# validation.specs

by Ran Gao
0th

Percentile

##### Validates user-specified parameters

This function checks the validity of user specified parameters including rate parameters for count variables, proportion parameters for binary and ordinary variables, mean and variance parameters for normal data, as well as the validity of entries in the correlation matrix. This function also computes the lower and upper limits for each pairwise correlation based on the marginal probabilities for range violation checks.

##### Usage
validation.specs(no.pois, no.bin, no.ord, no.norm, corr.mat, prop.vec.bin,
prop.vec.ord, lamvec, nor.mean, nor.var)validation_specs(no.pois, no.bin, no.ord, no.norm, corr.mat, prop.vec.bin,
prop.vec.ord, lamvec, nor.mean, nor.var) #deprecated
##### Arguments
no.pois

Number of count variables.

no.bin

Number of binary variables.

no.ord

Number of ordinal variables.

no.norm

Number of normal variables.

corr.mat

User specified correlation matrix for the multivariate data.

prop.vec.bin

Vector of probabilities corresponding to each of the binary variables.

prop.vec.ord

Vector of probabilities corresponding to each of the ordinal variables. For each of the ordinal variable, the i-th element of the probability vector is the cumulative probability defining the marginal distribution of the ordinal variable. If the variable has k categories, the i-th element of p will contain k-1 probabilities. The k-th element is implicitly 1.

lamvec

Vector of rate parameters for the count variables.

nor.mean

Vector of means for the normal variables.

nor.var

Vector of variances for the normal variables.

##### Details

This function computes the lower and upper bounds for all possible pairs that involve count, binary, ordinal and normal variables.

##### Value

The function returns TRUE if no specification problem is encountered. Otherwise, it returns an error message.

##### References

Demirtas, H. and Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. The American Statistician, 65(2), 104-109.

Demirtas, H., Hedeker, D., and Mermelstein, R.J. (2012). Simulation of massive public health data by power polynomials. Statistics in Medicine, 31(27), 3337-3346.

##### Aliases
• validation.specs
• validation_specs
##### Examples
# NOT RUN {
num_pois<-1
num_bin<-1
num_ord<-1
num_norm<-1
lambda<-c(1)
pbin<-c(0.3)
pord<-list(c(0.3,0.6))
normean<-15
norvar<-7
corr.mat=matrix(c(1,0.2,0.1,0.3, 0.2,1,0.5,0.4, 0.1,0.5,1, 0.7, 0.3, 0.4, 0.7, 1),4,4)
validation.specs(num_pois, num_bin, num_ord, num_norm,
corr.mat, pbin, pord, lambda, normean,norvar)

num_pois<-2
num_bin<-2
num_ord<-2
num_norm<-0
lambda<-c(1,2)
pbin<-c(0.3,0.5)
pord<-list(c(0.3,0.6),c(0.5,0.6))
corr.mat=matrix(0.64,6,6)
diag(corr.mat)=1
validation.specs(num_pois, num_bin, num_ord, num_norm,
corr.mat, pbin, pord, lambda, nor.mean=NULL, nor.var=NULL)

# An example with an invalid target correlation matrix (bound violation).
num_pois<-1
num_bin<-2
num_ord<-2
num_norm<-1
lamvec=c(1)
pbin=c(0.3, 0.7)
pord=list(c(0.2, 0.5), c(0.4, 0.7, 0.8))
nor.mean=2.1
nor.var=0.75
M=c(-0.35, 0.26, 0.34, 0.09, 0.14, 0.12, 0.30, -0.02, 0.17, 0.29, -0.04, 0.19,
0.10, 0.35, 0.39)
N=diag(6)
N[lower.tri(N)]=M
TV=N+t(N)
diag(TV)<-1
validation.specs(num_pois, num_bin, num_ord, num_norm, corr.mat=TV, pbin, pord,
lamvec, normean, norvar)

# An example with a non-positive definite correlation matrix.
pbin=c(0.3, 0.7)
TV1=TV
TV1[3,2]=TV[2,3]=5
validation.specs(num_pois, num_bin, num_ord, num_norm, corr.mat=TV1, pbin, pord,
lamvec, normean, norvar)
# }

Documentation reproduced from package PoisBinOrdNor, version 1.6.1, License: GPL-2 | GPL-3

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