albertopessia / drda Goto Github PK

Describe the bug

Thanks for this package! I noticed the above bug. When the input data frame is not sorted by dose, the plot utility does not work under base != 'n'.

To Reproduce

library(tidyverse)
library(drda)
voropm2 <- voropm2 %>% arrange(desc(dose))
fit <- drda(response ~ dose, data = voropm2,mean_function='loglogistic5',max_iter = 10000)
plot(fit,base="10")

yields:

Error in seq.default(x1, x2, by = ceiling((x2 - x1)/6)) : 
  invalid '(to - from)/by'

Expected behavior

I expect the sorting of the data frame to not impact results/plots generated. Right now I can work around the issue by sorting dataframes passed to the package, but this should probably be documented and eventually corrected.

Desktop (please complete the following information):

• OS: macOS
• Platform: aarch64-apple-darwin20 (64-bit)
• R: 4.2.1
• drda version: [e.g. 2.0.1]

Thanks @albertopessia for this user-friendly and well-documented package.

When I try to plot multiple dose-response curves in the same plot, the curves get distorted - due to change(s) in x or y axes. Consider the following example:

# Data
dose <- c( -4, -4.30, -4.60, -4.90, -5.20, -5.50, -5.80, -6.10, -6.40, -6.70, -7)
response <- c(3, 10, 24, 32, 45, 58, 71, 88, 92, 95, 99)

# Model 1
m1 <- drda(formula = response ~ dose, mean_function = "logistic4")

# Model 2
low_bound <- c(0, 100, -7, -Inf)
up_bound <- c(0, 100, -4, Inf)
m2 <- drda(formula = response ~ log_dose, upper_bound = up_bound, lower_bound = low_bound)

# Plot
plot(m1, m2) 

Defining axis limits appears to be the only working solution:

plot(m, m2, 
        xlim = c(-7, .4), 
        ylim = c(0, 100), 
        legend = c("Model_1", "Model_2")) 

Is there something obvious I am missing here or is this the expected behavior?

> sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] pracma_2.3.3 drda_1.0.0  

loaded via a namespace (and not attached):
 [1] fansi_0.5.0      assertthat_0.2.1 dplyr_1.0.7      crayon_1.4.1     utf8_1.2.2      
 [6] grid_4.0.2       R6_2.5.1         DBI_1.1.1        lifecycle_1.0.1  gtable_0.3.0    
[11] magrittr_2.0.1   scales_1.1.1     pillar_1.6.4     ggplot2_3.3.5    rlang_0.4.11    
[16] generics_0.1.0   vctrs_0.3.8      ellipsis_0.3.2   tools_4.0.2      glue_1.4.2      
[21] purrr_0.3.4      munsell_0.5.0    tinytex_0.34     xfun_0.26        compiler_4.0.2  
[26] pkgconfig_2.0.3  colorspace_2.0-2 tidyselect_1.1.1 tibble_3.1.5  

Hello

I am trying to use the nauc() function but get the following message

Error in if (tmp < xlim[2]) { : missing value where TRUE/FALSE needed
In addition: Warning message:
In log((ylim[2] - alpha)/(beta - ylim[2])) : NaNs produced.

Thank you so much for providing this nice package.

I have some issues with plotting.

• I generated a 4 parametric fit of this data x:

  Compound	Cell	conc	GI	relGI
  drug1	MDAMB436	1,00E-09	94,12125	0,9412125
  drug1	MDAMB436	5,00E-09	89,76748	0,8976748
  drug1	MDAMB436	1,67E-08	79,58089	0,7958089
  drug1	MDAMB436	5,00E-08	72,59264	0,7259264
  drug1	MDAMB436	1,67E-07	67,35769	0,6735769
  drug1	MDAMB436	5,00E-07	53,64598	0,5364598
  drug1	MDAMB436	1,67E-06	33,58508	0,3358508
  drug1	MDAMB436	5,00E-06	30,55685	0,3055685
  drug1	MDAMB436	5,00E-05	15,06649	0,1506649
  

  lb <- c(1, -1, -Inf, -Inf)
  ub <- c(1, Inf, Inf, Inf)
  fit=drda::drda(x$rel.gi ~ log10(x$conc),
  mean_function = "logistic4",
  lower_bound = lb,
  upper_bound = ub))

• summary of the fit:
  > summary(fit)

    Call: drda::drda(formula = x$rel.gi ~ log10(x$conc), mean_function = "logistic4", 
        lower_bound = lb, upper_bound = ub)
    
    Pearson Residuals:
        Min       1Q   Median       3Q      Max  
    -1.6240  -0.4998   0.1265   0.4788   1.2993  
    
    Parameters:
                      Estimate Std. Error Lower .95 Upper .95
    Maximum            1.00000         NA        NA        NA
    Height            -0.96278    0.07109  -1.10211    -0.823
    Growth rate        1.02122    0.11853   0.78890     1.254
    Midpoint at       -6.31416    0.19131  -6.68913    -5.939
    Residual std err.  0.03325    0.01108   0.01153     0.055
    
    Residual standard error on 6 degrees of freedom
    
    Log-likelihood: 19.687
    AIC: -31.374
    BIC: -30.585
    
    Optimization algorithm converged in 289 iterations
  

• plotting fails with error:

  plot(fit)
  Error in crossprod(G[i, ], V) : non-conformable argument

  8.crossprod(G[i, ], V) at curve_variance.R#31
  7.tcrossprod(crossprod(G[i, ], V), G[i, ]) at curve_variance.R#31
  6.curve_variance.drda(x, xx) at S3.R#50
  5.curve_variance(x, xx) at plot.R#547
  4.plot_params.logistic(x, dotargs[["base"]], dotargs[["xlim"]],
  dotargs[["ylim"]]) at S3.R#62
  3.plot_params(x, dotargs[["base"]], dotargs[["xlim"]], dotargs[["ylim"]]) at plot.R#99
  2.plot.drda(fit)
  1.plot(fit)

Please let me know if you need more details.

thanks for this package. It’s working well for me…

But is there any way to get standard error from predictions on a drda fit object (eg, predict(fit, se.fit=T) ).