The symbols used in the help pages and vignettes of bayesammi, with their meaning and the argument or result component that holds them. The notation follows one rule throughout: scalars are upper-case letters in normal face (\(P\), \(R\)), indices lower-case italic (\(i, j\)), vectors lower-case bold (\(\mathbf{y}\), \(\boldsymbol{\beta}\)) and matrices upper-case bold (\(\mathbf{X}\), \(\boldsymbol{\Sigma}\)).
Usage
bam_notation(format = c("table", "path", "tex"))See also
Other bayesammi utilities:
bam_reference(),
bam_save(),
bam_theme()
Author
Muhammad Yaseen (myaseen208@gmail.com)
Examples
bam_notation(
format = c("table", "path", "tex")[1]
)
#> symbol
#> <char>
#> 1: G
#> 2: E
#> 3: B
#> 4: K
#> 5: n
#> 6: i, j, l, k
#> 7: \\mathbf{Y} = (\\bar{y}_{ij})
#> 8: \\mu
#> 9: \\boldsymbol{\\tau} = (\\tau_i)
#> 10: \\boldsymbol{\\delta} = (\\delta_j)
#> 11: \\lambda_k
#> 12: \\boldsymbol{\\alpha}_k, \\boldsymbol{\\gamma}_k
#> 13: \\sigma^2
#> 14: \\sigma^2_j
#> 15: \\eta_{ij}
#> 16: g_i, h_j
#> 17: 1 + b_i
#> 18: d_{ij}
#> 19: \\sigma^2_{d,i}
#> 20: W_i, P_i, I_i
#> 21: \\theta_k
#> 22: n_{ij}
#> 23: S
#> 24: \\mathrm{DIC}, \\mathrm{WAIC}
#> 25: \\mathbf{K}_n
#> 26: \\sigma^2_\\mu, \\sigma^2_\\tau, \\sigma^2_\\delta, \\sigma^2_\\lambda
#> 27: \\nu, S^2_0
#> 28: \\mathrm{SSW}
#> 29: \\mathbf{A}^\\top, \\mathbf{1}, \\mathbf{I}
#> symbol
#> <char>
#> text kind
#> <char> <char>
#> 1: G scalar
#> 2: E scalar
#> 3: B scalar
#> 4: K scalar
#> 5: n scalar
#> 6: i j l k index
#> 7: Y matrix
#> 8: mu scalar
#> 9: tau vector
#> 10: delta vector
#> 11: lambda_k scalar
#> 12: alpha_k gamma_k vector
#> 13: sigma^2 scalar
#> 14: sigma_j^2 scalar
#> 15: eta_ij scalar
#> 16: g_i h_j scalar
#> 17: 1 + b_i scalar
#> 18: d_ij scalar
#> 19: sigma_d_i^2 scalar
#> 20: W_i P_i I_i scalar
#> 21: theta_k scalar
#> 22: n_ij scalar
#> 23: S scalar
#> 24: DIC WAIC scalar
#> 25: K_n matrix
#> 26: v_mu v_tau v_delta v_lambda scalar
#> 27: nu s0^2 scalar
#> 28: SSW scalar
#> 29: A^T 1 I operator
#> text kind
#> <char> <char>
#> meaning
#> <char>
#> 1: number of genotypes
#> 2: number of environments
#> 3: number of replicates in each environment (largest number of plots in a cell when unbalanced)
#> 4: number of bilinear terms
#> 5: number of observations BGE
#> 6: genotype, environment, replicate, bilinear term
#> 7: table of genotype-by-environment means
#> 8: overall mean
#> 9: genotype main effects (sum zero)
#> 10: environment main effects (sum zero)
#> 11: singular value of bilinear term k
#> 12: genotype and environment score vectors of term k (unit length)
#> 13: error variance
#> 14: error variance of environment j (.het = TRUE)
#> 15: expected value of genotype i in environment j
#> 16: genotype effect and environmental index of the stability models
#> 17: slope of genotype i on the environmental index
#> 18: deviation of genotype i from its regression in environment j
#> 19: deviation (Eberhart-Russell) or stability (Shukla) variance of genotype i
#> 20: Wricke's ecovalence, Lin and Binns' superiority, Annicchiarico's reliability
#> 21: share of term k in the fitted interaction (Za, WAAS)
#> 22: number of observed plots of genotype i in environment j
#> 23: number of kept posterior draws
#> 24: deviance and widely applicable information criteria
#> 25: orthonormal contrasts, (n - 1) x n, rows orthogonal to 1
#> 26: prior variances
#> 27: degrees of freedom and scale of the prior of the error variance
#> 28: within-cell sum of squares
#> 29: transpose, vector of ones, identity matrix
#> meaning
#> <char>
#> argument
#> <char>
#> 1: .gen
#> 2: .env
#> 3: .rep
#> 4: .k
#> 5:
#> 6:
#> 7: .y
#> 8:
#> 9:
#> 10:
#> 11:
#> 12:
#> 13:
#> 14: .het
#> 15:
#> 16:
#> 17:
#> 18:
#> 19: .model
#> 20:
#> 21:
#> 22:
#> 23: .nIter
#> 24: .waic
#> 25:
#> 26: .priors
#> 27: .priors
#> 28:
#> 29:
#> argument
#> <char>