Model a linear combination of a set of phenotypes using PCSS
model_combo calculates the linear model for a linear combination of
phenotypes as a function of a set of predictors.
model_combo(formula, phi, n, means, covs, ...)
formula |
an object of class |
phi |
named vector of linear weights for each variable in the
dependent variable in |
n |
sample size. |
means |
named vector of predictor and response means. |
covs |
named matrix of the covariance of all model predictors and the responses. |
... |
additional arguments |
an object of class "pcsslm".
An object of class "pcsslm" is a list containing at least the
following components:
call |
the matched call |
terms |
the |
coefficients |
a p x 4 matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. |
sigma |
the square root of the estimated variance of the random error. |
df |
degrees of freedom, a 3-vector p, n-p, p*, the first being the number of non-aliased coefficients, the last being the total number of coefficients. |
fstatistic |
a 3-vector with the value of the F-statistic with its numerator and denominator degrees of freedom. |
r.squared |
R^2, the 'fraction of variance explained by the model'. |
adj.r.squared |
the above R^2 statistic 'adjusted', penalizing for higher p. |
cov.unscaled |
a p x p matrix of (unscaled) covariances of the coef[j], j=1,...p. |
Sum Sq |
a 3-vector with the model's Sum of Squares Regression (SSR), Sum of Squares Error (SSE), and Sum of Squares Total (SST). |
Wolf JM, Barnard M, Xia X, Ryder N, Westra J, Tintle N (2020). “Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks.” Pacific Symposium on Biocomputing, 25, 719–730. ISSN 2335-6928, doi: 10.1142/9789811215636_0063, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6907735/.
Gasdaska A, Friend D, Chen R, Westra J, Zawistowski M, Lindsey W, Tintle N (2019). “Leveraging summary statistics to make inferences about complex phenotypes in large biobanks.” Pacific Symposium on Biocomputing, 24, 391–402. ISSN 2335-6928, doi: 10.1142/9789813279827_0036, https://pubmed.ncbi.nlm.nih.gov/30963077/.
ex_data <- pcsstools_example[c("g1", "x1", "x2", "x3", "y1", "y2", "y3")]
head(ex_data)
means <- colMeans(ex_data)
covs <- cov(ex_data)
n <- nrow(ex_data)
phi <- c("y1" = 1, "y2" = -1, "y3" = 0.5)
model_combo(
y1 + y2 + y3 ~ g1 + x1 + x2 + x3,
phi = phi, n = n, means = means, covs = covs
)
summary(lm(y1 - y2 + 0.5 * y3 ~ g1 + x1 + x2 + x3, data = ex_data))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.