graded_prob {lazy.irt} | R Documentation |
Calculate the probability contents associated with the integer score
graded_prob( x, mean, std, min = NULL, max = NULL, truncate = 0, method = 1, print = 0, plot = 0 )
x |
The integer score vector |
mean |
The mena of the underlying contimuous variable |
min |
The minimum value of x |
max |
The maximum value of x |
truncate |
= 1 to truncate the range of X between [min,max] |
method |
= 1 to use pnorm(x[i+1])-pnorm(x[i]) |
print |
= 1 to print the result |
plot |
= 1 to plot the result |
Let Y be distributed as N( mean, std ).
This function evaluates the probabilities:
p[1] = Pr( min < X <= x[1] ), p[2] = Pr( x[1] < X <= x[2] ), ...
p[length(x)] = Pr( x[n] < X <= max )
If method = 1,
p[i] = pnorm( b[i+1], mean, std ) - pnorm( b[i], mean, std )
else
p[i] = dnorm( x[i], mean, std )
If method = 2 or method = 1 and truncate = 1,
the probabilites will be normalized so that sum(p) = 1.
When truncate = 1, if the interval (min, max) is not wide enough to
cover the whole range of X, the resulting probabilities may not
sum to unity.
p A vector (same size as x)
# The rightmost/leftmost categoris are wider than the rest. # truncation has no effect graded_prob( 0:4, 2, 2, truncate=0, print=1 ) graded_prob( 0:4, 2, 2, truncate=1, print=1 ) graded_prob( 0:4, 2, 2, method=2, print=1 ) # The rightmost/leftmost categoris have the same length as the rest. graded_prob( 0:4, 2, 2, min=-0.5, max=4.5, truncate=0 ) graded_prob( 0:4, 2, 2, min=-0.5, max=4.5, truncate=1 )