View source: R/trendline_sum.R
trendline_sum | R Documentation |
Summarizing the results of linear or nonlinear regression model which built in the 'ggtrendline()' function. The function includes the following models:
"line2P" (formula as: y=a*x+b),
"line3P" (y=a*x^2+b*x+c),
"log2P" (y=a*ln(x)+b),
"exp2P" (y=a*exp(b*x)),
"exp3P" (y=a*exp(b*x)+c),
"power2P" (y=a*x^b),
and "power3P" (y=a*x^b+c).
trendline_sum( x, y, model = "line2P", Pvalue.corrected = TRUE, summary = TRUE, eDigit = 5 )
x, y |
the x and y arguments provide the x and y coordinates for the 'ggplot'. Any reasonable way of defining the coordinates is acceptable. |
model |
select which model to fit. Default is "line2P". The "model" should be one of c("line2P", "line3P", "log2P", "exp2P", "exp3P", "power2P", "power3P"), their formulas are as follows: |
Pvalue.corrected |
if P-value corrected or not, the value is one of c("TRUE", "FALSE"). |
summary |
summarizing the model fits. Default is TRUE. |
eDigit |
the numbers of digits for summarized results. Default is 3. |
The linear models (line2P, line3P, log2P) in this package are estimated by lm
function,
while the nonlinear models (exp2P, exp3P, power2P, power3P) are estimated by nls
function (i.e., least-squares method).
The argument 'Pvalue.corrected' is workful for non-linear regression only.
If "Pvalue.corrected = TRUE", the P-vlaue is calculated by using "Residual Sum of Squares" and "Corrected Total Sum of Squares (i.e. sum((y-mean(y))^2))".
If "Pvalue.corrected = TRUE", the P-vlaue is calculated by using "Residual Sum of Squares" and "Uncorrected Total Sum of Squares (i.e. sum(y^2))".
R^2, indicates the R-Squared value of each regression model.
p, indicates the p-value of each regression model.
N, indicates the sample size.
AIC, AICc, or BIC, indicate the Akaike's Information Criterion (AIC), the second-order AIC (AICc) for small samples, or Bayesian Information Criterion (BIC) for fitted model. Click AIC
for details. The smaller the AIC, AICc or BIC, the better the model.
RSS, indicate the value of "Residual Sum of Squares".
If the output of 'AICc' is 'Inf', not an exact number, please try to expand the sample size of your dataset to >=6.
ggtrendline
, SSexp2P
, SSexp3P
, SSpower2P
, SSpower3P
, nls
, selfStart
, AICc
library(ggtrendline) x <- c(1, 3, 6, 9, 13, 17) y <- c(5, 8, 11, 13, 13.2, 13.5) trendline_sum(x, y, model="exp3P", summary=TRUE, eDigit=3)
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