Description Usage Arguments Value Author(s) See Also Examples

For a given model object the function computes a linear function of the parameters and the corresponding standard errors, p-values and confidence intervals.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
ci.lin( obj,
ctr.mat = NULL,
subset = NULL,
subint = NULL,
diffs = FALSE,
fnam = !diffs,
vcov = FALSE,
alpha = 0.05,
df = Inf,
Exp = FALSE,
sample = FALSE )
ci.exp( ..., Exp = TRUE, pval=FALSE )
Wald( obj, H0=0, ... )
ci.mat( alpha = 0.05, df = Inf )
ci.pred( obj, newdata,
Exp = NULL,
alpha = 0.05 )
ci.ratio( r1, r2,
se1 = NULL,
se2 = NULL,
log.tr = !is.null(se1) & !is.null(se2),
alpha = 0.05,
pval = FALSE )
``` |

`obj` |
A model object (in general of class |

`ctr.mat` |
Contrast matrix to be multiplied to the parameter vector, i.e. the desired linear function of the parameters. |

`subset` |
The subset of the parameters to be used. If given as a
character vector, the elements are in turn matched against the
parameter names (using |

`subint` |
Character. |

`diffs` |
If TRUE, all differences between parameters
in the subset are computed. |

`fnam` |
Should the common part of the parameter names be included
with the annotation of contrasts? Ignored if |

`vcov` |
Should the covariance matrix of the set of parameters be
returned? If this is set, |

`alpha` |
Significance level for the confidence intervals. |

`df` |
Integer. Number of degrees of freedom in the t-distribution used to compute the quantiles used to construct the confidence intervals. |

`Exp` |
For |

`sample` |
Logical or numerical. If |

`pval` |
Logical. Should a column of P-values be included with the estimates
and confidence intervals output by |

`H0` |
Numeric. The null values for the selected/transformed parameters to be tested by a Wald test. Must have the same length as the selected parameter vector. |

`...` |
Parameters passed on to |

`newdata` |
Data frame of covariates where prediction is made. |

`r1,r2` |
Estimates of rates in two independent groups, with confidence intervals. |

`se1,se2` |
Standard errors of log-rates in the two groups. If
given, it is assumed that |

`log.tr` |
Logical, if true, it is assumed that |

`ci.lin`

returns a matrix with number of rows and row names as
`ctr.mat`

. The columns are Estimate, Std.Err, z, P, 2.5% and
97.5% (or according to the value of `alpha`

). If
`vcov=TRUE`

a list with components `est`

, the
desired functional of the parameters and `vcov`

, the variance
covariance matrix of this, is returned but not printed. If
`Exp==TRUE`

the confidence intervals for the parameters are
replaced with three columns: exp(estimate,c.i.).

`ci.exp`

returns only the exponentiated parameter estimates with
confidence intervals. It is merely a wrapper for `ci.lin`

,
fishing out the last 3 columns from `ci.lin(...,Exp=TRUE)`

. If
you just want the estimates and confidence limits, but not
exponentiated, use `ci.exp(...,Exp=FALSE)`

.

`Wald`

computes a Wald test for a subset of (possibly linear
combinations of) parameters being equal to the vector of null
values as given by `H0`

. The selection of the subset of
parameters is the same as for `ci.lin`

. Using the `ctr.mat`

argument makes it possible to do a Wald test for equality of
parameters. `Wald`

returns a named numerical vector of length 3,
with names `Chisq`

, `d.f.`

and `P`

.

`ci.mat`

returns a 2 by 3 matrix with rows `c(1,0,0)`

and
`c(0,-1,1)*1.96`

, devised to post-multiply to a p by 2 matrix with
columns of estimates and standard errors, so as to produce a p by 3 matrix
of estimates and confidence limits. Used internally in `ci.lin`

and
`ci.cum`

.
The 1.96 is replaced by the appropriate quantile from the normal or
t-distribution when arguments `alpha`

and/or `df`

are given.

`ci.pred`

returns a 3-column matrix with estimates and upper and
lower confidence intervals as columns. This is just a convenience
wrapper for `predict.glm(obj,se.fit=TRUE)`

which returns a rather
unhandy structure. The prediction with c.i. is made in the `link`

scale, and by default transformed by the inverse link, since the most
common use for this is for multiplicative Poisson or binomial models
with either log or logit link.

`ci.ratio`

returns the rate-ratio of two independent set of
rates given with confidence intervals or s.e.s. If `se1`

and
`se2`

are given and `log.tr=FALSE`

it is assumed that
`r1`

and `r2`

are rates and `se1`

and `se2`

are
standard errors of the log-rates.

Bendix Carstensen, BendixCarstensen.com & Michael Hills

See also `ci.cum`

for a function computing
cumulative sums of (functions of) parameter estimates.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
# Bogus data:
f <- factor( sample( letters[1:5], 200, replace=TRUE ) )
g <- factor( sample( letters[1:3], 200, replace=TRUE ) )
x <- rnorm( 200 )
y <- 7 + as.integer( f ) * 3 + 2 * x + 1.7 * rnorm( 200 )
# Fit a simple model:
mm <- lm( y ~ x + f + g )
ci.lin( mm )
ci.lin( mm, subset=3:6, diff=TRUE, fnam=FALSE )
ci.lin( mm, subset=3:6, diff=TRUE, fnam=TRUE )
ci.lin( mm, subset="f", diff=TRUE, fnam="f levels:" )
print( ci.lin( mm, subset="g", diff=TRUE, fnam="gee!:", vcov=TRUE ) )
# Use character defined subset to get ALL contrasts:
ci.lin( mm, subset="f", diff=TRUE )
# A Wald test of whether the g-parameters are 0
Wald( mm, subset="g" )
# Wald test of whether the three first f-parameters are equal:
( CM <- rbind( c(1,-1,0,0), c(1,0,-1,0)) )
Wald( mm, subset="f", ctr.mat=CM )
# or alternatively
( CM <- rbind( c(1,-1,0,0), c(0,1,-1,0)) )
Wald( mm, subset="f", ctr.mat=CM )
# Confidence intervals for ratio of rates
ci.ratio( cbind(10,8,12.5), cbind(5,4,6.25) )
ci.ratio( cbind(8,12.5), cbind(4,6.25) )
``` |

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