[,-1] mod=logistic4p(x,y) print(mod) ## End(Not run)