glm.sdf {EdSurvey} | R Documentation |

Fits a logit or probit that
uses weights and variance estimates
appropriate for the `edsurvey.data.frame`

,
the `light.edsurvey.data.frame`

, or the `edsurvey.data.frame.list`

.

```
glm.sdf(formula, family = binomial(link = "logit"), data,
weightVar = NULL, relevels = list(),
varMethod=c("jackknife", "Taylor"), jrrIMax = 1,
omittedLevels = TRUE, defaultConditions = TRUE, recode = NULL,
returnNumberOfPSU=FALSE, returnVarEstInputs = FALSE)
logit.sdf(
formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor"),
jrrIMax = 1,
omittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
returnNumberOfPSU = FALSE,
returnVarEstInputs = FALSE
)
probit.sdf(
formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor"),
jrrIMax = 1,
omittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
returnVarEstInputs = FALSE
)
```

`formula` |
a |

`family` |
the |

`data` |
an |

`weightVar` |
character indicating the weight variable to use (see Details).
The |

`relevels` |
a list; used to change the contrasts from the default treatment contrasts to the treatment contrasts with a chosen omitted group. The name of each element should be the variable name, and the value should be the group to be omitted. |

`varMethod` |
a character set to “jackknife” or “Taylor” that indicates the variance estimation method to be used. See Details. |

`jrrIMax` |
the |

`omittedLevels` |
a logical value. When set to the default value of |

`defaultConditions` |
a logical value. When set to the default value of |

`recode` |
a list of lists to recode variables. Defaults to |

`returnNumberOfPSU` |
a logical value set to |

`returnVarEstInputs` |
a logical value set to |

This function implements an estimator that correctly handles left-hand side
variables that are logical, allows for survey sampling weights, and estimates
variances using the jackknife replication or Taylor series.
The vignette titled
*Statistical Methods Used in EdSurvey*
describes estimation of the reported statistics and how it depends on `varMethod`

.

The coefficients are estimated using the sample weights according to the section “Estimation of Weighted Means When Plausible Values Are Not Present” or the section “Estimation of Weighted Means When Plausible Values Are Present,” depending on if there are assessment variables or variables with plausible values in them.

How the standard errors of the coefficients are estimated depends on the
presence of plausible values (assessment variables),
But once it is obtained, the *t* statistic
is given by

`t=\frac{\hat{\beta}}{\sqrt{\mathrm{var}(\hat{\beta})}}`

where
` \hat{\beta} `

is the estimated coefficient and `\mathrm{var}(\hat{\beta})`

is
its variance of that estimate.

`logit.sdf`

and `probit.sdf`

are included for convenience only;
they give the same results as a call to `glm.sdf`

with the binomial family
and the link function named in the function call (logit or probit).
By default, `glm`

fits a logistic regression when `family`

is not set,
so the two are expected to give the same results in that case.
Other types of generalized linear models are not supported.

All variance estimation methods are shown in the vignette titled
*Statistical Methods Used in EdSurvey*.
When the predicted
value does not have plausible values and `varMethod`

is set to
`jackknife`

, the variance of the coefficients
is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Not Present, Using the Jackknife Method.”

When plausible values are present and `varMethod`

is set to
`jackknife`

, the
variance of the coefficients is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Present, Using the Jackknife Method.”

When the predicted
value does not have plausible values and `varMethod`

is set to
`Taylor`

, the variance of the coefficients
is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Not Present, Using the Taylor Series Method.”

When plausible values are present and `varMethod`

is set to
`Taylor`

, the
variance of the coefficients is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Present, Using the Taylor Series Method.”

An `edsurveyGlm`

with the following elements:

`call` |
the function call |

`formula` |
the formula used to fit the model |

`coef` |
the estimates of the coefficients |

`se` |
the standard error estimates of the coefficients |

`Vimp` |
the estimated variance caused by uncertainty in the scores (plausible value variables) |

`Vjrr` |
the estimated variance from sampling |

`M` |
the number of plausible values |

`nPSU` |
the number of PSUs used in the calculation |

`varm` |
the variance estimates under the various plausible values |

`coefm` |
the values of the coefficients under the various plausible values |

`coefmat` |
the coefficient matrix (typically produced by the summary of a model) |

`weight` |
the name of the weight variable |

`npv` |
the number of plausible values |

`njk` |
the number of the jackknife replicates used |

`varMethod` |
always |

`varEstInputs` |
when |

Of the common hypothesis tests for joint parameter testing, only the Wald
test is widely used with plausible values and sample weights. As such, it
replaces, if imperfectly, the Akaike Information Criteria (AIC), the
likelihood ratio test, chi-squared, and analysis of variance (ANOVA, including *F*-tests).
See `waldTest`

or
the vignette titled
*Methods and Overview of Using EdSurvey for Running Wald Tests*.

Paul Bailey

```
## Not run:
# read in the example data (generated, not real student data)
sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))
# by default uses the jackknife variance method using replicate weights
table(sdf$b013801)
logit1 <- logit.sdf(I(b013801 %in% c("26-100", ">100")) ~ dsex + b017451, data=sdf)
# use summary to get detailed results
summary(logit1)
# Taylor series variance estimation
logit1t <- logit.sdf(I(b013801 %in% c("26-100", ">100")) ~ dsex + b017451, data=sdf,
varMethod="Taylor")
summary(logit1t)
logit2 <- logit.sdf(I(composite >= 300) ~ dsex + b013801, data=sdf)
summary(logit2)
logit3 <- glm.sdf(I(composite >= 300) ~ dsex + b013801, data=sdf,
family=quasibinomial(link="logit"))
# Wald test for joint hypothesis that all coefficients in b013801 are zero
waldTest(logit3, "b013801")
summary(logit3)
## End(Not run)
```

[Package *EdSurvey* version 2.7.1 Index]