test_that( "Coxph basic_single_null match", { fname <- 'll_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) time1 <- "t0" time2 <- "t1" event <- "lung" names <- c( "dose" ) term_n <- c(0) tform <- c( "loglin" ) keep_constant <- c(0) a_n <- c(0.0) modelform <- "M" fir <- 0 der_iden <- 0 verbose <- FALSE control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(-1,-1), 'halfmax' = -1, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=1) model_control <- list( 'strata'=FALSE, 'basic'=FALSE, 'single'=FALSE, 'null'=FALSE) e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=c( "loglin" ), keep_constant=keep_constant, a_n=a_n, modelform="M", fir=0, der_iden=der_iden, control=control,strat_col="fac", model_control=model_control) for (j in c(TRUE,FALSE)){ for (k in c(TRUE,FALSE)){ for (l in c(TRUE,FALSE)){ model_control <- list( 'strata'=FALSE, 'basic'=j, 'single'=k, 'null'=l) if (verbose){print(model_control)} a_n <- c(0.0) e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=c( "loglin" ), keep_constant=keep_constant, a_n=a_n, modelform="M", fir=0, der_iden=der_iden, control=control,strat_col="fac", model_control=model_control) expect_equal(e0$LogLik,e1$LogLik,tolerance=1e-2) if (verbose){print( "---------------" )} } } } model_control <- list( 'strata'=TRUE, 'basic'=FALSE, 'single'=FALSE, 'null'=FALSE) e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=c( "loglin" ), keep_constant=keep_constant, a_n=a_n, modelform="M", fir=0, der_iden=der_iden, control=control,strat_col="fac", model_control=model_control) for (j in c(TRUE,FALSE)){ for (k in c(TRUE,FALSE)){ for (l in c(TRUE,FALSE)){ model_control <- list( 'strata'=TRUE, 'basic'=j, 'single'=k, 'null'=l) if (verbose){print(model_control)} a_n <- c(0.0) e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=c( "loglin" ), keep_constant=keep_constant, a_n=a_n, modelform="M", fir=0, der_iden=der_iden, control=control,strat_col="fac", model_control=model_control) expect_equal(e0$LogLik,e1$LogLik,tolerance=1e-2) if (verbose){print( "---------------" )} } } } }) test_that( "Coxph strata_basic_single_CR", { fname <- 'll_comp_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) time1 <- "t0" time2 <- "t1" df$censor <- (df$lung==0) event <- "censor" names <- c( "dose", "fac" ) term_n <- c(0,0) tform <- c( "loglin", "loglin" ) keep_constant <- c(1,0) a_n <- c(0,0) modelform <- "M" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiter' = 20, 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=3, 'ties'='breslow', 'double_step'=1) plot_options <- list( "name"=paste(tempfile(), "run",sep="" ), "verbose"=FALSE, "studyid"="studyid", "age_unit"="years" ) dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options) # # t_ref <- dft$t surv_ref <- dft$surv t_c <- df$t1 cens_weight <- approx(t_ref, surv_ref, t_c,rule=2)$y # event <- "lung" a_n <- c(-0.1,-0.1) keep_constant <- c(0,0) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=1) verbose <- FALSE j_iterate <- 1 LL_comp <- c(-69.5158518365541, -69.5158518365541, -77.9763170154962, -77.9763170154962, -59.9516653570854, -60.0527317081394, -75.3402770170926, -75.3690992325999, -69.5158518365541, -69.5158518365541, -77.9763170154962, -77.9763170154962, -59.9516653570854, -60.0527317081394, -75.3402770170926, -75.3690992325999, -111.30091610792, -111.30091610792, -119.981426633545, -119.981426633545, -100.832886031781, -101.00697716481, -117.014696948034, -117.053896416502, -111.30091610792, -111.30091610792, -119.981426633545, -119.981426633545, -100.832886031781, -101.00697716481, -117.014696948034, -117.053896416502) for (i in c(TRUE,FALSE)){ for (j in c(TRUE,FALSE)){ for (k in c(TRUE,FALSE)){ for (l in c(TRUE,FALSE)){ model_control <- list( 'strata'=i, 'basic'=j, 'single'=k, 'cr'=l) if (verbose){print(model_control)} a_n <- c(-0.1,-0.1) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=1) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="rand", model_control=model_control, cens_weight=cens_weight) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 a_n <- c(-0.1,-0.1) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='efron', 'double_step'=0) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="rand", model_control=model_control, cens_weight=cens_weight) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 if (verbose){print( "---------------" )} } } } } }) test_that( "Pois strata_single", { fname <- 'll_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) time1 <- "t0" df$pyr <- df$t1-df$t0 pyr <- "pyr" event <- "lung" set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) names <- c( "dose", "rand", "rand" ) term_n <- c(2,1,0) tform <- c( "loglin", "lin", "plin" ) keep_constant <- c(0,0,0) a_n <- c(0.01,0.1,0.1) modelform <- "PAE" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=0) strat_col <- "fac" verbose <- FALSE j_iterate <- 1 LL_comp <- c(-463.5574, -464.9279, -461.2769, -462.1182, -3033.332, -2734.64, -992.622, -1334.36) for (i in c(TRUE,FALSE)){ for (j in c(TRUE,FALSE)){ model_control <- list( 'strata'=i, 'single'=j) if (verbose){print(model_control)} a_n <- c(0.01,0.1,0.1) modelform <- "PAE" e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col,model_control) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 a_n <- c(0.01,0.1,0.1) modelform <- "A" e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col,model_control) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 if (verbose){print( "---------------" )} } } }) test_that( "Pois comb_forms", { fname <- 'll_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) time1 <- "t0" df$pyr <- df$t1-df$t0 pyr <- "pyr" event <- "lung" set.seed(3742) df$rand <- floor(runif(nrow(df), min=1, max=5)) names <- c( "dose", "rand", "rand", "dose", "dose" ) term_n <- c(1,0,0, 0, 0) tform <- c( "loglin", "lin", "plin", "loglin_slope", "loglin_top" ) keep_constant <- c(0,0,0, 0, 0) a_n <- c(0.01,0.1,0.1, 1.0, 0.1) modelform <- "PAE" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=0) strat_col <- "fac" verbose <- FALSE modelforms <- c( "A", "PAE", "M", "PA" ) j_iterate <- 1 LL_comp <- c(-820.709, -471.0312, -471.0312, -463.1375, -707.56, -678.2228, -678.2228, -471.4805) for (modelform in modelforms){ model_control <- list( 'strata'=FALSE, 'single'=FALSE) if (verbose){print(model_control)} a_n <- c(0.01,0.1,0.1, 1.0, 0.1) e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col,model_control) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 if (verbose){print( "---------------" )} } term_n <- c(1,1,1, 0, 0) for (modelform in modelforms){ model_control <- list( 'strata'=FALSE, 'single'=FALSE) if (verbose){print(model_control)} a_n <- c(0.01,0.1,0.1, 1.0, 0.1) e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col,model_control) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 if (verbose){print( "---------------" )} } }) test_that( "Pois strata_single expanded", { fname <- 'll_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) time1 <- "t0" df$pyr <- df$t1-df$t0 pyr <- "pyr" event <- "lung" set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) names <- c( "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand" ) term_n <- c(0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1) tform <- c( "loglin_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope" ) keep_constant <- c(0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) a_n <- c(1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1) modelform <- "PAE" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=0) strat_col <- "fac" verbose <- FALSE j_iterate <- 1 LL_comp <- c(-496.7366, -475.4213, -461.9726, -461.1227, -4497.178, -3577.953, -2561.685, -2339.961) for (i in c(TRUE,FALSE)){ for (j in c(TRUE,FALSE)){ model_control <- list( 'strata'=i, 'single'=j) if (verbose){print(model_control)} a_n <- c(1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1) modelform <- "PAE" e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col,model_control) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 a_n <- c(1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1) modelform <- "A" e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col,model_control) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 if (verbose){print( "---------------" )} } } }) test_that( "risk check omnibus plain", { fname <- 'll_comp_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) time1 <- "t0" time2 <- "t1" df$censor <- (df$lung==0) # event <- "lung" names <- c( "dose", "fac", "dose", "fac", "rand" ) term_n <- c(0,0,1,1,1) tform <- c( "loglin", "lin", "lin", "plin", "loglin" ) keep_constant <- c(0,0,0,0,0) a_n <- c(-0.1,0.1,0.2,0.3,-0.5) modelform <- "M" fir <- 0 der_iden <- 0 cens_weight <- c(0) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) verbose <- FALSE df_order <- data.table( "term_n"=term_n, "tform"=tform, "keep_constant"=keep_constant, "a_n"=a_n, "names"=names, "order"=1:5) model_list <- c( 'M', 'A', 'PA', 'PAE' ) means <- c(0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931) medians <- c(0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932) sums <- c(53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931) for (model_i in 1:4){ modelform <- model_list[model_i] for (fir in c(0,1)){ for(i in 1:5){ model_control <- list( 'strata'=FALSE, 'basic'=FALSE, 'single'=FALSE, 'cr'=FALSE) # df_order$order <- sample(df_order$order) setorderv(df_order, c( "order" )) term_n <- df_order$term_n tform <- df_order$tform keep_constant <- df_order$keep_constant a_n <- df_order$a_n names <- df_order$names # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, control=control, model_control=model_control)$Risk a_i <- (model_i-1)*10 + fir*5 + i expect_equal(mean(e),means[a_i],tolerance=1e-2) expect_equal(median(e),medians[a_i],tolerance=1e-2) expect_equal(sum(e),sums[a_i],tolerance=1e-2) } } } }) test_that( "risk check omnibus gmix", { fname <- 'll_comp_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) time1 <- "t0" time2 <- "t1" df$censor <- (df$lung==0) # event <- "lung" control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) verbose <- FALSE model_list <- c( 'GMIX-R', 'GMIX-E', 'GMIX' ) names <- c( "dose", "fac", "dose", "fac", "rand" ) term_n <- c(0,0,1,1,2) tform <- c( "loglin", "loglin", "plin", "plin", "loglin" ) keep_constant <- c(0,0,0,0,0) a_n <- c(-0.1,0.1,0.2,0.3,-0.5) df_order <- data.table( "term_n"=term_n, "tform"=tform, "keep_constant"=keep_constant, "a_n"=a_n, "names"=names, "order"=1:5) means <- c(0.6918334,0.6918334,0.6918334,2.9080098,3.8839872,2.7077608,0.6918334,1.2728663,1.8214142,2.9080098,0.6918334,1.4807626,1.8214142,3.8839872,0.6918334,1.4807626,1.2728663,2.7077608) medians <- c(0.5871897,0.5871897,0.5871897,2.8144283,3.7521540,2.4285966,0.5871897,1.0226682,1.7472830,2.8144283,0.5871897,1.2200643,1.7472830,3.7521540,0.5871897,1.2200643,1.0226682,2.4285966) sums <- c(691.8334,691.8334,691.8334,2908.0098,3883.9872,2707.7608,691.8334,1272.8663,1821.4142,2908.0098,691.8334,1480.7626,1821.4142,3883.9872,691.8334,1480.7626,1272.8663,2707.7608) count <- 0 for (model_i in 1:3){ modelform <- model_list[model_i] if (modelform=='GMIX' ){ for (fir in c(0,1,2)){ for (term_i in 0:3){ model_control <- list( 'strata'=FALSE, 'basic'=FALSE, 'single'=FALSE, 'cr'=FALSE) if (fir==0){ model_control$gmix_term <- c(0,term_i%%2, floor(term_i/2)) } else if (fir==1){ model_control$gmix_term <- c(term_i%%2,0, floor(term_i/2)) } else if (fir==2){ model_control$gmix_term <- c(term_i%%2, floor(term_i/2),1) } # df_order$order <- sample(df_order$order) setorderv(df_order, c( "order" )) term_n <- df_order$term_n tform <- df_order$tform keep_constant <- df_order$keep_constant a_n <- df_order$a_n names <- df_order$names # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, control=control, model_control=model_control)$Risk count <- count + 1 expect_equal(mean(e),means[count],tolerance=1e-2) expect_equal(median(e),medians[count],tolerance=1e-2) expect_equal(sum(e),sums[count],tolerance=1e-2) } } } else { for (fir in c(0,1,2)){ model_control <- list( 'strata'=FALSE, 'basic'=FALSE, 'single'=FALSE, 'cr'=FALSE) # df_order$order <- sample(df_order$order) setorderv(df_order, c( "order" )) term_n <- df_order$term_n tform <- df_order$tform keep_constant <- df_order$keep_constant a_n <- df_order$a_n names <- df_order$names # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, control=control, model_control=model_control)$Risk count <- count + 1 expect_equal(mean(e),means[count],tolerance=1e-2) expect_equal(median(e),medians[count],tolerance=1e-2) expect_equal(sum(e),sums[count],tolerance=1e-2) } } } }) test_that( "risk check omnibus dose", { fname <- 'll_comp_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) time1 <- "t0" time2 <- "t1" df$censor <- (df$lung==0) # event <- "lung" names <- c( "dose", "fac", "dose", "fac", "rand" ) term_n <- c(0,0,1,1,1) tform <- c( "loglin", "lin", "lin", "plin", "loglin" ) keep_constant <- c(0,0,0,0,0) a_n <- c(-0.1,0.1,0.2,0.3,-0.5) modelform <- "M" fir <- 0 der_iden <- 0 cens_weight <- c(0) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) verbose <- FALSE df_order <- data.table( "term_n"=term_n, "tform"=tform, "keep_constant"=keep_constant, "a_n"=a_n, "names"=names, "order"=1:5) model_list <- c( 'M', 'A', 'PA', 'PAE' ) names <- c( "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac" ) term_n <- c(0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1) tform <- c( "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope" ) keep_constant <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) a_n <- c(-0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1) df_order <- data.table( "term_n"=term_n, "tform"=tform, "keep_constant"=keep_constant, "a_n"=a_n, "names"=names, "order"=1:22) means <- c(3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529) medians <- c(2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858) sums <- c(3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529) for (model_i in 1:4){ modelform <- model_list[model_i] for (fir in c(0,1)){ for(i in 1:22){ model_control <- list( 'strata'=FALSE, 'basic'=FALSE, 'single'=FALSE, 'cr'=FALSE) # df_order$order <- sample(df_order$order) setorderv(df_order, c( "order" )) term_n <- df_order$term_n tform <- df_order$tform keep_constant <- df_order$keep_constant a_n <- df_order$a_n names <- df_order$names # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, control=control, model_control=model_control)$Risk a_i <- (model_i-1)*44 + fir*22 + i expect_equal(mean(e),means[a_i],tolerance=1e-2) expect_equal(median(e),medians[a_i],tolerance=1e-2) expect_equal(sum(e),sums[a_i],tolerance=1e-2) } } } }) test_that( "check deviation calc", { fname <- 'll_comp_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) time1 <- "t0" time2 <- "t1" # event <- "lung" names <- c( "dose", "fac", "rand" ) term_n <- c(0,0,1) tform <- c( "loglin", "loglin", "loglin" ) keep_constant <- c(0,0,0) a_n <- c(-0.1,0.1,0.2) modelform <- "M" fir <- 0 der_iden <- 0 cens_weight <- c(0) verbose <- FALSE devs <- c() modelform <- "M" model_control <- list( 'strata'=FALSE, 'basic'=FALSE, 'single'=FALSE, 'cr'=FALSE) for (i in 1:3){ a_n <- c(0.6465390, 0.4260961, 0.1572781) keep_constant <- c(0,0,0) keep_constant[i] <- 1 # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="fac", model_control=model_control) devs <- c(devs, sum(e$Standard_Deviation)) } a_n <- c(0.6465390, 0.4260961, 0.1572781) keep_constant <- c(0,0,0) # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="fac", model_control=model_control) devs <- c(devs, sum(e$Standard_Deviation)) expect_equal(devs,c(0.6091269, 0.5356671, 0.7385757, 0.9448081),tolerance=1e-4) }) test_that( "check Linear Constraints", { fname <- 'l_pl_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) df$pyr <- df$t1 - df$t0 time1 <- "t0" time2 <- "t1" pyr <- 'pyr' event <- "lung" names <- c( "dose", "fac" ) term_n <- c(0,0) tform <- c( "loglin", "plin" ) keep_constant <- c(0,0) model_control <- list( 'strata'=F, 'basic'=F, 'single'=F, 'null'=F, 'constraint'=T) Constraint_Matrix <- matrix(c(1,-1),nrow=1) Constraint_const <- c(0.0) set.seed(3742) for (i in 1:20){ a_n <- 2*runif(2)-1 del <- abs(a_n[1]-a_n[2]) a_n0 <- rep(sum(a_n)/2,2) a_n <- a_n0 - c(-del/2,del/2) modelform <- "M" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiter' = 20, 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col="fac", model_control=model_control,cons_mat=Constraint_Matrix, cons_vec=Constraint_const) expect_equal(e$beta_0,c(0.357333, 0.357333),tolerance=1e-2) } for (i in 1:20){ a_n <- 2*runif(2)-1 del <- abs(a_n[1]-a_n[2]) a_n0 <- rep(sum(a_n)/2,2) a_n <- a_n0 + c(-del/2,del/2) modelform <- "M" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiter' = 20, 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1) e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control,strat_col="fac",model_control=model_control,cons_mat=Constraint_Matrix, cons_vec=Constraint_const) expect_equal(e$beta_0,c(-0.472812, -0.472812),tolerance=1e-2) } }) test_that( "Coxph strata_basic_single_CR", { fname <- 'll_comp_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) time1 <- "t0" time2 <- "t1" df$censor <- (df$lung==0) event <- "censor" names <- c( "dose", "fac" ) term_n <- c(0,0) tform <- c( "loglin", "loglin" ) keep_constant <- c(1,0) a_n <- c(0,0) modelform <- "M" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiter' = 20, 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=3, 'ties'='breslow', 'double_step'=1) plot_options <- list( "name"=paste(tempfile(), "run",sep="" ), "verbose"=FALSE, "studyid"="studyid", "age_unit"="years" ) dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options) # # t_ref <- dft$t surv_ref <- dft$surv t_c <- df$t1 cens_weight <- approx(t_ref, surv_ref, t_c,rule=2)$y # event <- "lung" a_n <- c(-0.1,-0.1) keep_constant <- c(0,0) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(-1,-1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=1) verbose <- FALSE j_iterate <- 1 LL_comp <- c(-69.51585, -69.51585, -77.97632, -77.97632, -59.95167, -60.05273, -75.34028, -75.3691, -69.51585, -69.51585, -77.97632, -77.97632, -59.95167, -60.05273, -75.34028, -75.3691, -111.3009, -111.3009, -119.9814, -119.9814, -100.8329, -101.007, -117.0147, -117.0539, -111.3009, -111.3009, -119.9814, -119.9814, -100.8329, -101.007, -117.0147, -117.0539) for (i in c(TRUE,FALSE)){ for (j in c(TRUE,FALSE)){ for (k in c(TRUE,FALSE)){ for (l in c(TRUE,FALSE)){ model_control <- list( 'strata'=i, 'basic'=j, 'single'=k, 'cr'=l) if (verbose){print(model_control)} a_n <- c(-0.1,-0.1) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=1) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="rand", model_control=model_control, cens_weight=cens_weight) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 a_n <- c(-0.1,-0.1) control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='efron', 'double_step'=0) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="rand", model_control=model_control, cens_weight=cens_weight) expect_equal(e$LogLik,LL_comp[j_iterate],tolerance=1e-2) j_iterate <- j_iterate + 1 if (verbose){print( "---------------" )} } } } } }) test_that( "Pois double_step change_all calcs", { fname <- 'll_0.csv' colTypes <- c( "double", "double", "double", "integer", "integer" ) df <- fread(fname,nThread=min(c(detectCores(),2)),data.table=TRUE,header=TRUE,colClasses=colTypes,verbose=FALSE,fill=TRUE) time1 <- "t0" df$pyr <- df$t1-df$t0 pyr <- "pyr" event <- "lung" set.seed(3742) df$rand <- floor(runif(nrow(df), min=0, max=5)) names <- c( "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand" ) term_n <- c(0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1) tform <- c( "loglin_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope" ) keep_constant <- c(0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) a_n <- c(1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1) modelform <- "PAE" fir <- 0 der_iden <- 0 control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=0) strat_col <- "fac" verbose <- FALSE j_iterate <- 1 LL_comp <- c(-496.7366, -475.4213, -461.9726, -461.1227, -4497.178, -3577.953, -2561.685, -2339.961) for (i in c(0,1)){ for (j in seq_len(length(term_n))){ model_control <- list( 'strata'=F, 'single'=F) if (verbose){print(model_control)} a_n <- c(1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1, -0.1 ,-0.1 ,1 ,-0.1 ,1 ,2 ,0.3 ,1.5 ,0.2 ,0.7 ,1) modelform <- "PAE" control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 5, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=F, 'dose_abs_max'=100.0, 'verbose'=4, 'ties'='breslow', 'double_step'=i) expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, j-1, control,strat_col,model_control)) if (verbose){print( "---------------" )} } } })