calcLRs.YsByX: Calculate linear regressions for time series in Y as function...

In wStockhausen/wtsDisMELSConn: Functions for dealing with DisMELS IBM model output, including calculating and analyzing connectivity matrices

Description

Function to calculate linear regressions between time series in Y as function of time series in X.

Usage

calcLRs.YsByX(mdfrX, mdfrY, yvars = NULL, xlab = "index", ylab = NULL,
  nrows = 2, coord_fixed = FALSE, labelByGroup = FALSE, verbose = FALSE)

Arguments

mdfrX	- melted dataframe with independent (X) time series
mdfrY	- melted dataframe with dependent (Y) time series
yvars	- vector of dependent variables to process (or NULL for all)
xlab	- label associated with independent variables
ylab	- label associated with dependent variables
nrows	- number of rows for LR plots
coord_fixed	- flag to use equal x/y unit dimensions in LR plots
labelByGroup	- flag to use "group" column to organize linear regression analysis
verbose	- flag to print messages

Details

none.

Value

list with named elements 'res', 'summary' and 'plot':

• res - a list with sublists by unique X with elements

  • lms - list with linear model (lm) results by Y for X

  • summary - dataframe with summaries of linear model results

  • plots - list of ggplot2 objects (p1=standardized time series, p2=fits to lm's)

• summary - a dataframe summarizing all LR results. Columns are

  • ygroup - dependent variable group label

  • y - dependent variable label

  • xgroup - independent variable group label

  • x - independent variable label

  • n - number of valid data points

  • rho - Pearson's correlation coefficient

  • rsq - R-squared for linear fit

  • p - P-value (uncorrected for multiple comparisons)

• plot - a ggplot object.

• data - dataframe with data involved in linear models

wStockhausen/wtsDisMELSConn documentation built on May 3, 2019, 7:36 p.m.