Self-Starting Nls 'exp2P' Regression Model

Description

This selfStart model evaluates the power regression function (formula as: y=a*exp(b*x)). It has an initial attribute that will evaluate initial estimates of the parameters 'a' and 'b' for a given set of data.

Usage

SSexp2P(predictor, a, b)

Arguments

Value

No return value (called for side effects).

See Also

ggtrendline, SSexp3P, SSpower3P, nls, selfStart

Examples

library(ggtrendline)
x<-1:5
y<-c(2,4,8,20,25)
xy<-data.frame(x,y)
getInitial(y ~ SSexp2P(x,a,b), data = xy)
## Initial values are in fact the converged values

fitexp2P <- nls(y~SSexp2P(x,a,b), data=xy)
summary(fitexp2P)

prediction <- predFit(fitexp2P , data.frame(x=x), se.fit = TRUE,
                          level = 0.95, interval = "confidence")
yfitexp2P <- prediction$fit
yfitexp2P  # output a matrix of predictions and bounds with column names fit, lwr, and upr.

Self-Starting Nls 'exp3P' Regression Model

Description

This selfStart model evaluates the exponential regression function (formula as: y=a*exp(b*x)+c). It has an initial attribute that will evaluate initial estimates of the parameters a, b, and c for a given set of data.

Usage

SSexp3P(predictor, a, b, c)

Arguments

Value

No return value (called for side effects).

See Also

ggtrendline, SSexp3P, SSpower3P, nls, selfStart

Examples

library(ggtrendline)
x<-1:5
y<-c(2,4,8,16,28)
xy<-data.frame(x,y)
getInitial(y ~ SSexp3P(x,a,b,c), data = xy)
## Initial values are in fact the converged values

fitexp3P <- nls(y~SSexp3P(x,a,b,c), data=xy)
summary(fitexp3P)

prediction <- predFit(fitexp3P , data.frame(x=x), se.fit = TRUE,
                          level = 0.95, interval = "confidence")
yfitexp3P <- prediction$fit
yfitexp3P  # output a matrix of predictions and bounds with column names fit, lwr, and upr.

Self-Starting Nls 'power2P' Regression Model

Description

This selfStart model evaluates the power regression function (formula as: y=a*x^b). It has an initial attribute that will evaluate initial estimates of the parameters 'a' and 'b' for a given set of data.

Usage

SSpower2P(predictor, a, b)

Arguments

Value

No return value (called for side effects).

See Also

ggtrendline, SSexp3P, SSpower3P, nls, selfStart

Examples

library(ggtrendline)
x<-1:5
y<-c(2,4,8,20,25)
xy<-data.frame(x,y)
getInitial(y ~ SSpower2P(x,a,b), data = xy)
## Initial values are in fact the converged values

fitpower2P <- nls(y~SSpower2P(x,a,b), data=xy)
summary(fitpower2P)

prediction <- predFit(fitpower2P , data.frame(x=x), se.fit = TRUE,
                          level = 0.95, interval = "confidence")
yfitpower2P <- prediction$fit
yfitpower2P # output a matrix of predictions and bounds with column names fit, lwr, and upr.

Self-Starting Nls 'power3P' Regression Model

Description

This selfStart model evaluates the power regression function (formula as: y=a*x^b+c). It has an initial attribute that will evaluate initial estimates of the parameters a, b, and c for a given set of data.

Usage

SSpower3P(predictor, a, b, c)

Arguments

Value

No return value (called for side effects).

See Also

ggtrendline, SSexp3P, SSpower3P, nls, selfStart

Examples

library(ggtrendline)
x<-1:5
y<-c(2,4,8,20,25)
xy<-data.frame(x,y)
getInitial(y ~ SSpower3P(x,a,b,c), data = xy)
## Initial values are in fact the converged values

fitpower3P <- nls(y~SSpower3P(x,a,b,c), data=xy)
summary(fitpower3P)

prediction <- predFit(fitpower3P , data.frame(x=x), se.fit = TRUE,
                          level = 0.95, interval = "confidence")
yfitpower3P <- prediction$fit
yfitpower3P # output a matrix of predictions and bounds with column names fit, lwr, and upr.

Add Trendline and Confidence Interval to 'ggplot'

Description

Add trendline and confidence interval of linear or nonlinear regression model to 'ggplot', by using different models 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).

Usage

ggtrendline(
  x,
  y,
  model = "line2P",
  linecolor = "black",
  linetype = 1,
  linewidth = 0.6,
  CI.level = 0.95,
  CI.fill = "grey60",
  CI.alpha = 0.3,
  CI.color = "black",
  CI.lty = 2,
  CI.lwd = 0.5,
  summary = TRUE,
  show.eq = TRUE,
  yhat = FALSE,
  eq.x = NULL,
  eq.y = NULL,
  show.Rsquare = TRUE,
  show.pvalue = TRUE,
  Pvalue.corrected = TRUE,
  Rname = 0,
  Pname = 0,
  rrp.x = NULL,
  rrp.y = NULL,
  text.col = "black",
  eDigit = 3,
  eSize = 3,
  xlab = NULL,
  ylab = NULL
)

Arguments

Details

The values of each parameter of regression model can be found by typing trendline_sum function in this package.

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).

Value

No return value (called for side effects).

References

Ritz C., and Streibig J. C. (2007) Nonlinear Regression with R. Springer.

Greenwell B. M., and Schubert Kabban C. M. (2014) investr: An R Package for Inverse Estimation. The R Journal, 6(1), 90-100.

See Also

ggtrendline, stat_eq, stat_rrp, trendline_sum, nls, selfStart

Examples

# library(ggplot2)
library(ggtrendline)
x <- c(1, 3, 6, 9,  13,   17)
y <- c(5, 8, 11, 13, 13.2, 13.5)

ggtrendline(x, y, model = "line2P")  # default
ggtrendline(x, y, model = "log2P", CI.fill = NA)  # CI lines only, without CI filling

ggtrendline(x, y, model = "exp2P", linecolor = "blue", linetype = 1, linewidth = 1) # set line
ggtrendline(x, y, model = "exp3P", CI.level = 0.99,
            CI.fill = "red", CI.alpha = 0.1, CI.color = NA, CI.lty = 2, CI.lwd = 1.5) # set CI

Predictions from a Fitted Model

Description

Generic prediction method for various types of fitted models. predFit can be used to obtain standard errors of fitted values and adjusted/unadjusted confidence/prediction intervals for objects of class "lm", "nls".

Usage

predFit(object, ...)

## Default S3 method:
predFit(object, ...)

## S3 method for class 'nls'
predFit(
  object,
  newdata,
  se.fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  adjust = c("none", "Bonferroni", "Scheffe"),
  k,
  ...
)

Arguments

Value

No return value (called for side effects).

Note

predFit function is from 'investr' package written by Brandon M. Greenwell.

References

Greenwell B. M., and Schubert-Kabban, C. M. (2014) investr: An R Package for Inverse Estimation. The R Journal, 6(1), 90-100.

See Also

predFit

Add Equation to 'ggplot'

Description

Add regression equation to 'ggplot', by using different models 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).

Usage

stat_eq(
  x,
  y,
  model = "line2P",
  show.eq = TRUE,
  xname = "x",
  yname = "y",
  yhat = FALSE,
  eq.x = NULL,
  eq.y = NULL,
  text.col = "black",
  eDigit = 3,
  eSize = 3
)

Arguments

Details

The values of each parameter of regression model can be found by typing trendline_sum function in this package.

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).

Value

No return value (called for side effects).

See Also

ggtrendline, stat_rrp, trendline_sum

Add R square and P-value to 'ggplot'

Description

Add R-square and P-value of regression models to 'ggplot', by using models 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).

Usage

stat_rrp(
  x,
  y,
  model = "line2P",
  Pvalue.corrected = TRUE,
  show.Rsquare = TRUE,
  show.pvalue = TRUE,
  Rname = 0,
  Pname = 0,
  rrp.x = NULL,
  rrp.y = NULL,
  text.col = "black",
  eDigit = 3,
  eSize = 3
)

Arguments

Details

The values of each parameter of regression model can be found by typing trendline_sum function in this package.

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 only valid for non-linear regression.

If "Pvalue.corrected = TRUE", the P-value is calculated by using "Residual Sum of Squares" and "Corrected Total Sum of Squares (i.e. sum((y-mean(y))^2))".

If "Pvalue.corrected = FALSE", the P-value is calculated by using "Residual Sum of Squares" and "Uncorrected Total Sum of Squares (i.e. sum(y^2))".

Value

No return value (called for side effects).

See Also

ggtrendline, stat_eq, trendline_sum

Summarized Results of Each Regression Model

Description

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).

Usage

trendline_sum(
  x,
  y,
  model = "line2P",
  Pvalue.corrected = TRUE,
  summary = TRUE,
  eDigit = 5
)

Arguments

Details

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))".

Value

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".

Note

If the output of 'AICc' is 'Inf', not an exact number, please try to expand the sample size of your dataset to >=6.

See Also

ggtrendline, SSexp2P, SSexp3P, SSpower2P, SSpower3P, nls, selfStart, AICc

Examples

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)