`fimlreg.Rd`

Estimate regression coefficients based on Full Information Maximum Likelihood Estimation, which can couple missing data, including response missing or covariates missing.

fimlreg(...) # S3 method for formula fimlreg(formula, data=NULL, ...) # S3 method for numeric fimlreg(Y, X, ...)

formula | an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'. |
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Y | a numeric vector, the reponse variable. |

X | a numeric matrix that may include NAs, the covariate matrix. |

data | an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which clse is called. |

... | Optional arguments. |

Note that arguments ... of stats::nlm are the parameters of algorithm, see the details in help file of "nlm". "fimlreg" can cople with any type of missing data.

Return a list including following components:

A named vector of coefficients

The formula used

The raw data

Liu Wei

data(nhanes) ## example one: include missing value fiml1 <- fimlreg(age~., data=nhanes) print(fiml1)#> $beta #> intercept bmi hyp chl #> 2.37216291 -0.13518966 0.46886921 0.01253721 #> #> $formula #> age ~ . #> <environment: 0x00000000168f1200> #># example two: No missing vlaue ## example two: No missing value n <- 100 group <- rnorm(n, sd=4) weight <- 3.2*group + 1.5 + rnorm(n, sd=0.1) fimllm <- fimlreg(weight~group, data=data.frame(weight=weight, group=group)) print(fimllm)#> $beta #> intercept group #> 1.511669 3.199354 #> #> $formula #> weight ~ group #> <environment: 0x00000000168f1200> #>