Description Format Details Source Examples
The Misra1c
data frame has 14 rows and 2 columns.
This is the same data as Misra1a
but a different model is fit.
This data frame contains the following columns:
A numeric vector of volume values.
A numeric vector of pressure values.
These data are the result of a NIST study regarding dental research in monomolecular adsorption. The response variable is volume, and the predictor variable is pressure.
Misra, D., NIST (1978). Dental Research Monomolecular Adsorption Study.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | Try <- function(expr) if (!inherits(val <- try(expr), "try-error")) val
plot(y ~ x, data = Misra1c)
Try(fm1 <- nls(y ~ b1*(1-(1+2*b2*x)**(-.5)), data = Misra1c, trace = TRUE,
start = c(b1 = 500, b2 = 0.0001) ))
Try(fm1a <- nls(y ~ b1*(1-(1+2*b2*x)**(-.5)), data = Misra1c, trace = TRUE,
alg = "port", start = c(b1 = 500, b2 = 0.0001) ))
Try(fm2 <- nls(y ~ b1*(1-(1+2*b2*x)**(-.5)), data = Misra1c, trace = TRUE,
start = c(b1 = 600, b2 = 0.0002) ))
Try(fm2a <- nls(y ~ b1*(1-(1+2*b2*x)**(-.5)), data = Misra1c, trace = TRUE,
alg = "port", start = c(b1 = 600, b2 = 0.0002) ))
Try(fm3 <- nls(y ~ 1-(1+2*b2*x)**(-.5), data = Misra1c, trace = TRUE,
start = c(b2 = 0.0001), algorithm = "plinear" ))
Try(fm4 <- nls(y ~ 1-(1+2*b2*x)**(-.5), data = Misra1c, trace = TRUE,
start = c(b2 = 0.0002), algorithm = "plinear" ))
|
11603.02 : 5e+02 1e-04
8452.255 : 2.973034e+02 2.223794e-04
138.0325 : 6.344276e+02 1.933620e-04
0.05964058 : 6.337918e+02 2.089646e-04
0.04097522 : 6.364150e+02 2.081371e-04
0.04096684 : 6.364273e+02 2.081363e-04
Nonlinear regression model
model: y ~ b1 * (1 - (1 + 2 * b2 * x)^(-0.5))
data: Misra1c
b1 b2
6.364e+02 2.081e-04
residual sum-of-squares: 0.04097
Number of iterations to convergence: 5
Achieved convergence tolerance: 1.222e-06
0: 5801.5082: 500.000 0.000100000
1: 522.80178: 831.329 0.000184686
2: 12.564879: 629.050 0.000204239
3: 0.020834412: 636.280 0.000208227
4: 0.020483419: 636.427 0.000208136
5: 0.020483418: 636.427 0.000208136
6: 0.020483418: 636.427 0.000208136
Nonlinear regression model
model: y ~ b1 * (1 - (1 + 2 * b2 * x)^(-0.5))
data: Misra1c
b1 b2
6.364e+02 2.081e-04
residual sum-of-squares: 0.04097
Algorithm "port", convergence message: both X-convergence and relative convergence (5)
262.4566 : 6e+02 2e-04
0.1559867 : 6.357031e+02 2.088635e-04
0.0409676 : 6.364173e+02 2.081389e-04
0.04096684 : 6.364272e+02 2.081363e-04
Nonlinear regression model
model: y ~ b1 * (1 - (1 + 2 * b2 * x)^(-0.5))
data: Misra1c
b1 b2
6.364e+02 2.081e-04
residual sum-of-squares: 0.04097
Number of iterations to convergence: 3
Achieved convergence tolerance: 1.091e-06
0: 131.22829: 600.000 0.000200000
1: 9.2635332: 620.165 0.000208610
2: 0.020527623: 636.422 0.000208126
3: 0.020483418: 636.427 0.000208136
4: 0.020483418: 636.427 0.000208136
Nonlinear regression model
model: y ~ b1 * (1 - (1 + 2 * b2 * x)^(-0.5))
data: Misra1c
b1 b2
6.364e+02 2.081e-04
residual sum-of-squares: 0.04097
Algorithm "port", convergence message: relative convergence (4)
14.7926 : 0.0001 1226.0429
0.1176382 : 1.997722e-04 6.593247e+02
0.04097147 : 2.080709e-04 6.365991e+02
0.04096684 : 2.081361e-04 6.364277e+02
0.04096684 : 2.081363e-04 6.364273e+02
Nonlinear regression model
model: y ~ 1 - (1 + 2 * b2 * x)^(-0.5)
data: Misra1c
b2 .lin
2.081e-04 6.364e+02
residual sum-of-squares: 0.04097
Number of iterations to convergence: 4
Achieved convergence tolerance: 2.155e-06
0.1134958 : 0.0002 658.6757
0.04097105 : 2.080739e-04 6.365911e+02
0.04096684 : 2.081361e-04 6.364276e+02
0.04096684 : 2.081363e-04 6.364273e+02
Nonlinear regression model
model: y ~ 1 - (1 + 2 * b2 * x)^(-0.5)
data: Misra1c
b2 .lin
2.081e-04 6.364e+02
residual sum-of-squares: 0.04097
Number of iterations to convergence: 3
Achieved convergence tolerance: 1.44e-06
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