@Ahmed Mohamed
Estoy de acuerdo con Jim. No estoy seguro de si la plataforma no lineal JMP puede realizar análisis de crestas. Es fácil poner límites a las estimaciones de los parámetros, pero no estoy familiarizado con la fórmula de pérdida. Sé que JSL es más fácil para la mayoría de los pasos de datos de SAS.
El JSL a continuación muestra algunos de los primeros pasos para ingresar y ejecutar un ajuste lineal simple y no lineal simple.
//conversion
Names Default to Here(1);
dt_cs = Open("$Downloads/elisa_cs.csv");
dt_cs << select where(! (:plate=="A" & :series==1));
dt_cs << delete Rows(); //only keep A 1
dt_calib = Data Table( "elisa_cs" ) << Stack(
columns( :rep1, :rep2, :rep3 ),
Source Label Column( "Label" ),
Stacked Data Column( "signal" ),
Output Table Name("calib")
);
dt_calib << New Column("w", numeric, continuous, << Set each value(1/:signal^1.3));
report1 = dt_calib << Fit Model(
Weight( :w ),
Y( :signal ),
Effects( :concentration ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run
);
report1 << Save Columns( {"Prediction Formula"}); //predictions are saved to the data table
lin_est = report1 << get estimates; //vector
show(lin_est);
gb = dt_calib << Graph Builder(
Size( 522, 456 ),
Show Control Panel( 0 ),
Show Legend( 0 ),
Variables(
X( :concentration ),
Y( :signal ),
Y( :Pred Formula signal, Position( 1 ), Side( "Right" ) )
),
Elements( Points( X, Y( 1 ), Legend( 6 ) ), Line( X, Y( 2 ), Legend( 11 ) ) ),
SendToReport(
Dispatch(
{},
"Graph Builder",
OutlineBox,
{Set Title( "Fit of the Linear Model" ), Image Export Display( Normal )}
),
Dispatch(
{},
"concentration",
ScaleBox,
{Scale( "Log" ), Format( "Best", 6 ), Min( 1 ), Max( 1000 ), Inc( 1 ),
Minor Ticks( 0 )}
),
Dispatch(
{},
"signal",
ScaleBox,
{Format( "Fixed Dec", 12, 0 ), Min( 0 ), Max( 4 ), Inc( 1 ),
Minor Ticks( 1 ), Label Row(
{Show Major Grid( 1 ), Show Minor Grid( 1 )}
)}
),
Dispatch(
{},
"Pred Formula signal",
ScaleBox,
{Format( "Fixed Dec", 12, 0 ), Min( 0 ), Max( 4 ), Inc( 1 ),
Minor Ticks( 1 )}
),
Dispatch(
{},
"400",
ScaleBox,
{Legend Model(
11,
Properties(
0,
{Line Color( 19 ), Line Width( 3 )},
Item ID( "Mean(Pred Formula signal)", 1 )
)
)}
)
)
);
//Note since this is a simple weighted regression the analysis and plot can be created with Bivariate
//here the predicteds are saved, not the formula
report1_biv = dt_calib << Bivariate(
Y( :signal ),
X( :concentration ),
Weight( :w ),
Fit Line( {Confid Shaded Fit( 1 ), Line Color( {212, 73, 88} ), Save Predicteds} ),
SendToReport(
Dispatch(
{},
"1",
ScaleBox,
{Scale( "Log" ), Min( 1 ), Max( 1000 ), Inc( 1 ), Minor Ticks( 0 )}
),
Dispatch(
{},
"2",
ScaleBox,
{Min( 0 ), Max( 4 ), Inc( 1 ), Minor Ticks( 1 ),
Label Row( {Show Major Grid( 1 ), Show Minor Grid( 1 )} )}
),
Dispatch(
{},
"Bivar Plot",
FrameBox,
{Grid Line Order( 2 ), Reference Line Order( 3 )}
)
)
);
//=========================================================================================
//NonLinear fit
nlmod = dt_calib << New Column( "NonLin Model",
Numeric,
"Continuous",
Format( "Best", 12 ),
Formula(
Parameter(
{top = 3, bottom = 0.2, c50 = 250, slope = 1},
top + (bottom - top) / (1 + (:concentration / c50) ^ slope)
)
)
);
report2_nlin = dt_calib << Nonlinear(
Y( :signal ),
X( :NonLin Model ),
Weight( :w ),
Newton,
Finish,
SendToReport(
Dispatch(
{"Plot"},
"1",
ScaleBox,
{Scale( "Log" ), Format( "Best", 6 ), Min( 1 ), Max( 1000 ), Inc( 1 ),
Minor Ticks( 0 )}
),
Dispatch(
{"Plot"},
"2",
ScaleBox,
{Min( 0 ), Max( 4 ), Inc( 1 ), Minor Ticks( 1 ),
Label Row( {Show Major Grid( 1 ), Show Minor Grid( 1 )} )}
)
)
);
report2 _nlin << Confidence Limits;
report2 _nlin << Save Estimates; //updates the column nlmod
nlin_est = report2_nlin << get estimates; //vector
show(nlin_est);
//==== up to proc nlmixedSu solicitud no es una solicitud trivial. Se necesitarían muchas horas de trabajo para que una persona con conocimientos de SAS, JMP y estadística realizara la tarea. Le sugiero que se ponga en contacto con una casa contratada para pagarles por la conversión.
Supongo que necesita convertir el código porque no tiene acceso a SAS y/o no está familiarizado con SAS.
Si el problema es no tener acceso a SAS, se puede acceder a SAS University Edition y, con cambios menores, lograr que el programa se ejecute correctamente.