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columns_tidy.yaml
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columns_tidy.yaml
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acf: Autocorrelation.
adj.p.value: P-value adjusted for multiple comparisons.
alternative: Alternative hypothesis (character).
at.value: Value(s) used to calculate AMEs.
at.variable: Variable(s) used to calculate average marginal effects (AMEs).
atmean: Marginal effects were calculated as either partial effects for the mean observation
(TRUE), or as mean partial effects (FALSE).
autocorrelation: Autocorrelation.
bias: Bias of the statistic.
ci.width: Expected width of confidence interval.
class: The class under consideration.
cluster: A factor describing the cluster from 1:k.
coef.type: Type of coefficient.
column1: Name or index of the first column being described.
column2: Name or index of the second column being described.
comp: ID of the model component.
comparison: Levels being compared.
component: ID of the cluster or component being considered.
conf.high: Upper bound on the confidence interval for the estimate.
conf.low: Lower bound on the confidence interval for the estimate.
contrast: Levels being compared.
cumulative: Cumulative percentage of variation explained.
cutoff: The cutoff used for classification. Observations with predicted probabilities
above this value were assigned class 1, and observations with predicted probabilities
below this value were assigned class 0.
delta: True difference in means.
den.df: Degrees of freedom of the denominator.
denominator: The denominator, which is tau=kendall_score/denominator.
dev.ratio: Fraction of null deviance explained at each value of lambda.
df: Degrees of freedom used by this term in the model.
distance: Distance between items.
estimate: The estimated value of the regression term.
estimate1: Sometimes two estimates are computed, such as in a two-sample t-test.
estimate2: Sometimes two estimates are computed, such as in a two-sample t-test.
event: Observed number of events.
exp: Weighted expected number of events in each group.
expected: Expected number of events.
fpr: False positive rate.
freq: Vector of frequencies at which the spectral density is estimated.
GCV: Generalized cross validation error estimate.
group: The group (if specified) in the lavaan model.
group1: First group being compared.
group2: Second group being compared.
index: Index (i.e. date or time) for a `ts` or `zoo` object.
item1: First item.
item2: Second item.
kendall_score: Kendall score.
lag: Lag values.
lambda: Value of penalty parameter lambda.
letters: Compact letter display denoting all pair-wise comparisons.
lhs: Left hand side.
logLik: Log-likelihood at the fitted value of the parameters.
mcmc.error: The MCMC error.
mean: The mean for each component. In case of 2+ dimensional models, a column with
the mean is added for each dimension. NA for noise component.
meansq: Mean sum of squares. Equal to total sum of squares divided by degrees of freedom.
method: Method used.
'n': Number of observations by component.
'N': Number of subjects in each group.
n.censor: Number of censored events.
n.event: Number of events at time t.
n.risk: Number of individuals at risk at time zero.
null.value: Value to which the estimate is compared.
num.df: Degrees of freedom.
nzero: Number of non-zero coefficients for the given lambda.
obs: weighted observed number of events in each group.
op: The operator in the model syntax (e.g. `~~` for covariances, or `~` for regression
parameters).
outcome: Outcome of manifest variable.
p: True proportion.
p.value: The two-sided p-value associated with the observed statistic.
p.value.Sargan: p-value for Sargan test of overidentifying restrictions.
p.value.weakinst: p-value for weak instruments test.
p.value.Wu.Hausman: p-value for Wu-Hausman weak instruments test for endogeneity.
parameter: The parameter being modeled.
PC: An integer vector indicating the principal component.
percent: Percentage of variation explained.
power: Power achieved for given value of n.
proportion: The mixing proportion of each component
pyears: Person-years of exposure.
quantile: ~
response: Which response column the coefficients correspond to (typically `Y1`, `Y2`,
etc).
rhs: Right hand side.
robust.se: robust version of standard error estimate.
row: Row ID of the original observation.
scale: Scaling factor of estimated coefficient.
sd: Standard deviation.
series: Name of the series (present only for multivariate time series).
sig.level: Significance level (Type I error probability).
size: Number of points assigned to cluster.
spec: Vector (for univariate series) or matrix (for multivariate series) of estimates
of the spectral density at frequencies corresponding to freq.
state: State (if multistate survfit object inputted).
statistic: The value of a T-statistic to use in a hypothesis that the regression term
is non-zero.
statistic.Sargan: Statistic for Sargan test of overidentifying restrictions.
statistic.weakinst: Statistic for Wu-Hausman test.
statistic.Wu.Hausman: Statistic for Wu-Hausman weak instruments test for endogeneity.
std_estimate: The standardized regression coefficients.
std.all: Standardized estimates based on both the variances of both (continuous) observed
and latent variables.
std.dev: Standard deviation explained by this PC.
std.error: The standard error of the regression term.
std.lv: Standardized estimates based on the variances of the (continuous) latent variables
only.
std.nox: Standardized estimates based on both the variances of both (continuous) observed
and latent variables, but not the variances of exogenous covariates.
step: Which step of lambda choices was used.
strata: Strata if stratified survfit object inputted.
stratum: The error stratum.
study: The estimate type (summary vs individual study).
sumsq: Sum of squares explained by this term.
tau: Quantile.
term: The name of the regression term.
time: Point in time.
tpr: The true positive rate at the given cutoff.
type: Either "weighted" or "unweighted".
uniqueness: Proportion of residual, or unexplained variance.
value: The value/estimate of the component. Results from data reshaping.
var_kendall_score: Variance of the kendall_score.
variable: Variable under consideration.
variance: In case of one-dimensional and spherical models, the variance for each component,
omitted otherwise. `NA` for noise component.
withinss: The within-cluster sum of squares.
y.level: The response level.
y.value: The response level.
z: z score.