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. Can also
be a list of two data frames (see below), in which case all other
arguments than |

`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 of length 2 with components `coef`

(a
vector), the desired functional of the parameters and `vcov`

(a
square matrix), 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)`

.

If `ctr.mat`

is a list of two data frames, the difference of the
predictions (on the linear predictor scale) from using the first
versus the last as newdata arguments to predict is computed. Columns
that are identical in the two data frames can be omitted (see
example). If the second data frame has only one row, this is
replicated to match the number of rows in the first. This facility is
primarily aimed at teasing out RRs that are non-linear functions of a
quantitative variable without setting up contrast matrices using the
same code as in the model.

`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 30 31 32 33 34 35 36 37 38 39 40 | ```
# 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 )
# Suppose the x-effect differs across levels of g:
mi <- update( mm, . ~ . + g:x )
ci.lin( mi )
# RR a vs. b by x:
nda <- data.frame( x=-3:3, g="a", f="b" )
ndb <- data.frame( x=-3:3, g="b", f="b" )
#
ci.lin( mi, list(nda,ndb) )
# Same result if f column is omitted because "f" columns are identical
ci.lin( mi, list(nda[,-3],ndb[,-3]) )
# 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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