Description Usage Arguments Value Examples
Plot very basic Goodness Of Fit for a pim
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 | ## S3 method for class 'pim'
summary(object,...)
## S3 method for class 'summary.pim'
print(x,...)
## S3 method for class 'pim'
vcov(object,...)
design.matrix(object, ...)
## S3 method for class 'pim'
design.matrix(object,...)
responses(object, ...)
## S3 method for class 'pim'
responses(object,...)
designcols(object, ...)
## S3 method for class 'pim'
designcols(object,...)
outcomeformula(object, ...)
## S3 method for class 'pim'
outcomeformula(object,...)
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object |
|
x |
|
... |
Ignored currently. |
For summary.pim
: an object of class summary.pim
. It holds the original call in an item call,
and has an additional item coefficients, that is a matrix holding columns:
Estimate |
The coefficient estimate. |
Std. Error |
Their standard error. |
Z value |
The standardized value (Test statistic for true coefficient zero). |
Pr(>|z|) |
The p-value. |
For print.summary.pim
: invisibly returns x
For vcov.pim
: a (co)variance matrix
For design.matrix
: The design matrix (of pseudo-observations)
For responses
: The true responses (pseudo-observations)
For designcols
: The orginal column names of the design matrix.
These typically also contain the part of the formula used to build them, so
indicate their source.
For outcomeformula
: The left hand side of the formula used to.
create the design matrix (i.e. to generate the pseudo-outcomes).
1 2 3 4 5 6 7 8 9 10 11 | set.seed(1)
myiris<-iris
myiris$xord<-ordered(sample(letters[1:3], nrow(myiris), replace=TRUE))
myiris$out<-runif(nrow(myiris))
irisprt<-myiris[sample(nrow(myiris), 10),] #10 random rows from iris
pim1<-pim(out~Sepal.Length, data=irisprt, link="logit")
summary(pim1)
vcov(pim1)
design.matrix(pim1)
responses(pim1)
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