| Meth {MethComp} | R Documentation |
Creates a dataframe with columns meth, item, (repl) and
y.
Meth( meth,
item,
repl,
y,
...,
print = FALSE)
## S3 method for class 'Meth':
summary( object, ... )
## S3 method for class 'Meth':
plot(
x, y = NULL,
col.LoA = "blue",
col.pt = "black",
cex.name = 2,
var.range,
diff.range,
var.names = FALSE,
... )
## S3 method for class 'Meth':
subset(x, ... )
## S3 method for class 'Meth':
sample( x,
how = "random",
N = if( how=="items" ) nlevels( x$item ) else nrow(x),
... )
## S3 method for class 'Meth':
transform(`_data`, ... )
meth |
Vector of methods, numeric, character or factor.
May also be a dataframe. If this has columns, meth,
item, (repl) and y, these are used. |
item |
Vector of items. If meth is a dataframe, item is
taken as the columns of the meth dataframe to use as vectors
of meth, item, (repl) and y. |
repl |
Vector of replicate numbers. |
y |
Vector of measurements.
If y is a numeric or character vector and length(y) > 1,
it is assumed that the dataframe meth is in wide form, and the
corresponding column names are taken as method names. In this case
item can be omitted in which case items are just taken as the
names
If y is a list it is
assumed (and checked) that the list elements are numerical vectors of
the same lengt as the meth, item and repl vectors.
For the plot method the argument is
either a vector of indices or names of methods to plot. |
print |
Logical: Should a summary result be printed? |
object |
A Meth object. |
x |
A Meth object. |
col.LoA |
What color should be used for the limits of agreement. |
col.pt |
What color should be used for the points. |
cex.name |
Character expansion factor for plotting method names |
var.range |
The range of the axes in the scatter plot and the x-axis in the Bland-Altman plot be? |
diff.range |
The range of yaxis in the Bland-Altman plot. Defaults to a range as the x-axis, but centered around 0. |
var.names |
If logical: should the individual panels be labelled with the variable names?. If character, then the values of the character will be used to label the methods. |
how |
What sampling strategy should be used. Charater strion, one of
"random", "linked" or "item". Only the first
letter is significant. See details for explanation. |
N |
How many observations should be sampled? |
_data |
A Meth object. |
... |
Ignored by the Meth and the summary and sample
functions. In the plot function, parameters passed on the
panel function plotting methods against each other, as well as those
plotting differences against means. |
In order to perform analyses of method comparisons it is convenient to have a
dataframe with classifying factors , meth, item, and possibly
repl and the response variable y. This function creates such a
dataframe, and gives it a class, Meth, for which there is a number of
methods: tab - tabulation, plot - plotting and a couple of
analysis methods (not fixed yet).
sample.Meth samples a Meth object with replacement.
If how=="random", a random sample of the rows are sampled, the existing
values of meth, item and y are kept but new replicate
numbers are generated.
If how=="linked", a random sample of the linked observations (i.e.
observations with identical item and repl values) are sampled
with replacement.
The Meth function returns a Meth object which is a
dataframe with columns meth, item, (repl) and y.
summary.Meth returns a table classified by method and no. of
replicate measurements, extended with columns of the total number of items,
total number of observations and the range of the measurements.
The subset returns a subset of the Meth object with complete
interformtion a sample of the items.
sample
Bendix Carstensen, bxc@steno.dk
data(fat) # Different ways of selecting columns and generating replicate numbers Sub1 <- Meth(fat,c(2,1,3,4),print=TRUE) Sub2 <- Meth(fat,c(2,1,NA,4),print=TRUE) Sub3 <- Meth(fat,c(2,1,4),print=TRUE) summary( Sub3 ) plot( Sub3 ) # More than two methods data( sbp ) plot( Meth( sbp ) ) # Creating non-unique replicate numbers per (meth,item) creates a warning: data( hba1c ) hb1 <- with( hba1c, Meth( dev, item, d.ana-d.samp, y, print=TRUE ) ) hb2 <- with( subset(hba1c,type=="Cap"), Meth( dev, item, d.ana-d.samp, y, print=TRUE ) ) summary( hb1 ) summary( hb2 )